Yarn-dyed fabric defect classification based on convolutional neural network

NASA Astrophysics Data System (ADS)

Jing, Junfeng; Dong, Amei; Li, Pengfei

2017-07-01

Considering that the manual inspection of the yarn-dyed fabric can be time consuming and less efficient, a convolutional neural network (CNN) solution based on the modified AlexNet structure for the classification of the yarn-dyed fabric defect is proposed. CNN has powerful ability of feature extraction and feature fusion which can simulate the learning mechanism of the human brain. In order to enhance computational efficiency and detection accuracy, the local response normalization (LRN) layers in AlexNet are replaced by the batch normalization (BN) layers. In the process of the network training, through several convolution operations, the characteristics of the image are extracted step by step, and the essential features of the image can be obtained from the edge features. And the max pooling layers, the dropout layers, the fully connected layers are also employed in the classification model to reduce the computation cost and acquire more precise features of fabric defect. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show the capability of defect classification via the modified Alexnet model and indicate its robustness.

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.

Extracted facial feature of racial closely related faces

NASA Astrophysics Data System (ADS)

Liewchavalit, Chalothorn; Akiba, Masakazu; Kanno, Tsuneo; Nagao, Tomoharu

2010-02-01

Human faces contain a lot of demographic information such as identity, gender, age, race and emotion. Human being can perceive these pieces of information and use it as an important clue in social interaction with other people. Race perception is considered the most delicacy and sensitive parts of face perception. There are many research concerning image-base race recognition, but most of them are focus on major race group such as Caucasoid, Negroid and Mongoloid. This paper focuses on how people classify race of the racial closely related group. As a sample of racial closely related group, we choose Japanese and Thai face to represents difference between Northern and Southern Mongoloid. Three psychological experiment was performed to study the strategies of face perception on race classification. As a result of psychological experiment, it can be suggested that race perception is an ability that can be learn. Eyes and eyebrows are the most attention point and eyes is a significant factor in race perception. The Principal Component Analysis (PCA) was performed to extract facial features of sample race group. Extracted race features of texture and shape were used to synthesize faces. As the result, it can be suggested that racial feature is rely on detailed texture rather than shape feature. This research is a indispensable important fundamental research on the race perception which are essential in the establishment of human-like race recognition system.

A theoretical study in extracting the essential features and dynamics of molecular motions: Intrinsic geometry methods for PF(5) pseudorotations and statistical methods for argon clusters

NASA Astrophysics Data System (ADS)

Panahi, Nima S.

We studied the problem of understanding and computing the essential features and dynamics of molecular motions through the development of two theories for two different systems. First, we studied the process of the Berry Pseudorotation of PF5 and the rotations it induces in the molecule through its natural and intrinsic geometric nature by setting it in the language of fiber bundles and graph theory. With these tools, we successfully extracted the essentials of the process' loops and induced rotations. The infinite number of pseudorotation loops were broken down into a small set of essential loops called "super loops", with their intrinsic properties and link to the physical movements of the molecule extensively studied. In addition, only the three "self-edge loops" generated any induced rotations, and then only a finite number of classes of them. Second, we studied applying the statistical methods of Principal Components Analysis (PCA) and Principal Coordinate Analysis (PCO) to capture only the most important changes in Argon clusters so as to reduce computational costs and graph the potential energy surface (PES) in three dimensions respectively. Both methods proved successful, but PCA was only partially successful since one will only see advantages for PES database systems much larger than those both currently being studied and those that can be computationally studied in the next few decades to come. In addition, PCA is only needed for the very rare case of a PES database that does not already include Hessian eigenvalues.

Extraction of Pharmacokinetic Evidence of Drug–Drug Interactions from the Literature

PubMed Central

Kolchinsky, Artemy; Lourenço, Anália; Wu, Heng-Yi; Li, Lang; Rocha, Luis M.

2015-01-01

Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmacoepidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1≈0.93, MCC≈0.74, iAUC≈0.99) and sentences (F1≈0.76, MCC≈0.65, iAUC≈0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. We also found that some drug-related named entity recognition tools and dictionaries led to slight but significant improvements, especially in classification of evidence sentences. Based on our thorough analysis of classifiers and feature transforms and the high classification performance achieved, we demonstrate that literature mining can aid DDI discovery by supporting automatic extraction of specific types of experimental evidence. PMID:25961290

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition.

PubMed

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-03-27

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K -nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction.

Feature Extraction for Track Section Status Classification Based on UGW Signals

PubMed Central

Yang, Yuan; Shi, Lin

2018-01-01

Track status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain track section, often determining whether it is free or occupied by a train. Different technologies have already been involved in the design of monitoring systems, including ultrasonic guided waves (UGW). This work proposes the use of the UGW signals captured by a track monitoring system to extract the features that are relevant for determining the corresponding track section status. For that purpose, three features of UGW signals have been considered: the root mean square value, the energy, and the main frequency components. Experimental results successfully validated how these features can be used to classify the track section status into free, occupied and broken. Furthermore, spatial and temporal dependencies among these features were analysed in order to show how they can improve the final classification performance. Finally, a preliminary high-level classification system based on deep learning networks has been envisaged for future works. PMID:29673156

Recognition of Simple 3D Geometrical Objects under Partial Occlusion

NASA Astrophysics Data System (ADS)

Barchunova, Alexandra; Sommer, Gerald

In this paper we present a novel procedure for contour-based recognition of partially occluded three-dimensional objects. In our approach we use images of real and rendered objects whose contours have been deformed by a restricted change of the viewpoint. The preparatory part consists of contour extraction, preprocessing, local structure analysis and feature extraction. The main part deals with an extended construction and functionality of the classifier ensemble Adaptive Occlusion Classifier (AOC). It relies on a hierarchical fragmenting algorithm to perform a local structure analysis which is essential when dealing with occlusions. In the experimental part of this paper we present classification results for five classes of simple geometrical figures: prism, cylinder, half cylinder, a cube, and a bridge. We compare classification results for three classical feature extractors: Fourier descriptors, pseudo Zernike and Zernike moments.

Static and dynamic superheated water extraction of essential oil components from Thymus vulgaris L.

PubMed

Dawidowicz, Andrzej L; Rado, Ewelina; Wianowska, Dorota

2009-09-01

Superheated water extraction (SWE) performed in both static and dynamic condition (S-SWE and D-SWE, respectively) was applied for the extraction of essential oil from Thymus vulgaris L. The influence of extraction pressure, temperature, time, and flow rate on the total yield of essential oil and the influence of extraction temperature on the extraction of some chosen components are discussed in the paper. The SWE extracts are related to PLE extracts with n-hexane and essential oil obtained by steam distillation. The superheated water extraction in dynamic condition seems to be a feasible option for the extraction of essential oil components from T. vulgaris L.

Relations between Automatically Extracted Motion Features and the Quality of Mother-Infant Interactions at 4 and 13 Months

PubMed Central

Egmose, Ida; Varni, Giovanna; Cordes, Katharina; Smith-Nielsen, Johanne; Væver, Mette S.; Køppe, Simo; Cohen, David; Chetouani, Mohamed

2017-01-01

Bodily movements are an essential component of social interactions. However, the role of movement in early mother-infant interaction has received little attention in the research literature. The aim of the present study was to investigate the relationship between automatically extracted motion features and interaction quality in mother-infant interactions at 4 and 13 months. The sample consisted of 19 mother-infant dyads at 4 months and 33 mother-infant dyads at 13 months. The coding system Coding Interactive Behavior (CIB) was used for rating the quality of the interactions. Kinetic energy of upper-body, arms and head motion was calculated and used as segmentation in order to extract coarse- and fine-grained motion features. Spearman correlations were conducted between the composites derived from the CIB and the coarse- and fine-grained motion features. At both 4 and 13 months, longer durations of maternal arm motion and infant upper-body motion were associated with more aversive interactions, i.e., more parent-led interactions and more infant negativity. Further, at 4 months, the amount of motion silence was related to more adaptive interactions, i.e., more sensitive and child-led interactions. Analyses of the fine-grained motion features showed that if the mother coordinates her head movements with her infant's head movements, the interaction is rated as more adaptive in terms of less infant negativity and less dyadic negative states. We found more and stronger correlations between the motion features and the interaction qualities at 4 compared to 13 months. These results highlight that motion features are related to the quality of mother-infant interactions. Factors such as infant age and interaction set-up are likely to modify the meaning and importance of different motion features. PMID:29326626

Relations between Automatically Extracted Motion Features and the Quality of Mother-Infant Interactions at 4 and 13 Months.

PubMed

Egmose, Ida; Varni, Giovanna; Cordes, Katharina; Smith-Nielsen, Johanne; Væver, Mette S; Køppe, Simo; Cohen, David; Chetouani, Mohamed

2017-01-01

Bodily movements are an essential component of social interactions. However, the role of movement in early mother-infant interaction has received little attention in the research literature. The aim of the present study was to investigate the relationship between automatically extracted motion features and interaction quality in mother-infant interactions at 4 and 13 months. The sample consisted of 19 mother-infant dyads at 4 months and 33 mother-infant dyads at 13 months. The coding system Coding Interactive Behavior (CIB) was used for rating the quality of the interactions. Kinetic energy of upper-body, arms and head motion was calculated and used as segmentation in order to extract coarse- and fine-grained motion features. Spearman correlations were conducted between the composites derived from the CIB and the coarse- and fine-grained motion features. At both 4 and 13 months, longer durations of maternal arm motion and infant upper-body motion were associated with more aversive interactions, i.e., more parent-led interactions and more infant negativity. Further, at 4 months, the amount of motion silence was related to more adaptive interactions, i.e., more sensitive and child-led interactions. Analyses of the fine-grained motion features showed that if the mother coordinates her head movements with her infant's head movements, the interaction is rated as more adaptive in terms of less infant negativity and less dyadic negative states. We found more and stronger correlations between the motion features and the interaction qualities at 4 compared to 13 months. These results highlight that motion features are related to the quality of mother-infant interactions. Factors such as infant age and interaction set-up are likely to modify the meaning and importance of different motion features.

A Robust Zero-Watermarking Algorithm for Audio

NASA Astrophysics Data System (ADS)

Chen, Ning; Zhu, Jie

2007-12-01

In traditional watermarking algorithms, the insertion of watermark into the host signal inevitably introduces some perceptible quality degradation. Another problem is the inherent conflict between imperceptibility and robustness. Zero-watermarking technique can solve these problems successfully. Instead of embedding watermark, the zero-watermarking technique extracts some essential characteristics from the host signal and uses them for watermark detection. However, most of the available zero-watermarking schemes are designed for still image and their robustness is not satisfactory. In this paper, an efficient and robust zero-watermarking technique for audio signal is presented. The multiresolution characteristic of discrete wavelet transform (DWT), the energy compression characteristic of discrete cosine transform (DCT), and the Gaussian noise suppression property of higher-order cumulant are combined to extract essential features from the host audio signal and they are then used for watermark recovery. Simulation results demonstrate the effectiveness of our scheme in terms of inaudibility, detection reliability, and robustness.

Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.

PubMed

Zhu, Jianwei; Zhang, Haicang; Li, Shuai Cheng; Wang, Chao; Kong, Lupeng; Sun, Shiwei; Zheng, Wei-Mou; Bu, Dongbo

2017-12-01

Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge. In this study, we present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. DCNN has showed excellent performance in image feature extraction and image recognition; the rational underlying the application of DCNN for fold recognition is that contact likelihood maps are essentially analogy to images, as they both display compositional hierarchy. Experimental results on the LINDAHL dataset suggest that even using the extracted fold-specific features alone, our approach achieved success rate comparable to the state-of-the-art approaches. When further combining these features with traditional alignment-related features, the success rate of our approach increased to 92.3%, 82.5% and 78.8% at family, superfamily and fold levels, respectively, which is about 18% higher than the state-of-the-art approach at fold level, 6% higher at superfamily level and 1% higher at family level. An independent assessment on SCOP_TEST dataset showed consistent performance improvement, indicating robustness of our approach. Furthermore, bi-clustering results of the extracted features are compatible with fold hierarchy of proteins, implying that these features are fold-specific. Together, these results suggest that the features extracted from predicted contacts are orthogonal to alignment-related features, and the combination of them could greatly facilitate fold recognition at superfamily/fold levels and template-based prediction of protein structures. Source code of DeepFR is freely available through https://github.com/zhujianwei31415/deepfr, and a web server is available through http://protein.ict.ac.cn/deepfr. zheng@itp.ac.cn or dbu@ict.ac.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

A structural SVM approach for reference parsing.

PubMed

Zhang, Xiaoli; Zou, Jie; Le, Daniel X; Thoma, George R

2011-06-09

Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases. References, typically appearing at the end of journal articles, can also provide valuable information for extracting other bibliographic data. Therefore, parsing individual reference to extract author, title, journal, year, etc. is sometimes a necessary preprocessing step in building citation-indexing systems. The regular structure in references enables us to consider reference parsing a sequence learning problem and to study structural Support Vector Machine (structural SVM), a newly developed structured learning algorithm on parsing references. In this study, we implemented structural SVM and used two types of contextual features to compare structural SVM with conventional SVM. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing. We also compared SVM and structural SVM to Conditional Random Field (CRF). The experimental results show that structural SVM and CRF achieve similar accuracies at token- and chunk-levels. When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. The comparison of these two methods with CRF using the same set of binary features show that both structural SVM and CRF perform better than SVM, indicating their stronger sequence learning ability in reference parsing.

Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

NASA Astrophysics Data System (ADS)

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

2018-02-01

Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

Fundus Image Features Extraction for Exudate Mining in Coordination with Content Based Image Retrieval: A Study

NASA Astrophysics Data System (ADS)

Gururaj, C.; Jayadevappa, D.; Tunga, Satish

2018-06-01

Medical field has seen a phenomenal improvement over the previous years. The invention of computers with appropriate increase in the processing and internet speed has changed the face of the medical technology. However there is still scope for improvement of the technologies in use today. One of the many such technologies of medical aid is the detection of afflictions of the eye. Although a repertoire of research has been accomplished in this field, most of them fail to address how to take the detection forward to a stage where it will be beneficial to the society at large. An automated system that can predict the current medical condition of a patient after taking the fundus image of his eye is yet to see the light of the day. Such a system is explored in this paper by summarizing a number of techniques for fundus image features extraction, predominantly hard exudate mining, coupled with Content Based Image Retrieval to develop an automation tool. The knowledge of the same would bring about worthy changes in the domain of exudates extraction of the eye. This is essential in cases where the patients may not have access to the best of technologies. This paper attempts at a comprehensive summary of the techniques for Content Based Image Retrieval (CBIR) or fundus features image extraction, and few choice methods of both, and an exploration which aims to find ways to combine these two attractive features, and combine them so that it is beneficial to all.

WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids.

PubMed

Hayat, Maqsood; Khan, Asifullah

2013-05-01

Membrane protein is the prime constituent of a cell, which performs a role of mediator between intra and extracellular processes. The prediction of transmembrane (TM) helix and its topology provides essential information regarding the function and structure of membrane proteins. However, prediction of TM helix and its topology is a challenging issue in bioinformatics and computational biology due to experimental complexities and lack of its established structures. Therefore, the location and orientation of TM helix segments are predicted from topogenic sequences. In this regard, we propose WRF-TMH model for effectively predicting TM helix segments. In this model, information is extracted from membrane protein sequences using compositional index and physicochemical properties. The redundant and irrelevant features are eliminated through singular value decomposition. The selected features provided by these feature extraction strategies are then fused to develop a hybrid model. Weighted random forest is adopted as a classification approach. We have used two benchmark datasets including low and high-resolution datasets. tenfold cross validation is employed to assess the performance of WRF-TMH model at different levels including per protein, per segment, and per residue. The success rates of WRF-TMH model are quite promising and are the best reported so far on the same datasets. It is observed that WRF-TMH model might play a substantial role, and will provide essential information for further structural and functional studies on membrane proteins. The accompanied web predictor is accessible at http://111.68.99.218/WRF-TMH/ .

Extraction of urban vegetation with Pleiades multiangular images

NASA Astrophysics Data System (ADS)

Lefebvre, Antoine; Nabucet, Jean; Corpetti, Thomas; Courty, Nicolas; Hubert-Moy, Laurence

2016-10-01

Vegetation is essential in urban environments since it provides significant services in terms of health, heat, property value, ecology ... As part of the European Union Biodiversity Strategy Plan for 2020, the protection and development of green-infrastructures is strengthened in urban areas. In order to evaluate and monitor the quality of the green infra-structures, this article investigates contributions of Pléiades multi-angular images to extract and characterize low and high urban vegetation. From such images one can extract both spectral and elevation information from optical images. Our method is composed of 3 main steps : (1) the computation of a normalized Digital Surface Model from the multi-angular images ; (2) Extraction of spectral and contextual features ; (3) a classification of vegetation classes (tree and grass) performed with a random forest classifier. Results performed in the city of Rennes in France show the ability of multi-angular images to extract DEM in urban area despite building height. It also highlights its importance and its complementarity with contextual information to extract urban vegetation.

Comparative analysis of essential oil composition of Iranian and Indian Nigella sativa L. extracted using supercritical fluid extraction and solvent extraction

PubMed Central

Ghahramanloo, Kourosh Hasanzadeh; Kamalidehghan, Behnam; Akbari Javar, Hamid; Teguh Widodo, Riyanto; Majidzadeh, Keivan; Noordin, Mohamed Ibrahim

2017-01-01

The objective of this study was to compare the oil extraction yield and essential oil composition of Indian and Iranian Nigella sativa L. extracted by using Supercritical Fluid Extraction (SFE) and solvent extraction methods. In this study, a gas chromatography equipped with a mass spectrophotometer detector was employed for qualitative analysis of the essential oil composition of Indian and Iranian N. sativa L. The results indicated that the main fatty acid composition identified in the essential oils extracted by using SFE and solvent extraction were linoleic acid (22.4%–61.85%) and oleic acid (1.64%–18.97%). Thymoquinone (0.72%–21.03%) was found to be the major volatile compound in the extracted N. sativa oil. It was observed that the oil extraction efficiency obtained from SFE was significantly (P<0.05) higher than that achieved by the solvent extraction technique. The present study showed that SFE can be used as a more efficient technique for extraction of N. Sativa L. essential oil, which is composed of higher linoleic acid and thymoquinone contents compared to the essential oil obtained by the solvent extraction technique. PMID:28814830

Comparative analysis of essential oil composition of Iranian and Indian Nigella sativa L. extracted using supercritical fluid extraction and solvent extraction.

PubMed

Ghahramanloo, Kourosh Hasanzadeh; Kamalidehghan, Behnam; Akbari Javar, Hamid; Teguh Widodo, Riyanto; Majidzadeh, Keivan; Noordin, Mohamed Ibrahim

2017-01-01

The objective of this study was to compare the oil extraction yield and essential oil composition of Indian and Iranian Nigella sativa L. extracted by using Supercritical Fluid Extraction (SFE) and solvent extraction methods. In this study, a gas chromatography equipped with a mass spectrophotometer detector was employed for qualitative analysis of the essential oil composition of Indian and Iranian N. sativa L. The results indicated that the main fatty acid composition identified in the essential oils extracted by using SFE and solvent extraction were linoleic acid (22.4%-61.85%) and oleic acid (1.64%-18.97%). Thymoquinone (0.72%-21.03%) was found to be the major volatile compound in the extracted N. sativa oil. It was observed that the oil extraction efficiency obtained from SFE was significantly ( P <0.05) higher than that achieved by the solvent extraction technique. The present study showed that SFE can be used as a more efficient technique for extraction of N. Sativa L. essential oil, which is composed of higher linoleic acid and thymoquinone contents compared to the essential oil obtained by the solvent extraction technique.

Biomass enzymatic saccharification is determined by the non-KOH-extractable wall polymer features that predominately affect cellulose crystallinity in corn.

PubMed

Jia, Jun; Yu, Bin; Wu, Leiming; Wang, Hongwu; Wu, Zhiliang; Li, Ming; Huang, Pengyan; Feng, Shengqiu; Chen, Peng; Zheng, Yonglian; Peng, Liangcai

2014-01-01

Corn is a major food crop with enormous biomass residues for biofuel production. Due to cell wall recalcitrance, it becomes essential to identify the key factors of lignocellulose on biomass saccharification. In this study, we examined total 40 corn accessions that displayed a diverse cell wall composition. Correlation analysis showed that cellulose and lignin levels negatively affected biomass digestibility after NaOH pretreatments at p<0.05 & 0.01, but hemicelluloses did not show any significant impact on hexoses yields. Comparative analysis of five standard pairs of corn samples indicated that cellulose and lignin should not be the major factors on biomass saccharification after pretreatments with NaOH and H2SO4 at three concentrations. Notably, despite that the non-KOH-extractable residues covered 12%-23% hemicelluloses and lignin of total biomass, their wall polymer features exhibited the predominant effects on biomass enzymatic hydrolysis including Ara substitution degree of xylan (reverse Xyl/Ara) and S/G ratio of lignin. Furthermore, the non-KOH-extractable polymer features could significantly affect lignocellulose crystallinity at p<0.05, leading to a high biomass digestibility. Hence, this study could suggest an optimal approach for genetic modification of plant cell walls in bioenergy corn.

Biomass Enzymatic Saccharification Is Determined by the Non-KOH-Extractable Wall Polymer Features That Predominately Affect Cellulose Crystallinity in Corn

PubMed Central

Wu, Leiming; Wang, Hongwu; Wu, Zhiliang; Li, Ming; Huang, Pengyan; Feng, Shengqiu; Chen, Peng; Zheng, Yonglian; Peng, Liangcai

2014-01-01

Corn is a major food crop with enormous biomass residues for biofuel production. Due to cell wall recalcitrance, it becomes essential to identify the key factors of lignocellulose on biomass saccharification. In this study, we examined total 40 corn accessions that displayed a diverse cell wall composition. Correlation analysis showed that cellulose and lignin levels negatively affected biomass digestibility after NaOH pretreatments at p<0.05 & 0.01, but hemicelluloses did not show any significant impact on hexoses yields. Comparative analysis of five standard pairs of corn samples indicated that cellulose and lignin should not be the major factors on biomass saccharification after pretreatments with NaOH and H2SO4 at three concentrations. Notably, despite that the non-KOH-extractable residues covered 12%–23% hemicelluloses and lignin of total biomass, their wall polymer features exhibited the predominant effects on biomass enzymatic hydrolysis including Ara substitution degree of xylan (reverse Xyl/Ara) and S/G ratio of lignin. Furthermore, the non-KOH-extractable polymer features could significantly affect lignocellulose crystallinity at p<0.05, leading to a high biomass digestibility. Hence, this study could suggest an optimal approach for genetic modification of plant cell walls in bioenergy corn. PMID:25251456

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition

PubMed Central

Han, Te; Jiang, Dongxiang; Zhang, Xiaochen; Sun, Yankui

2017-01-01

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction. PMID:28346385

Chemical Composition of Volatiles; Antimicrobial, Antioxidant and Cholinesterase Inhibitory Activity of Chaerophyllum aromaticum L. (Apiaceae) Essential Oils and Extracts.

PubMed

Petrović, Goran M; Stamenković, Jelena G; Kostevski, Ivana R; Stojanović, Gordana S; Mitić, Violeta D; Zlatković, Bojan K

2017-05-01

The present study reports the chemical composition of the headspace volatiles (HS) and essential oils obtained from fresh Chaerophyllum aromaticum root and aerial parts in full vegetative phase, as well as biological activities of their essential oils and MeOH extracts. In HS samples, the most dominant components were monoterpene hydrocarbons. On the other hand, the essential oils consisted mainly of sesquiterpenoids, representing 73.4% of the root and 63.4% of the aerial parts essential oil. The results of antibacterial assay showed that the aerial parts essential oil and MeOH extract have no antibacterial activity, while the root essential oil and extract showed some activity. Both of the tested essential oils exhibited anticholinesterase activity (47.65% and 50.88%, respectively); MeOH extract of the root showed only 8.40% inhibition, while aerial part extract acted as an activator of cholinesterase. Regarding the antioxidant activity, extracts were found to be more effective than the essential oils. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

Deep Learning in Label-free Cell Classification

PubMed Central

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-01-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells. PMID:26975219

Deep Learning in Label-free Cell Classification

NASA Astrophysics Data System (ADS)

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

2016-03-01

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

The influence of purge times on the yields of essential oil components extracted from plants by pressurized liquid extraction.

PubMed

Wianowska, Dorota

2014-01-01

The influence of different purge times on the yield of the main essential oil constituents of rosemary (Rosmarinus officinalis L.), thyme (Thymus vulgaris L.), and chamomile (Chamomilla recutita L.) was investigated. The pressurized liquid extraction process was performed by applying different extraction temperatures and solvents. The results presented in the paper show that the estimated yield of essential oil components extracted from the plants in the pressurized liquid extraction process is purge time-dependent. The differences in the estimated yields are mainly connected with the evaporation of individual essential oil components and the applied solvent during the purge; the more volatile an essential oil constituent is, the greater is its loss during purge time, and the faster the evaporation of the solvent during the purge process is, the higher the concentration of less volatile essential oil components in the pressurized liquid extraction receptacle. The effect of purge time on the estimated yield of individual essential oil constituents is additionally differentiated by the extraction temperature and the extraction ability of the applied solvent.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

PubMed

Ashraf, Rehan; Ahmed, Mudassar; Jabbar, Sohail; Khalid, Shehzad; Ahmad, Awais; Din, Sadia; Jeon, Gwangil

2018-01-25

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Research on simulated infrared image utility evaluation using deep representation

NASA Astrophysics Data System (ADS)

Zhang, Ruiheng; Mu, Chengpo; Yang, Yu; Xu, Lixin

2018-01-01

Infrared (IR) image simulation is an important data source for various target recognition systems. However, whether simulated IR images could be used as training data for classifiers depends on the features of fidelity and authenticity of simulated IR images. For evaluation of IR image features, a deep-representation-based algorithm is proposed. Being different from conventional methods, which usually adopt a priori knowledge or manually designed feature, the proposed method can extract essential features and quantitatively evaluate the utility of simulated IR images. First, for data preparation, we employ our IR image simulation system to generate large amounts of IR images. Then, we present the evaluation model of simulated IR image, for which an end-to-end IR feature extraction and target detection model based on deep convolutional neural network is designed. At last, the experiments illustrate that our proposed method outperforms other verification algorithms in evaluating simulated IR images. Cross-validation, variable proportion mixed data validation, and simulation process contrast experiments are carried out to evaluate the utility and objectivity of the images generated by our simulation system. The optimum mixing ratio between simulated and real data is 0.2≤γ≤0.3, which is an effective data augmentation method for real IR images.

Antimicrobial and antioxidant activity of essential oil and different plant extracts of Psidium cattleianum Sabine.

PubMed

Scur, M C; Pinto, F G S; Pandini, J A; Costa, W F; Leite, C W; Temponi, L G

2016-02-01

The goals of the study were to determinethe antimicrobial and antioxidant activities of essential oil and plant extracts aqueous and ethanolic of Psidium cattleianum Sabine; the chemical composition of the essential oil of P. cattleianum; and the phytochemical screening of aqueous and ethanolic extracts of the same plant. Regarding the antimicrobial activity, the ethanolic extract exhibited moderate antimicrobial activity with respect to bacteria K. pneumoniae and S. epidermidis, whereas, regarding other microorganisms, it showed activity considered weak. The aqueous extract and the essential oil showed activity considered weak, although they inhibited the growth of microorganisms. About the antioxidant potential, the ethanolic and aqueous extracts exhibited a scavenging index exceeding 90%, while the essential oil didn´t show significant antioxidant activity. Regarding the phytochemical composition, the largest class of volatile compounds identified in the essential oil of P. cattleianum included the following terpenic hydrocarbons: α-copaene (22%); eucalyptol (15%), δ-cadinene (9.63%) and α-selinene (6.5%). The phytochemical screening of extracts showed the presence of tannins, flavonoids, and triterpenoids for aqueous and ethanolic extracts. The extracts and essential oils inhibit the growth of microrganisms and plant extracts showed significant antioxidant activity. Also, the phytochemical characterization of the essential oil showed the presence of compounds interest commercial, as well as extracts showed the presence of important classes and compounds with biological activities.

Classification of underwater target echoes based on auditory perception characteristics

NASA Astrophysics Data System (ADS)

Li, Xiukun; Meng, Xiangxia; Liu, Hang; Liu, Mingye

2014-06-01

In underwater target detection, the bottom reverberation has some of the same properties as the target echo, which has a great impact on the performance. It is essential to study the difference between target echo and reverberation. In this paper, based on the unique advantage of human listening ability on objects distinction, the Gammatone filter is taken as the auditory model. In addition, time-frequency perception features and auditory spectral features are extracted for active sonar target echo and bottom reverberation separation. The features of the experimental data have good concentration characteristics in the same class and have a large amount of differences between different classes, which shows that this method can effectively distinguish between the target echo and reverberation.

Efficient Spatio-Temporal Local Binary Patterns for Spontaneous Facial Micro-Expression Recognition

PubMed Central

Wang, Yandan; See, John; Phan, Raphael C.-W.; Oh, Yee-Hui

2015-01-01

Micro-expression recognition is still in the preliminary stage, owing much to the numerous difficulties faced in the development of datasets. Since micro-expression is an important affective clue for clinical diagnosis and deceit analysis, much effort has gone into the creation of these datasets for research purposes. There are currently two publicly available spontaneous micro-expression datasets—SMIC and CASME II, both with baseline results released using the widely used dynamic texture descriptor LBP-TOP for feature extraction. Although LBP-TOP is popular and widely used, it is still not compact enough. In this paper, we draw further inspiration from the concept of LBP-TOP that considers three orthogonal planes by proposing two efficient approaches for feature extraction. The compact robust form described by the proposed LBP-Six Intersection Points (SIP) and a super-compact LBP-Three Mean Orthogonal Planes (MOP) not only preserves the essential patterns, but also reduces the redundancy that affects the discriminality of the encoded features. Through a comprehensive set of experiments, we demonstrate the strengths of our approaches in terms of recognition accuracy and efficiency. PMID:25993498

American Thyroid Association statement on the essential elements of interdisciplinary communication of perioperative information for patients undergoing thyroid cancer surgery.

PubMed

Carty, Sally E; Doherty, Gerard M; Inabnet, William B; Pasieka, Janice L; Randolph, Gregory W; Shaha, Ashok R; Terris, David J; Tufano, Ralph P; Tuttle, R Michael

2012-04-01

Thyroid cancer specialists require specific perioperative information to develop a management plan for patients with thyroid cancer, but there is not yet a model for effective interdisciplinary data communication. The American Thyroid Association Surgical Affairs Committee was asked to define a suggested essential perioperative dataset representing the critical information that should be readily available to participating members of the treatment team. To identify and agree upon a multidisciplinary set of critical perioperative findings requiring communication, we examined diverse best-practice documents relating to thyroidectomy and extracted common features felt to enhance precise, direct communication with nonsurgical caregivers. Suggested essential datasets for the preoperative, intraoperative, and immediate postoperative findings and management of patients undergoing surgery for thyroid cancer were identified and are presented. For operative reporting, the essential features of both a dictated narrative format and a synoptic computer format are modeled in detail. The importance of interdisciplinary communication is discussed with regard to the extent of required resection, the final pathology findings, surgical complications, and other factors that may influence risk stratification, adjuvant treatment, and surveillance. Accurate communication of the important findings and sequelae of thyroidectomy for cancer is critical to individualized risk stratification as well as to the clinical issues of thyroid cancer care that are often jointly managed in the postoperative setting. True interdisciplinary care is essential to providing optimal care and surveillance.

Arabic OCR: toward a complete system

NASA Astrophysics Data System (ADS)

El-Bialy, Ahmed M.; Kandil, Ahmed H.; Hashish, Mohamed; Yamany, Sameh M.

1999-12-01

Latin and Chinese OCR systems have been studied extensively in the literature. Yet little work was performed for Arabic character recognition. This is due to the technical challenges found in the Arabic text. Due to its cursive nature, a powerful and stable text segmentation is needed. Also; features capturing the characteristics of the rich Arabic character representation are needed to build the Arabic OCR. In this paper a novel segmentation technique which is font and size independent is introduced. This technique can segment the cursive written text line even if the line suffers from small skewness. The technique is not sensitive to the location of the centerline of the text line and can segment different font sizes and type (for different character sets) occurring on the same line. Features extraction is considered one of the most important phases of the text reading system. Ideally, the features extracted from a character image should capture the essential characteristics of this character that are independent of the font type and size. In such ideal case, the classifier stores a single prototype per character. However, it is practically challenging to find such ideal set of features. In this paper, a set of features that reflect the topological aspects of Arabia characters is proposed. These proposed features integrated with a topological matching technique introduce an Arabic text reading system that is semi Omni.

Analysis of essential oils from Voacanga africana seeds at different hydrodistillation extraction stages: chemical composition, antioxidant activity and antimicrobial activity.

PubMed

Liu, Xiong; Yang, Dongliang; Liu, Jiajia; Ren, Na

2015-01-01

In this study, essential oils from Voacanga africana seeds at different extraction stages were investigated. In the chemical composition analysis, 27 compounds representing 86.69-95.03% of the total essential oils were identified and quantified. The main constituents in essential oils were terpenoids, alcohols and fatty acids accounting for 15.03-24.36%, 21.57-34.43% and 33.06-57.37%, respectively. Moreover, the analysis also revealed that essential oils from different extraction stages possessed different chemical compositions. In the antioxidant evaluation, all analysed oils showed similar antioxidant behaviours, and the concentrations of essential oils providing 50% inhibition of DPPH-scavenging activity (IC50) were about 25 mg/mL. In the antimicrobial experiments, essential oils from different extraction stages exhibited different antimicrobial activities. The antimicrobial activity of oils was affected by extraction stages. By controlling extraction stages, it is promising to obtain essential oils with desired antimicrobial activities.

Chemical compositions and antioxidant/antimicrobial activities of various samples prepared from Schinus terebinthifolius leaves cultivated in Egypt.

PubMed

El-Massry, Khaled F; El-Ghorab, Ahmed H; Shaaban, Hamdy A; Shibamoto, Takayuki

2009-06-24

Essential oil, dichloromethane extract, and ethanol extract were prepared from fresh Schinus terebinthifolius leaves cultivated in Egypt. The essential oil was analyzed by gas chromatography and gas chromatography/mass spectrometry. The essential oil comprised 4.97% monoterpenes, 56.96% sesquiterpenes, 34.37% oxygenated monoterpenes, and 3.32% oxygenated sesquiterpenes. The major compounds in the essential oil were cis-beta-terpineol (GC peak area%, 17.87%), (E)-caryophyllene (17.56%), beta-cedrene (9.76%), and citronellal (7.03%). The major phenolic compounds identified in the ethanol extract were caffeic acid (5.07 mg/100 mg extract), coumaric acid (1.64 mg), and syringic acid (1.59 mg). The antioxidant activity of ethanol extract, which was comparable with that of butylhydroquinone, was superior to essential oil and dichloromethane extract in 2,2-diphenylpicrylhydrazyl and beta-carotene/bleaching assays. The dichloromethane extract exhibited the greatest antimicrobial activity against 6 strains, followed by the ethanol extract and the essential oil.

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

PubMed

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

In vitro total phenolics, flavonoids contents and antioxidant activity of essential oil, various organic extracts from the leaves of tropical medicinal plant Tetrastigma from Sabah.

PubMed

Hossain, M Amzad; Shah, Muhammad Dawood; Gnanaraj, Charles; Iqbal, Muhammad

2011-09-01

To detect the in vitro total phenolics, flavonoids contents and antioxidant activity of essential oil, various organic extracts from the leaves of tropical medicinal plant Tetrastigma from Sabah. The dry powder leaves of Tetrastigma were extracted with different organic solvent such as hexane, ethyl acetate, chloroform, butanol and aqueous methanol. The total phenolic and total flavonoids contents of the essential oil and various organic extracts such as hexane, ethyl acetate, chloroform, butanol and aqueous ethanol were determined by Folin - Ciocalteu method and the assayed antioxidant activity was determined in vitro models such as antioxidant capacity by radical scavenging activity using α, α-diphenyl- β-picrylhydrazyl (DPPH) method. The total phenolic contents of the essential oil and different extracts as gallic acid equivalents were found to be highest in methanol extract (386.22 mg/g) followed by ethyl acetate (190.89 mg/g), chloroform (175.89 mg/g), hexane (173.44 mg/g), and butanol extract (131.72 mg/g) and the phenolic contents not detected in essential oil. The antioxidant capacity of the essential oil and different extracts as ascorbic acid standard was in the order of methanol extract > ethyl acetate extract >chloroform> butanol > hexane extract also the antioxidant activity was not detected in essential oil. The findings show that the extent of antioxidant activity of the essential oil and all extracts are in accordance with the amount of phenolics present in that extract. Leaves of Tetrastigma being rich in phenolics may provide a good source of antioxidant. Copyright © 2011 Hainan Medical College. Published by Elsevier B.V. All rights reserved.

21 CFR 182.50 - Certain other spices, seasonings, essential oils, oleoresins, and natural extracts.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Provisions § 182.50 Certain other spices, seasonings, essential oils, oleoresins, and natural extracts. Certain other spices, seasonings, essential oils, oleoresins, and natural extracts that are generally... 21 Food and Drugs 3 2014-04-01 2014-04-01 false Certain other spices, seasonings, essential oils...

Bank gully extraction from DEMs utilizing the geomorphologic features of a loess hilly area in China

NASA Astrophysics Data System (ADS)

Yang, Xin; Na, Jiaming; Tang, Guoan; Wang, Tingting; Zhu, Axing

2018-04-01

As one of most active gully types in the Chinese Loess Plateau, bank gullies generally indicate soil loss and land degradation. This study addressed the lack of detailed, large scale monitoring of bank gullies and proposed a semi-automatic method for extracting bank gullies, given typical topographic features based on 5 m resolution DEMs. First, channel networks, including bank gullies, are extracted through an iterative channel burn-in algorithm. Second, gully heads are correctly positioned based on the spatial relationship between gully heads and their corresponding gully shoulder lines. Third, bank gullies are distinguished from other gullies using the newly proposed topographic measurement of "relative gully depth (RGD)." The experimental results from the loess hilly area of the Linjiajian watershed in the Chinese Loess Plateau show that the producer accuracy reaches 87.5%. The accuracy is affected by the DEM resolution and RGD parameters, as well as the accuracy of the gully shoulder line. The application in the Madigou watershed with a high DEM resolution validated the duplicability of this method in other areas. The overall performance shows that bank gullies can be extracted with acceptable accuracy over a large area, which provides essential information for research on soil erosion, geomorphology, and environmental ecology.

Aboveground Biomass Estimation Using Reconstructed Feature of Airborne Discrete-Return LIDAR by Auto-Encoder Neural Network

NASA Astrophysics Data System (ADS)

Li, T.; Wang, Z.; Peng, J.

2018-04-01

Aboveground biomass (AGB) estimation is critical for quantifying carbon stocks and essential for evaluating carbon cycle. In recent years, airborne LiDAR shows its great ability for highly-precision AGB estimation. Most of the researches estimate AGB by the feature metrics extracted from the canopy height distribution of the point cloud which calculated based on precise digital terrain model (DTM). However, if forest canopy density is high, the probability of the LiDAR signal penetrating the canopy is lower, resulting in ground points is not enough to establish DTM. Then the distribution of forest canopy height is imprecise and some critical feature metrics which have a strong correlation with biomass such as percentiles, maximums, means and standard deviations of canopy point cloud can hardly be extracted correctly. In order to address this issue, we propose a strategy of first reconstructing LiDAR feature metrics through Auto-Encoder neural network and then using the reconstructed feature metrics to estimate AGB. To assess the prediction ability of the reconstructed feature metrics, both original and reconstructed feature metrics were regressed against field-observed AGB using the multiple stepwise regression (MS) and the partial least squares regression (PLS) respectively. The results showed that the estimation model using reconstructed feature metrics improved R2 by 5.44 %, 18.09 %, decreased RMSE value by 10.06 %, 22.13 % and reduced RMSEcv by 10.00 %, 21.70 % for AGB, respectively. Therefore, reconstructing LiDAR point feature metrics has potential for addressing AGB estimation challenge in dense canopy area.

Advanced analytical techniques for the extraction and characterization of plant-derived essential oils by gas chromatography with mass spectrometry.

PubMed

Waseem, Rabia; Low, Kah Hin

2015-02-01

In recent years, essential oils have received a growing interest because of the positive health effects of their novel characteristics such as antibacterial, antifungal, and antioxidant activities. For the extraction of plant-derived essential oils, there is the need of advanced analytical techniques and innovative methodologies. An exhaustive study of hydrodistillation, supercritical fluid extraction, ultrasound- and microwave-assisted extraction, solid-phase microextraction, pressurized liquid extraction, pressurized hot water extraction, liquid-liquid extraction, liquid-phase microextraction, matrix solid-phase dispersion, and gas chromatography (one- and two-dimensional) hyphenated with mass spectrometry for the extraction through various plant species and analysis of essential oils has been provided in this review. Essential oils are composed of mainly terpenes and terpenoids with low-molecular-weight aromatic and aliphatic constituents that are particularly important for public health. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[Orthogonal experiment using SFE-CO2 in extraction of essential oil from fresh Houttuynia cordata and analysis of essential oil by GC-MS].

PubMed

Meng, Jiang; Dong, Xiao-ping; Zhou, Yi-sheng; Jiang, Zhi-hong; Leung, Kelvin Sze-Yin; Zhao, Zhong-zhen

2007-02-01

To optimize the extraction procedure of essential oil from H. cordata using the SFE-CO2 and analyze the chemical composition of the essential oil. The extraction procedure of essential oil from fresh H. cordata was optimized with the orthogonal experiment. Essential oil of fresh H. cordata was analysed by GC-MS. The optimize preparative procedure was as follow: essential oil of H. cordata was extracted at a temperature of 35 degrees C, pressure of 15,000 kPa for 20 min. 38 chemical components were identified and the relative contents were quantified. The optimum preparative procedure is reliable and can guarantee the quality of essential oil.

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.

Comparison study of moisture content, colour properties and essential oil compounds extracted by hydrodistillation and supercritical fluid extraction between stem and leaves of lemongrass (Cymbopogun citratus)

NASA Astrophysics Data System (ADS)

Kamaruddin, Shazlin; Mustapha, Wan Aida Wan; Haiyee, Zaibunnisa Abdul

2018-04-01

The objectives of this study were to compare the properties of moisture content, colour and essential oil compounds between stem and leaves of lemongrass (Cymbopogun citratus). The essential oil was extracted using two different methods which are hydrodistillation and supercritical fluid extraction (SFE). There was no significant difference of moisture content between stem and leaves of lemongrass. The lightness (L) and yellowness (+b) values of the stems were significantly higher (p<0.05) compared to the leaves. The highest yield of essential oil was obtained by extraction using supercritical fluid extraction (SFE) in leaves (˜ 0.7%) by treatment at 1700psi and 50°C. The main compound of extracted essential oil was citral (geranial and neral).

A concept for extraction of habitat features from laser scanning and hypersprectral imaging for evaluation of Natura 2000 sites - the ChangeHabitats2 project approach

NASA Astrophysics Data System (ADS)

Székely, B.; Kania, A.; Pfeifer, N.; Heilmeier, H.; Tamás, J.; Szöllősi, N.; Mücke, W.

2012-04-01

The goal of the ChangeHabitats2 project is the development of cost- and time-efficient habitat assessment strategies by employing effective field work techniques supported by modern airborne remote sensing methods, i.e. hyperspectral imagery and laser scanning (LiDAR). An essential task of the project is the design of a novel field work technique that on the one hand fulfills the reporting requirements of the Flora-Fauna-Habitat (FFH-) directive and on the other hand serves as a reference for the aerial data analysis. Correlations between parameters derived from remotely sensed data and terrestrial field measurements shall be exploited in order to create half- or fully-automated methods for the extraction of relevant Natura2000 habitat parameters. As a result of these efforts a comprehensive conceptual model has been developed for extraction and integration of Natura 2000 relevant geospatial data. This scheme is an attempt to integrate various activities within ChangeHabitats2 project defining pathways of development, as well as encompassing existing data processing chains, theoretical approaches and field work. The conceptual model includes definition of processing levels (similar to those existing in remote sensing), whereas these levels cover the range from the raw data to the extracted habitat feature. For instance, the amount of dead wood (standing or lying on the surface) is an important evaluation criterion for the habitat. The tree trunks lying on the ground surface typically can be extracted from the LiDAR point cloud, and the amount of wood can be estimated accordingly. The final result will be considered as a habitat feature derived from laser scanning data. Furthermore, we are also interested not only in the determination of the specific habitat feature, but also in the detection of its variations (especially in deterioration). In this approach the variation of this important habitat feature is considered to be a differential habitat feature, that can be immediately used in the evaluation of the Natura 2000 sites. The goal of the project is the identification of many potential habitat features that can be extracted or implied from remotely sensed data, and the development of processing chains to provide data that can be used in the everyday field work of ecological site assessment. This is a contribution of ChangeHabitats2 project financed by the European Union within the Industry Academia Partnership Pathways (IAPP), as a part of FP7 Marie Curie Actions.

Chemical composition and antibacterial activity of methanolic extract and essential oil of Iranian Teucrium polium against some of phytobacteria.

PubMed

Purnavab, S; Ketabchi, S; Rowshan, V

2015-01-01

The antibacterial activity of essential oil and methanolic extract of Teucrium polium was determined against Pseudomonas aeruginosa, Pantoea agglomerans, Brenneria nigrifluens, Rhizobium radiobacter, Rhizobium vitis, Streptomyces scabies, Ralstonia solanacearum, Xanthomonas campestris and Pectobacterium cartovorum by disc diffusion method. Minimum inhibitory concentration and minimum bactericidal concentration were determined by using the serial dilution method. Chemical composition of essential oil and methanolic extract was determined by GC-MS and HPLC. α-Pinene (25.769%) and myrcene (12.507) were of the highest percentage in T. polium essential oil, and sinapic acid (15.553 mg/g) and eugenol (6.805 mg/g) were the major compounds in the methanolic extract. Our results indicate that both methanolic extract and essential oil did not show antibacterial activity against P. aeruginosa. Also the essential oil did not show antibacterial activity against P. cartovorum. In general, both methanolic extract and essential oil showed the same antibacterial activity against R. solanacearum, P. agglomerans, B. nigrifluens and S. scabies.

Improved solvent-free microwave extraction of essential oil from dried Cuminum cyminum L. and Zanthoxylum bungeanum Maxim.

PubMed

Wang, Ziming; Ding, Lan; Li, Tiechun; Zhou, Xin; Wang, Lu; Zhang, Hanqi; Liu, Li; Li, Ying; Liu, Zhihong; Wang, Hongju; Zeng, Hong; He, Hui

2006-01-13

Solvent-free microwave extraction (SFME) is a recently developed green technique which is performed in atmospheric conditions without adding any solvent or water. SFME has already been applied to extraction of essential oil from fresh plant materials or dried materials prior moistened. The essential oil is evaporated by the in situ water in the plant materials. In this paper, it was observed that an improved SFME, in which a kind of microwave absorption solid medium, such as carbonyl iron powders (CIP), was added and mixed with the sample, can be applied to extraction of essential oil from the dried plant materials without any pretreatment. Because the microwave absorption capacity of CIP is much better than that of water, the extraction time while using the improved SFME is no more than 30 min using a microwave power of 85 W. Compared to the conventional SFME, the advantages of improved SFME were to speed up the extraction rate and need no pretreatment. Improved SFME has been compared with conventional SFME, microwave-assisted hydrodistillation (MAHD) and conventional hydrodistillation (HD) for the extraction of essential oil from dried Cuminum cyminum L. and Zanthoxylum bungeanum Maxim. By using GC-MS system the compositions of essential oil extracted by applying four kinds of extraction methods were identified. There was no obvious difference in the quality of essential oils obtained by the four kinds of extraction methods.

Composition of the Essential Oil of Aristolochia Manshurientsis Kom

NASA Astrophysics Data System (ADS)

Zhao, Xiuhong; Xin, Guang; Zhao, Lichun; Xiao, Zhigang; Xue, Bai

2018-03-01

This study demonstrated the chemical constituents of the essential oil of Aristolochia manshurientsis Kom and improved the essential oil efficiency by the enzyme-assisted extraction followed by hydrodistillation. The essential oils of Aristolochia manshurientsis Kom acquired by hydrodistillation after the solvent extraction with and without the assistance of cellulase have been investigated by gas chromatography/Mass spectrometry (GC-MS). The predominant constituents of both types of essential oils are camphene, 1,7,7-trimethyl-bicyclo [2.2.1] hept-2-yl acetate, 1,6-dimethyl-4-(1-methylethyl) naphthalene, caryophyllene oxide, borneol, and (-)-Spathulenol. The enzyme-assisted extraction not only increased extracting efficiency of the essential oil from 4.93% to 9.36%, but also facilitated the extraction of additional eight compounds such as 2-methano(-6,6-dimethyl) bicycle [3.1.1] hept-2-ene, (+)--terpineol and 1-propyl-3-(propen-1-yl) adamantane, which were not identified from the non-enzyme extraction sample.

Chemical composition and bioactivity of different oregano (Origanum vulgare) extracts and essential oil.

PubMed

Teixeira, Bárbara; Marques, António; Ramos, Cristina; Serrano, Carmo; Matos, Olívia; Neng, Nuno R; Nogueira, José M F; Saraiva, Jorge Alexandre; Nunes, Maria Leonor

2013-08-30

There is a growing interest in industry to replace synthetic chemicals by natural products with bioactive properties. Aromatic plants are excellent sources of bioactive compounds that can be extracted using several processes. As far as oregano is concerned, studies are lacking addressing the effect of extraction processes in bioactivity of extracts. This study aimed to characterise the in vitro antioxidant and antibacterial properties of oregano (Origanum vulgare) essential oil and extracts (in hot and cold water, and ethanol), and the chemical composition of its essential oil. The major components of oregano essential oil were carvacrol, β-fenchyl alcohol, thymol, and γ-terpinene. Hot water extract had the strongest antioxidant properties and the highest phenolic content. All extracts were ineffective in inhibiting the growth of the seven tested bacteria. In contrast, the essential oil inhibited the growth of all bacteria, causing greater reductions on both Listeria strains (L. monocytogenes and L. innocua). O. vulgare extracts and essential oil from Portuguese origin are strong candidates to replace synthetic chemicals used by the industry. © 2013 Society of Chemical Industry.

Microwave-assisted extraction of Nigella sativa L. essential oil and evaluation of its antioxidant activity.

PubMed

Abedi, Abdol-Samad; Rismanchi, Marjan; Shahdoostkhany, Mehrnoush; Mohammadi, Abdorreza; Mortazavian, Amir Mohammad

2017-11-01

It has been previously reported that the essential oil of Nigella sativa L. seeds and its major active component, thymoquinone (TQ), possess a broad variety of biological activities and therapeutic properties. In this work, microwave-assisted extraction (MAE) of the essential oil from Nigella sativa L. seeds and its antioxidant activity were studied. Response surface methodology based on central composite design was used to evaluate the effects of extraction time, irradiation power and moisture content on extraction yield and TQ content. Optimal parameters obtained by CCD and RSM were extraction time 30 min, irradiation power 450 W, and moisture content 50%. The extraction yield and TQ content of the essential oil were 0.33 and 20% under the optimum conditions, respectively. In contrast, extraction yield and TQ amount of oil obtained by hydrodistillation (HD) were 0.23 and 3.71%, respectively. The main constituents of the essential oil extracted by MAE and HD were p -cymene, TQ, α-thujene and longifolene, comprising more than 60% of total peak area. The antioxidant capacity of essential oils extracted by different methods were evaluated using 2,2-diphenyl-1-picrylhydrazyl and Ferric reducing antioxidant power assays, and compared with traditional antioxidants. The results showed that MAE method was a viable alternative to HD for the essential oil extraction from N. sativa seeds due to the excellent extraction efficiency, higher thymoquinone content, and stronger antioxidant activity.

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.

Antioxidant and Antibacterial Activities of Crude Extracts and Essential Oils of Syzygium cumini Leaves

PubMed Central

Mohamed, Amal A.; Ali, Sami I.; El-Baz, Farouk K.

2013-01-01

This research highlights the chemical composition, antioxidant and antibacterial activities of essential oils and various crude extracts (using methanol and methylene chloride) from Syzygium cumini leaves. Essential oils were analyzed by gas chromatography-mass spectrometry (GC-MS).The abundant constituents of the oils were: α-pinene (32.32%), β-pinene (12.44%), trans-caryophyllene (11.19%), 1, 3, 6-octatriene (8.41%), delta-3-carene (5.55%), α-caryophyllene (4.36%), and α-limonene (3.42%).The antioxidant activities of all extracts were examined using two complementary methods, namely diphenylpicrylhydrazyl (DPPH) and ferric reducing power (FRAP). In both methods, the methanol extract exhibited a higher activity than methylene chloride and essential oil extracts. A higher content of both total phenolics and flavonoids were found in the methanolic extract compared with other extracts. Furthermore, the methanol extract had higher antibacterial activity compared to methylene chloride and the essential oil extracts. Due to their antioxidant and antibacterial properties, the leaf extracts from S. cumini may be used as natural preservative ingredients in food and/or pharmaceutical industries. PMID:23593183

Antioxidant and antibacterial activities of crude extracts and essential oils of Syzygium cumini leaves.

PubMed

Mohamed, Amal A; Ali, Sami I; El-Baz, Farouk K

2013-01-01

This research highlights the chemical composition, antioxidant and antibacterial activities of essential oils and various crude extracts (using methanol and methylene chloride) from Syzygium cumini leaves. Essential oils were analyzed by gas chromatography-mass spectrometry (GC-MS).The abundant constituents of the oils were: α-pinene (32.32%), β-pinene (12.44%), trans-caryophyllene (11.19%), 1, 3, 6-octatriene (8.41%), delta-3-carene (5.55%), α-caryophyllene (4.36%), and α-limonene (3.42%).The antioxidant activities of all extracts were examined using two complementary methods, namely diphenylpicrylhydrazyl (DPPH) and ferric reducing power (FRAP). In both methods, the methanol extract exhibited a higher activity than methylene chloride and essential oil extracts. A higher content of both total phenolics and flavonoids were found in the methanolic extract compared with other extracts. Furthermore, the methanol extract had higher antibacterial activity compared to methylene chloride and the essential oil extracts. Due to their antioxidant and antibacterial properties, the leaf extracts from S. cumini may be used as natural preservative ingredients in food and/or pharmaceutical industries.

[Antiradical properties of essential oils and extracts from clove bud and pimento].

PubMed

Misharina, T A; Alinkina, E S; Medvedeva, I B

2015-01-01

The antiradical properties of essential oils and extracts from the clove bud (Eugenia caryophyllata Thumb.) and berries of tree (Pimenta dioica (L.) Meriff) were studied and compared with the properties of synthetic antioxidant ionol (2,6-ditret-butyl-4-hydroxytoluene, BHT) in model reactions with the stable free 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical. The essential oils of clove bud and pimento had qualitatively close composition of the main components but differed by their quantitative content. In the studied samples, eugenol was the main compound with high antiradical activity. The reaction rates of essential oils and extracts with the DPPH radical were practically the same for essential oils and twice the reaction rate of BHT. The values of antiradical efficiency (AE) were also close for essential oils and were twice that for extracts and ionol. A synergetic action of components in the essential oil and extract of pimento on antiradical efficiency values was found.

Edge enhancement and noise suppression for infrared image based on feature analysis

NASA Astrophysics Data System (ADS)

Jiang, Meng

2018-06-01

Infrared images are often suffering from background noise, blurred edges, few details and low signal-to-noise ratios. To improve infrared image quality, it is essential to suppress noise and enhance edges simultaneously. To realize it in this paper, we propose a novel algorithm based on feature analysis in shearlet domain. Firstly, as one of multi-scale geometric analysis (MGA), we introduce the theory and superiority of shearlet transform. Secondly, after analyzing the defects of traditional thresholding technique to suppress noise, we propose a novel feature extraction distinguishing image structures from noise well and use it to improve the traditional thresholding technique. Thirdly, with computing the correlations between neighboring shearlet coefficients, the feature attribute maps identifying the weak detail and strong edges are completed to improve the generalized unsharped masking (GUM). At last, experiment results with infrared images captured in different scenes demonstrate that the proposed algorithm suppresses noise efficiently and enhances image edges adaptively.

Deep Learning in Label-free Cell Classification

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

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individualmore » cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.« less

Deep Learning in Label-free Cell Classification

DOE PAGES

Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; ...

2016-03-15

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individualmore » cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.« less

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

PubMed

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

2013-01-01

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

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

PubMed Central

Li, Hui; Wang, Rong; Gan, Yong

2017-01-01

Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area. PMID:28744305

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

PubMed

Wang, Xiao; Li, Hui; Wang, Rong; Zhang, Qiuwen; Zhang, Weiwei; Gan, Yong

2017-01-01

Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area.

Correlative feature analysis of FFDM images

NASA Astrophysics Data System (ADS)

Yuan, Yading; Giger, Maryellen L.; Li, Hui; Sennett, Charlene

2008-03-01

Identifying the corresponding image pair of a lesion is an essential step for combining information from different views of the lesion to improve the diagnostic ability of both radiologists and CAD systems. Because of the non-rigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this study, we present a computerized framework that differentiates the corresponding images from different views of a lesion from non-corresponding ones. A dual-stage segmentation method, which employs an initial radial gradient index(RGI) based segmentation and an active contour model, was initially applied to extract mass lesions from the surrounding tissues. Then various lesion features were automatically extracted from each of the two views of each lesion to quantify the characteristics of margin, shape, size, texture and context of the lesion, as well as its distance to nipple. We employed a two-step method to select an effective subset of features, and combined it with a BANN to obtain a discriminant score, which yielded an estimate of the probability that the two images are of the same physical lesion. ROC analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing between corresponding and non-corresponding pairs. By using a FFDM database with 124 corresponding image pairs and 35 non-corresponding pairs, the distance feature yielded an AUC (area under the ROC curve) of 0.8 with leave-one-out evaluation by lesion, and the feature subset, which includes distance feature, lesion size and lesion contrast, yielded an AUC of 0.86. The improvement by using multiple features was statistically significant as compared to single feature performance. (p<0.001)

Historical feature pattern extraction based network attack situation sensing algorithm.

PubMed

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

PubMed Central

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously. PMID:24892054

Autofocus algorithm for synthetic aperture radar imaging with large curvilinear apertures

NASA Astrophysics Data System (ADS)

Bleszynski, E.; Bleszynski, M.; Jaroszewicz, T.

2013-05-01

An approach to autofocusing for large curved synthetic aperture radar (SAR) apertures is presented. Its essential feature is that phase corrections are being extracted not directly from SAR images, but rather from reconstructed SAR phase-history data representing windowed patches of the scene, of sizes sufficiently small to allow the linearization of the forward- and back-projection formulae. The algorithm processes data associated with each patch independently and in two steps. The first step employs a phase-gradient-type method in which phase correction compensating (possibly rapid) trajectory perturbations are estimated from the reconstructed phase history for the dominant scattering point on the patch. The second step uses phase-gradient-corrected data and extracts the absolute phase value, removing in this way phase ambiguities and reducing possible imperfections of the first stage, and providing the distances between the sensor and the scattering point with accuracy comparable to the wavelength. The features of the proposed autofocusing method are illustrated in its applications to intentionally corrupted small-scene 2006 Gotcha data. The examples include the extraction of absolute phases (ranges) for selected prominent point targets. They are then used to focus the scene and determine relative target-target distances.

Chemistry Perfumes Your Daily Life

NASA Astrophysics Data System (ADS)

Fortineau, Anne-Dominique

2004-01-01

The use of perfumes can be tracked back to many early civilizations. Historically, perfumes were composed exclusively of natural ingredients, mainly essential oils, and were reserved for the wealthiest people. The use of synthetic ingredients marked the beginning of modern perfumery at the end of the 19th century. Nowadays, perfumes are available to everyone and are present in a wide range of consumer goods. The methods used to extract perfume ingredients from their natural sources have changed over time as technology has advanced. Depending on the material, expression, distillation, and solvent extraction are the main techniques used to extract the odorant components from a natural source. Synthetic organic chemistry has provided perfumers with novel ingredients that are cheaper and more stable than many natural materials over the years. Today, more than 3000 fragrance ingredients are estimated to be available to perfumers to create a harmonious composition of head (for example, citrusy), heart (for example, fruity floral) and base notes (for example, musky). Future developments and uses of perfume are endless and as Ernest Beaux, the perfumer who created Chanel N85, once said, "In perfumery the future lies primarily in the hands of the chemists." Featured on the Cover See Featured Molecules .

A comparison of essential oils obtained from lavandin via different extraction processes: Ultrasound, microwave, turbohydrodistillation, steam and hydrodistillation.

PubMed

Périno-Issartier, Sandrine; Ginies, Christian; Cravotto, Giancarlo; Chemat, Farid

2013-08-30

A total of eight extraction techniques ranging from conventional methods (hydrodistillation (HD), steam distillation (SD), turbohydrodistillation (THD)), through innovative techniques (ultrasound assisted extraction (US-SD) and finishing with microwave assisted extraction techniques such as In situ microwave-generated hydrodistillation (ISMH), microwave steam distillation (MSD), microwave hydrodiffusion and gravity (MHG), and microwave steam diffusion (MSDf)) were used to extract essential oil from lavandin flowers and their results were compared. Extraction time, yield, essential oil composition and sensorial analysis were considered as the principal terms of comparison. The essential oils extracted using the more innovative processes were quantitatively (yield) and qualitatively (aromatic profile) similar to those obtained from the conventional techniques. The method which gave the best results was the microwave hydrodiffusion and gravity (MHG) method which gave reduced extraction time (30min against 220min for SD) and gave no differences in essential oil yield and sensorial perception. Copyright © 2013 Elsevier B.V. All rights reserved.

Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics.

PubMed

Abdolali, Fatemeh; Zoroofi, Reza Aghaeizadeh; Otake, Yoshito; Sato, Yoshinobu

2017-02-01

Accurate detection of maxillofacial cysts is an essential step for diagnosis, monitoring and planning therapeutic intervention. Cysts can be of various sizes and shapes and existing detection methods lead to poor results. Customizing automatic detection systems to gain sufficient accuracy in clinical practice is highly challenging. For this purpose, integrating the engineering knowledge in efficient feature extraction is essential. This paper presents a novel framework for maxillofacial cysts detection. A hybrid methodology based on surface and texture information is introduced. The proposed approach consists of three main steps as follows: At first, each cystic lesion is segmented with high accuracy. Then, in the second and third steps, feature extraction and classification are performed. Contourlet and SPHARM coefficients are utilized as texture and shape features which are fed into the classifier. Two different classifiers are used in this study, i.e. support vector machine and sparse discriminant analysis. Generally SPHARM coefficients are estimated by the iterative residual fitting (IRF) algorithm which is based on stepwise regression method. In order to improve the accuracy of IRF estimation, a method based on extra orthogonalization is employed to reduce linear dependency. We have utilized a ground-truth dataset consisting of cone beam CT images of 96 patients, belonging to three maxillofacial cyst categories: radicular cyst, dentigerous cyst and keratocystic odontogenic tumor. Using orthogonalized SPHARM, residual sum of squares is decreased which leads to a more accurate estimation. Analysis of the results based on statistical measures such as specificity, sensitivity, positive predictive value and negative predictive value is reported. The classification rate of 96.48% is achieved using sparse discriminant analysis and orthogonalized SPHARM features. Classification accuracy at least improved by 8.94% with respect to conventional features. This study demonstrated that our proposed methodology can improve the computer assisted diagnosis (CAD) performance by incorporating more discriminative features. Using orthogonalized SPHARM is promising in computerized cyst detection and may have a significant impact in future CAD systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Evaluation of anxiolytic and sedative effect of essential oil and hydroalcoholic extract of Ocimum basilicum L. and chemical composition of its essential oil.

PubMed

Rabbani, Mohammed; Sajjadi, Seyed Ebrahim; Vaezi, Arefeh

2015-01-01

Ocimum basilicum belongs to Lamiaceae family and has been used for the treatment of wide range of diseases in traditional medicine in Iranian folk medicine. Due to the progressive need to anti-anxiety medications and because of the similarity between O. basilicum and Salvia officinalis, which has anti-anxiety effects, we decided to investigate the anxiolytic and sedative activity of hydroalcoholic extract and essential oil of O. basilicum in mice by utilizing an elevated plus maze and locomotor activity meter. The chemical composition of the plant essential oil was also determined. The essential oil and hydroalcoholic extract of this plant were administered intraperitoneally to male Syrian mice at various doses (100, 150 and 200 mg/kg of hydroalcoholic extract and 200 mg/kg of essential oil) 30 min before starting the experiment. The amount of hydroalcoholic extract was 18.6% w/w and the essential oil was 0.34% v/w. The major components of the essential oil were methyl chavicol (42.8%), geranial (13.0%), neral (12.2%) and β-caryophyllene (7.2%). HE at 150 and 200 mg/kg and EO at 200 mg/kg significantly increased the time passed in open arms in comparison to control group. This finding was not significant for the dose of 100 mg/kg of the extract. None of the dosages had significant effect on the number of entrance to the open arms. Moreover, both the hydroalcoholic extract and the essential oil decreased the locomotion of mice in comparison to the control group. This study shows the anxiolytic and sedative effect of hydroalcoholic extract and essential oil of O. basilicum. The anti-anxiety and sedative effect of essential oil was higher than the hydroalcoholic extract with the same doses. These effects could be due to the phenol components of O. basilicum.

Evaluation of anxiolytic and sedative effect of essential oil and hydroalcoholic extract of Ocimum basilicum L. and chemical composition of its essential oil

PubMed Central

Rabbani, Mohammed; Sajjadi, Seyed Ebrahim; Vaezi, Arefeh

2015-01-01

Ocimum basilicum belongs to Lamiaceae family and has been used for the treatment of wide range of diseases in traditional medicine in Iranian folk medicine. Due to the progressive need to anti-anxiety medications and because of the similarity between O. basilicum and Salvia officinalis, which has anti-anxiety effects, we decided to investigate the anxiolytic and sedative activity of hydroalcoholic extract and essential oil of O. basilicum in mice by utilizing an elevated plus maze and locomotor activity meter. The chemical composition of the plant essential oil was also determined. The essential oil and hydroalcoholic extract of this plant were administered intraperitoneally to male Syrian mice at various doses (100, 150 and 200 mg/kg of hydroalcoholic extract and 200 mg/kg of essential oil) 30 min before starting the experiment. The amount of hydroalcoholic extract was 18.6% w/w and the essential oil was 0.34% v/w. The major components of the essential oil were methyl chavicol (42.8%), geranial (13.0%), neral (12.2%) and β-caryophyllene (7.2%). HE at 150 and 200 mg/kg and EO at 200 mg/kg significantly increased the time passed in open arms in comparison to control group. This finding was not significant for the dose of 100 mg/kg of the extract. None of the dosages had significant effect on the number of entrance to the open arms. Moreover, both the hydroalcoholic extract and the essential oil decreased the locomotion of mice in comparison to the control group. This study shows the anxiolytic and sedative effect of hydroalcoholic extract and essential oil of O. basilicum. The anti-anxiety and sedative effect of essential oil was higher than the hydroalcoholic extract with the same doses. These effects could be due to the phenol components of O. basilicum. PMID:26779273

Experimental and theoretical description of the optical properties of Myrcia sylvatica essential oil.

PubMed

Silva Prado, Andriele da; Leal, Luciano Almeida; de Brito, Patrick Pascoal; de Almeida Fonseca, Antonio Luciano; Blawid, Stefan; Ceschin, Artemis Marti; Veras Mourão, Rosa Helena; da Silva Júnior, Antônio Quaresma; Antonio da Silva Filho, Demétrio; Ribeiro Junior, Luiz Antonio; Ferreira da Cunha, Wiliam

2017-07-01

We present an extensive study of the optical properties of Myrcia sylvatica essential oil with the goal of investigating the suitability of its material system for uses in organic photovoltaics. The methods of extraction, experimental analysis, and theoretical modeling are described in detail. The precise composition of the oil in our samples is determined via gas chromatography, mass spectrometry, and X-ray scattering techniques. The measurements indicate that, indeed, the material system of Myrcia sylvatica essential oil may be successfully employed for the design of organic photovoltaic devices. The optical absorption of the molecules that compose the oil are calculated using time-dependent density functional theory and used to explain the measured UV-Vis spectra of the oil. We show that it is sufficient to consider the α-bisabolol/cadalene pair, two of the main constituents of the oil, to obtain the main features of the UV-Vis spectra. This finding is of importance for future works that aim to use Myrcia sylvatica essential oil as a photovoltaic material.

Medical image registration based on normalized multidimensional mutual information

NASA Astrophysics Data System (ADS)

Li, Qi; Ji, Hongbing; Tong, Ming

2009-10-01

Registration of medical images is an essential research topic in medical image processing and applications, and especially a preliminary and key step for multimodality image fusion. This paper offers a solution to medical image registration based on normalized multi-dimensional mutual information. Firstly, affine transformation with translational and rotational parameters is applied to the floating image. Then ordinal features are extracted by ordinal filters with different orientations to represent spatial information in medical images. Integrating ordinal features with pixel intensities, the normalized multi-dimensional mutual information is defined as similarity criterion to register multimodality images. Finally the immune algorithm is used to search registration parameters. The experimental results demonstrate the effectiveness of the proposed registration scheme.

Structural classification of proteins using texture descriptors extracted from the cellular automata image.

PubMed

Kavianpour, Hamidreza; Vasighi, Mahdi

2017-02-01

Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods. In this work, a binary representation of protein sequences is introduced based on reduced amino acids alphabets according to surrounding hydrophobicity index. Many important features which are hidden in these long binary sequences can be clearly displayed through their cellular automata images. The extracted features from these images are used to build a classification model by support vector machine. Comparing to previous studies on the several benchmark datasets, the promising classification rates obtained by tenfold cross-validation imply that the current approach can help in revealing some inherent features deeply hidden in protein sequences and improve the quality of predicting protein structural class.

Essential oils and herbal extracts as antimicrobial agents in cosmetic emulsion.

PubMed

Herman, Anna; Herman, Andrzej Przemysław; Domagalska, Beata Wanda; Młynarczyk, Andrzej

2013-06-01

The cosmetic industry adapts to the needs of consumers seeking to limit the use of preservatives and develop of preservative-free or self-preserving cosmetics, where preservatives are replaced by raw materials of plant origin. The aim of study was a comparison of the antimicrobial activity of extracts (Matricaria chamomilla, Aloe vera, Calendula officinalis) and essential oils (Lavandulla officinallis, Melaleuca alternifolia, Cinnamomum zeylanicum) with methylparaben. Extracts (2.5 %), essential oils (2.5 %) and methylparaben (0.4 %) were tested against Pseudomonas aeruginosa ATCC 27853, Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 29213, Candida albicans ATCC 14053. Essentials oils showed higher inhibitory activity against tested microorganism strain than extracts and methylparaben. Depending on tested microorganism strain, all tested extracts and essential oils show antimicrobial activity 0.8-1.7 and 1-3.5 times stronger than methylparaben, respectively. This shows that tested extracts and essential oils could replace use of methylparaben, at the same time giving a guarantee of microbiological purity of the cosmetic under its use and storage.

Chemical Composition, Anticonvulsant Activity, and Toxicity of Essential Oil and Methanolic Extract of Elettaria cardamomum.

PubMed

Masoumi-Ardakani, Yaser; Mandegary, Ali; Esmaeilpour, Khadijeh; Najafipour, Hamid; Sharififar, Fariba; Pakravanan, Mahboobeh; Ghazvini, Hamed

2016-11-01

Elettaria cardamomum is an aromatic spice (cardamom) native to the humid Asian areas, which contains some compounds with a potential anticonvulsant activity. Various pharmacological properties such as anti-inflammatory, analgesic, antioxidant, and antimicrobial effects have been related to this plant. This research was conducted to examine the probable protective impact of the essential oil and methanolic extract of E. cardamomum against chemically (pentylentetrazole)- and electrically (maximal electroshock)-induced seizures in mice. In addition, neurotoxicity, acute lethality, and phytochemistry of the essential oil and methanolic extract were estimated. The TLC method showed the presence of kaempferol, rutin, and quercetin in the extract, and the concentration of quercetin in the extract was 0.5 µg/mL. The major compounds in the essential oil were 1,8-cineole (45.6 %), α -terpinyl acetate (33.7 %), sabinene (3.8 %), 4-terpinen-4-ol (2.4 %), and myrcene (2.2 %), respectively. The extract and essential oil showed significant neurotoxicity in the rotarod test at the doses of 1.5 g/kg and 0.75 mL/kg, respectively. No mortalities were observed up to the doses of 2 g/kg and 0.75 mL/kg for the extract and essential oil. The essential oil was effective in both the pentylentetrazole and maximal electroshock models; however, the extract was only effective in the pentylentetrazole model. The study suggested that E. cardamomum methanolic extract had no significant lethality in mice. Both the essential oil and methanolic extract showed movement toxicity. Anticonvulsant effects of E. cardamomum were negligible against the seizures induced by pentylentetrazole and maximal electroshock. Georg Thieme Verlag KG Stuttgart · New York.

In vitro antioxidant and antihemolytic effects of the essential oil and methanolic extract of Allium rotundum L.

PubMed

Assadpour, S; Nabavi, S M; Nabavi, S F; Dehpour, A A; Ebrahimzadeh, M A

2016-12-01

A plethora of scientific evidence showed that several plant species from the genus Allium (Alliaceae) possess multiple therapeutic effects. Present paper aimed to examine the antioxidant and antihemolytic activities of the essential oil and methanol extract Allium rotundum L. through different in vitro assays. 1,1-diphenyl-2-picryl hydroxyl radical (DPPH), nitric oxide as well as hydrogen peroxide scavenging, Fe2+ chelating, reducing power and also hemoglobin-induced linoleic acid peroxidation assay systems have been utilized to examine antioxidant effects of these samples. Total amounts of phenolic and flavonoid contents were calculated. The antihemolytic effect was investigated against hemolysis induced by hydrogen peroxide in rat erythrocytes. Also, mineral contents of plant were evaluated by atomic absorption spectroscopy. IC50 for DPPH radical-scavenging activity were 284 ± 11.64 for methanol extract and 1264 ± 45.60 µg ml-1 for essential oil, respectively. The extract has shown better reducing effects versus essential oil. The extract also demonstrated better activity in nitric oxide-scavenging activity. IC50 were 464 ± 19.68 for extract and 1093 ± 38.25 µg ml-1 for essential oil. The extract shows better activity than essential oil in Fe2+ chelating system. IC50 were 100 ± 3.75 for extract and 1223 ± 36.25 µg ml-1 for essential oil. The A. rotundum extract and essential oil showed significant H2O2 scavenging effects at dose-dependent manners. IC50 was 786 ± 29.08 mg ml-1 for essential oil. The amounts of eight elements were determined. The concentrations of elements were in the order: Mn> Fe> Zn> Cu> Ni> Cd. The extract showed a higher antioxidant effect in all tested models including DPPH, nitric oxide, reducing power as well as iron chelating and antihemolytic activities than essential oil. The latter showed more potent antioxidant activity in scavenging H2O2 and lipid peroxidation model. Antioxidant activities of extract may be attributed at least in part, due to its phenolic and flavonoid contents.

Sentence alignment using feed forward neural network.

PubMed

Fattah, Mohamed Abdel; Ren, Fuji; Kuroiwa, Shingo

2006-12-01

Parallel corpora have become an essential resource for work in multi lingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross language information retrieval and machine translation applications. In this paper, we present a new approach to align sentences in bilingual parallel corpora based on feed forward neural network classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuate score, and cognate score values. A set of manually prepared training data has been assigned to train the feed forward neural network. Another set of data was used for testing. Using this new approach, we could achieve an error reduction of 60% over length based approach when applied on English-Arabic parallel documents. Moreover this new approach is valid for any language pair and it is quite flexible approach since the feature parameter vector may contain more/less or different features than that we used in our system such as lexical match feature.

Evaluation of the essential oil of Foeniculum vulgare Mill (fennel) fruits extracted by three different extraction methods by GC/MS.

PubMed

Hammouda, Faiza M; Saleh, Mahmoud A; Abdel-Azim, Nahla S; Shams, Khaled A; Ismail, Shams I; Shahat, Abdelaaty A; Saleh, Ibrahim A

2014-01-01

Hydrodistillation (HD) and steam-distillation, or solvent extraction methods of essential oils have some disadvantages like thermal decomposition of extracts, its contamination with solvent or solvent residues and the pollution of residual vegetal material with solvent which can be also an environmental problem. Thus, new green techniques, such as supercritical fluid extraction and microwave assisted techniques, are potential solutions to overcome these disadvantages. The aim of this study was to evaluate the essential oil of Foeniculum vulgare subsp. Piperitum fruits extracted by three different extraction methods viz. Supercritical fluid extraction (SFE) using CO2, microwave-assisted extraction (MAE) and hydro-distillation (HD) using gas chromatography-mass spectrometry (GC/MS). The results revealed that both MAE and SFE enhanced the extraction efficiency of the interested components. MAE gave the highest yield of oil as well as higher percentage of Fenchone (28%), whereas SFE gave the highest percentage of anethol (72%). Microwave-assisted extraction (MAE) and supercritical fluid extraction (SFE) not only enhanced the essential oil extraction but also saved time, reduced the solvents use and produced, ecologically, green technologies.

Ionic liquid-based microwave-assisted extraction of essential oil and biphenyl cyclooctene lignans from Schisandra chinensis Baill fruits.

PubMed

Ma, Chun-hui; Liu, Ting-ting; Yang, Lei; Zu, Yuan-gang; Chen, Xiaoqiang; Zhang, Lin; Zhang, Ying; Zhao, Chunjian

2011-12-02

Ionic liquid-based microwave-assisted extraction (ILMAE) has been successfully applied in extracting essential oil and four kinds of biphenyl cyclooctene lignans from Schisandra chinensis Baill. 0.25 M 1-lauryl-3-methylimidazolium bromide ionic liquid is selected as solvent. The optimum parameters of dealing with 25.0 g sample are 385 W irradiation power, 40 min microwave extraction time and 1:12 solid-liquid ratio. The yields of essential oil and lignans are 12.12±0.37 ml/kg and 250.2±38.2 mg/kg under the optimum conditions. The composition of the essential oil extracted by hydro-distillation, steam-distillation and ILMAE is analyzed by GC-MS. With ILMAE method, the energy consumption time has not only been shortened to 40 min (hydro-distillation 3.0 h for extracting essential oil and reflux extraction 4.0 h for extracting lignans, respectively), but also the extraction efficiency has been improved (extraction of lignans and distillation of essential oil at the same time) and reduces the environmental pollution. S. chinensis materials treated by different methods are observed by scanning electronic microscopy. Micrographs provide more evidence to prove that ILMAE is a better and faster method. The experimental results also indicate that ILMAE is a simple and efficient technique for sample preparation. Copyright © 2011 Elsevier B.V. All rights reserved.

The Application of Special Computing Techniques to Speed-Up Image Feature Extraction and Processing Techniques.

DTIC Science & Technology

1981-12-01

ocessors has led to the possibility of implementing a large number of image processing functions in near real time . ~CC~ jnro _ j:% UNLSSFE (b-.YC ASIIAINO...to the possibility of implementing a large number of image processing functions in near " real - time ," a result which is essential to establishing a...for example, and S) rapid image handling for near real - time in- teraction by a user at a display. For example, for a large resolution image, say

[Comparison of Chemical Components of Essential Oil from Ocimum basilicum var. pilosum Extracted by Supercritical CO2 Fluid and Steam Distillation].

PubMed

Wang, Zhao-yu; Zheng, Jia-huan; Shi, Sheng-ying; Luo, Zhi-xiong; Ni, Shun-yu; Lin, Jing-ming

2015-11-01

To compare the chemical components of essential oil prepared by steam distillation extraction (SD) and supercritical CO2 fluid extraction (SFE-CO2) from Ocimum basilicum var. pilosum whole plant. The essential oil of Ocimum basilicum var. pilosum were extracted by SD and SFE-CO2. The chemical components of essential oil were separated and analyzed by gas chromatography-mass spectrometry( GC-MS). Their relative contents were determined by normalization of peak area. 40 and 42 compounds were detected in the essential oil prepared by SD and SFE-CO2 respectively. 25 compounds were common. Thereare significant differences of the chemical components between the Ocimum basilicum var. pilosum essential oil prepared by SD and thatby SFE-CO2. Different methods showed different extraction efficiency with a special compound. It might be a good idea to unite several methods in the modern traditional Chinese medicine industry.

Comparison of heat and mass transfer of different microwave-assisted extraction methods of essential oil from Citrus limon (Lisbon variety) peel.

PubMed

Golmakani, Mohammad-Taghi; Moayyedi, Mahsa

2015-11-01

Dried and fresh peels of Citrus limon were subjected to microwave-assisted hydrodistillation (MAHD) and solvent-free microwave extraction (SFME), respectively. A comparison was made between MAHD and SFME with the conventional hydrodistillation (HD) method in terms of extraction kinetic, chemical composition, and antioxidant activity. Higher yield results from higher extraction rates by microwaves and could be due to a synergy of two transfer phenomena: mass and heat acting in the same way. Gas chromatography/mass spectrometry (GC/MS) analysis did not indicate any noticeable differences between the constituents of essential oils obtained by MAHD and SFME, in comparison with HD. Antioxidant analysis of the extracted essential oils indicated that microwave irradiation did not have adverse effects on the radical scavenging activity of the extracted essential oils. The results of this study suggest that MAHD and SFME can be termed as green technologies because of their less energy requirements per ml of essential oil extraction.

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

PubMed

Ma, Xu; Cheng, Yongmei; Hao, Shuai

2016-12-10

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

Ultrasonic-assisted extraction of essential oil from Botryophora geniculate using different extracting solvents

NASA Astrophysics Data System (ADS)

Habibullah, Wilfred, Cecilia Devi

2016-11-01

This study compares the performance of ionic liquids to substitute conventional solvents (hexane, dichloromethane and methanol) to extract essential oil from Botryophora geniculate plant. Two different Ionic liquids ([C3MIM][Ac], [C4MIM][Ac]) with co-solvent diethyl ether were used in the ultrasonic-assisted extraction. The effect of various experimental conditions such as time, temperature and solvent were studied. Gas chromatography-mass spectroscopy (GC-MS) was used to analyze essential oils. The results showed that in ultrasonic-assisted extraction using ionic liquids as a solvent gave highest yield (9.5%) in 30 min at temperature 70°C. When using ultrasonic bath with hexane, dichloromethane and methanol, yields was (3.34%), (3.6%) and (3.81%) at 90 min, respectively were obtained. The ultrasonic-assisted extraction under optimal extraction conditions (time 30 min, temperature of 70°C) gave the best yield for the essential oil extraction.

Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images.

PubMed

Yan, Yiming; Tan, Zhichao; Su, Nan; Zhao, Chunhui

2017-08-24

In this paper, a building extraction method is proposed based on a stacked sparse autoencoder with an optimized structure and training samples. Building extraction plays an important role in urban construction and planning. However, some negative effects will reduce the accuracy of extraction, such as exceeding resolution, bad correction and terrain influence. Data collected by multiple sensors, as light detection and ranging (LIDAR), optical sensor etc., are used to improve the extraction. Using digital surface model (DSM) obtained from LIDAR data and optical images, traditional method can improve the extraction effect to a certain extent, but there are some defects in feature extraction. Since stacked sparse autoencoder (SSAE) neural network can learn the essential characteristics of the data in depth, SSAE was employed to extract buildings from the combined DSM data and optical image. A better setting strategy of SSAE network structure is given, and an idea of setting the number and proportion of training samples for better training of SSAE was presented. The optical data and DSM were combined as input of the optimized SSAE, and after training by an optimized samples, the appropriate network structure can extract buildings with great accuracy and has good robustness.

Supercritical carbon dioxide (SC-CO2) extraction of essential oil from Swietenia mahagoni seeds

NASA Astrophysics Data System (ADS)

Norodin, N. S. M.; Salleh, L. M.; Hartati; Mustafa, N. M.

2016-11-01

Swietenia mahagoni (Mahogany) is a traditional plant that is rich with bioactive compounds. In this study, process parameters such as particle size, extraction time, solvent flowrate, temperature and pressure were studied on the extraction of essential oil from Swietenia mahagoni seeds by using supercritical carbon dioxide (SC-CO2) extraction. Swietenia mahagoni seeds was extracted at a pressure of 20-30 MPa and a temperature of 40-60°C. The effect of particle size on overall extraction of essential oil was done at 30 MPa and 50°C while the extraction time of essential oil at various temperatures and at a constant pressure of 30 MPa was studied. Meanwhile, the effect of flowrate CO2 was determined at the flowrate of 2, 3 and 4 ml/min. From the experimental data, the extraction time of 120 minutes, particle size of 0.5 mm, the flowrate of CO2 of 4 ml/min, at a pressure of 30 MPa and the temperature of 60°C were the best conditions to obtain the highest yield of essential oil.

Spectral feature design in high dimensional multispectral data

NASA Technical Reports Server (NTRS)

Chen, Chih-Chien Thomas; Landgrebe, David A.

1988-01-01

The High resolution Imaging Spectrometer (HIRIS) is designed to acquire images simultaneously in 192 spectral bands in the 0.4 to 2.5 micrometers wavelength region. It will make possible the collection of essentially continuous reflectance spectra at a spectral resolution sufficient to extract significantly enhanced amounts of information from return signals as compared to existing systems. The advantages of such high dimensional data come at a cost of increased system and data complexity. For example, since the finer the spectral resolution, the higher the data rate, it becomes impractical to design the sensor to be operated continuously. It is essential to find new ways to preprocess the data which reduce the data rate while at the same time maintaining the information content of the high dimensional signal produced. Four spectral feature design techniques are developed from the Weighted Karhunen-Loeve Transforms: (1) non-overlapping band feature selection algorithm; (2) overlapping band feature selection algorithm; (3) Walsh function approach; and (4) infinite clipped optimal function approach. The infinite clipped optimal function approach is chosen since the features are easiest to find and their classification performance is the best. After the preprocessed data has been received at the ground station, canonical analysis is further used to find the best set of features under the criterion that maximal class separability is achieved. Both 100 dimensional vegetation data and 200 dimensional soil data were used to test the spectral feature design system. It was shown that the infinite clipped versions of the first 16 optimal features had excellent classification performance. The overall probability of correct classification is over 90 percent while providing for a reduced downlink data rate by a factor of 10.

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.

Inhibitory Effect of Black and Red Pepper and Thyme Extracts and Essential Oils on Enterohemorrhagic Escherichia coli and DNase Activity of Staphylococcus aureus.

PubMed

Zarringhalam, Maryam; Zaringhalam, Jalal; Shadnoush, Mehdi; Safaeyan, Firouzeh; Tekieh, Elaheh

2013-01-01

In this study, extracts and essential oils of Black and Red pepper and Thyme were tested for antibacterial activity against Escherichia coli O157: H7 and Staphylococcus aureus. Black and Red pepper and Thyme were provided from Iranian agricultural researches center. 2 g of each plant powder was added to 10 cc ethanol 96°. After 24 h, the crude extract was separated as an alcoholic extract and concentrated by distillation method. Plants were examined for determining their major component and essential oils were separated. Phytochemical analyses were done for detection of some effective substances in extracts. The antibacterial activity against Escherichia coli O157: H7 and Staphylococcus aureus was tested and the results showed that all extracts and essential oils were effective and essential oils were more active. The extracts and oils that showed antimicrobial activity were later tested to determine the Minimum Inhibitory Dilution (MID) for those bacteria. They were also effective on the inhibition of DNase activity. This study was indicated that extracts and essential oils of Black and Red pepper and Thyme can play a significant role in inhibition of Escherichia coli O157: H7 and Staphylococcus aureus.

Inhibitory Effect of Black and Red Pepper and Thyme Extracts and Essential Oils on Enterohemorrhagic Escherichia coli and DNase Activity of Staphylococcus aureus

PubMed Central

Zarringhalam, Maryam; Zaringhalam, Jalal; Shadnoush, Mehdi; Safaeyan, Firouzeh; Tekieh, Elaheh

2013-01-01

In this study, extracts and essential oils of Black and Red pepper and Thyme were tested for antibacterial activity against Escherichia coli O157: H7 and Staphylococcus aureus. Black and Red pepper and Thyme were provided from Iranian agricultural researches center. 2 g of each plant powder was added to 10 cc ethanol 96°. After 24 h, the crude extract was separated as an alcoholic extract and concentrated by distillation method. Plants were examined for determining their major component and essential oils were separated. Phytochemical analyses were done for detection of some effective substances in extracts. The antibacterial activity against Escherichia coli O157: H7 and Staphylococcus aureus was tested and the results showed that all extracts and essential oils were effective and essential oils were more active. The extracts and oils that showed antimicrobial activity were later tested to determine the Minimum Inhibitory Dilution (MID) for those bacteria. They were also effective on the inhibition of DNase activity. This study was indicated that extracts and essential oils of Black and Red pepper and Thyme can play a significant role in inhibition of Escherichia coli O157: H7 and Staphylococcus aureus. PMID:24250643

Antimicrobial activities of essential oil and hexane extract of Florence fennel [Foeniculum vulgare var. azoricum (Mill.) Thell.] against foodborne microorganisms.

PubMed

Cetin, Bülent; Ozer, Hakan; Cakir, Ahmet; Polat, Taşkin; Dursun, Atilla; Mete, Ebru; Oztürk, Erdoğan; Ekinci, Melek

2010-02-01

The objective of this study was to determine the chemical compositions of the essential oil and hexane extract isolated from the inflorescence, leaf stems, and aerial parts of Florence fennel and the antimicrobial activities of the essential oil, hexane extract, and their major component, anethole, against a large variety of foodborne microorganisms. Gas chromatography and gas chromatography-mass spectrometry analysis showed that the essential oils obtained from inflorescence, leaf stems, and whole aerial parts contained (E)-anethole (59.28-71.69%), limonene (8.30-10.73%), apiole (trace to 9.23%), beta-fenchyl acetate (3.02-4.80%), and perillene (2.16-3.29%) as the main components. Likewise, the hexane extract of the plant sample exhibited a similar chemical composition, and it contained (E)-anethole (53.00%), limonene (27.16%), gamma-terpinene (4.09%), and perillene (3.78%). However, the hexane extract also contained less volatile components such as n-hexadecanoic acid (1.62%), methyl palmitate (1.17%), and linoleic acid (1.15%). The in vitro antimicrobial assays showed that the essential oil, anethole, and hexane extract were effective against most of the foodborne pathogenic, saprophytic, probiotic, and mycotoxigenic microorganisms tested. The results of the present study revealed that (E)-anethole, the main component of Florence fennel essential oil, is responsible for the antimicrobial activity and that the essential oils as well as the hexane extract can be used as a food preservative. This study is the first report showing the antimicrobial activities of essential oil and hexane extract of Florence fennel against probiotic bacteria.

Optimization study of Chromalaena odorata essential oil extracted using solventless extraction technique

NASA Astrophysics Data System (ADS)

Nasshorudin, Dalila; Ahmad, Muhammad Syarhabil; Mamat, Awang Soh; Rosli, Suraya

2015-05-01

Solventless extraction process of Chromalaena odorata using reduced pressure and temperature has been investigated. The percentage yield of essential oil produce was calculated for every experiment with different experimental condition. The effect of different parameters, such as temperature and extraction time on the yield was investigated using the Response Surface Methodology (RSM) through Central Composite Design (CCD). The temperature and extraction time were found to have significant effect on the yield of extract. A final essential oil yield was 0.095% could be extracted under the following optimized conditions; a temperature of 80 °C and a time of 8 hours.

21 CFR 582.50 - Certain other spices, seasonings, essential oils, oleoresins, and natural extracts.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 6 2011-04-01 2011-04-01 false Certain other spices, seasonings, essential oils, oleoresins, and natural extracts. 582.50 Section 582.50 Food and Drugs FOOD AND DRUG ADMINISTRATION..., oleoresins, and natural extracts. Certain other spices, seasonings, essential oils, oleoresins, and natural...

21 CFR 182.50 - Certain other spices, seasonings, essential oils, oleoresins, and natural extracts.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 3 2011-04-01 2011-04-01 false Certain other spices, seasonings, essential oils, oleoresins, and natural extracts. 182.50 Section 182.50 Food and Drugs FOOD AND DRUG ADMINISTRATION..., oleoresins, and natural extracts. Certain other spices, seasonings, essential oils, oleoresins, and natural...

Optimization of conditions of solvent-free microwave extraction and study on antioxidant capacity of essential oil from Schisandra chinensis (Turcz.) Baill.

PubMed

Ma, Chun-hui; Yang, Lei; Zu, Yuan-gang; Liu, Ting-ting

2012-10-15

In this article, solvent-free microwave extraction (SFME) of essential oil from Schisandra chinensis (Turcz.) Baill was studied. A multivariate study based on central composite design (CCD) was used to evaluate the influence of three major variables affecting the performance of SFME. The optimum parameters were extraction time 30 min, irradiation power 385 W and moisture content of the fruits was 68%. The extraction yield of essential oil was 11 ml/kg under the optimum conditions. The antioxidant capacity of essential oils extracted by different methods were determined, and compared with traditional antioxidants. GC-MS showed the different composition of essential oil extracted by hydro-distillation (HD), steam-distillation (SD) and SFME. S. chinensis materials treated by different methods were observed by scanning electronic microscopy (SEM) and thermo-gravimetric analysis (TGA). Micrographs and thermo gravimetric loss provided more evidences to prove SFME of essential oil is more completed than HD and SD. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

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

Cytotoxic effects of Pinus eldarica essential oil and extracts on HeLa and MCF-7 cell lines.

PubMed

Sarvmeili, Najmeh; Jafarian-Dehkordi, Abbas; Zolfaghari, Behzad

2016-12-01

Several attempts have so far been made in the search of new anticancer agents of plant origin. Some studies have reported that different species of Pine genus possess cytotoxic activities against various cancer cell lines. In the present study, we evaluated the cytotoxic effects of Pinus eldarica bark and leaf extracts or leaf essential oil on HeLa and MCF-7 tumor cell lines. Hydroalcoholic and phenolic extracts and the essential oil of plant were prepared. Total phenolic contents of the extracts were measured using Folin-Ciocalteu reagent. Essential oil components were determined by gas chromatography-mass spectroscopy (GC-MS). Cytotoxic activity of the extracts and essential oil against HeLa and MCF-7 tumor cell lines were assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. The polyphenolic content of hydroalcoholic and phenolic extracts of the bark and hydroalcoholic extract of the leaf were 48.31%, 47.2%, and 8.47%, respectively. According to the GC-MS analysis, the major components of the leaf oil of P. eldarica were: β -caryophyllene (14.8%), germacrene D (12.9%), α-terpinenyl acetate (8.15%), α -pinene (5.7%), and -α humulene (5.9%). Bark extracts and leaf essential oil of P. eldarica significantly reduced the viability of both HeLa and MCF-7 cells in a concentration dependent manner. However, leaf extract showed less inhibitory effects against both cell lines. The essential oil of P. eldarica was more cytotoxic than its hydroalcoholic and phenolic extracts. The terpenes and phenolic compounds were probably responsible for cytotoxicity of P. eldarica . Therefore, P. eldarica might have a good potential for active anticancer agents.

Cytotoxic effects of Pinus eldarica essential oil and extracts on HeLa and MCF-7 cell lines

PubMed Central

Sarvmeili, Najmeh; Jafarian-Dehkordi, Abbas; Zolfaghari, Behzad

2016-01-01

Several attempts have so far been made in the search of new anticancer agents of plant origin. Some studies have reported that different species of Pine genus possess cytotoxic activities against various cancer cell lines. In the present study, we evaluated the cytotoxic effects of Pinus eldarica bark and leaf extracts or leaf essential oil on HeLa and MCF-7 tumor cell lines. Hydroalcoholic and phenolic extracts and the essential oil of plant were prepared. Total phenolic contents of the extracts were measured using Folin-Ciocalteu reagent. Essential oil components were determined by gas chromatography-mass spectroscopy (GC-MS). Cytotoxic activity of the extracts and essential oil against HeLa and MCF-7 tumor cell lines were assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. The polyphenolic content of hydroalcoholic and phenolic extracts of the bark and hydroalcoholic extract of the leaf were 48.31%, 47.2%, and 8.47%, respectively. According to the GC-MS analysis, the major components of the leaf oil of P. eldarica were: β -caryophyllene (14.8%), germacrene D (12.9%), α–terpinenyl acetate (8.15%), α –pinene (5.7%), and –α humulene (5.9%). Bark extracts and leaf essential oil of P. eldarica significantly reduced the viability of both HeLa and MCF-7 cells in a concentration dependent manner. However, leaf extract showed less inhibitory effects against both cell lines. The essential oil of P. eldarica was more cytotoxic than its hydroalcoholic and phenolic extracts. The terpenes and phenolic compounds were probably responsible for cytotoxicity of P. eldarica. Therefore, P. eldarica might have a good potential for active anticancer agents. PMID:28003841

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.

Classifier dependent feature preprocessing methods

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M., II; Peterson, Gilbert L.

2008-04-01

In mobile applications, computational complexity is an issue that limits sophisticated algorithms from being implemented on these devices. This paper provides an initial solution to applying pattern recognition systems on mobile devices by combining existing preprocessing algorithms for recognition. In pattern recognition systems, it is essential to properly apply feature preprocessing tools prior to training classification models in an attempt to reduce computational complexity and improve the overall classification accuracy. The feature preprocessing tools extended for the mobile environment are feature ranking, feature extraction, data preparation and outlier removal. Most desktop systems today are capable of processing a majority of the available classification algorithms without concern of processing while the same is not true on mobile platforms. As an application of pattern recognition for mobile devices, the recognition system targets the problem of steganalysis, determining if an image contains hidden information. The measure of performance shows that feature preprocessing increases the overall steganalysis classification accuracy by an average of 22%. The methods in this paper are tested on a workstation and a Nokia 6620 (Symbian operating system) camera phone with similar results.

[Comparison of essential oil enriched with ultrafiltration method and extraction method respectively from essential oil-in-water emulsion of Citri Reticulatae Pericarpium Viride by GC-MS].

PubMed

Yin, Ailing; Han, Zhifeng; Shen, Jie; Guo, Liwei; Cao, Guiping

2011-10-01

To study on the separation from essential oil-in-water emulsion of Citri Reticulatae Pericarpium Viride by ultrafiltration and acetoacetate extraction methods respectively, and the comparison of the oil yields and chemical compositions. Essential oil-in-water emulsion of Citri Reticulatae Pericarpium Viride was separated by ultrafiltration and acetoacetate extraction methods respectively, and the chemical compositions were analyzed and compared by GC-MS. Ultrafiltration method could enrich essential oil more and its chemical compositions were more similar to the essential oil prepared by steam distillation method. Ultrafiltration method is a good medium to separate essential oil from essential oil-in-water emulsion of Citri Reticulatae Pericarpium Viride.

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors.

PubMed

Xi, Xugang; Tang, Minyan; Miran, Seyed M; Luo, Zhizeng

2017-05-27

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection.

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

PubMed Central

Xi, Xugang; Tang, Minyan; Miran, Seyed M.; Luo, Zhizeng

2017-01-01

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection. PMID:28555016

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) ...

Geospatial mapping of Antarctic coastal oasis using geographic object-based image analysis and high resolution satellite imagery

NASA Astrophysics Data System (ADS)

Jawak, Shridhar D.; Luis, Alvarinho J.

2016-04-01

An accurate spatial mapping and characterization of land cover features in cryospheric regions is an essential procedure for many geoscientific studies. A novel semi-automated method was devised by coupling spectral index ratios (SIRs) and geographic object-based image analysis (OBIA) to extract cryospheric geospatial information from very high resolution WorldView 2 (WV-2) satellite imagery. The present study addresses development of multiple rule sets for OBIA-based classification of WV-2 imagery to accurately extract land cover features in the Larsemann Hills, east Antarctica. Multilevel segmentation process was applied to WV-2 image to generate different sizes of geographic image objects corresponding to various land cover features with respect to scale parameter. Several SIRs were applied to geographic objects at different segmentation levels to classify land mass, man-made features, snow/ice, and water bodies. We focus on water body class to identify water areas at the image level, considering their uneven appearance on landmass and ice. The results illustrated that synergetic usage of SIRs and OBIA can provide accurate means to identify land cover classes with an overall classification accuracy of ≍97%. In conclusion, our results suggest that OBIA is a powerful tool for carrying out automatic and semiautomatic analysis for most cryospheric remote-sensing applications, and the synergetic coupling with pixel-based SIRs is found to be a superior method for mining geospatial information.

Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites.

PubMed

Maalek, Reza; Lichti, Derek D; Ruwanpura, Janaka Y

2018-03-08

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites.

Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites

PubMed Central

Maalek, Reza; Lichti, Derek D; Ruwanpura, Janaka Y

2018-01-01

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites. PMID:29518062

Accuracy improvement in laser stripe extraction for large-scale triangulation scanning measurement system

NASA Astrophysics Data System (ADS)

Zhang, Yang; Liu, Wei; Li, Xiaodong; Yang, Fan; Gao, Peng; Jia, Zhenyuan

2015-10-01

Large-scale triangulation scanning measurement systems are widely used to measure the three-dimensional profile of large-scale components and parts. The accuracy and speed of the laser stripe center extraction are essential for guaranteeing the accuracy and efficiency of the measuring system. However, in the process of large-scale measurement, multiple factors can cause deviation of the laser stripe center, including the spatial light intensity distribution, material reflectivity characteristics, and spatial transmission characteristics. A center extraction method is proposed for improving the accuracy of the laser stripe center extraction based on image evaluation of Gaussian fitting structural similarity and analysis of the multiple source factors. First, according to the features of the gray distribution of the laser stripe, evaluation of the Gaussian fitting structural similarity is estimated to provide a threshold value for center compensation. Then using the relationships between the gray distribution of the laser stripe and the multiple source factors, a compensation method of center extraction is presented. Finally, measurement experiments for a large-scale aviation composite component are carried out. The experimental results for this specific implementation verify the feasibility of the proposed center extraction method and the improved accuracy for large-scale triangulation scanning measurements.

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.

Optimized hardware framework of MLP with random hidden layers for classification applications

NASA Astrophysics Data System (ADS)

Zyarah, Abdullah M.; Ramesh, Abhishek; Merkel, Cory; Kudithipudi, Dhireesha

2016-05-01

Multilayer Perceptron Networks with random hidden layers are very efficient at automatic feature extraction and offer significant performance improvements in the training process. They essentially employ large collection of fixed, random features, and are expedient for form-factor constrained embedded platforms. In this work, a reconfigurable and scalable architecture is proposed for the MLPs with random hidden layers with a customized building block based on CORDIC algorithm. The proposed architecture also exploits fixed point operations for area efficiency. The design is validated for classification on two different datasets. An accuracy of ~ 90% for MNIST dataset and 75% for gender classification on LFW dataset was observed. The hardware has 299 speed-up over the corresponding software realization.

In vivo toxicity and antitumor activity of essential oils extract from agarwood (Aquilaria crassna).

PubMed

Dahham, Saad Sabbar; Hassan, Loiy E Ahmed; Ahamed, Mohamed B Khadeer; Majid, Aman Shah Abdul; Majid, Amin Malik Shah Abdul; Zulkepli, Nik Noriman

2016-07-22

Aquilaria crassna has been used in traditional Asian medicine to treat vomiting, rheumatism, asthma, and cough. Furthermore, earlier studies from our laboratory have revealed that the essential oil extract from agarwood inhibited colorectal carcinoma cells. Despite of the wide range of ethno-pharmacological uses of agarwood, its toxicity has not been previously evaluated through systematic toxicological studies. Therefore, the potential safety of essential oil extract and its in vivo anti-tumor activity had been investigated. In the acute toxicity study, Swiss female mice were given a single dose of the essential oil extract at 2000 mg/kg/day orally and screened for two weeks after administration. Meanwhile, in the sub-chronic study, two different doses of the extract were administered for 28 days. Mortality, clinical signs, body weight changes, hematological and biochemical parameters, gross findings, organ weights, and histological parameters were monitored during the study. Other than that, in vivo anti-tumor study was assessed by using subcutaneous tumors model established in nude mice. The acute toxicity study showed that the LD50 of the extract was greater than 2000 mg/kg. In the repeated dose for 28-day oral toxicity study, the administration of 100 mg/kg and 500 mg/kg of essential oil per body weight revealed insignificant difference in food and water intakes, bodyweight change, hematological and biochemical parameters, relative organ weights, gross findings or histopathology compared to the control group. Nevertheless, the essential oil extract, when supplemented to nude mice, caused significant growth inhibition of the subcutaneous tumor of HCT 116 colorectal carcinoma cells. Collectively, the data obtained indicated that essential oil extract from agarwood might be a safe material, and this essential oil is suggested as a potential anti-colon cancer candidate.

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.

Activity antifungal of the essential oils; aqueous and ethanol extracts from Citrus aurantium L.

PubMed

Metoui, N; Gargouri, S; Amri, I; Fezzani, T; Jamoussi, B; Hamrouni, L

2015-01-01

Our study is about the essential oil of Citrus aurantium L. in Tunisia and its plant extract. The yield of this essential oil is 0, 56% but the yield of the extract of plant was 17.1% for the aqueous extract ant 18.3% for the ethanolic extract. The analysis of chemical composition by using GC and GC/MS showed the essential oil of C. aurantium L. species to be rich in monoterpenes such as α-terpineol, lianolyl acetate, linalool and limonene. The antifungal activity of this oil showed us an inhibition of the germination of mushrooms, in the same way we could note that the biologic activities are generally assigned to the chemotypes high content in oxygenated monoterpene.

Antioxidant activity of essential oil and extracts of Valeriana jatamansi roots.

PubMed

Thusoo, Sakshima; Gupta, Sahil; Sudan, Rasleen; Kour, Jaspreet; Bhagat, Sahil; Hussain, Rashid; Bhagat, Madhulika

2014-01-01

Valeriana jatamansi is an indigenous medicinal plant used in the treatment of a number of diseases. In the present study, chemical composition of the essential oil was determined by GC-MS. Seven major components were identified in Valeriana jatamansi essential oil, namely, β-vatirenene, β-patchoulene, dehydroaromadendrene, β-gurjunene, patchoulic alcohol, β-guaiene, and α-muurolene. Methanolic, aqueous, and chloroform extracts of Valeriana jatamansi roots were also prepared and analyzed for their polyphenols and flavonoid content. Antioxidant activity of essential oil and different extracts of Valeriana jatamansi roots was determined by DPPH radical scavenging and chelation power assay. A linear correlation has been obtained by comparing the antioxidant activity and polyphenols and flavonoid content of the extracts. Results indicated that antioxidant activity of methanolic extract could be attributed to the presence of rich amount of polyphenols and flavonoid. Essential oil of Valeriana jatamansi roots showed moderate antioxidant activity.

Automatic digital surface model (DSM) generation from aerial imagery data

NASA Astrophysics Data System (ADS)

Zhou, Nan; Cao, Shixiang; He, Hongyan; Xing, Kun; Yue, Chunyu

2018-04-01

Aerial sensors are widely used to acquire imagery for photogrammetric and remote sensing application. In general, the images have large overlapped region, which provide a lot of redundant geometry and radiation information for matching. This paper presents a POS supported dense matching procedure for automatic DSM generation from aerial imagery data. The method uses a coarse-to-fine hierarchical strategy with an effective combination of several image matching algorithms: image radiation pre-processing, image pyramid generation, feature point extraction and grid point generation, multi-image geometrically constraint cross-correlation (MIG3C), global relaxation optimization, multi-image geometrically constrained least squares matching (MIGCLSM), TIN generation and point cloud filtering. The image radiation pre-processing is used in order to reduce the effects of the inherent radiometric problems and optimize the images. The presented approach essentially consists of 3 components: feature point extraction and matching procedure, grid point matching procedure and relational matching procedure. The MIGCLSM method is used to achieve potentially sub-pixel accuracy matches and identify some inaccurate and possibly false matches. The feasibility of the method has been tested on different aerial scale images with different landcover types. The accuracy evaluation is based on the comparison between the automatic extracted DSMs derived from the precise exterior orientation parameters (EOPs) and the POS.

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.

VAS: A Vision Advisor System combining agents and object-oriented databases

NASA Technical Reports Server (NTRS)

Eilbert, James L.; Lim, William; Mendelsohn, Jay; Braun, Ron; Yearwood, Michael

1994-01-01

A model-based approach to identifying and finding the orientation of non-overlapping parts on a tray has been developed. The part models contain both exact and fuzzy descriptions of part features, and are stored in an object-oriented database. Full identification of the parts involves several interacting tasks each of which is handled by a distinct agent. Using fuzzy information stored in the model allowed part features that were essentially at the noise level to be extracted and used for identification. This was done by focusing attention on the portion of the part where the feature must be found if the current hypothesis of the part ID is correct. In going from one set of parts to another the only thing that needs to be changed is the database of part models. This work is part of an effort in developing a Vision Advisor System (VAS) that combines agents and objected-oriented databases.

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.

Texture classification of lung computed tomography images

NASA Astrophysics Data System (ADS)

Pheng, Hang See; Shamsuddin, Siti M.

2013-03-01

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

Automated segmentation and feature extraction of product inspection items

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1997-03-01

X-ray film and linescan images of pistachio nuts on conveyor trays for product inspection are considered. The final objective is the categorization of pistachios into good, blemished and infested nuts. A crucial step before classification is the separation of touching products and the extraction of features essential for classification. This paper addresses new detection and segmentation algorithms to isolate touching or overlapping items. These algorithms employ a new filter, a new watershed algorithm, and morphological processing to produce nutmeat-only images. Tests on a large database of x-ray film and real-time x-ray linescan images of around 2900 small, medium and large nuts showed excellent segmentation results. A new technique to detect and segment dark regions in nutmeat images is also presented and tested on approximately 300 x-ray film and approximately 300 real-time linescan x-ray images with 95-97 percent detection and correct segmentation. New algorithms are described that determine nutmeat fill ratio and locate splits in nutmeat. The techniques formulated in this paper are of general use in many different product inspection and computer vision problems.

Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks.

PubMed

Luque, Amalia; Gómez-Bellido, Jesús; Carrasco, Alejandro; Barbancho, Julio

2018-06-03

The analysis and classification of the sounds produced by certain animal species, notably anurans, have revealed these amphibians to be a potentially strong indicator of temperature fluctuations and therefore of the existence of climate change. Environmental monitoring systems using Wireless Sensor Networks are therefore of interest to obtain indicators of global warming. For the automatic classification of the sounds recorded on such systems, the proper representation of the sound spectrum is essential since it contains the information required for cataloguing anuran calls. The present paper focuses on this process of feature extraction by exploring three alternatives: the standardized MPEG-7, the Filter Bank Energy (FBE), and the Mel Frequency Cepstral Coefficients (MFCC). Moreover, various values for every option in the extraction of spectrum features have been considered. Throughout the paper, it is shown that representing the frame spectrum with pure FBE offers slightly worse results than using the MPEG-7 features. This performance can easily be increased, however, by rescaling the FBE in a double dimension: vertically, by taking the logarithm of the energies; and, horizontally, by applying mel scaling in the filter banks. On the other hand, representing the spectrum in the cepstral domain, as in MFCC, has shown additional marginal improvements in classification performance.

An Analysis of Activities in Saudi Arabian Middle School Science Textbooks and Workbooks for the Inclusion of Essential Features of Inquiry

ERIC Educational Resources Information Center

Aldahmash, Abdulwali H.; Mansour, Nasser S.; Alshamrani, Saeed M.; Almohi, Saeed

2016-01-01

This study examines Saudi Arabian middle school science textbooks' coverage of the essential features of scientific inquiry. All activities in the middle school science textbooks and workbooks were analyzed by using the scientific inquiry "essential features" rubric. The results indicated that the essential features are included in about…

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.

Prediction of essential proteins based on gene expression programming.

PubMed

Zhong, Jiancheng; Wang, Jianxin; Peng, Wei; Zhang, Zhen; Pan, Yi

2013-01-01

Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins. Many methods have been proposed to combine some of them to predict essential proteins. However, it is still a big challenge for designing an effective method to predict them by integrating different features, and explaining how these selected features decide the essentiality of protein. Gene expression programming (GEP) is a learning algorithm and what it learns specifically is about relationships between variables in sets of data and then builds models to explain these relationships. In this work, we propose a GEP-based method to predict essential protein by combing some biological features and topological features. We carry out experiments on S. cerevisiae data. The experimental results show that the our method achieves better prediction performance than those methods using individual features. Moreover, our method outperforms some machine learning methods and performs as well as a method which is obtained by combining the outputs of eight machine learning methods. The accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.

Automated facial attendance logger for students

NASA Astrophysics Data System (ADS)

Krithika, L. B.; Kshitish, S.; Kishore, M. R.

2017-11-01

From the past two decades, various spheres of activity are in the aspect of ‘Face recognition’ as an essential tool. The complete series of actions of face recognition is composed of 3 stages: Face Detection, Feature Extraction and Recognition. In this paper, we make an effort to put forth a new application of face recognition and detection in education. The proposed system scans the classroom and detects the face of the students in class and matches the scanned face with the templates that is available in the database and updates the attendance of the respective students.

Application of PLE for the determination of essential oil components from Thymus vulgaris L.

PubMed

Dawidowicz, Andrzej L; Rado, Ewelina; Wianowska, Dorota; Mardarowicz, Marek; Gawdzik, Jan

2008-08-15

Essential plants, due to their long presence in human history, their status in culinary arts, their use in medicine and perfume manufacture, belong to frequently examined stock materials in scientific and industrial laboratories. Because of a large number of freshly cut, dried or frozen plant samples requiring the determination of essential oil amount and composition, a fast, safe, simple, efficient and highly automatic sample preparation method is needed. Five sample preparation methods (steam distillation, extraction in the Soxhlet apparatus, supercritical fluid extraction, solid phase microextraction and pressurized liquid extraction) used for the isolation of aroma-active components from Thymus vulgaris L. are compared in the paper. The methods are mainly discussed with regard to the recovery of components which typically exist in essential oil isolated by steam distillation. According to the obtained data, PLE is the most efficient sample preparation method in determining the essential oil from the thyme herb. Although co-extraction of non-volatile ingredients is the main drawback of this method, it is characterized by the highest yield of essential oil components and the shortest extraction time required. Moreover, the relative peak amounts of essential components revealed by PLE are comparable with those obtained by steam distillation, which is recognized as standard sample preparation method for the analysis of essential oils in aromatic plants.

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.

Isolation of essential oil from different plants and herbs by supercritical fluid extraction.

PubMed

Fornari, Tiziana; Vicente, Gonzalo; Vázquez, Erika; García-Risco, Mónica R; Reglero, Guillermo

2012-08-10

Supercritical fluid extraction (SFE) is an innovative, clean and environmental friendly technology with particular interest for the extraction of essential oil from plants and herbs. Supercritical CO(2) is selective, there is no associated waste treatment of a toxic solvent, and extraction times are moderate. Further, supercritical extracts were often recognized of superior quality when compared with those produced by hydro-distillation or liquid-solid extraction. This review provides a comprehensive and updated discussion of the developments and applications of SFE in the isolation of essential oils from plant matrices. SFE is normally performed with pure CO(2) or using a cosolvent; fractionation of the extract is commonly accomplished in order to isolate the volatile oil compounds from other co-extracted substances. In this review the effect of pressure, temperature and cosolvent on the extraction and fractionation procedure is discussed. Additionally, a comparison of the extraction yield and composition of the essential oil of several plants and herbs from Lamiaceae family, namely oregano, sage, thyme, rosemary, basil, marjoram and marigold, which were produced in our supercritical pilot-plant device, is presented and discussed. Copyright © 2012 Elsevier B.V. All rights reserved.

Chemical composition and antioxidant activity of phenolic compounds and essential oils from Calamintha nepeta L.

PubMed

Khodja, Nabyla Khaled; Boulekbache, Lila; Chegdani, Fatima; Dahmani, Karima; Bennis, Faiza; Madani, Khodir

2018-05-24

Background Essential oils, infusion and decoction extracts of Calamintha nepeta L. were evaluated for their bioactive substances (polyphenols and essential oils) and antioxidant activities. Methods The amounts of phenolic compounds were determined by colorimetric assays and identified by high performance and liquid chromatography coupled with ultraviolet detector (HPLC-UV) method. The chemical composition of essential oils was determined by gas-chromatography coupled with mass spectrometry (GC/MS) method. For the evaluation of the antioxidant activity of essential oils and extracts, two different assays (reducing power and DPPH radical scavenging activity) were used. Results Infusion extract presented the highest phenolic content, followed by the decoction one, while the lowest amount was observed in essential oils. The amount of flavonoids of the decocted extract was higher than that of the infused one. The phenolic profile of C. nepeta infusion and decoction extracts revealed the presence of 28 and 13 peaks, respectively. Four phenolics compounds were identified in infusion (gallic acid (GA), rosmarinic acid (RA), caffeine (C) and caffeic acid (CA)) and two were identified in decoction (GA and RA). The chemical composition of essential oils revealed the presence of 29 compounds, accounting for the 99.7% of the total oils. Major compounds of essential oil (EO) were trans-menthone (50.06%) and pulegone (33.46%). Infusion and decoction extracts revealed an interesting antioxidant activity which correlates positively with their total phenolic contents. Conclusions These results showed that Calamintha nepeta could be considered as a valuable source of phenolics and essential oils with potent antioxidant activity.

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.

Analysis of phytochemical constituents of Eucalyptus citriodora L. responsible for antifungal activity against post-harvest fungi.

PubMed

Javed, S; Shoaib, A; Mahmood, Z; Mushtaq, S; Iftikhar, S

2012-01-01

In vitro antifungal activity and phytochemical constituents of essential oil, aqueous, methanol and chloroform extract of Eucalyptus citriodora Hook leaves were investigated. A qualitative phytochemical analysis was performed for the detection of alkaloids, cardiac glycosides, flavonoids, saponins, sterols, tannins and phenols. Methanolic extract holds all identified biochemical constituents except for the tannin. While these biochemical constituents were found to be absent in essential oil, aqueous and chloroform extracts with the exception of sterols, cardiac glycosides and phenols in essential oil and sterols and phenols in aqueous and chloroform extracts. Antimycotic activity of four fractions of E. citriodora was investigated through agar-well diffusion method against four post-harvest fungi, namely, Aspergillus flavus Link ex Gray, Aspergillus fumigatus Fres., Aspergillus nidulans Eidam ex Win and Aspergillus terreus Thom. The results revealed maximum fungal growth inhibition by methanolic extract (14.5%) followed by essential oil (12.9%), chloroform extract (10.15%) and aqueous extract (10%).

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.

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.

[Gas chromatography for analysis of essential oils. Characteristics of essential oil of Dracocephalum species and the influence of extraction method on its composition].

PubMed

Lemberkovics, Eva; Kakasy, András Zoltán; Héthelyi, B Eva; Simándi, Béla; Böszörményi, Andrea; Balázs, Andrea; Szoke, Eva

2007-01-01

In this work the essential oil composition of some less known Dracocephalum species was studied and compared the effectiveness, selectivity and influence of different extraction methods (hydrodistillation, Soxhlet extraction with organic solvents and supercritical fluid extraction) on essential oils. For investigations in Hungary and Transylvania cultivated plant material was used. The analysis of essential oils was carried out by GC and GC-MS methods. The components were identified by standard addition, retention factors and mass spectra. The percentile evaluation of each volatile constituents was made on basis of GC-FID chromatograms. The accuracy of measurements was characterized by relative standard deviation. In the essential oil of D. renati Emb. (studied firstly by us) 18.3% of limonene was measured and carvone, citrals and linalyl acetate monoterpenes, methyl chavicol and some sesquiterpene (e.g. bicyclovetivenol) determined in lower quantities. We established that more than 50% of essential oil of D. grandiflorum L. was formed by sesquiterpenes (beta-caryophyllene and- oxide, beta-bourbonene, beta-cubebene, aromadendrene) and the essential oil of D. ruyschiana L. contained pinocamphone isomers in more than 60%. The oxygenated acyclic monoterpenes, the characteristic constituents of Moldavian dragonhead were present in some tenth percent only in D. renati oil. We found significant differences in the composition of the SFE extract and traditional essential oil of D. moldavica L. The supercritical fractions collected at the beginning of the extraction process were richer in valuable ester component (geranyl acetate) than the essential oil obtained by hydrodistillation. The fractions collected at the end of supercritical were poor in oxygenated monoterpenes but rich in minor compounds of traditional oil, e.g. palmitic acid.

21 CFR 182.50 - Certain other spices, seasonings, essential oils, oleoresins, and natural extracts.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Certain other spices, seasonings, essential oils... GENERALLY RECOGNIZED AS SAFE General Provisions § 182.50 Certain other spices, seasonings, essential oils, oleoresins, and natural extracts. Certain other spices, seasonings, essential oils, oleoresins, and natural...

21 CFR 582.50 - Certain other spices, seasonings, essential oils, oleoresins, and natural extracts.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 6 2010-04-01 2010-04-01 false Certain other spices, seasonings, essential oils... GENERALLY RECOGNIZED AS SAFE General Provisions § 582.50 Certain other spices, seasonings, essential oils, oleoresins, and natural extracts. Certain other spices, seasonings, essential oils, oleoresins, and natural...

21 CFR 182.50 - Certain other spices, seasonings, essential oils, oleoresins, and natural extracts.

Code of Federal Regulations, 2012 CFR

2012-04-01

... GENERALLY RECOGNIZED AS SAFE General Provisions § 182.50 Certain other spices, seasonings, essential oils, oleoresins, and natural extracts. Certain other spices, seasonings, essential oils, oleoresins, and natural... 21 Food and Drugs 3 2012-04-01 2012-04-01 false Certain other spices, seasonings, essential oils...

21 CFR 582.50 - Certain other spices, seasonings, essential oils, oleoresins, and natural extracts.

Code of Federal Regulations, 2013 CFR

2013-04-01

... GENERALLY RECOGNIZED AS SAFE General Provisions § 582.50 Certain other spices, seasonings, essential oils, oleoresins, and natural extracts. Certain other spices, seasonings, essential oils, oleoresins, and natural... 21 Food and Drugs 6 2013-04-01 2013-04-01 false Certain other spices, seasonings, essential oils...

21 CFR 582.50 - Certain other spices, seasonings, essential oils, oleoresins, and natural extracts.

Code of Federal Regulations, 2012 CFR

2012-04-01

... GENERALLY RECOGNIZED AS SAFE General Provisions § 582.50 Certain other spices, seasonings, essential oils, oleoresins, and natural extracts. Certain other spices, seasonings, essential oils, oleoresins, and natural... 21 Food and Drugs 6 2012-04-01 2012-04-01 false Certain other spices, seasonings, essential oils...

21 CFR 182.50 - Certain other spices, seasonings, essential oils, oleoresins, and natural extracts.

Code of Federal Regulations, 2013 CFR

2013-04-01

... GENERALLY RECOGNIZED AS SAFE General Provisions § 182.50 Certain other spices, seasonings, essential oils, oleoresins, and natural extracts. Certain other spices, seasonings, essential oils, oleoresins, and natural... 21 Food and Drugs 3 2013-04-01 2013-04-01 false Certain other spices, seasonings, essential oils...

21 CFR 582.50 - Certain other spices, seasonings, essential oils, oleoresins, and natural extracts.

Code of Federal Regulations, 2014 CFR

2014-04-01

... GENERALLY RECOGNIZED AS SAFE General Provisions § 582.50 Certain other spices, seasonings, essential oils, oleoresins, and natural extracts. Certain other spices, seasonings, essential oils, oleoresins, and natural... 21 Food and Drugs 6 2014-04-01 2014-04-01 false Certain other spices, seasonings, essential oils...

Antioxidant activity, phenolic content, and peroxide value of essential oil and extracts of some medicinal and aromatic plants used as condiments and herbal teas in Turkey.

PubMed

Ozcan, Mehmet Musa; Erel, Ozcan; Herken, Emine Etöz

2009-02-01

The antioxidant activity, total peroxide values, and total phenol contents of several medicinal and aromatic plant essential oil and extracts from Turkey were examined. Total phenolic contents were determined using a spectrophotometric technique and calculated as gallic acid equivalents. Total antioxidant activity of essential oil and extracts varied from 0.6853 to 1.3113 and 0.3189 to 0.6119 micromol of Trolox equivalents/g, respectively. The total phenolic content of essential oil ranged from 0.0871 to 0.5919 mg of gallic acid/g dry weight. However, the total phenolic contents of extracts were found to be higher compared with those of essential oils. The amount of total peroxide values of oils varied from 7.31 (pickling herb) to 58.23 (bitter fennel flower) mumol of H(2)O(2)/g. As a result, it is shown that medicinal plant derivatives such as extract and essential oils can be useful as a potential source of total phenol, peroxide, and antioxidant capacity for protection of processed foods.

Antioxidant Activity of Essential Oil Extracted by SC-CO₂ from Seeds of Trachyspermum ammi.

PubMed

Singh, Aarti; Ahmad, Anees

2017-07-11

Bcakground: Extracts obtained from natural sources such as plants are of immense importance for humans. Methods: Therefore this study was conducted to obtain essential oil from the seeds of T. ammi by conventional and non-conventional methods. Hydrodistillation (HD), Solvent Extraction (SE), Ultrasonication (US), and Supercritical Carbon-dioxide (SC-CO₂) extraction techniques were used to extract essential oil from the powdered seeds of T. ammi . A quality control method for each extracted oil was developed using HPTLC, FTIR, and GC-MS. The optimization process was carried out using fractional factorial design (FFD) under which three parameters were considered: pressure (150, 175, and 300 bar), temperature (25, 30, and 40 °C), and CO₂ flow rate (5, 10, 15 g/min). Results: The yield of essential oil obtained from the HD, SE, US, and SC-CO₂ methods were 1.20%, 1.82%, 2.30%, and 2.64% v/w , respectively. Antioxidant activity was determined by the DPPH and superoxide scavenging methods and the IC 50 (Inhibition Concentration) values of the T. ammi oil sample were found to be 36.41 and 20.55 µg mL -1 , respectively. Conclusion: The present paper reported that different extraction methods lead to different yields of essential oils and the choice of a suitable method is extremely important to obtain more preferred compounds. The yield was higher in the SC-CO₂ method and it is a sustainable and green extraction technique. Many important constituents were detected in analytical techniques. Antioxidant activities carried out showed that essential oil extracted from T. ammi seeds possess significant antioxidant activity.

Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering.

PubMed

Rodríguez-Sotelo, J L; Peluffo-Ordoñez, D; Cuesta-Frau, D; Castellanos-Domínguez, G

2012-10-01

The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration. The output of the algorithm is a feature weighting vector. The performance of the method was assessed using a heartbeat clustering test on real ECG records. The quantitative cluster validity measures yielded a correctly classified heartbeat rate of 98.69% (specificity), 85.88% (sensitivity) and 95.04% (general clustering performance), which is even higher than the performance achieved by other similar ECG clustering studies. The number of features was reduced on average from 100 to 18, and the temporal cost was a 43% lower than in previous ECG clustering schemes. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Larvicidal activity of essential oil and methanol extract of Nepeta menthoides against malaria vector Anopheles stephensi.

PubMed

Mahnaz, Khanavi; Alireza, Fallah; Hassan, Vatandoost; Mahdi, Sedaghat; Reza, Abai Mohammad; Abbas, Hadjiakhoondi

2012-12-01

To investigate the larvicidal activity of essential oil and methanol extract of the Nepeta menthoides (N. menthoides) against main malaria vector, Anopheles stephensi (An. stephensi). The essential oil of plant was obtained by Clevenger type apparatus and the methanol extract was supplied with Percolation method. Larvicidal activity was tested by WHO method. Twenty five fourth-instar larvae of An. stephensi were used in the larvicidal assay and four replicates were tested for each concentration. Five different concentrations of the oil and extract were tested for calculation of LC(50) and LC(90) values. The LC(50) and LC(90) values were determined by probit analysis. LC(50) was 69.5 and 234.3 ppm and LC(90) was 175.5 and 419.9 ppm for the extract and essential oil respectively. According to the results of this study methanolic extract of plant exhibited more larvicidal activity than essential oil. This could be useful for investigation of new natural larvicidal compounds. Copyright © 2012 Hainan Medical College. Published by Elsevier B.V. All rights reserved.

Toxicity of some essential oils and plant extracts against Sitophilus oryzea, Aconthocelides obtectus and Rhizoperta dominica

NASA Astrophysics Data System (ADS)

Şimşek, Şeyda; Yaman, Cennet; Yarımoǧlu, Berkan; Yılmaz, Ayşegül

2017-04-01

In the present study, experiments were conducted to investigate fumigant toxicity of the essential oil from Myrtus sp plants for adult Aconthocelides obtectus, Sitophilus oryzea and Rhizoperta dominica in vitro conditions. The essential oils were isolated with the water distillation method by Neo-Clevenger apparatus. During the study 10% (v/v) doses of oils in 20 cc of compressed rubber-capped glass tubes were used. After 24 hours mortality rates of the essential oil were compared. Myrtus sp essential oil showed the highest fumigant toxicity on A. obtectus (46.66%). The lowest fumigant toxicity on S. oryzea (8.88%). The contact toxicity plant extracts (Prangos ferulacea, Alkanna orientalis, Myrtus communis) were tested against S. oryzea under laboratory conditions. Single dose contact toxicity effects of plant extracts were tested on S. oryzea adults via applying 1 µl extract suspension (10% w/v) to individual insect. The greatest contact toxicity to S. oryzea adults was observed with M. communis (43.33%) and A. orientalis (41.11%) extracts. P. ferulacea (34.44%) extracts produced moderate toxicity to S. oryzea adults.

Optimisation of supercritical carbon dioxide extraction of essential oil of flowers of tea (Camellia sinensis L.) plants and its antioxidative activity.

PubMed

Chen, Zhenchun; Mei, Xin; Jin, Yuxia; Kim, Eun-Hye; Yang, Ziyin; Tu, Youying

2014-01-30

To extract natural volatile compounds from tea (Camellia sinensis) flowers without thermal degradation and residue of organic solvents, supercritical fluid extraction (SFE) using carbon dioxide was employed to prepare essential oil of tea flowers in the present study. Four important parameters--pressure, temperature, static extraction time, and dynamic extraction time--were selected as independent variables in the SFE. The optimum extraction conditions were the pressure of 30 MPa, temperature of 50°C, static time of 10 min, and dynamic time of 90 min. Based on gas chromatography-mass spectrometry analysis, 59 compounds, including alkanes (45.4%), esters (10.5%), ketones (7.1%), aldehydes (3.7%), terpenes (3.7%), acids (2.1%), alcohols (1.6%), ethers (1.3%) and others (10.3%) were identified in the essential oil of tea flowers. Moreover, the essential oil of tea flowers showed relatively stronger DPPH radical scavenging activity than essential oils of geranium and peppermint, although its antioxidative activity was weaker than those of essential oil of clove, ascorbic acid, tert-butylhydroquinone, and butylated hydroxyanisole. Essential oil of tea flowers using SFE contained many types of volatile compounds and showed considerable DPPH scavenging activity. The information will contribute to the future application of tea flowers as raw materials in health-care food and food flavour industries. © 2013 Society of Chemical Industry.

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.

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.

Portable microwave assisted extraction: An original concept for green analytical chemistry.

PubMed

Perino, Sandrine; Petitcolas, Emmanuel; de la Guardia, Miguel; Chemat, Farid

2013-11-08

This paper describes a portable microwave assisted extraction apparatus (PMAE) for extraction of bioactive compounds especially essential oils and aromas directly in a crop or in a forest. The developed procedure, based on the concept of green analytical chemistry, is appropriate to obtain direct in-field information about the level of essential oils in natural samples and to illustrate green chemical lesson and research. The efficiency of this experiment was validated for the extraction of essential oil of rosemary directly in a crop and allows obtaining a quantitative information on the content of essential oil, which was similar to that obtained by conventional methods in the laboratory. Copyright © 2013 Elsevier B.V. All rights reserved.

Aqueous Extract Composition of Spent Ginger (Zingiber officinale var. Amarum) from Essential Oil Distillation

NASA Astrophysics Data System (ADS)

Manuhara, G. J.; Mentari, G. P.; Khasanah, L. U.; Utami, R.

2018-03-01

Ginger (Zingiber officinale var Amarum) is widely used as raw material for essential oil production in Indonesia and contain high functional compounds. After producing essential oil, distillation leave less valuable spent ginger. This research was conducted to determine the bioactive compounds remained in aqueous extract of the spent ginger. The extracts were produced at various combination of temperature (55, 75, 95°C) and duration (15, 30, 45 minutes). The extract composition was observed using Gas Chromatography - Mass Spectrometry analysis. The temperature and time of maceration extraction affected the content of compounds in spent ginger aqueous extracts. The extracts contained four largest components of α-curcumene, α-zingiberene, β-sesquiphellandrene and β-bisabolene. The aqueous extracts from spent ginger contained the compounds which may contribute to distinctive flavor of ginger and also bioactive function.

Sieve-based relation extraction of gene regulatory networks from biological literature

PubMed Central

2015-01-01

Background Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. Results We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming data into skip-mention sequences is appropriate for detecting relations between distant mentions. Conclusions Linear-chain conditional random fields, along with appropriate data transformations, can be efficiently used to extract relations. The sieve-based architecture simplifies the system as new sieves can be easily added or removed and each sieve can utilize the results of previous ones. Furthermore, sieves with conditional random fields can be trained on arbitrary text data and hence are applicable to broad range of relation extraction tasks and data domains. PMID:26551454

Sieve-based relation extraction of gene regulatory networks from biological literature.

PubMed

Žitnik, Slavko; Žitnik, Marinka; Zupan, Blaž; Bajec, Marko

2015-01-01

Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments. To extract them in an explicit, computer readable format, these relations were at first extracted manually from databases. Manual curation was later replaced with automatic or semi-automatic tools with natural language processing capabilities. The current challenge is the development of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. We develop a computational approach for extraction of gene regulatory networks from textual data. Our method is designed as a sieve-based system and uses linear-chain conditional random fields and rules for relation extraction. With this method we successfully extracted the sporulation gene regulation network in the bacterium Bacillus subtilis for the information extraction challenge at the BioNLP 2013 conference. To enable extraction of distant relations using first-order models, we transform the data into skip-mention sequences. We infer multiple models, each of which is able to extract different relationship types. Following the shared task, we conducted additional analysis using different system settings that resulted in reducing the reconstruction error of bacterial sporulation network from 0.73 to 0.68, measured as the slot error rate between the predicted and the reference network. We observe that all relation extraction sieves contribute to the predictive performance of the proposed approach. Also, features constructed by considering mention words and their prefixes and suffixes are the most important features for higher accuracy of extraction. Analysis of distances between different mention types in the text shows that our choice of transforming data into skip-mention sequences is appropriate for detecting relations between distant mentions. Linear-chain conditional random fields, along with appropriate data transformations, can be efficiently used to extract relations. The sieve-based architecture simplifies the system as new sieves can be easily added or removed and each sieve can utilize the results of previous ones. Furthermore, sieves with conditional random fields can be trained on arbitrary text data and hence are applicable to broad range of relation extraction tasks and data domains.

[Ionic liquid based ultrasonication-assisted extraction of essential oil from the leaves of Persicaria minor and conductor-like screening model for realistic solvents study].

PubMed

Habib, Ullah; Cecilia, D Wilfred; Maizatul, S Shaharun

2017-06-08

Ionic liquids (ILs) based ultrasonic-assisted extract has been applied for the extraction of essential oil from Persicaria minor leaves. The effects of temperature, sonication time, and particle size of the plant material on the yield of essential oil were investigated. Among the different ILs employed, 1-ethyl-3-methylimidazolium acetate was the most effective, providing a 9.55% yield of the essential oil under optimum conditions (70 ℃, 25 min, IL:hexane ratio of 7:10 (v/v), particle size 60-80 mesh). The performance of 1-ethyl-3-methylimidazolium acetate in the extraction was attributed to its low viscosity and ability to disintegrate the structural matrix of the plant material. The ability of 1-ethyl-3-methylimidazolium acetate was also confirmed using the conductor like-screening model for realistic solvents. This research proves that ILs can be used to extract essential oils from lignocellulosic biomass.

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.

ESIM: Edge Similarity for Screen Content Image Quality Assessment.

PubMed

Ni, Zhangkai; Ma, Lin; Zeng, Huanqiang; Chen, Jing; Cai, Canhui; Ma, Kai-Kuang

2017-10-01

In this paper, an accurate full-reference image quality assessment (IQA) model developed for assessing screen content images (SCIs), called the edge similarity (ESIM), is proposed. It is inspired by the fact that the human visual system (HVS) is highly sensitive to edges that are often encountered in SCIs; therefore, essential edge features are extracted and exploited for conducting IQA for the SCIs. The key novelty of the proposed ESIM lies in the extraction and use of three salient edge features-i.e., edge contrast, edge width, and edge direction. The first two attributes are simultaneously generated from the input SCI based on a parametric edge model, while the last one is derived directly from the input SCI. The extraction of these three features will be performed for the reference SCI and the distorted SCI, individually. The degree of similarity measured for each above-mentioned edge attribute is then computed independently, followed by combining them together using our proposed edge-width pooling strategy to generate the final ESIM score. To conduct the performance evaluation of our proposed ESIM model, a new and the largest SCI database (denoted as SCID) is established in our work and made to the public for download. Our database contains 1800 distorted SCIs that are generated from 40 reference SCIs. For each SCI, nine distortion types are investigated, and five degradation levels are produced for each distortion type. Extensive simulation results have clearly shown that the proposed ESIM model is more consistent with the perception of the HVS on the evaluation of distorted SCIs than the multiple state-of-the-art IQA methods.

Anticonvulsant Effect of the Aqueous Extract and Essential Oil of Carum Carvi L. Seeds in a Pentylenetetrazol Model of Seizure in Mice

PubMed Central

Showraki, Alireza; Emamghoreishi, Masoumeh; Oftadegan, Somayeh

2016-01-01

Background: Carum carvi L. (caraway), known as black zeera in Iran, has been indicated for the treatment of epilepsy in Iranian folk medicine. This study evaluated whether the aqueous extract and essential oil of caraway seeds have anticonvulsant effects in mice. Methods: The anticonvulsant effects of the aqueous extract (200, 400, 800, 1600, and 3200 mg/kg, i.p.) and essential oil (25, 50, 100, 200, and 400 mg/kg, i.p.) of caraway were assessed using pentylenetetrazol (PTZ; 95 mg/kg i.p.) induced convulsions. Diazepam (3 mg/kg) was used as positive control. The latency time before the onset of myoclonic, clonic, and tonic convulsions and the percentage of mortality were recorded. In addition, the effect of caraway on neuromuscular coordination was evaluated using the rotarod performance test. Results: The extract and essential oil dose-dependently increased the latency time to the onset of myoclonic (ED50, 1257 and 62.2 mg/kg, respectively) and clonic (ED50, 929 and 42.3 mg/kg, respectively) seizures. The extract and essential oil of caraway prevented the animals from tonic seizure with ED50s of 2142.4 and 97.6 mg/kg, respectively. The extract and essential oil of caraway protected 28.6 and 71.4% of the animals from PTZ-induced death, respectively, and had no significant effect on neuromuscular coordination. Conclusion: This study showed that the aqueous extract and essential oil of caraway had anticonvulsant properties. However, the essential oil was more potent and effective than was the aqueous extract as an anticonvulsant. Additionally, the anticonvulsant effect of caraway was not due to a muscle relaxant activity. These findings support the acclaimed antiepileptic effect of caraway in folk medicine and propose its potential use in petit mal seizure in humans. PMID:27217604

Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion

NASA Astrophysics Data System (ADS)

Lin, Jinshan; Chen, Qian

2013-07-01

Vibration data of faulty rolling bearings are usually nonstationary and nonlinear, and contain fairly weak fault features. As a result, feature extraction of rolling bearing fault data is always an intractable problem and has attracted considerable attention for a long time. This paper introduces multifractal detrended fluctuation analysis (MF-DFA) to analyze bearing vibration data and proposes a novel method for fault diagnosis of rolling bearings based on MF-DFA and Mahalanobis distance criterion (MDC). MF-DFA, an extension of monofractal DFA, is a powerful tool for uncovering the nonlinear dynamical characteristics buried in nonstationary time series and can capture minor changes of complex system conditions. To begin with, by MF-DFA, multifractality of bearing fault data was quantified with the generalized Hurst exponent, the scaling exponent and the multifractal spectrum. Consequently, controlled by essentially different dynamical mechanisms, the multifractality of four heterogeneous bearing fault data is significantly different; by contrast, controlled by slightly different dynamical mechanisms, the multifractality of homogeneous bearing fault data with different fault diameters is significantly or slightly different depending on different types of bearing faults. Therefore, the multifractal spectrum, as a set of parameters describing multifractality of time series, can be employed to characterize different types and severity of bearing faults. Subsequently, five characteristic parameters sensitive to changes of bearing fault conditions were extracted from the multifractal spectrum and utilized to construct fault features of bearing fault data. Moreover, Hilbert transform based envelope analysis, empirical mode decomposition (EMD) and wavelet transform (WT) were utilized to study the same bearing fault data. Also, the kurtosis and the peak levels of the EMD or the WT component corresponding to the bearing tones in the frequency domain were carefully checked and used as the bearing fault features. Next, MDC was used to classify the bearing fault features extracted by EMD, WT and MF-DFA in the time domain and assess the abilities of the three methods to extract fault features from bearing fault data. The results show that MF-DFA seems to outperform each of envelope analysis, statistical parameters, EMD and WT in feature extraction of bearing fault data and then the proposed method in this paper delivers satisfactory performances in distinguishing different types and severity of bearing faults. Furthermore, to further ascertain the nature causing the multifractality of bearing vibration data, the generalized Hurst exponents of the original bearing vibration data were compared with those of the shuffled and the surrogated data. Consequently, the long-range correlations for small and large fluctuations of data seem to be chiefly responsible for the multifractality of bearing vibration data.

A framework for semisupervised feature generation and its applications in biomedical literature mining.

PubMed

Li, Yanpeng; Hu, Xiaohua; Lin, Hongfei; Yang, Zhihao

2011-01-01

Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features, i.e., example-distinguishing features (EDFs) and class-distinguishing features (CDFs) from original feature set, and then generalizes EDFs into higher-level features based on their coupling degrees with CDFs in unlabeled data. The advantage is: EDFs with extreme sparsity in labeled data can be enriched by their co-occurrences with CDFs in unlabeled data so that the performance of these low-frequency features can be greatly boosted and new information from unlabeled can be incorporated. We apply this approach to three tasks in biomedical literature mining: gene named entity recognition (NER), protein-protein interaction extraction (PPIE), and text classification (TC) for gene ontology (GO) annotation. New features are generated from over 20 GB unlabeled PubMed abstracts. The experimental results on BioCreative 2, AIMED corpus, and TREC 2005 Genomics Track show that 1) FCG can utilize well the sparse features ignored by supervised learning. 2) It improves the performance of supervised baselines by 7.8 percent, 5.0 percent, and 5.8 percent, respectively, in the tree tasks. 3) Our methods achieve 89.1, 64.5 F-score, and 60.1 normalized utility on the three benchmark data sets.

Essential oils: extraction, bioactivities, and their uses for food preservation.

PubMed

Tongnuanchan, Phakawat; Benjakul, Soottawat

2014-07-01

Essential oils are concentrated liquids of complex mixtures of volatile compounds and can be extracted from several plant organs. Essential oils are a good source of several bioactive compounds, which possess antioxidative and antimicrobial properties. In addition, some essential oils have been used as medicine. Furthermore, the uses of essential oils have received increasing attention as the natural additives for the shelf-life extension of food products, due to the risk in using synthetic preservatives. Essential oils can be incorporated into packaging, in which they can provide multifunctions termed "active or smart packaging." Those essential oils are able to modify the matrix of packaging materials, thereby rendering the improved properties. This review covers up-to-date literatures on essential oils including sources, chemical composition, extraction methods, bioactivities, and their applications, particularly with the emphasis on preservation and the shelf-life extension of food products. © 2014 Institute of Food Technologists®

Correlative feature analysis on FFDM

PubMed Central

Yuan, Yading; Giger, Maryellen L.; Li, Hui; Sennett, Charlene

2008-01-01

Identifying the corresponding images of a lesion in different views is an essential step in improving the diagnostic ability of both radiologists and computer-aided diagnosis (CAD) systems. Because of the nonrigidity of the breasts and the 2D projective property of mammograms, this task is not trivial. In this pilot study, we present a computerized framework that differentiates between corresponding images of the same lesion in different views and noncorresponding images, i.e., images of different lesions. A dual-stage segmentation method, which employs an initial radial gradient index (RGI) based segmentation and an active contour model, is applied to extract mass lesions from the surrounding parenchyma. Then various lesion features are automatically extracted from each of the two views of each lesion to quantify the characteristics of density, size, texture and the neighborhood of the lesion, as well as its distance to the nipple. A two-step scheme is employed to estimate the probability that the two lesion images from different mammographic views are of the same physical lesion. In the first step, a correspondence metric for each pairwise feature is estimated by a Bayesian artificial neural network (BANN). Then, these pairwise correspondence metrics are combined using another BANN to yield an overall probability of correspondence. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the individual features and the selected feature subset in the task of distinguishing corresponding pairs from noncorresponding pairs. Using a FFDM database with 123 corresponding image pairs and 82 noncorresponding pairs, the distance feature yielded an area under the ROC curve (AUC) of 0.81±0.02 with leave-one-out (by physical lesion) evaluation, and the feature metric subset, which included distance, gradient texture, and ROI-based correlation, yielded an AUC of 0.87±0.02. The improvement by using multiple feature metrics was statistically significant compared to single feature performance. PMID:19175108

ELiXIR—Solid-State Luminaire With Enhanced Light Extraction by Internal Reflection

NASA Astrophysics Data System (ADS)

Allen, Steven C.; Steckl, Andrew J.

2007-06-01

A phosphor-converted light-emitting diode (pcLED) luminaire featuring enhanced light extraction by internal reflection (ELiXIR) with efficacy of 60 lm/W producing 18 lumens of yellowish green light at 100 mA is presented. The luminaire consists of a commercial blue high power LED, a polymer hemispherical shell lens with interior phosphor coating, and planar aluminized reflector. High extraction efficiency of the phosphor-converted light is achieved by separating the phosphor from the LED and using internal reflection to steer the light away from lossy reflectors and the LED package and out of the device. At 10 and 500 mA, the luminaire produces 2.1 and 66 lumens with efficacies of 80 and 37 lm/W, respectively. Technological improvements over existing commercial LEDs, such as more efficient pcLED packages or, alternatively, higher efficiency green or yellow for color mixing, will be essential to achieving 150 200 lm/W solid-state lighting. Advances in both areas are demonstrated.

Chemical Composition and Antioxidant Activity of Essential Oils and Methanol Extracts of Different Parts from Juniperus rigida Siebold & Zucc.

PubMed

Liu, Qiaoxiao; Li, Dengwu; Wang, Wei; Wang, Dongmei; Meng, Xiaxia; Wang, Yongtao

2016-09-01

The chemical composition and antioxidant activity of essential oils and MeOH extracts of stems, needles, and berries from Juniperus rigida were studied. The results indicated that the yield of essential oil from stems (2.5%) was higher than from needles (0.8%) and berries (1.0%). The gas chromatography/mass spectrometer (GC/MS) analysis indicated that 21, 17, and 14 compounds were identified from stems, needles, and berries essential oils, respectively. Caryophyllene, α-caryophyllene, and caryophyllene oxide were primary compounds in both stems and needles essential oils. However, α-pinene and β-myrcene mainly existed in berries essential oils and α-ionone only in needles essential oils. The high-performance liquid chromatography (HPLC) analysis indicated that the phenolic profiles of three parts exhibited significant differences. Needles extracts had the highest content of chlorogenic acid, catechin, podophyllotoxin, and amentoflavone, and for berries extracts, the content of those compounds was the lowest. Meanwhile, three in vitro methods (DPPH, ABTS, and FRAP) were used to evaluate antioxidant activity. Stems essential oil and needles extracts exhibited the powerful antioxidant activity than other parts. This is the first comprehensive study on the different parts of J. rigida. The results suggested that stems and needles of J. rigida are useful supplements for healthy products as new resources. © 2016 Wiley-VHCA AG, Zürich.

The extraction of essential oil from patchouli leaves (Pogostemon cablin Benth) using microwave hydrodistillation and solvent-free microwave extraction methods

NASA Astrophysics Data System (ADS)

Putri, D. K. Y.; Kusuma, H. S.; Syahputra, M. E.; Parasandi, D.; Mahfud, M.

2017-12-01

Patchouli plant (Pogostemon cablin Benth) is one of the important essential oil-producing plant, contributes more than 50% of total exports of Indonesia’s essential oil. However, the extraction of patchouli oil that has been done in Indonesia is generally still used conventional methods that require enormous amount of energy, high solvent usage, and long time of extraction. Therefore, in this study, patchouli oil extraction was carried out by using microwave hydrodistillation and solvent-free microwave extraction methods. Based on this research, it is known that the extraction of patchouli oil using microwave hydrodistillation method with longer extraction time (240 min) only produced patchouli oil’s yield 1.2 times greater than solvent-free microwave extraction method which require faster extraction time (120 min). Otherwise the analysis of electric consumption and the environmental impact, the solvent-free microwave extraction method showed a smaller amount when compared with microwave hydrodistillation method. It is conclude that the use of solvent-free microwave extraction method for patchouli oil extraction is suitably method as a new green technique.

Chemical Variability, Antioxidant and Antifungal Activities of Essential Oils and Hydrosol Extract of Calendula arvensis L. from Western Algeria.

PubMed

Belabbes, Rania; Dib, Mohammed El Amine; Djabou, Nassim; Ilias, Faiza; Tabti, Boufeldja; Costa, Jean; Muselli, Alain

2017-05-01

The chemical composition of the essential oils and hydrosol extract from aerial parts of Calendula arvensis L. was investigated using GC-FID and GC/MS. Intra-species variations of the chemical compositions of essential oils from 18 Algerian sample locations were investigated using statistical analysis. Chemical analysis allowed the identification of 53 compounds amounting to 92.3 - 98.5% with yields varied of 0.09 - 0.36% and the main compounds were zingiberenol 1 (8.7 - 29.8%), eremoligenol (4.2 - 12.5%), β-curcumene (2.1 - 12.5%), zingiberenol 2 (4.6 - 19.8%) and (E,Z)-farnesol (3.5 - 23.4%). The study of the chemical variability of essential oils allowed the discrimination of two main clusters confirming that there is a relation between the essential oil compositions and the harvest locations. Different concentrations of essential oil and hydrosol extract were prepared and their antioxidant activity were assessed using three methods (2,2-diphenyl-1-picrylhydrazyl, Ferric-Reducing Antioxidant Power Assay and β-carotene). The results showed that hydrosol extract presented an interesting antioxidant activity. The in vitro antifungal activity of hydrosol extract produced the best antifungal inhibition against Penicillium expansum and Aspergillus niger, while, essential oil was inhibitory at relatively higher concentrations. Results showed that the treatments of pear fruits with essential oil and hydrosol extract presented a very interesting protective activity on disease severity of pears caused by P. expansum. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

PRE-CLINICAL EVALUATION OF EXTRACTS AND ESSENTIAL OILS FROM BETEL-LIKE SCENT PIPER SPECIES IDENTIFIED POTENTIAL CANCER TREATMENT

PubMed Central

Sanubol, Arisa; Chaveerach, Arunrat; Tanee, Tawatchai; Sudmoon, Runglawan

2017-01-01

Background: Nine Piper species with betel-like scents are sources of industrial and medicinal aromatic chemicals, but there is lack of information on cytotoxicity and genotoxicity for human safety, including how these plants impact human cervical cancer cell line. Methods: Plant leaves were extracted with hexane and hydro-distilled for essential oils. The extracts and oils were pre-clinically studied based on cyto - and genotoxicity using microculture tetrazolium (MTT) and comet assays. Results: The crude extracts showed an IC50 in leukocytes and HeLa cells of 58.59-97.31 mg/ml and 34.91-101.79 mg/ml, the LD50 is higher than 5000 mg/kg. With lower values than the crude extracts, the essential oils showed an IC50 in leukocytes and HeLa cells of 0.023-0.059 μg/ml and 0.025-0.043 μg/ml the LD50 is less than 50 mg/kg. IC50 values showed that the essential oils were highly toxic than the crude extracts. At the level of human genetic materials, the crude extracts of two species, including P. betloides and P. crocatum, showed a significant toxicity (p < 0.05) in leukocytes. The other samples were non-toxic. The crude extracts of all samples showed significant genotoxicity in HeLa cells. The essential oils of all studied Piper species showed insignificant toxicity in leukocytes. For HeLa cells, the eight-studied species showed significant toxicity in HeLa cells, whereas only P. submultinerve showed insignificant toxicity. Conclusion: The crude extracts and essential oils should be tested as putative cervical cancer treatments due to less toxicity in human normal cells. PMID:28480386

PRE-CLINICAL EVALUATION OF EXTRACTS AND ESSENTIAL OILS FROM BETEL-LIKE SCENT PIPER SPECIES IDENTIFIED POTENTIAL CANCER TREATMENT.

PubMed

Sanubol, Arisa; Chaveerach, Arunrat; Tanee, Tawatchai; Sudmoon, Runglawan

2017-01-01

Nine Piper species with betel-like scents are sources of industrial and medicinal aromatic chemicals, but there is lack of information on cytotoxicity and genotoxicity for human safety, including how these plants impact human cervical cancer cell line. Plant leaves were extracted with hexane and hydro-distilled for essential oils. The extracts and oils were pre-clinically studied based on cyto - and genotoxicity using microculture tetrazolium (MTT) and comet assays. The crude extracts showed an IC 50 in leukocytes and HeLa cells of 58.59-97.31 mg/ml and 34.91-101.79 mg/ml, the LD 50 is higher than 5000 mg/kg. With lower values than the crude extracts, the essential oils showed an IC 50 in leukocytes and HeLa cells of 0.023-0.059 μg/ml and 0.025-0.043 μg/ml the LD 50 is less than 50 mg/kg. IC 50 values showed that the essential oils were highly toxic than the crude extracts. At the level of human genetic materials, the crude extracts of two species, including P. betloides and P. crocatum , showed a significant toxicity ( p < 0.05) in leukocytes. The other samples were non-toxic. The crude extracts of all samples showed significant genotoxicity in HeLa cells. The essential oils of all studied Piper species showed insignificant toxicity in leukocytes. For HeLa cells, the eight-studied species showed significant toxicity in HeLa cells, whereas only P. submultinerve showed insignificant toxicity. The crude extracts and essential oils should be tested as putative cervical cancer treatments due to less toxicity in human normal cells.

Antioxidant Activity of Essential Oil Extracted by SC-CO2 from Seeds of Trachyspermum ammi

PubMed Central

Singh, Aarti; Ahmad, Anees

2017-01-01

Bcakground: Extracts obtained from natural sources such as plants are of immense importance for humans. Methods: Therefore this study was conducted to obtain essential oil from the seeds of T. ammi by conventional and non-conventional methods. Hydrodistillation (HD), Solvent Extraction (SE), Ultrasonication (US), and Supercritical Carbon-dioxide (SC-CO2) extraction techniques were used to extract essential oil from the powdered seeds of T. ammi. A quality control method for each extracted oil was developed using HPTLC, FTIR, and GC-MS. The optimization process was carried out using fractional factorial design (FFD) under which three parameters were considered: pressure (150, 175, and 300 bar), temperature (25, 30, and 40 °C), and CO2 flow rate (5, 10, 15 g/min). Results: The yield of essential oil obtained from the HD, SE, US, and SC-CO2 methods were 1.20%, 1.82%, 2.30%, and 2.64% v/w, respectively. Antioxidant activity was determined by the DPPH and superoxide scavenging methods and the IC50 (Inhibition Concentration) values of the T. ammi oil sample were found to be 36.41 and 20.55 µg mL−1, respectively. Conclusion: The present paper reported that different extraction methods lead to different yields of essential oils and the choice of a suitable method is extremely important to obtain more preferred compounds. The yield was higher in the SC-CO2 method and it is a sustainable and green extraction technique. Many important constituents were detected in analytical techniques. Antioxidant activities carried out showed that essential oil extracted from T. ammi seeds possess significant antioxidant activity. PMID:28930268

In vitro efficacy of essential oils and extracts of Schinus molle L. against Ctenocephalides felis felis.

PubMed

Batista, Lilian C De S O; Cid, Yara P; De Almeida, Ana Paula; Prudêncio, Edlene R; Riger, Cristiano J; De Souza, Marco A A; Coumendouros, Katherine; Chaves, Douglas S A

2016-04-01

Extracts and essential oils from plants are important natural sources of pesticides. These compounds are considered an alternative to control ectoparasites of veterinary importance. Schinus molle, an endemic species of Brazil, produces a high level of essential oil and several other compounds. The aim of this work was to determinate the chemical composition of extracts and essential oils of S. molle and further to evaluate the activity against eggs and adults of Ctenocephalides felis felis, a predominant flea that infests dogs and cats in Brazil. In an in vitro assay, the non-polar (n-hexane) extract showed 100% efficacy (800 µg cm(-2); LD50 = 524·80 µg cm(-2)) at 24 and 48 h. Its major compound was lupenone (50·25%). Essential oils from fruits and leaves were evaluated, and had 100% efficacy against adult fleas at 800 µg cm(-2) (LD50 = 353·95 µg cm(-2)) and at 50 µg cm(-2) (LD50 = 12·02 µg cm(-2)), respectively. On the other hand, the essential oil from fruits and leaves was not active against flea eggs. This is the first study that reports the insecticidal effects of essential oils and extracts obtained from Schinus molle against Ctenocephalides felis felis.

Photometric Supernova Classification with Machine Learning

NASA Astrophysics Data System (ADS)

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

2016-08-01

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

Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network

PubMed Central

Zhang, Kai; Long, Erping; Cui, Jiangtao; Zhu, Mingmin; An, Yingying; Zhang, Jia; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni

2017-01-01

Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model. PMID:28306716

Ant-cuckoo colony optimization for feature selection in digital mammogram.

PubMed

Jona, J B; Nagaveni, N

2014-01-15

Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

FuzzObserver

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Bayard, David

2006-01-01

Fuzzy Feature Observation Planner for Small Body Proximity Observations (FuzzObserver) is a developmental computer program, to be used along with other software, for autonomous planning of maneuvers of a spacecraft near an asteroid, comet, or other small astronomical body. Selection of terrain features and estimation of the position of the spacecraft relative to these features is an essential part of such planning. FuzzObserver contributes to the selection and estimation by generating recommendations for spacecraft trajectory adjustments to maintain the spacecraft's ability to observe sufficient terrain features for estimating position. The input to FuzzObserver consists of data from terrain images, including sets of data on features acquired during descent toward, or traversal of, a body of interest. The name of this program reflects its use of fuzzy logic to reason about the terrain features represented by the data and extract corresponding trajectory-adjustment rules. Linguistic fuzzy sets and conditional statements enable fuzzy systems to make decisions based on heuristic rule-based knowledge derived by engineering experts. A major advantage of using fuzzy logic is that it involves simple arithmetic calculations that can be performed rapidly enough to be useful for planning within the short times typically available for spacecraft maneuvers.

Optimization of microwave assisted extraction of essential oils from Iranian Rosmarinus officinalis L. using RSM.

PubMed

Akhbari, Maryam; Masoum, Saeed; Aghababaei, Fahimeh; Hamedi, Sepideh

2018-06-01

In this study, the efficiencies of conventional hydro-distillation and novel microwave hydro-distillation methods in extraction of essential oil from Rosemary officinalis leaves have been compared. In order to attain the best yield and also highest quality of the essential oil in the microwave assisted method, the optimal values of operating parameters such as extraction time, microwave irradiation power and water volume to plant mass ratio were investigated using central composite design under response surface methodology. Optimal conditions for obtaining the maximum extraction yield in the microwave assisted method were predicted as follows: extraction time of 85 min, microwave power of 888 W, and water volume to plant mass ratio of 0.5 ml/g. The extraction yield at these predicted conditions was computed as 0.7756%. The qualities of the obtained essential oils under designed experiments were optimized based on total contents of four major compounds (α-pinene, 1,8-cineole, camphor and verbenone) which determined by gas chromatography equipped with mass spectroscopy (GC-MS). The highest essential oil quality (55.87%) was obtained at extraction time of 68 min; microwave irradiation power of 700 W; and water volume to plant mass ratio of zero.

Potential application of aromatic plant extracts to prevent cheese blowing.

PubMed

Librán, C M; Moro, A; Zalacain, A; Molina, A; Carmona, M; Berruga, M I

2013-07-01

This study aimed to inhibit the growth of Escherichia coli and Clostridium tyrobutyricum, common bacteria responsible for early and late cheese blowing defects respectively, by using novel aqueous extracts obtained by dynamic solid-liquid extraction and essential oils obtained by solvent free microwave extraction from 12 aromatic plants. In terms of antibacterial activity, a total of 13 extracts inhibited one of the two bacteria, and only two essential oils, Lavandula angustifolia Mill. and Lavandula hybrida, inhibited both. Four aqueous extracts were capable of inhibiting C. tyrobutyricum, but none were effective against E. coli. After extracts' chemical composition identification, relationship between the identified compounds and their antibacterial activity were performed by partial least square regression models revealing that compounds such as 1,8 cineole, linalool, linalyl acetate, β-phellandrene or verbene (present in essential oils), pinocarvone, pinocamphone or coumaric acid derivate (in aqueous extracts) were compounds highly correlated to the antibacterial activity.

Free-radical scavenging activity and antibacterial impact of Greek oregano isolates obtained by SFE.

PubMed

Stamenic, Marko; Vulic, Jelena; Djilas, Sonja; Misic, Dusan; Tadic, Vanja; Petrovic, Slobodan; Zizovic, Irena

2014-12-15

The antioxidant and antibacterial properties of Greek oregano extracts obtained by fractional supercritical fluid extraction (SFE) with carbon dioxide were investigated and compared with the properties of essential oil obtained by hydrodistillation. According to DPPH, hydroxyl radical and superoxide anion radical scavenging activity assays, the supercritical extracts expressed stronger antioxidant activity comparing to the essential oil. The most effective was the supercritical extract obtained by fractional extraction at 30 MPa and 100°C after the volatile fraction had been extracted at lower pressure. At the same time this extract showed strong antibacterial activity against staphylococci, including MRSA strain, but did not affect Escherichia coli of normal intestinal flora. The essential oil obtained by hydrodistillation showed stronger antibacterial activity against E. coli, Salmonella and Klebsiella pneumoniae, comparing to the supercritical extracts but at the same affected the normal gut flora. Copyright © 2014 Elsevier Ltd. All rights reserved.

Bifunctional ultrasound assisted extraction and determination of Elettaria cardamomum Maton essential oil.

PubMed

Sereshti, Hassan; Rohanifar, Ahmad; Bakhtiari, Sadjad; Samadi, Soheila

2012-05-18

A new hyphenated extraction method composed of ultrasound assisted extraction (UAE)-optimized ultrasound assisted emulsification microextraction (USAEME) was developed for the extraction and preconcentration of the essential oil of Elettaria cardamomum Maton. The essential oil was analyzed by gas chromatography-mass spectrometry (GC-MS) and optimization was performed using gas chromatography-flame ionization detection (GC-FID). Ultrasound played two different roles in the extraction of the essential oil. First, as a source of sufficient energy to break the oil-containing glands in order to release the oil, and second as an emulsifier to disperse the organic phase within water. The effective parameters (factors) of USAEME including volume of extraction solvent (C(2)H(4)Cl(2)), extraction temperature and ultrasonic time were optimized by using a central composite design (CCD). The optimal conditions were 120 μL for extraction solvent volume, 32.5 °C for temperature and 10.5 min for ultrasonic time. The linear dynamic ranges (LDRs) were 0.01-50 mg L(-1) with the determination coefficients in the range of 0.9990-0.9999. The limits of detection (LODs) and the relative standard deviations (RSDs) were 0.001-0.007 mg L(-1) and 3.6-6.3%, respectively. The enrichment factors were 93-98. The main components of the extracted essential oil were α-terpenyl acetate (46.0%), 1,8-cineole (27.7%), linalool (5.3%), α-terpineol (4.0%), linalyl acetate (3.5%). Copyright © 2012 Elsevier B.V. All rights reserved.

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…

A graph lattice approach to maintaining and learning dense collections of subgraphs as image features.

PubMed

Saund, Eric

2013-10-01

Effective object and scene classification and indexing depend on extraction of informative image features. This paper shows how large families of complex image features in the form of subgraphs can be built out of simpler ones through construction of a graph lattice—a hierarchy of related subgraphs linked in a lattice. Robustness is achieved by matching many overlapping and redundant subgraphs, which allows the use of inexpensive exact graph matching, instead of relying on expensive error-tolerant graph matching to a minimal set of ideal model graphs. Efficiency in exact matching is gained by exploitation of the graph lattice data structure. Additionally, the graph lattice enables methods for adaptively growing a feature space of subgraphs tailored to observed data. We develop the approach in the domain of rectilinear line art, specifically for the practical problem of document forms recognition. We are especially interested in methods that require only one or very few labeled training examples per category. We demonstrate two approaches to using the subgraph features for this purpose. Using a bag-of-words feature vector we achieve essentially single-instance learning on a benchmark forms database, following an unsupervised clustering stage. Further performance gains are achieved on a more difficult dataset using a feature voting method and feature selection procedure.

Antimicrobial activity of essential oil and aqueous and ethanol extracts of Teucrium polium L. subsp. gabesianum (L.H.) from Tunisia.

PubMed

Ben Othman, Mahmoud; Bel Hadj Salah-Fatnassi, Karima; Ncibi, Saida; Elaissi, Amer; Zourgui, Lazhar

2017-07-01

The antimicrobial effects of essential oil, ethanol and aqueous extracts of Teucrium polium L. were investigated against 13 microorganisms. Extracts and essential oil were obtained from maceration, decoction and hydrodistillation respectively. Samples were tested for their antimicrobial activity using the disk diffusion, the agar dilution and the agar incorporation method. Essential oil was analysed using GC/MS, results showed that β-pinene (35.97%) and α-pinene (13.32%) were the main components. Furthermore, essential oil exhibited the highest antimicrobial activity, it was most effective against Proteus mirabilis, Staphylococcus aureus and Citrobacter freundei where inhibition zone ranged between 15 and 25 mm, and with the microbial inhibitory concentration (MIC) values of 0.078-0.156 mg/ml. The oil and ethanol extract showed the best antifungal activity against Microsporum canis , Scopulariopsis brevicaulis , and Trichophyton rubrum with the inhibition percentage (I%) ranging from 18.94 to 100%. However, none of the samples exhibited antifungal activity against Aspergillus fumigatus . In this study, the obtained results showed significant effects of essential oils and ethanol extracts of T. polium which may used as a substitute to the synthetic drugs against certain microbial diseases.

Microwave-Assisted Hydro-Distillation of Essential Oil from Rosemary: Comparison with Traditional Distillation

PubMed Central

Moradi, Sara; Fazlali, Alireza; Hamedi, Hamid

Background: Hydro-distillation (HD) method is a traditional technique which is used in most industrial companies. Microwave-assisted Hydro-distillation (MAHD) is an advanced HD technique utilizing a microwave oven in the extraction process. Methods: In this research, MAHD of essential oils from the aerial parts (leaves) of rosemary (Rosmarinus officinalis L.) was studied and the results were compared with those of the conventional HD in terms of extraction time, extraction efficiency, chemical composition, quality of the essential oils and cost of the operation. Results: Microwave hydro-distillation was superior in terms of saving energy and extraction time (30 min, compared to 90 min in HD). Chromatography was used for quantity analysis of the essential oils composition. Quality of essential oil improved in MAHD method due to an increase of 17% in oxygenated compounds. Conclusion: Consequently, microwave hydro-distillation can be used as a substitute of traditional hydro-distillation. PMID:29296263

Microwave-Assisted Hydro-Distillation of Essential Oil from Rosemary: Comparison with Traditional Distillation.

PubMed

Moradi, Sara; Fazlali, Alireza; Hamedi, Hamid

2018-01-01

Hydro-distillation (HD) method is a traditional technique which is used in most industrial companies. Microwave-assisted Hydro-distillation (MAHD) is an advanced HD technique utilizing a microwave oven in the extraction process. In this research, MAHD of essential oils from the aerial parts (leaves) of rosemary ( Rosmarinus officinalis L. ) was studied and the results were compared with those of the conventional HD in terms of extraction time, extraction efficiency, chemical composition, quality of the essential oils and cost of the operation. Microwave hydro-distillation was superior in terms of saving energy and extraction time (30 min , compared to 90 min in HD). Chromatography was used for quantity analysis of the essential oils composition. Quality of essential oil improved in MAHD method due to an increase of 17% in oxygenated compounds. Consequently, microwave hydro-distillation can be used as a substitute of traditional hydro-distillation.

Antimicrobial and antifungal activities of the extracts and essential oils of Bidens tripartita.

PubMed

Tomczykowa, Monika; Tomczyk, Michał; Jakoniuk, Piotr; Tryniszewska, Elzbieta

2008-01-01

The aim of this study was to determine the antibacterial and antifungal properties of the extracts, subextracts and essential oils of Bidens tripartita flowers and herbs. In the study, twelve extracts and two essential oils were investigated for activity against different Gram-positive Bacillus subtilis, Micrococcus luteus, Staphylococcus aureus, Gram-negative bacteria Escherichia coli, E. coli (beta-laktamase+), Klebsiella pneumoniae (ESBL+), Pseudomonas aeruginosa and some fungal organisms Candida albicans, C. parapsilosis, Aspergillus fumigatus, A. terreus using a broth microdilution and disc diffusion methods. The results obtained indicate antimicrobial activity of the tested extracts (except butanolic extracts), which however did not inhibit the growth of fungi used in this study. Bacteriostatic effect of both essential oils is insignificant, but they have strong antifungal activity. These results support the use of B. tripartita to treat a microbial infections and it is indicated as an antimicrobial and antifungal agent, which may act as pharmaceuticals and preservatives.

A surprising method for green extraction of essential oil from dry spices: Microwave dry-diffusion and gravity.

PubMed

Farhat, Asma; Fabiano-Tixier, Anne-Sylvie; Visinoni, Franco; Romdhane, Mehrez; Chemat, Farid

2010-11-19

Without adding any solvent or water, we proposed a novel and green approach for the extraction of secondary metabolites from dried plant materials. This "solvent, water and vapor free" approach based on a simple principle involves the application of microwave irradiation and earth gravity to extract the essential oil from dried caraway seeds. Microwave dry-diffusion and gravity (MDG) has been compared with a conventional technique, hydrodistillation (HD), for the extraction of essential oil from dried caraway seeds. Essential oils isolated by MDG were quantitatively (yield) and qualitatively (aromatic profile) similar to those obtained by HD, but MDG was better than HD in terms of rapidity (45min versus 300min), energy saving, and cleanliness. The present apparatus permits fast and efficient extraction, reduces waste, avoids water and solvent consumption, and allows substantial energy savings. Copyright © 2010 Elsevier B.V. All rights reserved.

Headspace single drop microextraction coupled with microwave extraction of essential oil from plant materials.

PubMed

Zhai, Yujuan; Sun, Shuo; Wang, Ziming; Zhang, Yupu; Liu, He; Sun, Ye; Zhang, Hanqi; Yu, Aimin

2011-05-01

Headspace single drop microextraction (HS-SDME) coupled with microwave extraction (ME) was developed and applied to the extraction of the essential oil from dried Syzygium aromaticum (L.) Merr. et Perry and Cuminum cyminum L. The operational parameters, such as microdrop volume, microwave absorption medium (MAM), extraction time, and microwave power were optimized. Ten microliters of decane was used as the microextraction solvent. Ionic liquid and carbonyl iron powder were used as MAM. The extraction time was less than 7 min at the microwave power of 440 W. The proposed method was compared with hydrodistillation (HD). There were no obvious differences in the constituents of essential oils obtained by the two methods.

[Extraction and analysis of chemical components of essential oil in Thymus vulgaris of tissue culture].

PubMed

Li, Xiao-Dong; Yang, Li; Xu, Shi-Qian; Li, Jian-Guo; Cheng, Zhi-Hui; Dang, Jian-Zhang

2011-10-01

To extract the essential oils from the Seedlings, the Aseptic Seedlings and the Tissue Culture Seedlings of Thymus vulgaris and analyze their chemical components and the relative contents. The essential oils were extracted by steam distillation, the chemical components and the relative contents were identified and analyzed by gas chromatography-mass spectrometry (GC/MS) and peak area normalization method. The main chemical components of essential oil in these three samples had no significant difference, they all contained the main components of essential oil in Thymus vulgaris: Thymol, Carvacrol, o-Cymene, gamma-Terpinene, Caryophyllene et al. and only had a slight difference in the relative content. This study provides important theoretical foundation and data reference for further study on production of essential oil in thyme by tissue culture technology.

Effect of extraction technique on the content and antioxidant activity of crude extract of Anacyclus clavatus flowers and their essential oil composition.

PubMed

Aliboudhar, Hamza; Tigrine-Kordjani, Nacéra

2014-01-01

Anacyclus clavatus is a plant used as food and remedy. The objective of this work was to study the effect of extraction technique on the antioxidant property, total phenol and flavonoid contents of crude extracts from A. clavatus flowers and their essential oil composition. 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, ferric-reducing power, β-carotene and total antioxidant capacity assays have demonstrated the significant antioxidant ability of different crude extracts obtained by using the following extraction methods: Soxhlet, microwave heating, heat reflux (HRE) and maceration. The activity of the extract obtained by HRE was the highest (112.06 ± 2.89 μg/mL) evaluated by the DPPH assay. Extraction of essential oil was performed by microwave-assisted hydro-distillation (MAHD) and by hydro-distillation (HD). A significant difference was observed in both essential oils, despite the common main family and major constituents, such as artemisia ketone (10.0 ± 0.8% for MAHD vs. 6.5 ± 0.5 for HD) and pinocarvone (4.1 ± 0.4% for MAHD vs. 1.1 ± 0.1% for HD).

[Extraction and analysis of the essential oil in Pogostemon cablin by enzymatic hydrolysis and inhibitory activity against Hela cell proliferation].

PubMed

Yu, Jing; Qi, Yue; Luo, Gang; Duan, Hong-quan; Zhou, Jing

2012-05-01

To optimize the extraction method of essential oil in Pogostemon cablin and analyze its inhibitory activity against Hela cell proliferation. The Pogostemon cablin was treated by hemicellulase before steam distillation. The enzyme dosage, treatment time, treatment temperature, pH were optimized through orthogonal experimental design. The components of essential oil were identified by gas chromatography-mass spectrometry (GC-MS). Inhibitory activity of patchouli oil against Hela cell proliferation was determined by MTP method. The optimum extraction process was as follows: pH 4.5, temperature 45 degrees C, the ratio of hemicellulase to Pogostemon cablin was 1% and enzymatic hydrolysis for 1.0 hour. Extraction ratio of the patchouli oil in steam distillation and hemicellulase extraction method was 2.2220 mg/g, 3.1360 mg/g respectively. Patchouli oil could inhibit Hela cell proliferation. IC50 of the patchouli oil in steam distillation and hemicellulase extraction method was 12.2 +/- 0.46 microg/mL and 0.36 +/- 0.03 microg/mL respectively. In comparison with steam distillation method, extraction ratios of essential oil and the inhibitory activity against Hela cell proliferation can be increased by the hemicellulase extraction method.

Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting.

PubMed

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

2017-03-01

Automatic sleep staging is essential for alleviating the burden of the physicians of analyzing a large volume of data by visual inspection. It is also a precondition for making an automated sleep monitoring system feasible. Further, computerized sleep scoring will expedite large-scale data analysis in sleep research. Nevertheless, most of the existing works on sleep staging are either multichannel or multiple physiological signal based which are uncomfortable for the user and hinder the feasibility of an in-home sleep monitoring device. So, a successful and reliable computer-assisted sleep staging scheme is yet to emerge. In this work, we propose a single channel EEG based algorithm for computerized sleep scoring. In the proposed algorithm, we decompose EEG signal segments using Ensemble Empirical Mode Decomposition (EEMD) and extract various statistical moment based features. The effectiveness of EEMD and statistical features are investigated. Statistical analysis is performed for feature selection. A newly proposed classification technique, namely - Random under sampling boosting (RUSBoost) is introduced for sleep stage classification. This is the first implementation of EEMD in conjunction with RUSBoost to the best of the authors' knowledge. The proposed feature extraction scheme's performance is investigated for various choices of classification models. The algorithmic performance of our scheme is evaluated against contemporary works in the literature. The performance of the proposed method is comparable or better than that of the state-of-the-art ones. The proposed algorithm gives 88.07%, 83.49%, 92.66%, 94.23%, and 98.15% for 6-state to 2-state classification of sleep stages on Sleep-EDF database. Our experimental outcomes reveal that RUSBoost outperforms other classification models for the feature extraction framework presented in this work. Besides, the algorithm proposed in this work demonstrates high detection accuracy for the sleep states S1 and REM. Statistical moment based features in the EEMD domain distinguish the sleep states successfully and efficaciously. The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data

NASA Astrophysics Data System (ADS)

Gegiuc, Alexandru; Similä, Markku; Karvonen, Juha; Lensu, Mikko; Mäkynen, Marko; Vainio, Jouni

2018-01-01

For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.

Extracting contours of oval-shaped objects by Hough transform and minimal path algorithms

NASA Astrophysics Data System (ADS)

Tleis, Mohamed; Verbeek, Fons J.

2014-04-01

Circular and oval-like objects are very common in cell and micro biology. These objects need to be analyzed, and to that end, digitized images from the microscope are used so as to come to an automated analysis pipeline. It is essential to detect all the objects in an image as well as to extract the exact contour of each individual object. In this manner it becomes possible to perform measurements on these objects, i.e. shape and texture features. Our measurement objective is achieved by probing contour detection through dynamic programming. In this paper we describe a method that uses Hough transform and two minimal path algorithms to detect contours of (ovoid-like) objects. These algorithms are based on an existing grey-weighted distance transform and a new algorithm to extract the circular shortest path in an image. The methods are tested on an artificial dataset of a 1000 images, with an F1-score of 0.972. In a case study with yeast cells, contours from our methods were compared with another solution using Pratt's figure of merit. Results indicate that our methods were more precise based on a comparison with a ground-truth dataset. As far as yeast cells are concerned, the segmentation and measurement results enable, in future work, to retrieve information from different developmental stages of the cell using complex features.

Chemical composition and in vitro antifungal and antioxidant activity of the essential oil and methanolic extract of Teucrium sauvagei Le Houerou.

PubMed

Salah, K Bel Hadj; Mahjoub, M A; Chaumont, J P; Michel, L; Millet-Clerc, J; Chraeif, I; Ammar, S; Mighri, Z; Aouni, M

2006-10-01

The chemical composition and the in vitro antifungal and antioxidant activity of the essential oil and the methanolic leaf extracts of Teucrium sauvagei Le Houerou, an endemic medicinal plant growing in Tunisia, have been studied. More than 35 constituents having an abundance >or=0.2% were identified in the oil. beta-Eudesmol, T-cadinol, alpha-thujene, gamma-cadinene, and sabinene were the prevalent constituents. Results of the antifungal activity tests indicated that the methanolic extract inhibited the in vitro growth of seven dermatophytes, whereas the essential oil showed average inhibition against only three dermatophytes. In vitro antioxidant properties of the essential oil and the methanolic extract were determined by DPPH (2,2-diphenyl-1-picrylhydrazyl) and ABTS (2,2'-azinobis(3-ethylbenzothiazoline-6-sulphonic acid)) assays and compared to those of the synthetic antioxidant Trolox. Due to their antifungal and antioxidant properties, the essential oil and the methanolic extract of T. sauvagei may be of use as natural preservative ingredients in food and/or pharmaceutical industries.

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.

Antifungal activities of Hedychium essential oils and plant extracts against mycotoxigenic fungi

USDA-ARS?s Scientific Manuscript database

Plant-derived antifungal compounds are preferred to chemicals to reduce the risk of toxic effects on humans, livestock and the environment. Essential oil extracted from rhizomes and plant extracts of ornamental ginger lily (Hedychium spp.) were evaluated for their antifungal activity against two fu...

Economic Analysis of an Integrated Annatto Seeds-Sugarcane Biorefinery Using Supercritical CO2 Extraction as a First Step

PubMed Central

Albarelli, Juliana Q.; Santos, Diego T.; Cocero, María José; Meireles, M. Angela A.

2016-01-01

Recently, supercritical fluid extraction (SFE) has been indicated to be utilized as part of a biorefinery, rather than as a stand-alone technology, since besides extracting added value compounds selectively it has been shown to have a positive effect on the downstream processing of biomass. To this extent, this work evaluates economically the encouraging experimental results regarding the use of SFE during annatto seeds valorization. Additionally, other features were discussed such as the benefits of enhancing the bioactive compounds concentration through physical processes and of integrating the proposed annatto seeds biorefinery to a hypothetical sugarcane biorefinery, which produces its essential inputs, e.g., CO2, ethanol, heat and electricity. For this, first, different configurations were modeled and simulated using the commercial simulator Aspen Plus® to determine the mass and energy balances. Next, each configuration was economically assessed using MATLAB. SFE proved to be decisive to the economic feasibility of the proposed annatto seeds-sugarcane biorefinery concept. SFE pretreatment associated with sequential fine particles separation process enabled higher bixin-rich extract production using low-pressure solvent extraction method employing ethanol, meanwhile tocotrienols-rich extract is obtained as a first product. Nevertheless, the economic evaluation showed that increasing tocotrienols-rich extract production has a more pronounced positive impact on the economic viability of the concept. PMID:28773616

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.

A modified approach for isolation of essential oil from fruit of Amorpha fruticosa Linn using microwave-assisted hydrodistillation concatenated liquid-liquid extraction.

PubMed

Chen, Fengli; Jia, Jia; Zhang, Qiang; Gu, Huiyan; Yang, Lei

2017-11-17

In this work, a modified technique was developed to separate essential oil from the fruit of Amorpha fruticosa using microwave-assisted hydrodistillation concatenated liquid-liquid extraction (MHD-LLE). The new apparatus consists of two series-wound separation columns for separating essential oil, one is the conventional oil-water separation column, and the other is the extraction column of components from hydrosol using an organic solvent. Therefore, the apparatus can simultaneously collect the essential oil separated on the top of hydrosol and the components extracted from hydrosol using an organic solvent. Based on the yield of essential oil in the first and second separation columns, the effects of parameters were investigated by single factor experiments and Box-Behnken design. Under the optimum conditions (2mL ethyl ether as the extraction solvent in the second separation column, 12mL/g liquid-solid ratio, 4.0min homogenate time, 35min microwave irradiation time and 540W microwave irradiation power), satisfactory yields for the essential oil in the first separation column (10.31±0.33g/kg) and second separation column (0.82±0.03g/kg) were obtained. Compared with traditional methods, the developed method gave a higher yield of essential oil in a shorter time. In addition, GC-MS analysis of the essential oil indicated significant differences of the relative contents of individual volatile components in the essential oils obtained in the two separation columns. Therefore, the MHD-LLE technique developed here is a good alternative for the isolation of essential oil from A. fruticosa fruit as well as other herbs. Copyright © 2017 Elsevier B.V. All rights reserved.

The antibacterial and antifungal activity of essential oils extracted from Guatemalan medicinal plants.

PubMed

Miller, Andrew B; Cates, Rex G; Lawrence, Michael; Soria, J Alfonso Fuentes; Espinoza, Luis V; Martinez, Jose Vicente; Arbizú, Dany A

2015-04-01

Essential oils are prevalent in many medicinal plants used for oral hygiene and treatment of diseases. Medicinal plant species were extracted to determine the essential oil content. Those producing sufficient oil were screened for activity against Staphylococcus aureus, Escherichia coli, Streptococcus mutans, Lactobacillus acidophilus, and Candida albicans. Plant samples were collected, frozen, and essential oils were extracted by steam distillation. Minimum inhibitory concentrations (MIC) were determined using a tube dilution assay for those species yielding sufficient oil. Fifty-nine of the 141 plant species produced sufficient oil for collection and 12 species not previously reported to produce essential oils were identified. Essential oil extracts from 32 species exhibited activity against one or more microbes. Oils from eight species were highly inhibitory to S. mutans, four species were highly inhibitory to C. albicans, and 19 species yielded MIC values less than the reference drugs. RESULTS suggest that 11 species were highly inhibitory to the microbes tested and merit further investigation. Oils from Cinnamomum zeylanicum Blume (Lauraceae), Citrus aurantiifolia (Christm.) Swingle (Rutaceae), Lippia graveolens Kunth (Verbenaceae), and Origanum vulgare L. (Lamiaceae) yielded highly significant or moderate activity against all microbes and have potential as antimicrobial agents. Teas prepared by decoction or infusion are known methods for extracting essential oils. Oils from 11 species were highly active against the microbes tested and merit investigation as to their potential for addressing health-related issues and in oral hygiene.

An open data mining framework for the analysis of medical images: application on obstructive nephropathy microscopy images.

PubMed

Doukas, Charalampos; Goudas, Theodosis; Fischer, Simon; Mierswa, Ingo; Chatziioannou, Aristotle; Maglogiannis, Ilias

2010-01-01

This paper presents an open image-mining framework that provides access to tools and methods for the characterization of medical images. Several image processing and feature extraction operators have been implemented and exposed through Web Services. Rapid-Miner, an open source data mining system has been utilized for applying classification operators and creating the essential processing workflows. The proposed framework has been applied for the detection of salient objects in Obstructive Nephropathy microscopy images. Initial classification results are quite promising demonstrating the feasibility of automated characterization of kidney biopsy images.

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.

Contribution of microwave accelerated distillation in the extraction of the essential oil of Zygophyllum album L.

PubMed

Tigrine-Kordjani, Nacéra; Meklati, Brahim Youcef; Chemat, Farid

2011-01-01

The aerial parts of Zygophyllum album L. are used in folk medicine as an antidiabetic agent and as a drug active against several pathologies. In this work we present the chemical composition of Algerian essential oils obtained by microwave accelerated distillation (MAD) extraction, a solventless method assisted by microwave. Under the same analytical conditions and using GC-FID and GC-MS, the chemical composition of the essential oil of Zygophyllum album L. extracted by MAD was compared with that achieved using hydrodistillation (HD). The extracted compounds were hydrosoluble, and they were removed from the aqueous solution by a liquid extraction with an organic solvent. Employing MAD (100°C, 30  min), the essential oil contained mainly oxygenated monoterpenes with major constituents: carvone and α-terpineol. However, most of the compounds present in the hydrodistilled volatile fraction were not terpene species, with β-damascenone as a major constituent. The MAD method appears to be more efficient than HD: after 30  min extraction time, the obtained yields (i.e. 0.002%) were comparable to those provided by HD after 3  h extraction. MAD seems to be more convenient since the volatile fraction is richer in oxygenated monoterpenes, species that are recognised for their olfactory value and their contribution to the fragrance of the essential oil. Copyright © 2010 John Wiley & Sons, Ltd.

Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

PubMed Central

Nie, Haitao; Long, Kehui; Ma, Jun; Yue, Dan; Liu, Jinguo

2015-01-01

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes. PMID:25714094

Essential oil and methanolic extract of Zataria multiflora Boiss with anticholinesterase effect.

PubMed

Sharififar, Fariba; Mirtajadini, Mansour; Azampour, Mohammad Jaber; Zamani, Ehsan

2012-01-01

One of the most common strategies in the treatment of cognitive disorders is enhancing the acetylcholine level in the brain through the inhibition of acetylcholinesterase. Despite the effectiveness of current modern drugs, more attention has been paid for finding new anticholinesterase agents from medicinal plants. Zatraia multiflora Boiss. is an endemic plant to Iran which has different uses in traditional medicine as anti-inflammatory, antimicrobial, anti spasmodic. We intended to evaluate the in vitro anticholinesterase and free radical scavenging activity of the essential oil and methanolic extract of Z. multiflora. The essential oil and methanolic extract of the plant were evaluated for anticholinesterase activity using modified Ellman method. The free radical scavenging effect of the samples were studied by using of the diphenylpicrylhydrazyl (DPPH). IC50 and the percent of inhibition of acetylcholinesterase was calculated from regression equation. The results showed that both the essential oil and methanolic extract of the plant exhibited high anticholinesterase activity (95.3 +/- 3.4 and 87.9 +/- 2.2% inhibition, respectively) which was similar to eserine (96.2 +/- 1.7% inhibition). The IC50 value of essential oil was determined as 0.97 +/- 0.12 microg mL(-1) in comparison to eserine (0.13 +/- 0.02 microg mL(-1)). The results of antioxidant assay showed that both the essential oil and methanolic extract potentially inhibit DPPH free radical (94.8 +/- 2.4 and 93.2 +/- 1.7% inhibition, respectively). The essential oil and methanolic extract of Z. multiflora have beneficial effect in health promotion and this plant would be good candidate for further studies.

[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.

Antioxidant activity of Piper nigrum L. essential oil extracted by supercritical CO₂ extraction and hydro-distillation.

PubMed

Bagheri, Hossein; Abdul Manap, Mohd Yazid Bin; Solati, Zeinab

2014-04-01

The aim of this study was to optimize the antioxidant activity of Piper nigrum L. essential oil extracted using the supercritical carbon dioxide (SC-CO₂) technique. Response surface methodology was applied using a three-factor central composite design to evaluate the effects of three independent extraction variables: pressure of 15-30 MPa, temperature of 40-50 °C and dynamic extraction time of 40-80 min. The DPPH radical scavenging method was used to evaluate the antioxidant activity of the extracts. The results showed that the best antioxidant activity was achieved at 30 MPa, 40 °C and 40 min. The extracts were analyzed by GC-FID and GC-MS. The main components extracted using SC-CO₂ extraction in optimum conditions were β-caryophyllene (25.38 ± 0.62%), limonene (15.64 ± 0.15%), sabinene (13.63 ± 0.21%), 3-carene (9.34 ± 0.04%), β-pinene (7.27 ± 0.05%), and α-pinene (4.25 ± 0.06%). The essential oil obtained through this technique was compared with the essential oil obtained using hydro-distillation. For the essential oil obtained by hydro-distillation, the most abundant compounds were β-caryophyllene (18.64 ± 0.84%), limonene (14.95 ± 0.13%), sabinene (13.19 ± 0.17%), 3-carene (8.56 ± 0.11%), β-pinene (9.71 ± 0.12%), and α-pinene (7.96 ± 0.14%). Radical scavenging activity of the extracts obtained by SC-CO₂ and hydro-distillation showed an EC₅₀ of 103.28 and 316.27 µg mL(-1) respectively. Copyright © 2014 Elsevier B.V. All rights reserved.

[Apoptosis and activity changes of telomerase induced by essential oil from pine needles in HepG2 cell line].

PubMed

Wei, Feng-xiang; Li, Mei-yu; Song, Yu-hong; Li, Hong-zhi

2008-08-01

To study the effects of essential oil extracted from pine needles on HepG2 cell line. HepG2 cells were treated with essential oil extracted from pine needles. Cell growth rate was determined with MTF assay, cell morphologic changes were examined under transmission electromicroscope and HE straining. Flow cytometry was used to exmine apoptotic cells. Bcl-2 gene expression was determined by flow cytometry and telomerase activity by TRAP assay. Essential oils from pine needles could not only repress the growth of HepG2 cells significantly, but also induce apoptosis to them. Both dose-effect and time-effect relationship could be confirmed. Typical morphology changes of apoptosis such as nuclear enrichment and karyorrhexis were observed through transmission electromicroscope and HE straining. Telomerase activity was down regulated in the essential oil extracted from pine needles induced apoptotic cells. The expression of bcl-2 gene was suppressed after the essential oil from pine needles treatement. The essential oil extracted from pine needles can inhibit cell growth of HepG2 cell line and induce apoptosis, which may associate with inhibition of telomerase activity and bcl-2 may be involved in the regulation of telomerase activity.

Microwave-Assisted Simplified Simultaneous Distillation Coupled with Ionic Liquid Pretreatment for the Analysis of Essential Oil in Schisandra sphenanthera.

PubMed

Yu, Guo-Wei; Nie, Jing; Song, Zhi-Yu; Li, Zu-Guang; Lee, Maw-Rong; Wang, Shen-Peng

2017-11-01

Simultaneous distillation extraction (SDE) is quite useful for the separation of volatile compounds from an analyte when their contents are quite low. In this study, a simplified SDE approach is applied for the extraction of essential oil from Schisandra sphenanthera, with microwave as heating source, [Bmim][Cl] as the medium for pretreatment, and gas chromatography-mass spectrometry as the analytical approach. Consequently, the improvement resulted from [Bmim][Cl] pretreatment is demonstrated by taking comparison with blank experiments. Totally 61 compounds have been detected in the essential oil obtained by using [Bmim][Cl] pretreatment, while without [Bmim][Cl] pretreatment, only 53 compounds can be detected. Moreover, [Bmim][Cl] pretreatment can also resulted in a higher yield of essential oil. The experimental results demonstrate that the simplified SDE coupled with ionic liquid pretreatment is a feasible approach for the extraction of essential oil from S. sphenanthera with high efficiency as 0.85% of essential oil yield has been obtained, and can be potentially extended to the extraction of essential oil or other target volatile compounds with low content. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

Extraction and GC determination of volatile aroma compounds from extracts of three plant species of the Apiaceae family

NASA Astrophysics Data System (ADS)

Stan, M.; Soran, M. L.; Varodi, C.; Lung, I.; Copolovici, L.; MǎruÅ£oiu, C.

2013-11-01

Parsley (Petroselinum crispum), dill (Anethum graveolens) and celery (Apium graveolens), three aromatic plants belonging to the Apiaceae (Umbelliferae) botanical family, were selected as sources of essential or volatile oils. Essential oils are composed of a large diversity of volatile aroma compounds. Plant-derived essential oils and extracts have long been used as natural agents in food preservation, pharmaceuticals and medicinal therapies. In the present study, the plant extracts from leaves of parsley, dill and celery, were obtained by maceration, ultrasound-assisted extraction and microwave-assisted extraction. All extractions were performed at 30°C, using different solvents (ethanol, diethyl ether, n-hexane) and solvent mixtures (1:1, v/v). The most effective solvent system for the extraction of volatile aroma compounds was diethyl ether - n-hexane (1:1, v/v). Extraction efficiency and determination of aroma volatiles were performed by GC-FID and GC-MS, respectively. The major volatile compounds present in plant extracts were myristicin, α-phellandrene, β-phellandrene, 1,3,8-p-menthatriene, apiol, dill ether and allyl phenoxyacetate.

Chemical Variability and Biological Activities of Brassica rapa var. rapifera Parts Essential Oils Depending on Geographic Variation and Extraction Technique.

PubMed

Saka, Boualem; Djouahri, Abderrahmane; Djerrad, Zineb; Terfi, Souhila; Aberrane, Sihem; Sabaou, Nasserdine; Baaliouamer, Aoumeur; Boudarene, Lynda

2017-06-01

In the present work, the Brassica rapa var. rapifera parts essential oils and their antioxidant and antimicrobial activities were investigated for the first time depending on geographic origin and extraction technique. Gas-chromatography (GC) and GC/mass spectrometry (MS) analyses showed several constituents, including alcohols, aldehydes, esters, ketones, norisoprenoids, terpenic, nitrogen and sulphur compounds, totalizing 38 and 41 compounds in leaves and root essential oils, respectively. Nitrogen compounds were the main volatiles in leaves essential oils and sulphur compounds were the main volatiles in root essential oils. Qualitative and quantitative differences were found among B. rapa var. rapifera parts essential oils collected from different locations and extracted by hydrodistillation and microwave-assisted hydrodistillation techniques. Furthermore, our findings showed a high variability for both antioxidant and antimicrobial activities. The highlighted variability reflects the high impact of plant part, geographic variation and extraction technique on chemical composition and biological activities, which led to conclude that we should select essential oils to be investigated carefully depending on these factors, in order to isolate the bioactive components or to have the best quality of essential oil in terms of biological activities and preventive effects in food. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

Chemical composition profiling and antifungal activity of the essential oil and plant extracts of Mesembryanthemum edule (L.) bolus leaves.

PubMed

Omoruyi, Beauty Etinosa; Afolayan, Anthony Jide; Bradley, Graeme

2014-01-01

Essential oil from Mesembryanthemum edule leaves have been used by the Eastern Cape traditional healers for the treatment of respiratory tract infections, tuberculosis, dysentery, diabetic mellitus, laryngitis and vaginal infections. The investigation of bioactive compounds in the essential oil of this plant could help to verify the efficacy of the plant in the management or treatment of these illnesses. Various concentrations of the hydro-distilled essential oil, ranging from 0.005-5 mg/ml, were tested against some fungal strains, using the micro-dilution method. Minimum inhibitory activity was compared with four other different crude extracts of hexane, acetone, ethanol and aqueous samples from the same plant. The chemical composition of the essential oil, hexane, acetone and ethanol extracts was determined using GC-MS. GC/MS analysis of the essential oil resulted in the identification of 28 compounds, representing 99.99% of the total oil. Phytoconstituents of hexane, acetone and ethanol extracts yielded a total peak chromatogram of fifty nine compounds. A total amount of 10.6% and 36.61% of the constituents were obtained as monoterpenes and oxygenated monoterpenes. Sesquiterpene hydrocarbons (3.58%) were relatively low compared to the oxygenated sesquiterpenes (9.28%), while the major concentrated diterpenes and oxygenated diterpenes were 1.43% and 19.24 %, respectively and phytol 12.41%. Total amount of fatty acids and their methyl esters content, present in the oil extract, were found to be 19.25 %. Antifungal activity of the oil extract and four solvent extracts were tested against five pathogenic fungal strains. The oil extract showed antifungal activity against Candida albican, Candida krusei, Candida rugosa, Candida glabrata and Cryptococcus neoformans with MIC ranges of 0.02 0.31 mg/ml. Hexane extract was active against the five fungal strains with MICs ranging between 0.02-1.25 mg/ml. Acetone extracts were active against C. krusei only at 0.04mg/ml. No appreciable antifungal activity was found in either ethanol or water extracts when compared with commercial antibiotics. The profile of chemical constituents found in M. edule essential oil and its antifungal properties support the use of M. edule by traditional healers as well as in the pharmaceutical and food industries as a natural antibiotic and food preservative.

Toward End-to-End Face Recognition Through Alignment Learning

NASA Astrophysics Data System (ADS)

Zhong, Yuanyi; Chen, Jiansheng; Huang, Bo

2017-08-01

Plenty of effective methods have been proposed for face recognition during the past decade. Although these methods differ essentially in many aspects, a common practice of them is to specifically align the facial area based on the prior knowledge of human face structure before feature extraction. In most systems, the face alignment module is implemented independently. This has actually caused difficulties in the designing and training of end-to-end face recognition models. In this paper we study the possibility of alignment learning in end-to-end face recognition, in which neither prior knowledge on facial landmarks nor artificially defined geometric transformations are required. Specifically, spatial transformer layers are inserted in front of the feature extraction layers in a Convolutional Neural Network (CNN) for face recognition. Only human identity clues are used for driving the neural network to automatically learn the most suitable geometric transformation and the most appropriate facial area for the recognition task. To ensure reproducibility, our model is trained purely on the publicly available CASIA-WebFace dataset, and is tested on the Labeled Face in the Wild (LFW) dataset. We have achieved a verification accuracy of 99.08\\% which is comparable to state-of-the-art single model based methods.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Automated retinal vessel type classification in color fundus images

NASA Astrophysics Data System (ADS)

Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.

2013-02-01

Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

Detection and localization of damage using empirical mode decomposition and multilevel support vector machine

NASA Astrophysics Data System (ADS)

Dushyanth, N. D.; Suma, M. N.; Latte, Mrityanjaya V.

2016-03-01

Damage in the structure may raise a significant amount of maintenance cost and serious safety problems. Hence detection of the damage at its early stage is of prime importance. The main contribution pursued in this investigation is to propose a generic optimal methodology to improve the accuracy of positioning of the flaw in a structure. This novel approach involves a two-step process. The first step essentially aims at extracting the damage-sensitive features from the received signal, and these extracted features are often termed the damage index or damage indices, serving as an indicator to know whether the damage is present or not. In particular, a multilevel SVM (support vector machine) plays a vital role in the distinction of faulty and healthy structures. Formerly, when a structure is unveiled as a damaged structure, in the subsequent step, the position of the damage is identified using Hilbert-Huang transform. The proposed algorithm has been evaluated in both simulation and experimental tests on a 6061 aluminum plate with dimensions 300 mm × 300 mm × 5 mm which accordingly yield considerable improvement in the accuracy of estimating the position of the flaw.

Quantitative CT based radiomics as predictor of resectability of pancreatic adenocarcinoma

NASA Astrophysics Data System (ADS)

van der Putten, Joost; Zinger, Svitlana; van der Sommen, Fons; de With, Peter H. N.; Prokop, Mathias; Hermans, John

2018-02-01

In current clinical practice, the resectability of pancreatic ductal adenocarcinoma (PDA) is determined subjec- tively by a physician, which is an error-prone procedure. In this paper, we present a method for automated determination of resectability of PDA from a routine abdominal CT, to reduce such decision errors. The tumor features are extracted from a group of patients with both hypo- and iso-attenuating tumors, of which 29 were resectable and 21 were not. The tumor contours are supplied by a medical expert. We present an approach that uses intensity, shape, and texture features to determine tumor resectability. The best classification results are obtained with fine Gaussian SVM and the L0 Feature Selection algorithms. Compared to expert predictions made on the same dataset, our method achieves better classification results. We obtain significantly better results on correctly predicting non-resectability (+17%) compared to a expert, which is essential for patient treatment (negative prediction value). Moreover, our predictions of resectability exceed expert predictions by approximately 3% (positive prediction value).

Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features

PubMed Central

Zhang, Yaoyun; Jiang, Min; Wang, Jingqi; Xu, Hua

2016-01-01

Semantic role labeling (SRL), which extracts shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding clinical narratives. Since semantic roles are formed by syntactic constituents in the sentence, an effective parser, as well as an effective syntactic feature set are essential to build a practical SRL system. Our study initiates a formal evaluation and comparison of SRL performance on a clinical text corpus MiPACQ, using three state-of-the-art parsers, the Stanford parser, the Berkeley parser, and the Charniak parser. First, the original parsers trained on the open domain syntactic corpus Penn Treebank were employed. Next, those parsers were retrained on the clinical Treebank of MiPACQ for further comparison. Additionally, state-of-the-art syntactic features from open domain SRL were also examined for clinical text. Experimental results showed that retraining the parsers on clinical Treebank improved the performance significantly, with an optimal F1 measure of 71.41% achieved by the Berkeley parser. PMID:28269926

Object recognition and pose estimation of planar objects from range data

NASA Technical Reports Server (NTRS)

Pendleton, Thomas W.; Chien, Chiun Hong; Littlefield, Mark L.; Magee, Michael

1994-01-01

The Extravehicular Activity Helper/Retriever (EVAHR) is a robotic device currently under development at the NASA Johnson Space Center that is designed to fetch objects or to assist in retrieving an astronaut who may have become inadvertently de-tethered. The EVAHR will be required to exhibit a high degree of intelligent autonomous operation and will base much of its reasoning upon information obtained from one or more three-dimensional sensors that it will carry and control. At the highest level of visual cognition and reasoning, the EVAHR will be required to detect objects, recognize them, and estimate their spatial orientation and location. The recognition phase and estimation of spatial pose will depend on the ability of the vision system to reliably extract geometric features of the objects such as whether the surface topologies observed are planar or curved and the spatial relationships between the component surfaces. In order to achieve these tasks, three-dimensional sensing of the operational environment and objects in the environment will therefore be essential. One of the sensors being considered to provide image data for object recognition and pose estimation is a phase-shift laser scanner. The characteristics of the data provided by this scanner have been studied and algorithms have been developed for segmenting range images into planar surfaces, extracting basic features such as surface area, and recognizing the object based on the characteristics of extracted features. Also, an approach has been developed for estimating the spatial orientation and location of the recognized object based on orientations of extracted planes and their intersection points. This paper presents some of the algorithms that have been developed for the purpose of recognizing and estimating the pose of objects as viewed by the laser scanner, and characterizes the desirability and utility of these algorithms within the context of the scanner itself, considering data quality and noise.

Application of response surface methodology for the optimization of supercritical fluid extraction of essential oil from pomegranate (Punica granatum L.) peel.

PubMed

Ara, Katayoun Mahdavi; Raofie, Farhad

2016-07-01

Essential oils and volatile components of pomegranate ( Punica granatum L.) peel of the Malas variety from Meybod, Iran, were extracted using supercritical fluid extraction (SFE) and hydro-distillation methods. The experimental parameters of SFE that is pressure, temperature, extraction time, and modifier (methanol) volume were optimized using a central composite design after a (2 4-1 ) fractional factorial design. Detailed chemical composition of the essential oils and volatile components obtained by hydro-distillation and optimum condition of the supercritical CO 2 extraction were analyzed by GC-MS, and seventy-three and forty-six compounds were identified according to their retention indices and mass spectra, respectively. The optimum SFE conditions were 350 atm pressure, 55 °C temperature, 30 min extraction time, and 150 µL methanol. Results showed that oleic acid, palmitic acid and (-)-Borneol were major compounds in both extracts. The optimum extraction yield was 1.18 % (w/w) for SFE and 0.21 % (v/w) for hydro-distillation.

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.

An Analysis of Activities in Saudi Arabian Middle School Science Textbooks and Workbooks for the Inclusion of Essential Features of Inquiry

NASA Astrophysics Data System (ADS)

Aldahmash, Abdulwali H.; Mansour, Nasser S.; Alshamrani, Saeed M.; Almohi, Saeed

2016-12-01

This study examines Saudi Arabian middle school science textbooks' coverage of the essential features of scientific inquiry. All activities in the middle school science textbooks and workbooks were analyzed by using the scientific inquiry `essential features' rubric. The results indicated that the essential features are included in about 59 % of the analyzed science activities. However, feature 2, `making learner give priority to evidence in responding to questions' and feature 3, `allowing learner to formulate explanations from evidence' appeared more frequently than the other three features (feature 1: engaging learner in scientifically oriented questions, feature 4: helping learner connect explanations to scientific knowledge, and feature 5: helping learner communicate and justify explanations to others), whether in the activities as a whole, or in the activities included in each of the four science domains (physical science, Earth science, life science and chemistry). These features are represented in almost all activities. This means that almost all activities in the middle school science textbooks and the workbooks include features 2 and 3. Meanwhile, the mean level of inclusion of the five essential features of scientific inquiry found in the middle school science textbooks and workbooks as a whole is 2.55. However, results found for features 1, 4, 5 and for in-level inclusion of the inquiry features in each of the science domains indicate that the inclusion of the essential inquiry features is teacher-centred. As a result, neither science textbooks nor workbooks provide students with the opportunity or encouragement to develop their inquiry skills. Consequently, the results suggest important directions for educational administrators and policy-makers in the preparation and use of science educational content.

The extraction and chromatographic determination of the essentials oils from Ocimum basilicum L. by different techniques

NASA Astrophysics Data System (ADS)

Loredana Soran, Maria; Codruta Cobzac, Simona; Varodi, Codruta; Lung, Ildiko; Surducan, Emanoil; Surducan, Vasile

2009-08-01

Three different techniques (maceration, sonication and extraction in microwave field) were used for extraction of essential oils from Ocimum basilicum L. The extracts were analyzed by TLC/HPTLC technique and the fingerprint informations were obtained. The GC-FID was used to characterized the extraction efficiency and for identify the terpenic bioactive compounds. The most efficient extraction technique was maceration followed by microwave and ultrasound. The best extraction solvent system was ethyl ether + ethanol (1:1, v/v). The main compounds identified in Ocimum basilicum L. extracts were: α and β-pinene (mixture), limonene, citronellol, and geraniol.

Anti-spasmodic assessment of hydroalcoholic extract and essential oil of aerial part of Pycnocycla caespitosa Boiss. & Hausskn on rat ileum contractions.

PubMed

Sadraei, Hassan; Asghari, Gholamreza; Alipour, Mahdi

2016-01-01

Pycnocycla caespitosa is an essential oil-containing plant naturally growing in southwest of Iran. The extract of this plant has been used as remedy in traditional medicine. Another species of Pycnocyla (P. spinosa) possessed antispasmodic activity. The pharmacological objective of this study was to look for relaxant effect of hydroalcoholic extract and essential oil of P. caespitosa on rat isolated ileum contractions for comparison with loperamide. The essential oil and the hydroalcoholic extract were prepared by hydrodistillation and percolation techniques, respectively. For antispasmodic studies a section of rat ileum was suspended in an organ bath containing Tyrode's solution. The tissue was stimulated with electrical field stimulation (EFS), KCl (80 mM) and acetylcholine (ACh 0.5 μM). The tissue was kept under 1 g tension at 37°C and continuously gassed with O2. The essential oil content in the aerial parts of P. caespitosa was found to be 0.16 % ml/g. The essential oil was analyzed by gas chromatography and gas chromatography-mass spectrometry. Seventy constituents, representing 97 % of the oil were identified. The major components of the oil were carvacrol (7.1%), β-eudesmol (6.4 %), ρ-cymene (5.7%), caryophyllene oxide (3.6%), α-pinine (1.4%) and α-phelandrene (1.1%). The hydroalcoholic extract of P. caespitosa inhibited the response to KCl (IC50 = 48 ± 3 μg/ml), ACh (IC50 = 61 ± 14.7 μg/ml) and EFS (IC50 = 77 ± 17 μg/ml) in a concentration-dependent manner. The essential oil of P. caespitosa also inhibited rat ileum contractions. The IC50 values for KCl, ACh and EFS were 9.2 ± 1.2 μg/ml, 7.6 ± 0.8 μg/ml and 6.4 ± 0.8 μg/ml, respectively. The inhibitory effect of both the essential oil and the extract were reversible. This research confirms the anti-spasmodic activity of both the essential oil and the extract of P. caespitosa on smooth muscle contraction of ileum.

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.

SU-E-J-252: Reproducibility of Radiogenomic Image Features: Comparison of Two Semi-Automated Segmentation Methods

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

Lee, M; Woo, B; Kim, J

Purpose: Objective and reliable quantification of imaging phenotype is an essential part of radiogenomic studies. We compared the reproducibility of two semi-automatic segmentation methods for quantitative image phenotyping in magnetic resonance imaging (MRI) of glioblastoma multiforme (GBM). Methods: MRI examinations with T1 post-gadolinium and FLAIR sequences of 10 GBM patients were downloaded from the Cancer Image Archive site. Two semi-automatic segmentation tools with different algorithms (deformable model and grow cut method) were used to segment contrast enhancement, necrosis and edema regions by two independent observers. A total of 21 imaging features consisting of area and edge groups were extracted automaticallymore » from the segmented tumor. The inter-observer variability and coefficient of variation (COV) were calculated to evaluate the reproducibility. Results: Inter-observer correlations and coefficient of variation of imaging features with the deformable model ranged from 0.953 to 0.999 and 2.1% to 9.2%, respectively, and the grow cut method ranged from 0.799 to 0.976 and 3.5% to 26.6%, respectively. Coefficient of variation for especially important features which were previously reported as predictive of patient survival were: 3.4% with deformable model and 7.4% with grow cut method for the proportion of contrast enhanced tumor region; 5.5% with deformable model and 25.7% with grow cut method for the proportion of necrosis; and 2.1% with deformable model and 4.4% with grow cut method for edge sharpness of tumor on CE-T1W1. Conclusion: Comparison of two semi-automated tumor segmentation techniques shows reliable image feature extraction for radiogenomic analysis of GBM patients with multiparametric Brain MRI.« less

In vitro biological activity and essential oil composition of four indigenous South African Helichrysum species.

PubMed

Lourens, A C U; Reddy, D; Başer, K H C; Viljoen, A M; Van Vuuren, S F

2004-12-01

Helichrysum species are used widely to treat various medical conditions. In this study, the anti-microbial, anti-oxidant (DPPH assay) and anti-inflammatory activity (5-lipoxygenase assay) of Helichrysum dasyanthum, Helichrysum felinum, Helichrysum excisum and Helichrysum petiolare were investigated. The essential oil compositions of these species were determined. The acetone and methanol extracts as well as the essential oils exhibited activity against Gram-positive bacteria, while both the methanol and acetone extracts of all four species were active in the anti-oxidant assay. The essential oils, on the other hand, displayed activity in the 5-lipoxygenase assay, which was used as an indication of anti-inflammatory activity. Two extracts exhibited promising activity in the anti-microbial assay, the acetone extract of Helichrysum dasyanthum with a MIC value of 15.63 microg/ml and the methanol extract of Helichrysum excisum with a MIC value of 62.5 microg/ml. The acetone extract of Helichrysum dasyanthum was the most active free radical scavenger in the DPPH assay (IC(50) of 9.53 microg/ml) while values for the anti-inflammatory activity of the essential oils ranged between 25 and 32 microg/ml. The essential oil compositions of three species (Helichrysum dasyanthum, Helichrysum excisum and Helichrysum petiolare) were dominated by the presence of monoterpenes such as alpha-pinene, 1,8-cineole and p-cymene. In the oil of Helichrysum felinum, monoterpenes were largely absent. Its profile consisted of a variety of sesquiterpenes in low concentrations with beta-caryophyllene dominating.

Solvent-free microwave extraction of essential oil from aromatic herbs: from laboratory to pilot and industrial scale.

PubMed

Filly, Aurore; Fernandez, Xavier; Minuti, Matteo; Visinoni, Francesco; Cravotto, Giancarlo; Chemat, Farid

2014-05-01

Solvent-free microwave extraction (SFME) has been proposed as a green method for the extraction of essential oil from aromatic herbs that are extensively used in the food industry. This technique is a combination of microwave heating and dry distillation performed at atmospheric pressure without any added solvent or water. The isolation and concentration of volatile compounds is performed in a single stage. In this work, SFME and a conventional technique, hydro-distillation HD (Clevenger apparatus), are used for the extraction of essential oil from rosemary (Rosmarinus officinalis L.) and are compared. This preliminary laboratory study shows that essential oils extracted by SFME in 30min were quantitatively (yield and kinetics profile) and qualitatively (aromatic profile) similar to those obtained using conventional hydro-distillation in 2h. Experiments performed in a 75L pilot microwave reactor prove the feasibility of SFME up scaling and potential industrial applications. Copyright © 2013 Elsevier Ltd. All rights reserved.

GC/MS Evaluation and In Vitro Antioxidant Activity of Essential Oil and Solvent Extracts of an Endemic Plant Used as Folk Remedy in Turkey: Phlomis bourgaei Boiss.

PubMed Central

Sarikurkcu, Cengiz; Sabih Ozer, M.; Cakir, Ahmet; Eskici, Mustafa; Mete, Ebru

2013-01-01

This study was outlined to examine the chemical composition of hydrodistilled essential oil and in vitro antioxidant potentials of the essential oil and different solvent extracts of endemic Phlomis bourgaei Boiss. used as folk remedy in Turkey. The chemical composition of the oil was analyzed by GC and GC-MS, and the predominant components in the oil were found to be β-caryophyllene (37.37%), (Z)-β-farnesene (15.88%), and germacrene D (10.97%). Antioxidant potentials of the solvent extracts and the oil were determined by four testing systems including β-carotene/linoleic acid, DPPH, reducing power, and chelating effect. In β-carotene/linoleic acid assay, all extracts showed the inhibition of more than 50% at all concentrations. In DPPH, chelating effect, and reducing power test systems, the water extract with 88.68%, 77.45%, and 1.857 (absorbance at 700 nm), respectively, exhibited more excellent activity potential than other extracts (hexane, ethyl acetate and methanol) and the essential oil at 1.0 mg/mL concentration. The amount of the total phenolics and flavonoids was the highest in this extract (139.50 ± 3.98 μg gallic acid equivalents (GAEs)/mg extract and 22.71 ± 0.05 μg quercetin equivalents (QEs)/mg extract). PMID:23762120

GC/MS Evaluation and In Vitro Antioxidant Activity of Essential Oil and Solvent Extracts of an Endemic Plant Used as Folk Remedy in Turkey: Phlomis bourgaei Boiss.

PubMed

Sarikurkcu, Cengiz; Sabih Ozer, M; Cakir, Ahmet; Eskici, Mustafa; Mete, Ebru

2013-01-01

This study was outlined to examine the chemical composition of hydrodistilled essential oil and in vitro antioxidant potentials of the essential oil and different solvent extracts of endemic Phlomis bourgaei Boiss. used as folk remedy in Turkey. The chemical composition of the oil was analyzed by GC and GC-MS, and the predominant components in the oil were found to be β -caryophyllene (37.37%), (Z)- β -farnesene (15.88%), and germacrene D (10.97%). Antioxidant potentials of the solvent extracts and the oil were determined by four testing systems including β -carotene/linoleic acid, DPPH, reducing power, and chelating effect. In β -carotene/linoleic acid assay, all extracts showed the inhibition of more than 50% at all concentrations. In DPPH, chelating effect, and reducing power test systems, the water extract with 88.68%, 77.45%, and 1.857 (absorbance at 700 nm), respectively, exhibited more excellent activity potential than other extracts (hexane, ethyl acetate and methanol) and the essential oil at 1.0 mg/mL concentration. The amount of the total phenolics and flavonoids was the highest in this extract (139.50 ± 3.98  μ g gallic acid equivalents (GAEs)/mg extract and 22.71 ± 0.05 μ g quercetin equivalents (QEs)/mg extract).

Continuous nucleus extraction by optically-induced cell lysis on a batch-type microfluidic platform.

PubMed

Huang, Shih-Hsuan; Hung, Lien-Yu; Lee, Gwo-Bin

2016-04-21

The extraction of a cell's nucleus is an essential technique required for a number of procedures, such as disease diagnosis, genetic replication, and animal cloning. However, existing nucleus extraction techniques are relatively inefficient and labor-intensive. Therefore, this study presents an innovative, microfluidics-based approach featuring optically-induced cell lysis (OICL) for nucleus extraction and collection in an automatic format. In comparison to previous micro-devices designed for nucleus extraction, the new OICL device designed herein is superior in terms of flexibility, selectivity, and efficiency. To facilitate this OICL module for continuous nucleus extraction, we further integrated an optically-induced dielectrophoresis (ODEP) module with the OICL device within the microfluidic chip. This on-chip integration circumvents the need for highly trained personnel and expensive, cumbersome equipment. Specifically, this microfluidic system automates four steps by 1) automatically focusing and transporting cells, 2) releasing the nuclei on the OICL module, 3) isolating the nuclei on the ODEP module, and 4) collecting the nuclei in the outlet chamber. The efficiency of cell membrane lysis and the ODEP nucleus separation was measured to be 78.04 ± 5.70% and 80.90 ± 5.98%, respectively, leading to an overall nucleus extraction efficiency of 58.21 ± 2.21%. These results demonstrate that this microfluidics-based system can successfully perform nucleus extraction, and the integrated platform is therefore promising in cell fusion technology with the goal of achieving genetic replication, or even animal cloning, in the near future.

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.

Characterisation and antimicrobial activity of active polypropylene films containing oregano essential oil and Allium extract to be used in packaging for meat products.

PubMed

Llana-Ruiz-Cabello, María; Pichardo, Silvia; Bermudez, José María; Baños, Alberto; Ariza, Juan José; Guillamón, Enrique; Aucejo, Susana; Cameán, Ana M

2018-04-01

Cooked ham is more prone to spoilage than other meat products, making preservation a key step in its commercialisation. One of the most promising preservation strategies is the use of active packaging. Oregano essential oil (OEO) and Proallium® (an Allium extract) have previously been shown to be useful in polylactic acid (PLA)-active films for ready-to-eat salads. The present work aims to study the suitability of polypropylene (PP) films containing OEO and Proallium® in the preservation of cooked ham. Concerning the technological features of the studied material, no significant changes in the mechanical or optical properties of PP films containing the active substances were recorded in comparison to the PP film without extracts. However, films containing both active substances were more flexible than the control film and less strong, highlighting the plasticisation effect of the natural extracts. Moreover, physical properties changed when active substances were added to the film. Incorporation of 4% Proallium® affected the transparency of the film to a higher extent compared to 8% OEO, undergoing decreases in transparency of 40% and 45%, respectively. Moreover, only the film containing the highest amount of OEO (8%) significantly decreased the thickness. Both active substances showed antibacterial properties; however, Proallium®-active films seemed to be more effective against Brochothrix thermosphacta than PP films containing OEO, with all percentages of Proallium® killing the bacterial population present in the ham after 60 days. In addition, materials containing the lowest Proallium® content exhibited higher acceptability by consumers in the sensory analyses with 63-100% willing to purchase, better even than the control package (56-89%). In fact, 2% of Proallium® obtained the best results in the odour study performed by the panellists.

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.

Steam distillation extraction kinetics regression models to predict essential oil yield, composition, and bioactivity of chamomile oil

USDA-ARS?s Scientific Manuscript database

Chamomile (Matricaria chamomilla L.) is one of the most widely spread and used medicinal and essential oil crop in the world. Chamomile essential oil is extracted via steam distillation of the inflorescences (flowers). In this study, distillation time (DT) was found to be a crucial determinant of yi...

Determination of antioxidant capacity and a-amylase inhibitory activity of the essential oils from citronella grass and lemongrass

USDA-ARS?s Scientific Manuscript database

The objective of the present study was to determine the antioxidant capacity of and in vitro a-amylase inhibitory activity of the essential oils extracted from citronella grass and lemongrass. The chemical composition of the extracted essential oils was determined by GC-MS. The antioxidant capacity ...

In Vitro Antibacterial and Antibiotic Resistance Modifying Effect of Bioactive Plant Extracts on Methicillin-Resistant Staphylococcus epidermidis

PubMed Central

Chovanová, Romana; Vaverková, Štefánia

2013-01-01

The crude extracts of plants from Asteraceae and Lamiaceae family and essential oils from Salvia officinalis and Salvia sclarea were studied for their antibacterial as well as antibiotic resistance modifying activity. Using disc diffusion and broth microdilution assays we determined higher antibacterial effect of three Salvia spp. and by evaluating the leakage of 260 nm absorbing material we detected effect of extracts and, namely, of essential oils on the disruption of cytoplasmic membrane. The evaluation of in vitro interactions between plant extracts and oxacillin described in terms of fractional inhibitory concentration (FIC) indices revealed synergistic or additive effects of plant extracts and clearly synergistic effects of essential oil from Salvia officinalis with oxacillin in methicillin-resistant Staphylococcus epidermidis. PMID:24222768

Influence of extraction methods on antioxidant and antimicrobial properties of essential oil from Thymua danesis subsp. Lancifolius.

PubMed

Tavakolpour, Yousef; Moosavi-Nasab, Marzieh; Niakousari, Mehrdad; Haghighi-Manesh, Soroush

2016-03-01

The essential oil (EO) from dried ground powder leaves and stems of Thymua danesis was extracted using hydrodistillation (HD), ohmic extraction (OE), ultrasound-assisted HD and ultrasound-assisted OE methods. Then, the antioxidant, antimicrobial, and sensory properties of the EO were investigated both in vitro and in food systems. Thyme EO extracted by ultrasound-assisted HD method had promising antibacterial activities against Escherichia coli and Staphylococcus aureus and had the best antioxidant properties when tested in vitro. In food systems, higher concentrations of the EO were needed to exert similar antibacterial and antioxidant effects. Furthermore, thyme EO added yogurt and drink yogurt revealed better sensory properties than the control and fresh samples. Essential oil from Thymua danesis has a good potential to be used as an antioxidant, antimicrobial, and flavoring agent in food systems and the extraction method effects on the antioxidant and antimicrobial properties of the thyme extract.

Application of response surface methodology to optimise supercritical carbon dioxide extraction of essential oil from Cyperus rotundus Linn.

PubMed

Wang, Hongwu; Liu, Yanqing; Wei, Shoulian; Yan, Zijun

2012-05-01

Supercritical fluid extraction with carbon dioxide (SC-CO2 extraction) was performed to isolate essential oils from the rhizomes of Cyperus rotundus Linn. Effects of temperature, pressure, extraction time, and CO2 flow rate on the yield of essential oils were investigated by response surface methodology (RSM). The oil yield was represented by a second-order polynomial model using central composite rotatable design (CCRD). The oil yield increased significantly with pressure (p<0.0001) and CO2 flow rate (p<0.01). The maximum oil yield from the response surface equation was predicted to be 1.82% using an extraction temperature of 37.6°C, pressure of 294.4bar, extraction time of 119.8 min, and CO2 flow rate of 20.9L/h. Copyright © 2011 Elsevier Ltd. All rights reserved.

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

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.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

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

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

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

Chemical composition and biological activities of extracts and essential oil of Boswellia dalzielii leaves.

PubMed

Kohoude, Midéko Justin; Gbaguidi, Fernand; Agbani, Pierre; Ayedoun, Marc-Abel; Cazaux, Sylvie; Bouajila, Jalloul

2017-12-01

Boswellia dalzielii Hutch. (Burseraceae) is an aromatic plant. The leaves are used for beverage flavouring. This study investigates the chemical composition and biological activities of various extracts. The essential oil was prepared via hydrodistillation. Identification and quantification were realized via GC-MS and GC-FID. Consecutive extractions (cyclohexane, dichloromethane, ethyl acetate and methanol) were carried out and various chemical groups (phenolics, flavonoids, tannins, antocyanins and sugar) were quantified. The volatile compounds of organic extracts were identified before and after derivatization. Antioxidant, antihyperuricemia, anti-Alzheimer, anti-inflammatory and anticancer activities were evaluated. In the essential oil, 50 compounds were identified, including 3-carene (27.72%) and α-pinene (15.18%). 2,5-Dihydroxy acetophenone and β-d-xylopyranose were identified in the methanol extract. Higher phenolic (315.97 g GAE/kg dry mass) and flavonoid (37.19 g QE/kg dry mass) contents were observed in the methanol extract. The methanol extract has presented remarkable IC 50  =   6.10 mg/L for antiDPPH, 35.10 mg/L for antixanthine oxidase and 28.01 mg/L for anti-5-lipoxygenase. For acetylcholinesterase inhibition, the best IC 50 (76.20 and 67.10 mg/L) were observed, respectively, with an ethyl acetate extract and the essential oil. At 50 mg/L, the dichloromethane extract inhibited OVCAR-3 cell lines by 65.10%, while cyclohexane extract inhibited IGROV-1 cell lines by 92.60%. Biological activities were fully correlated with the chemical groups of the extracts. The ethyl acetate and methanol extracts could be considered as potential alternatives for use in dietary supplements for the prevention or treatment of diseases because of these extracts natural antioxidant, antihyperuricemic and anti-inflammatory activities.

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

New feature extraction method for classification of agricultural products from x-ray images

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

1999-01-01

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. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. 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. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

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.

Relation extraction for biological pathway construction using node2vec.

PubMed

Kim, Munui; Baek, Seung Han; Song, Min

2018-06-13

Systems biology is an important field for understanding whole biological mechanisms composed of interactions between biological components. One approach for understanding complex and diverse mechanisms is to analyze biological pathways. However, because these pathways consist of important interactions and information on these interactions is disseminated in a large number of biomedical reports, text-mining techniques are essential for extracting these relationships automatically. In this study, we applied node2vec, an algorithmic framework for feature learning in networks, for relationship extraction. To this end, we extracted genes from paper abstracts using pkde4j, a text-mining tool for detecting entities and relationships. Using the extracted genes, a co-occurrence network was constructed and node2vec was used with the network to generate a latent representation. To demonstrate the efficacy of node2vec in extracting relationships between genes, performance was evaluated for gene-gene interactions involved in a type 2 diabetes pathway. Moreover, we compared the results of node2vec to those of baseline methods such as co-occurrence and DeepWalk. Node2vec outperformed existing methods in detecting relationships in the type 2 diabetes pathway, demonstrating that this method is appropriate for capturing the relatedness between pairs of biological entities involved in biological pathways. The results demonstrated that node2vec is useful for automatic pathway construction.

Mesoporous-silica nanofluidic channels for quick enrichment/extraction of trace pesticide molecules

NASA Astrophysics Data System (ADS)

Xu, Pengcheng; Chen, Chuanzhao; Li, Xinxin

2015-11-01

As nanofluidic channels, uniaxially oriented mesoporous-silica is, for the first time, in-situ self-assembled in a microfluidic chip for quick enrichment/extraction of ng L-1(ppt)-level organo-phosphorous (OP) pesticide residue from aqueous solution to ethanol. This micro/nano combined pre-treatment chip is essential for following gas chromatography-mass spectrometry (GC-MS) quantitative analysis. Featuring huge surface area and dense silanol groups at the inwall surface, the mesoporous-silica is uniaxially self-assembled in a micro-reservoir to form a pile of nanofluidic channels (diameter = 2.1 nm). The captured/enriched pesticide molecules in the nanochannels can be efficiently extracted by much smaller volume of ethanol due to its much higher solubility to OP. In our affirming experiment, three mixed OP pesticides of dichlorvos, paraoxon and chlorpyrifos (in water) are captured/enriched by the nano-channels and eluted/extracted by only 0.6 mL ethanol. The whole process only takes 16 min. The GC-MS quantitative results for the extracted three pesticides indicate that the extraction recovery achieves 80%. The achieved limit of quantification (LOQ) and the limit of detection (LOD) are 100 ng L-1 and 30 ng L-1, respectively. The nanofluidic-channel pre-treatment technique is promising in various application fields like agriculture and food safety security.

Theoretical Coalescence: A Method to Develop Qualitative Theory: The Example of Enduring.

PubMed

Morse, Janice M

Qualitative research is frequently context bound, lacks generalizability, and is limited in scope. The purpose of this article was to describe a method, theoretical coalescence, that provides a strategy for analyzing complex, high-level concepts and for developing generalizable theory. Theoretical coalescence is a method of theoretical expansion, inductive inquiry, of theory development, that uses data (rather than themes, categories, and published extracts of data) as the primary source for analysis. Here, using the development of the lay concept of enduring as an example, I explore the scientific development of the concept in multiple settings over many projects and link it within the Praxis Theory of Suffering. As comprehension emerges when conducting theoretical coalescence, it is essential that raw data from various different situations be available for reinterpretation/reanalysis and comparison to identify the essential features of the concept. The concept is then reconstructed, with additional inquiry that builds description, and evidence is conducted and conceptualized to create a more expansive concept and theory. By utilizing apparently diverse data sets from different contexts that are linked by certain characteristics, the essential features of the concept emerge. Such inquiry is divergent and less bound by context yet purposeful, logical, and with significant pragmatic implications for practice in nursing and beyond our discipline. Theoretical coalescence is a means by which qualitative inquiry is broadened to make an impact, to accommodate new theoretical shifts and concepts, and to make qualitative research applied and accessible in new ways.

Theoretical Coalescence: A Method to Develop Qualitative Theory

PubMed Central

Morse, Janice M.

2018-01-01

Background Qualitative research is frequently context bound, lacks generalizability, and is limited in scope. Objectives The purpose of this article was to describe a method, theoretical coalescence, that provides a strategy for analyzing complex, high-level concepts and for developing generalizable theory. Theoretical coalescence is a method of theoretical expansion, inductive inquiry, of theory development, that uses data (rather than themes, categories, and published extracts of data) as the primary source for analysis. Here, using the development of the lay concept of enduring as an example, I explore the scientific development of the concept in multiple settings over many projects and link it within the Praxis Theory of Suffering. Methods As comprehension emerges when conducting theoretical coalescence, it is essential that raw data from various different situations be available for reinterpretation/reanalysis and comparison to identify the essential features of the concept. The concept is then reconstructed, with additional inquiry that builds description, and evidence is conducted and conceptualized to create a more expansive concept and theory. Results By utilizing apparently diverse data sets from different contexts that are linked by certain characteristics, the essential features of the concept emerge. Such inquiry is divergent and less bound by context yet purposeful, logical, and with significant pragmatic implications for practice in nursing and beyond our discipline. Conclusion Theoretical coalescence is a means by which qualitative inquiry is broadened to make an impact, to accommodate new theoretical shifts and concepts, and to make qualitative research applied and accessible in new ways. PMID:29360688

Extraction of essential oil from Bunium Persicum (Boiss.) by instant controlled pressure drop (DIC).

PubMed

Feyzi, Elnaz; Eikani, Mohammad H; Golmohammad, Fereshteh; Tafaghodinia, Bahram

2017-12-29

Essential oils extraction from Bunium Persicum (Boiss) was performed using instant controlled pressure drop (in French: Détente Instantanée Contrôlée or DIC) thechnology and optimum extraction conditions were obtained. Response surface methodology (RSM) was used to determine the optimal conditions and the results were 20s heating time, 3.5bar pressure, 0.44mm particle diameter and 9 cycles. Essential oils extraction was also compared with Hydrodistillation (HD), ultrasound-assisted extraction (UAE) and Soxhlet (SOX) extraction. Results show higher efficiency of the DIC than other methods and more oxygenated components were observed. Impact of DIC, HD, UAE and SOX on the morphological structure of the plant was studied by SEM. Antioxidant activity and total phenolic content (TPC) of the extract were determined and comapred by HD. Results show that DIC facilitates achieving to higher TPC and more antioxidant activity. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

NASA Astrophysics Data System (ADS)

Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

2017-04-01

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Anti-inflammatory activities of essential oil isolated from the calyx of Hibiscus sabdariffa L.

PubMed

Shen, Chun-Yan; Zhang, Tian-Tian; Zhang, Wen-Li; Jiang, Jian-Guo

2016-10-12

Hibiscus sabdariffa Linn., belonging to the family of Malvaceae, is considered to be a plant with health care applications in China. The main purpose of this study was to analyze the composition of its essential oil and assess its potential therapeutic effect on anti-inflammatory activity. A water steam distillation method was used to extract the essential oil from H. Sabdariffa. The essential oil components were determined by gas chromatography/mass spectrometry (GC-MS) analysis and a total of 18 volatile constituents were identified, the majority of which were fatty acids and ester compounds. Biological activity showed that the essential oil extracted from H. Sabdariffa exhibited excellent anti-inflammatory activity in lipopolysaccharide (LPS)-stimulated macrophage RAW 264.7 cells. The nitric oxide (NO) inhibition rate reached 67.46% when the concentration of the essential oil was 200 μg mL -1 . Further analysis showed that the anti-inflammatory activity of the essential oil extracted from H. Sabdariffa might be exerted through inhibiting the activation of NF-κB and MAPK (JNK and ERK1/2) signaling pathways to decrease NO and pro-inflammatory cytokine (IL-1, IL-6, TNF-α, COX-2, and iNOS) production. Thus, the essential oil extracted from H. Sabdariffa is a good source of a natural product with a beneficial effect against inflammation, and it may be applied as a food supplement and/or functional ingredient.

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.

Antimicrobial activity of the essential oil and extracts of Cordia curassavica (Boraginaceae).

PubMed

Hernandez, Tzasna; Canales, Margarita; Teran, Barbara; Avila, Olivia; Duran, Angel; Garcia, Ana Maria; Hernandez, Hector; Angeles-Lopez, Omar; Fernandez-Araiza, Mario; Avila, Guillermo

2007-04-20

In traditional Mexican medicine Cordia curassavica (Jacq) Roemer & Schultes is used to treat gastrointestinal, respiratory and dermatological disorders in Zapotitlán de las Salinas, Puebla (México). The aim of this work was to investigate antimicrobial activity of the essential oil, obtained by using Clevenger distillation apparatus, and hexane, chloroform and methanol extracts from aerial parts of Cordia curassavica. Antimicrobial activity was evaluated against 13 bacteria and five fungal strains. The oil and extracts exhibited antimicrobial activity against Gram-positive and Gram-negative bacteria and five fungal strains. Sarcina lutea and Vibrio cholerae were the strains more sensitive to the essential oil effect (MIC=62 microg/mL) and Vibrio cholerae for the hexane extract (MIC=125 microg/mL). Rhyzoctonia solani was the strain more sensitive to the essential oil effect (IC(50)=180 microg/mL) and Trichophyton mentagrophytes for the hexane extract (IC(50)=230 microg/mL). The essential oil was examined by GC and GC-MS. A total 11 constituents representing 96.28% of the essential oil were identified: 4-methyl,4-ethenyl-3-(1-methyl ethenyl)-1-(1-methyl methanol)cyclohexane (37.34%), beta-eudesmol (19.21%), spathulenol (11.25%) and cadina 4(5), 10(14) diene (7.93%) were found to be the major components. The present study tends to confirm the use in the folk medicine of Cordia curassavica in gastrointestinal, respiratory and dermatological diseases.

Biochemical characterization of Helichrysum italicum (Roth) G.Don subsp. italicum (Asteraceae) from Montenegro: phytochemical screening, chemotaxonomy, and antioxidant properties.

PubMed

Kladar, Nebojša V; Anačkov, Goran T; Rat, Milica M; Srđenović, Branislava U; Grujić, Nevena N; Šefer, Emilia I; Božin, Biljana N

2015-03-01

The chemical composition and antioxidant properties of the essential oil and EtOH extract of immortelle (Helichrysum italicum (Roth) G.Don subsp. italicum, Asteraceae) collected in Montenegro were evaluated. The essential oil was characterized by GC/MS analysis, and the content of total phenolics and flavonoids in the EtOH extract was determined using the FolinCiocalteu reagent. The free-radical-scavenging capacity (RSC) of both the essential oil and the EtOH extract was assessed with the 2,2-diphenyl-1-pycrylhydrazyl (DPPH) method. Moreover, the inhibition of hydroxyl radical ((.) OH) generation by the EtOH extract of immortelle was evaluated for the first time here. Neryl acetate (28.2%) and γ-curcumene (18.8%) were the main compounds in the essential oil, followed by neryl propionate (9.1%) and ar-curcumene (8.3%). The chemical composition of the oils of the examined and additional 16 selected Helichrysum italicum taxa described in literature were compared using principal component (PCA) and cluster (CA) analyses. The results of the statistical analyses implied the occurrence of at least four different main and three subchemotypes of essential oils. Considering the antioxidant properties, the EtOH extract of immortelle exhibited similar potential as propyl gallate and quercetin, while the essential oil exhibited relatively weak DPPH(.) -scavenging capacity. Copyright © 2015 Verlag Helvetica Chimica Acta AG, Zürich.

Essential Oil Extraction, Chemical Analysis and Anti-Candida Activity of Calamintha nepeta (L.) Savi subsp. glandulosa (Req.) Ball-New Approaches.

PubMed

Božović, Mijat; Garzoli, Stefania; Sabatino, Manuela; Pepi, Federico; Baldisserotto, Anna; Andreotti, Elisa; Romagnoli, Carlo; Mai, Antonello; Manfredini, Stefano; Ragno, Rino

2017-01-26

A comprehensive study on essential oils extracted from different Calamintha nepeta (L.) Savi subsp. glandulosa (Req.) Ball samples from Tarquinia (Italy) is reported. In this study, the 24-h steam distillation procedure for essential oil preparation, in terms of different harvesting and extraction times, was applied. The Gas chromatography-mass spectrometry (GC/MS) analysis showed that C. nepeta (L.) Savi subsp. glandulosa (Req.) Ball essential oils from Tarquinia belong to the pulegone-rich chemotype. The analysis of 44 samples revealed that along with pulegone, some other chemicals may participate in exerting the related antifungal activity. The results indicated that for higher activity, the essential oils should be produced with at least a 6-h steam distillation process. Even though it is not so dependent on the period of harvesting, it could be recommended not to harvest the plant in the fruiting stage, since no significant antifungal effect was shown. The maximum essential oil yield was obtained in August, with the highest pulegone percentage. To obtain the oil with a higher content of menthone, September and October should be considered as the optimal periods. Regarding the extraction duration, vegetative stage material gives the oil in the first 3 h, while material from the reproductive phase should be extracted at least at 6 or even 12 h.

In-vitro assessment of antioxidant and antimicrobial activities of methanol extracts and essential oil of Thymus hirtus sp. algeriensis.

PubMed

Fatma, Guesmi; Mouna, Ben Farhat; Mondher, Mejri; Ahmed, Landoulsi

2014-07-14

Owing to the complexity of the antioxidant materials and their mechanism of actions, it is obvious that no single testing method is capable of providing a comprehensive picture of the antioxidant profile. The essential oil of the Thymus specie may still possess other important activities in traditional medicine, it can be used in the treatment of fever and cough. This essential oil may also have an anticancer activity. The essential oils aerial parts hydrodistilled from Thymus hirtus sp. algeriensis, were characterised by GC/MS analysis and the methanolic extracts were chemically characterized by HPLC method. The essence of thyme was evaluated for its antioxidant and antibacterial activity. The Terpinen-4-ol are the principal class of metabolites (33.34%) among which 1.8-cineole (19.96%) and camphor (19.20%) predominate. In this study, quantitative values of antioxidant activity of crude methanolic extracts of Thymus hirtus sp. algeriensis were investigated. The essential oils was screened for their antibacterial activity against six common pathogenic microorganisms (Escherichia coli, Pseudomonas aeruginosa, Salmonella enteridis, Staphylococcus aureus, Bacillus subtilis and Listeria monocytogenes) by well diffusion method and agar dilution method (MIC). All the essences were found to inhibit the growth of both gram (+) and gram (-) bacteria organisms tested. These activities were correlated with the presence of phenolic compounds in active fractions. HPLC confirmed presence of phenolic compounds in methanol extracts. Methanol extracts and essential oils from aerial parts of Thymus hirtus sp. algeriensis, were examined for their potential as antioxidants. The technique for measuring antioxidant activity, which was developed using DPPH, ABTS and β-carotene bleaching, produced results as found in established literatures. The present results indicate clearly that methanol extracts and essential oils from Thymus hirtus sp. algeriensis possess antioxidant properties and could serve as free radical inhibitors or scavengers, acting possibly as primary antioxidants, also their essential oil have an antibacterial effect.

Essential oils and hydrophilic extracts from the leaves and flowers of Succisa pratensis Moench. and their biological activity.

PubMed

Witkowska-Banaszczak, Ewa; Długaszewska, Jolanta

2017-11-01

This study was undertaken to evaluate the antioxidant activity of methanol and water extracts from Succisa pratensis Moench (Dipsacaceae) leaves and flowers as well as the chemical composition of the essential oils found in them and the antimicrobial activity of the oils and extracts thereof. The essential oils from S. pratensis leaves and flowers were analysed by the GC-MS. The total phenolic content was determined with Folin-Ciocalteu, that of flavonoids with aluminium chloride and that of phenolic acids with Arnov's reagent. The antioxidant activity was investigated by the DPPH radical scavenging assay. Antimicrobial activity was studied in vitro against G-positive and G-negative bacteria, and fungi using disc diffusion and broth microdilution methods. Eighty-six components of the leaf essential oil and 50 of the flower essential oil were identified. The main components of the leaf essential oil were 2-hexyl-1-octanol (5.76%) and heptacosane (5.53%), whereas hexadecanoic acid (16.10%), 8-octadecen-1-ol acetate (9.86%), methyl linolenate (8.58%), pentacosane (6.63%) and heptacosane (5.50%) were found in the flower essential oil. The essential oils exerted high antimicrobial activity (range: 0.11 to >3.44mg/ml) against the following bacteria: Pseudomonas aeruginosa, Staphylococcus aureus and fungi: Trichophyton mentagrophytes, Candida albicans, whereas the methanol and water extracts showed moderate or weak activity. The strongest antioxidant activity was shown by methanol extracts from S. pratensis leaves, IC 50 = 0.09 mg/ml. There was a positive correlation between the total phenolic content and the antimicrobial activity, while for the antioxidant effect, it was not observed. The results suggest great antibacterial activity of the oils and high antioxidant activity of the methanol extract and may justify the application in treating infections. © 2017 Royal Pharmaceutical Society.

In-vitro assessment of antioxidant and antimicrobial activities of methanol extracts and essential oil of Thymus hirtus sp. algeriensis

PubMed Central

2014-01-01

Background Owing to the complexity of the antioxidant materials and their mechanism of actions, it is obvious that no single testing method is capable of providing a comprehensive picture of the antioxidant profile. The essential oil of the Thymus specie may still possess other important activities in traditional medicine, it can be used in the treatment of fever and cough. This essential oil may also have an anticancer activity. Methods The essential oils aerial parts hydrodistilled from Thymus hirtus sp. algeriensis, were characterised by GC/MS analysis and the methanolic extracts were chemically characterized by HPLC method. The essence of thyme was evaluated for its antioxidant and antibacterial activity. Result The Terpinen-4-ol are the principal class of metabolites (33.34%) among which 1.8-cineole (19.96%) and camphor (19.20%) predominate. In this study, quantitative values of antioxidant activity of crude methanolic extracts of Thymus hirtus sp. algeriensis were investigated. The essential oils was screened for their antibacterial activity against six common pathogenic microorganisms (Escherichia coli, Pseudomonas aeruginosa, Salmonella enteridis, Staphylococcus aureus, Bacillus subtilis and Listeria monocytogenes) by well diffusion method and agar dilution method (MIC). All the essences were found to inhibit the growth of both gram (+) and gram (−) bacteria organisms tested. These activities were correlated with the presence of phenolic compounds in active fractions. HPLC confirmed presence of phenolic compounds in methanol extracts. Conclusion Methanol extracts and essential oils from aerial parts of Thymus hirtus sp. algeriensis, were examined for their potential as antioxidants. The technique for measuring antioxidant activity, which was developed using DPPH, ABTS and β-carotene bleaching, produced results as found in established literatures. The present results indicate clearly that methanol extracts and essential oils from Thymus hirtus sp. algeriensis possess antioxidant properties and could serve as free radical inhibitors or scavengers, acting possibly as primary antioxidants, also their essential oil have an antibacterial effect. PMID:25022197

Efficacy of carbazole alkaloids, essential oil and extract of Murraya koenigii in enhancing subcutaneous wound healing in rats.

PubMed

Nagappan, Thilahgavani; Segaran, Thirukanthan Chandra; Wahid, Mohd Effendy Abdul; Ramasamy, Perumal; Vairappan, Charles S

2012-12-05

The traditional use of Murraya koenigii as Asian folk medicine prompted us to investigate its wound healing ability. Three carbazole alkaloids (mahanine (1), mahanimbicine (2), mahanimbine (3)), essential oil and ethanol extract of Murraya koenigii were investigated for their efficacy in healing subcutaneous wounds. Topical application of the three alkaloids, essential oil and crude extract on 8 mm wounds created on the dorsal skin of rats was monitored for 18 days. Wound contraction rate and epithelialization duration were calculated, while wound granulation and collagen deposition were evaluated via histological method. Wound contraction rates were obvious by day 4 for the group treated with extract (19.25%) and the group treated with mahanimbicine (2) (12.60%), while complete epithelialization was achieved on day 18 for all treatment groups. Wounds treated with mahanimbicine (2) (88.54%) and extract of M. koenigii (91.78%) showed the highest rate of collagen deposition with well-organized collagen bands, formation of fibroblasts, hair follicle buds and with reduced inflammatory cells compared to wounds treated with mahanine (1), mahanimbine (3) and essential oil. The study revealed the potential of mahanimbicine (2) and crude extract of M. koenigii in facilitation and acceleration of wound healing.

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.

Ultrasonic nebulization extraction-heating gas flow transfer-headspace single drop microextraction of essential oil from pericarp of Zanthoxylum bungeanum Maxim.

PubMed

Wei, Shigang; Zhang, Huihui; Wang, Yeqiang; Wang, Lu; Li, Xueyuan; Wang, Yinghua; Zhang, Hanqi; Xu, Xu; Shi, Yuhua

2011-07-22

The ultrasonic nebulization extraction-heating gas flow transfer coupled with headspace single drop microextraction (UNE-HGFT-HS-SDME) was developed for the extraction of essential oil from Zanthoxylum bungeanum Maxim. The gas chromatography-mass spectrometry was applied to the determination of the constituents in the essential oil. The contents of the constituents from essential oil obtained by the proposed method were found to be more similar to those obtained by hydro-distillation (HD) than those obtained by ultrasonic nebulization extraction coupled with headspace single drop microextraction (UNE-HS-SDME). The heating gas flow was firstly used in the analysis of the essential oil to transfer the analytes from the headspace to the solvent microdrop. The relative standard deviations for determining the five major constituents were in the range from 1.5 to 6.7%. The proposed method is a fast, sensitive, low cost and small sample consumption method for the determination of the volatile and semivolatile constituents in the plant materials. Copyright © 2011 Elsevier B.V. All rights reserved.

Face biometrics with renewable templates

NASA Astrophysics Data System (ADS)

van der Veen, Michiel; Kevenaar, Tom; Schrijen, Geert-Jan; Akkermans, Ton H.; Zuo, Fei

2006-02-01

In recent literature, privacy protection technologies for biometric templates were proposed. Among these is the so-called helper-data system (HDS) based on reliable component selection. In this paper we integrate this approach with face biometrics such that we achieve a system in which the templates are privacy protected, and multiple templates can be derived from the same facial image for the purpose of template renewability. Extracting binary feature vectors forms an essential step in this process. Using the FERET and Caltech databases, we show that this quantization step does not significantly degrade the classification performance compared to, for example, traditional correlation-based classifiers. The binary feature vectors are integrated in the HDS leading to a privacy protected facial recognition algorithm with acceptable FAR and FRR, provided that the intra-class variation is sufficiently small. This suggests that a controlled enrollment procedure with a sufficient number of enrollment measurements is required.

Integrated system for automated financial document processing

NASA Astrophysics Data System (ADS)

Hassanein, Khaled S.; Wesolkowski, Slawo; Higgins, Ray; Crabtree, Ralph; Peng, Antai

1997-02-01

A system was developed that integrates intelligent document analysis with multiple character/numeral recognition engines in order to achieve high accuracy automated financial document processing. In this system, images are accepted in both their grayscale and binary formats. A document analysis module starts by extracting essential features from the document to help identify its type (e.g. personal check, business check, etc.). These features are also utilized to conduct a full analysis of the image to determine the location of interesting zones such as the courtesy amount and the legal amount. These fields are then made available to several recognition knowledge sources such as courtesy amount recognition engines and legal amount recognition engines through a blackboard architecture. This architecture allows all the available knowledge sources to contribute incrementally and opportunistically to the solution of the given recognition query. Performance results on a test set of machine printed business checks using the integrated system are also reported.

Controlling the intermediate structure of an ionic liquid for f-block element separations

DOE PAGES

Abney, Carter W.; Do, Changwoo; Luo, Huimin; ...

2017-04-19

Recent research has revealed molecular structure beyond the inner coordination sphere is essential in defining the performance of separations processes, but nevertheless remains largely unexplored. Here we apply small angle neutron scattering (SANS) and x-ray absorption fine structure (XAFS) spectroscopy to investigate the structure of an ionic liquid system studied for f-block element separations. SANS data reveal dramatic changes in the ionic liquid microstructure (~150 Å) which we demonstrate can be controlled by judicious selection of counter ion. Mesoscale structural features (> 500 Å) are also observed as a function of metal concentration. XAFS analysis supports formation of extended aggregatemore » structures, similar to those observed in traditional solvent extraction processes, and suggest additional parallels may be drawn from further study. As a result, achieving precise tunability over the intermediate features is an important development in controlling mesoscale structure and realizing advanced new forms of soft matter.« less

Bio-refinery of orange peels waste: a new concept based on integrated green and solvent free extraction processes using ultrasound and microwave techniques to obtain essential oil, polyphenols and pectin.

PubMed

Boukroufa, Meryem; Boutekedjiret, Chahrazed; Petigny, Loïc; Rakotomanomana, Njara; Chemat, Farid

2015-05-01

In this study, extraction of essential oil, polyphenols and pectin from orange peel has been optimized using microwave and ultrasound technology without adding any solvent but only "in situ" water which was recycled and used as solvent. The essential oil extraction performed by Microwave Hydrodiffusion and Gravity (MHG) was optimized and compared to steam distillation extraction (SD). No significant changes in yield were noticed: 4.22 ± 0.03% and 4.16 ± 0.05% for MHG and SD, respectively. After extraction of essential oil, residual water of plant obtained after MHG extraction was used as solvent for polyphenols and pectin extraction from MHG residues. Polyphenols extraction was performed by ultrasound-assisted extraction (UAE) and conventional extraction (CE). Response surface methodology (RSM) using central composite designs (CCD) approach was launched to investigate the influence of process variables on the ultrasound-assisted extraction (UAE). The statistical analysis revealed that the optimized conditions of ultrasound power and temperature were 0.956 W/cm(2) and 59.83°C giving a polyphenol yield of 50.02 mgGA/100 g dm. Compared with the conventional extraction (CE), the UAE gave an increase of 30% in TPC yield. Pectin was extracted by conventional and microwave assisted extraction. This technique gives a maximal yield of 24.2% for microwave power of 500 W in only 3 min whereas conventional extraction gives 18.32% in 120 min. Combination of microwave, ultrasound and the recycled "in situ" water of citrus peels allow us to obtain high added values compounds in shorter time and managed to make a closed loop using only natural resources provided by the plant which makes the whole process intensified in term of time and energy saving, cleanliness and reduced waste water. 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.

Patient walk detection in hospital room using Microsoft Kinect V2.

PubMed

Liang Liu; Mehrotra, Sanjay

2016-08-01

This paper describes a system using Kinect sensor to detect patient walk automatically in a hospital room setting. The system is especially essential for the case when the patient is alone and the nursing staff is absent. The patient activities are represented by the features extracted from Kinect V2 skeletons. The analysis to the recognized walk could help us to better understand the health situation of the patient and the possible hospital acquired infection (HAI), and provide valuable information to healthcare givers for making a corresponding treatment decision and alteration. The Kinect V2 depth sensor provides the ground truth.

A new computational strategy for predicting essential genes.

PubMed

Cheng, Jian; Wu, Wenwu; Zhang, Yinwen; Li, Xiangchen; Jiang, Xiaoqian; Wei, Gehong; Tao, Shiheng

2013-12-21

Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms. We first collected features that were widely used by previous predictive models and assessed the relationships between gene features and gene essentiality using a stepwise regression model. We found two issues that could significantly reduce model accuracy: (i) the effect of multicollinearity among gene features and (ii) the diverse and even contrasting correlations between gene features and gene essentiality existing within and among different species. To address these issues, we developed a novel model called feature-based weighted Naïve Bayes model (FWM), which is based on Naïve Bayes classifiers, logistic regression, and genetic algorithm. The proposed model assesses features and filters out the effects of multicollinearity and diversity. The performance of FWM was compared with other popular models, such as support vector machine, Naïve Bayes model, and logistic regression model, by applying FWM to reciprocally predict essential genes among and within 21 species. Our results showed that FWM significantly improves the accuracy and robustness of essential gene prediction. FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.

Antioxidant and Antifungal Activity of Extracts of the Aerial Parts of Thymus capitatus (L.) Hoffmanns Against Four Phytopathogenic Fungi of Citrus sinensis

PubMed Central

Tabti, Leila; Dib, Mohammed El Amine; Gaouar, Nassira; Samira, Bouayad; Tabti, Boufeldja

2014-01-01

Background: Many medicinal plants from the Lamiaceae family can be easily found in Algeria. These plants have been used as traditional medicines by local ethnic groups. Thymus capitatus is known in Algeria as "Zaatar" and has been commonly used as a spice, and reported to have many biological effects. Objectives: This paper focused on the assessment of the antioxidant potential and antifungal activity of essential oil and solvent extracts of T. capitatus against the growth of certain fungi. Materials and Methods: Essential oil, ethanol and hexane extracts of T. capitatus were tested for their antioxidant and antifungal activities. The 2, 2-diphenyl-1-picrylhydrazyl (DPPH) assay was used to determine the free radical quenching capacity. Antifungal activity was assessed using the radial growth technique. Results: DPPH free radical scavenging effect of the extracts was compared with standard antioxidant ascorbic acid and showed significant results. The ethanol extract showed high activity at the concentration of 80 g/mL, but less than the standard ascorbic acid. The essential oil was effective against all the fungi used in the experiment. The highest inhibitory effect on the growth of Aspergillus niger, Aspergillus oryzae, Penicillium digitatum, and Fusarium solani was exhibited by the essential oil at concentrations between 0.1 and 0.5 μg/mL. Conclusions: These findings demonstrated that ethanol extract obtained from T. capitatus is a potential source of natural antioxidant, while the essential oil extract can be exploited as an ideal alternative to synthetic fungicides for use in the treatment of many fungal phytopathogens. PMID:24644439

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.

Essential oils: from extraction to encapsulation.

PubMed

El Asbahani, A; Miladi, K; Badri, W; Sala, M; Aït Addi, E H; Casabianca, H; El Mousadik, A; Hartmann, D; Jilale, A; Renaud, F N R; Elaissari, A

2015-04-10

Essential oils are natural products which have many interesting applications. Extraction of essential oils from plants is performed by classical and innovative methods. Numerous encapsulation processes have been developed and reported in the literature in order to encapsulate biomolecules, active molecules, nanocrystals, oils and also essential oils for various applications such as in vitro diagnosis, therapy, cosmetic, textile, food etc. Essential oils encapsulation led to numerous new formulations with new applications. This insures the protection of the fragile oil and controlled release. The most commonly prepared carriers are polymer particles, liposomes and solid lipid nanoparticles. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

An Efficient Algorithm for Server Thermal Fault Diagnosis Based on Infrared Image

NASA Astrophysics Data System (ADS)

Liu, Hang; Xie, Ting; Ran, Jian; Gao, Shan

2017-10-01

It is essential for a data center to maintain server security and stability. Long-time overload operation or high room temperature may cause service disruption even a server crash, which would result in great economic loss for business. Currently, the methods to avoid server outages are monitoring and forecasting. Thermal camera can provide fine texture information for monitoring and intelligent thermal management in large data center. This paper presents an efficient method for server thermal fault monitoring and diagnosis based on infrared image. Initially thermal distribution of server is standardized and the interest regions of the image are segmented manually. Then the texture feature, Hu moments feature as well as modified entropy feature are extracted from the segmented regions. These characteristics are applied to analyze and classify thermal faults, and then make efficient energy-saving thermal management decisions such as job migration. For the larger feature space, the principal component analysis is employed to reduce the feature dimensions, and guarantee high processing speed without losing the fault feature information. Finally, different feature vectors are taken as input for SVM training, and do the thermal fault diagnosis after getting the optimized SVM classifier. This method supports suggestions for optimizing data center management, it can improve air conditioning efficiency and reduce the energy consumption of the data center. The experimental results show that the maximum detection accuracy is 81.5%.

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.

Chemical and biological characterisation of solvent extracts and essential oils from leaves and fruit of two Australian species of Pittosporum (Pittosporaceae) used in aboriginal medicinal practice.

PubMed

Sadgrove, Nicholas John; Jones, Graham Lloyd

2013-02-13

Although no known medicinal use for Pittosporum undulatum Vent. (Pittosporaceae) has been recorded, anecdotal evidence suggests that Australian Aboriginal people used Pittosporum angustifolium Lodd., G. Lodd. & W. Lodd. topically for eczema, pruritis or to induce lactation in mothers following child-birth and internally for coughs, colds or cramps. Essential oil composition and bioactivity as well as differential solvent extract antimicrobial activity from Pittosporum angustifolium are investigated here first, to partially describe the composition of volatiles released in traditional applications of Pittosporum angustifolium for colds or as a lactagogue, and second to investigate antibacterial activity related to topical applications. Essential oils were also investigated from Pittosporum undulatum Vent., first to enhance essential oil data produced in previous studies, and second as a comparison to Pittosporum angustifolium. Essential oils were hydrodistilled from fruit and leaves of both species using a modified approach to lessen the negative (frothing) effect of saponins. This was achieved by floating pumice or pearlite obsidian over the mixture to crush the suds formed while boiling. Essential oil extracts were analysed using GC-MS, quantified using GC-FID then screened for antimicrobial activity using a micro-titre plate broth dilution assay (MIC). Using dichloromethane, methanol, hexane and H(2)O as solvents, extracts were produced from leaves and fruit of Pittosporum angustifolium and screened for antimicrobial activity and qualitative phytochemical character. Although the essential oil from leaves and fruit of Pittosporum undulatum demonstrated some component variation, the essential oil from fruits of Pittosporum angustifolium had major constituents that strongly varied according to the geographical location of collection, suggesting the existence of at least two chemotypes; one with high abundance of acetic acid decyl ester. This chemotype had high antimicrobial activity whilst the other chemotype had only moderate antimicrobial activity against the three microbial species investigated here. This result may support the occurrence of geographical specificity with regard to ethnopharmacological use. Antimicrobial activity screening of the solvent extracts from Pittosporum angustifolium revealed the leaves to be superior to fruit, with water being the most suitable extraction solvent. To the best of our knowledge, this study constitutes the first time essential oils, and solvent extracts from the fruits of Pittosporum angustifolium, have been examined employing comprehensive chemical and biological analysis. The essential oil composition presented in this paper, includes components with structural similarity as chemosemiotic compounds involved in mother-infant identification, which may have significance with regard to traditional applications as a lactagogue. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Comparison of microwave hydrodistillation and solvent-free microwave extraction of essential oil from Melaleuca leucadendra Linn

NASA Astrophysics Data System (ADS)

Ismanto, A. W.; Kusuma, H. S.; Mahfud, M.

2017-12-01

The comparison of solvent-free microwave extraction (SFME) and microwave hydrodistillation (MHD) in the extraction of essential oil from Melaleuca leucadendra Linn. was examined. Dry cajuput leaves were used in this study. The purpose of this study is also to determine optimal condition (microwave power). The relative electric consumption of SFME and MHD methods are both showing 0,1627 kWh/g and 0,3279 kWh/g. The results showed that solvent-free microwave extraction methods able to reduce energy consumption and can be regarded as a green technique for extraction of cajuput oil.

Larvicidal effects of mineral turpentine, low aromatic white spirits, aqueous extracts of Cassia alata, and aqueous extracts, ethanolic extracts and essential oil of betel leaf (Piper betle) on Chrysomya megacephala.

PubMed

Kumarasinghe, Sujith Prasad W; Karunaweera, Nadira D; Ihalamulla, Ranjan L; Arambewela, Lakshmi S R; Dissanayake, Roshinie D S C T

2002-12-01

Many methods have been employed, with variable success, in the treatment of cutaneous myiasis caused by Chrysomya species. Experiment 1: to assess the larvicidal effect of mineral turpentine (MT) and the main ingredient of MT, low aromatic white spirits (LAWS), on Chrysomya megacephala larvae in vitro. Experiment 2: to assess the larvicidal effects of aqueous extracts of winged senna (Cassia alata), and aqueous extracts, ethanolic extracts and essential oil of betel leaf (Piper betle). In experiment 1, two samples of LAWS were obtained from two industrialists (samples 1 and 2). Adult flies of C. megacephala were bred in the insectory of the Department of Parasitology, Faculty of Medicine, University of Colombo. Petri dishes were prepared with pads of cotton wool. These cotton pads were soaked separately in MT, LAWS samples 1 and 2, and normal saline as a control. Ten larvae were placed in each Petri dish. The activity of the larvae was observed and recorded half-hourly. MT and the two samples of LAWS were analyzed by chromatography. In experiment 2, volatile essential oil of betel was prepared using a standard steam distillation process. An ethanolic extract of betel was obtained after boiling the crushed leaf with water, and mixing the stock with ethanol. Betel oil dilutions of 1-4% were prepared using 1% Tween 80 (v/v aq) as a solvent, with 0.05 g/100 mL sodium lauryl sulphate (as stabilizer) and 0.01 g/100 mL methyl paraben (as a preservative). Cotton wool swabs soaked in 1, 2, 3 and 4% essential oil of betel in 1% Tween 80 (v/v aq) prepared as above, 1, 2, 3 and 4% ethanolic extract of betel, 50 and 25% aqueous extract of C. alata, and 50 and 25% aqueous extract of betel were placed in separate Petri dishes. Ten larvae were placed in each Petri dish. 1% Tween 80 solvent with the stabilizer and the preservative, but without betel essential oil, was used as a negative control and MT was used as a positive control. Larval motility was assessed as before. MT and the two LAWS samples killed the larvae in vitro within 4 h. Chromatography showed more unidentified constituents in MT than in pure LAWS, indicating additional substances in MT. The 4 and 3% preparations of the essential oil of betel were effective in killing 100% of the larvae of Chrysomya within 3 h 30 min. The 2% extract of betel essential oil killed 96.7% of larvae in 4 h. Ethanolic and aqueous extracts of betel, the aqueous extract of C. alata, normal saline and the Tween 80 solvent were not larvicidal. MT and LAWS, the main ingredient of MT, were effective in killing Chrysomya larvae. Essential oil obtained from betel leaves also showed a dose-dependent larvicidal effect on Chrysomya larvae. This natural product may be effective in the treatment of wound myiasis.

Effect of lithium salts addition on the ionic liquid based extraction of essential oil from Farfarae Flos.

PubMed

Li, Zhen-Yu; Zhang, Sha-Sha; Jie-Xing; Qin, Xue-Mei

2015-01-01

In this study, an ionic liquids (ILs) based extraction approach has been successfully applied to the extraction of essential oil from Farfarae Flos, and the effect of lithium chloride was also investigated. The results indicated that the oil yields can be increased by the ILs, and the extraction time can be reduced significantly (from 4h to 2h), compared with the conventional water distillation. The addition of lithium chloride showed different effect according to the structures of ILs, and the oil yields may be related with the structure of cation, while the chemical compositions of essential oil may be related with the anion. The reduction of extraction time and remarkable higher efficiency (5.41-62.17% improved) by combination of lithium salt and proper ILs supports the suitability of the proposed approach. Copyright © 2014 Elsevier B.V. All rights reserved.

Factorial design of essential oil extraction from Fagraea fragrans Roxb. flowers and evaluation of its biological activities for perfumery and cosmetic applications.

PubMed

Yingngam, B; Brantner, A H

2015-06-01

To optimize the extraction yields of essential oil from Fagraea fragrans Roxb. flowers in hydro-distillation using a central composite design (CCD) and to evaluate its biological activities for perfumery and cosmetic applications. Central composite design was applied to study the influences of operational parameters [water to flower weight (X(1)) and distillation time (X(2))] on the yields of essential oil (Y). Chemical compositions of the essential oil extracted from the optimized condition were identified by gas chromatography-mass spectrometry. Antioxidant activities of the essential oil were determined against ABTS(•+) and DPPH(•) radicals, and the cytotoxic effects were assessed on human embryonic kidney (HEK293) cells by the use of the MTT assay. Also, the aromatic properties of the essential oil were evaluated by five healthy trained volunteers. The best conditions to obtain the maximum essential oil yield were 7.5 mL g(-1) (X(1)) and 215 min (X(2)). The experimental yield of the essential oil (0.35 ± 0.02% v/w) was close to the value predicted by a mathematical model (0.35 ± 0.01% v/w). 3-Octadecyne, Z,Z,Z-7,10,13-hexadecatrienal, E-nerolidol, pentadecanal and linalool were the major constituents of the essential oil. The essential oil showed moderate antioxidant capacities with no toxic effects on HEK293 cells at 1-250 μg mL(-1). Also, the essential oil exhibited a very strong aroma and was classified to be top- to middle-notes. The results offer the effectively operational conditions in the extraction of essential oil from F. fragrans using hydro-distillation. The essential oil could be used as a natural fragrance, having antioxidant activity with slight cytotoxicity, for perfumery and cosmetic applications. © 2014 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Evaluation of antifungal activities of the essential oil and various extracts of Nigella sativa and its main component, thymoquinone against pathogenic dermatophyte strains.

PubMed

Mahmoudvand, H; Sepahvand, A; Jahanbakhsh, S; Ezatpour, B; Ayatollahi Mousavi, S A

2014-12-01

Plant extracts and plant-derived compounds are valuable sources as folk medicine for the treatment and prevention of a wide range of diseases including infectious diseases. In the present study, the antifungal activities of the essential oil and various extracts Nigella sativa and its active principle, thymoquinone against Trichophyton mentagrophytes, Microsporum canis and Microsporum gypseum as pathogenic dermatophyte strains have been evaluated. In addition, the cytotoxic effects of N. sativa against murine macrophage cells were determined. In this study, the antifungal activity was studied by disk diffusion method and assessment of minimum inhibitory concentration (MIC) of extracts using broth macrodilution method. In addition, the cytotoxic activity of N. sativa was evaluated by colorimetric assay (MTT). The components of the N. sativa essential oil were also identified by gas chromatography/mass spectroscopy (GC/MS) analysis. The results showed that the essential oil and various extracts of N. sativa particularly thymoquinone have potent antifungal effects on T. mentagrophytes, M. canis and M. gypseum as pathogenic dermatophyte strains. In the assessment of the cytotoxicity activity, it could be observed that N. sativa had no significant cytotoxicity in the murine macrophages at low concentrations. While, thymoquinone in comparison with essential oil and various extracts of N. sativa showed higher cytotoxicity on murine macrophage cells. In the GC/MS analysis, thymoquinone (42.4%), p-cymene (14.1%), carvacrol (10.3%) and longifolene (6.1%) were found to be the major components of N. sativa essential oil. The findings of this study suggest a first step in the search of new antidermatophytic drugs and aid the use of N. sativa seeds in the traditional medicine for dermatophytic infections. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Analysis of the essential oils of Alpiniae Officinarum Hance in different extraction methods

NASA Astrophysics Data System (ADS)

Yuan, Y.; Lin, L. J.; Huang, X. B.; Li, J. H.

2017-09-01

It was developed for the analysis of the essential oils of Alpiniae Officinarum Hance extracted by steam distillation (SD), ultrasonic assisted solvent extraction (UAE) and supercritical fluid extraction (SFE) via gas chromatography mass spectrometry (GC-MS) combined with retention index (RI) method. There were multiple volatile components of the oils extracted by the three above-mention methods respectively identified; meanwhile, each one was quantified by area normalization method. The results indicated that the content of 1,8-Cineole, the index constituent, by SD was similar as SFE, and higher than UAE. Although UAE was less time consuming and consumed less energy, the oil quality was poorer due to the use of organic solvents was hard to degrade. In addition, some constituents could be obtained by SFE but could not by SD. In conclusion, essential oil of different extraction methods from the same batch of materials had been proved broadly similarly, however, there were some differences in composition and component ratio. Therefore, development and utilization of different extraction methods must be selected according to the functional requirements of products.

Selectivity of Pinus sylvestris extract and essential oil to estrogen-insensitive breast cancer cells Pinus sylvestris against cancer cells.

PubMed

Hoai, Nguyen Thi; Duc, Ho Viet; Thao, Do Thi; Orav, Anne; Raal, Ain

2015-10-01

So far, the anticancer action of pine tree extracts has mainly been shown for the species distributed widely around the Asian countries. Therefore, this study was performed to examine the potential cytotoxicity of Scots pine (Pinus sylvestris L.) native also to the European region and growing widely in Estonia. The cytotoxic activity of methanol extract and essential oil of Scots pine needles was determined by sulforhodamine B assay in different human cancer cell lines. This needle extract was found to suppress the viability of several human cancer cell lines showing some selectivity to estrogen receptor negative breast cancer cells, MDA-MB-231(half maximal inhibitory concentration [IC50] 35 μg/ml) in comparison with estrogen receptor-positive breast cancer cells, MCF-7 (IC50 86 μg/ml). It is the strongest cytotoxic effect at all measured, thus far for the needles and leaves extracts derived from various pine species, and is also the first study comparing the anticancer effects of pine tree extracts on molecularly different human breast cancer cells. The essential oil showed the stronger cytotoxic effect to both negative and positive breast cancer cell lines (both IC50 29 μg/ml) than pine extract (IC50 42 and 80 μg/ml, respectively). The data from this report indicate that Scots pine needles extract and essential oil exhibits some potential as chemopreventive or chemotherapeutic agent for mammary tumors unresponsive to endocrine treatment.

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).

Treelets Binary Feature Retrieval for Fast Keypoint Recognition.

PubMed

Zhu, Jianke; Wu, Chenxia; Chen, Chun; Cai, Deng

2015-10-01

Fast keypoint recognition is essential to many vision tasks. In contrast to the classification-based approaches, we directly formulate the keypoint recognition as an image patch retrieval problem, which enjoys the merit of finding the matched keypoint and its pose simultaneously. To effectively extract the binary features from each patch surrounding the keypoint, we make use of treelets transform that can group the highly correlated data together and reduce the noise through the local analysis. Treelets is a multiresolution analysis tool, which provides an orthogonal basis to reflect the geometry of the noise-free data. To facilitate the real-world applications, we have proposed two novel approaches. One is the convolutional treelets that capture the image patch information locally and globally while reducing the computational cost. The other is the higher-order treelets that reflect the relationship between the rows and columns within image patch. An efficient sub-signature-based locality sensitive hashing scheme is employed for fast approximate nearest neighbor search in patch retrieval. Experimental evaluations on both synthetic data and the real-world Oxford dataset have shown that our proposed treelets binary feature retrieval methods outperform the state-of-the-art feature descriptors and classification-based approaches.

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

PubMed Central

Bakas, Spyridon; Akbari, Hamed; Sotiras, Aristeidis; Bilello, Michel; Rozycki, Martin; Kirby, Justin S.; Freymann, John B.; Farahani, Keyvan; Davatzikos, Christos

2017-01-01

Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method. PMID:28872634

On the probability density function and characteristic function moments of image steganalysis in the log prediction error wavelet subband

NASA Astrophysics Data System (ADS)

Bao, Zhenkun; Li, Xiaolong; Luo, Xiangyang

2017-01-01

Extracting informative statistic features is the most essential technical issue of steganalysis. Among various steganalysis methods, probability density function (PDF) and characteristic function (CF) moments are two important types of features due to the excellent ability for distinguishing the cover images from the stego ones. The two types of features are quite similar in definition. The only difference is that the PDF moments are computed in the spatial domain, while the CF moments are computed in the Fourier-transformed domain. Then, the comparison between PDF and CF moments is an interesting question of steganalysis. Several theoretical results have been derived, and CF moments are proved better than PDF moments in some cases. However, in the log prediction error wavelet subband of wavelet decomposition, some experiments show that the result is opposite and lacks a rigorous explanation. To solve this problem, a comparison result based on the rigorous proof is presented: the first-order PDF moment is proved better than the CF moment, while the second-order CF moment is better than the PDF moment. It tries to open the theoretical discussion on steganalysis and the question of finding suitable statistical features.

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

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.

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.

Phytochemical composition and antimicrobial activities of the essential oils and organic extracts from pelargonium graveolens growing in Tunisia

PubMed Central

2012-01-01

Background Pelargonium graveolens (P. graveolens) L. is an aromatic and medicinal plant belonging to the geraniacea family. Results The chemical compositions of the essential oil as well as the in vitro antimicrobial activities were investigated. The GC-MS analysis of the essential oil revealed 42 compounds. Linallol L, Citronellol, Geraniol, 6-Octen-1-ol, 3,7-dimethyl, formate and Selinene were identified as the major components. The tested oil and organic extracts exhibited a promising antimicrobial effect against a panel of microorganisms with diameter inhibition zones ranging from 12 to 34 mm and MICs values from 0.039 to10 mg/ml. The investigation of the phenolic content showed that EtOAc, MeOH and water extracts had the highest phenolic contents. Conclusion Overall, results presented here suggest that the essential oil and organic extracts of P. graveolens possesses antimicrobial and properties, and is therefore a potential source of active ingredients for food and pharmaceutical industry. PMID:23216669

Supercritical CO₂ extract and essential oil of aerial part of Ledum palustre L.--Chemical composition and anti-inflammatory activity.

PubMed

Baananou, Sameh; Bagdonaite, Edita; Marongiu, Bruno; Piras, Alessandra; Porcedda, Silvia; Falconieri, Danilo; Boughattas, Naceur A

2015-01-01

The anti-inflammatory activity of two extracts from the aerial parts of Ledum palustre has been reported. The volatile oil was obtained by supercritical fluid extraction (SFE) and the essential oil by hydrodistillation (HD). The oils were analysed by gas chromatography-mass spectrometry to monitor their composition. Both extracts shared as main compound (41.0-43.4%) ledol (23.3-26.7%) and ascaridole (15.1-4.5%). The anti-inflammatory activity was evaluated by the subcutaneous carrageenan injection-induced hind paw oedema. The treated animals received essential oil (SFE and HD), the reference group received ketoprofen or piroxicam and the control group received NaCl 0.9%. A statistical analysis was performed by the Student t-test. The results show that L. palustre essential oil enhanced a significant inhibition of oedema (50-73%) for HD oil and (52-80%) for SFE oil. These results were similar to those obtained with piroxicam (70%) and ketoprofen (55%).

Development of an ionic liquid-based microwave-assisted method for simultaneous extraction and distillation for determination of proanthocyanidins and essential oil in Cortex cinnamomi.

PubMed

Liu, Ye; Yang, Lei; Zu, Yuangang; Zhao, Chunjian; Zhang, Lin; Zhang, Ying; Zhang, Zhonghua; Wang, Wenjie

2012-12-15

Cortex cinnamomi is associated with many health benefits and is used in the food and pharmaceutical industries. In this study, an efficient ionic liquid-based microwave-assisted simultaneous extraction and distillation (ILMSED) technique was used to extract cassia oil and proanthocyanidins from Cortex cinnamomi; these were quantified by gas chromatography/mass spectrometry (GC-MS) and the vanillin-HCl colorimetric method, respectively. 0.5M 1-butyl-3-methylimidazolium bromide ionic liquid was selected as solvent. The optimum parameters of dealing with 20.0 g sample were 230 W microwave irradiation power, 15 min microwave extraction time and 10 liquid-solid ratio. The yields of essential oil and proanthocyanidins were 1.24 ± 0.04% and 4.58 ± 0.21% under the optimum conditions. The composition of the essential oil was analysed by GC-MS. Using the ILMSED method, the energy consumption was reduced and the extraction yields were improved. The proposed method was validated using stability, repeatability, and recovery experiments. The results indicated that the developed ILMSED method provided a good alternative for the extraction of both the essential oil and proanthocyanidins from Cortex cinnamomi. Copyright © 2012 Elsevier Ltd. All rights reserved.

Behavioral model of visual perception and recognition

NASA Astrophysics Data System (ADS)

Rybak, Ilya A.; Golovan, Alexander V.; Gusakova, Valentina I.

1993-09-01

In the processes of visual perception and recognition human eyes actively select essential information by way of successive fixations at the most informative points of the image. A behavioral program defining a scanpath of the image is formed at the stage of learning (object memorizing) and consists of sequential motor actions, which are shifts of attention from one to another point of fixation, and sensory signals expected to arrive in response to each shift of attention. In the modern view of the problem, invariant object recognition is provided by the following: (1) separated processing of `what' (object features) and `where' (spatial features) information at high levels of the visual system; (2) mechanisms of visual attention using `where' information; (3) representation of `what' information in an object-based frame of reference (OFR). However, most recent models of vision based on OFR have demonstrated the ability of invariant recognition of only simple objects like letters or binary objects without background, i.e. objects to which a frame of reference is easily attached. In contrast, we use not OFR, but a feature-based frame of reference (FFR), connected with the basic feature (edge) at the fixation point. This has provided for our model, the ability for invariant representation of complex objects in gray-level images, but demands realization of behavioral aspects of vision described above. The developed model contains a neural network subsystem of low-level vision which extracts a set of primary features (edges) in each fixation, and high- level subsystem consisting of `what' (Sensory Memory) and `where' (Motor Memory) modules. The resolution of primary features extraction decreases with distances from the point of fixation. FFR provides both the invariant representation of object features in Sensor Memory and shifts of attention in Motor Memory. Object recognition consists in successive recall (from Motor Memory) and execution of shifts of attention and successive verification of the expected sets of features (stored in Sensory Memory). The model shows the ability of recognition of complex objects (such as faces) in gray-level images invariant with respect to shift, rotation, and scale.

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

PubMed Central

Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, UK Arvind

2016-01-01

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

Antibacterial activity of GUAVA, Psidium guajava Linnaeus, leaf extracts on diarrhea-causing enteric bacteria isolated from Seabob shrimp, Xiphopenaeus kroyeri (Heller).

PubMed

Gonçalves, Flávia A; Andrade Neto, Manoel; Bezerra, José N S; Macrae, Andrew; Sousa, Oscarina Viana de; Fonteles-Filho, Antonio A; Vieira, Regine H S F

2008-01-01

Guava leaf tea of Psidium guajava Linnaeus is commonly used as a medicine against gastroenteritis and child diarrhea by those who cannot afford or do not have access to antibiotics. This study screened the antimicrobial effect of essential oils and methanol, hexane, ethyl acetate extracts from guava leaves. The extracts were tested against diarrhea-causing bacteria: Staphylococcus aureus, Salmonella spp. and Escherichia coli. Strains that were screened included isolates from seabob shrimp, Xiphopenaeus kroyeri (Heller) and laboratory-type strains. Of the bacteria tested, Staphylococcus aureus strains were most inhibited by the extracts. The methanol extract showed greatest bacterial inhibition. No statistically significant differences were observed between the tested extract concentrations and their effect. The essential oil extract showed inhibitory activity against S. aureus and Salmonella spp. The strains isolated from the shrimp showed some resistance to commercially available antibiotics. These data support the use of guava leaf-made medicines in diarrhea cases where access to commercial antibiotics is restricted. In conclusion, guava leaf extracts and essential oil are very active against S. aureus, thus making up important potential sources of new antimicrobial compounds.

Optimization of an accelerated solvent extraction dispersive liquid-liquid microextraction method for the separation and determination of essential oil from Ligusticum chuanxiong Hort by gas chromatography with mass spectrometry.

PubMed

Yang, Guang; Sun, Qiushi; Hu, Zhiyan; Liu, Hua; Zhou, Tingting; Fan, Guorong

2015-10-01

In this study, an accelerated solvent extraction dispersive liquid-liquid microextraction coupled with gas chromatography and mass spectrometry was established and employed for the extraction, concentration and analysis of essential oil constituents from Ligusticum chuanxiong Hort. Response surface methodology was performed to optimize the key parameters in accelerated solvent extraction on the extraction efficiency, and key parameters in dispersive liquid-liquid microextraction were discussed as well. Two representative constituents in Ligusticum chuanxiong Hort, (Z)-ligustilide and n-butylphthalide, were quantitatively analyzed. It was shown that the qualitative result of the accelerated solvent extraction dispersive liquid-liquid microextraction approach was in good agreement with that of hydro-distillation, whereas the proposed approach took far less extraction time (30 min), consumed less plant material (usually <1 g, 0.01 g for this study) and solvent (<20 mL) than the conventional system. To sum up, the proposed method could be recommended as a new approach in the extraction and analysis of essential oil. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.

PubMed

Ni, Qianwu; Chen, Lei

2017-01-01

Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification. In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model. Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure. The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Synergistic antibacterial activity of the essential oil of aguaribay (Schinus molle L.).

PubMed

de Mendonça Rocha, Pedro M; Rodilla, Jesus M; Díez, David; Elder, Heriberto; Guala, Maria Silvia; Silva, Lúcia A; Pombo, Eunice Baltazar

2012-10-12

Schinus molle L. (aguaribay, aroeira-falsa, "molle", family Anacardiaceae), a native of South America, produces an active antibacterial essential oil extracted from the leaves and fruits. This work reports a complete study of its chemical composition and determines the antibacterial activity of Schinus molle L. essential oil and its main components. The results showed that the crude extract essential oil has a potent antibacterial effect on Staphylococcus aureus ATCC 25923, a strong/moderate effect on Escherichia coli ATCC 25922 and moderate/weak one on Pseudomonas aeruginosa ATCC 27853.

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.

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

PubMed Central

2009-01-01

Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality. PMID:19758426

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.

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.

Optimization of supercritical carbon dioxide extraction of essential oil from Dracocephalum kotschyi Boiss: An endangered medicinal plant in Iran.

PubMed

Nejad-Sadeghi, Masoud; Taji, Saeed; Goodarznia, Iraj

2015-11-27

Extraction of the essential oil from a medicinal plant called Dracocephalum kotschyi Boiss was performed by green technology of supercritical carbon dioxide (SC-CO2) extraction. A Taguchi orthogonal array design with an OA16 (4(5)) matrix was used to evaluate the effects of five extraction variables: pressure of 150-310bar, temperature of 40-60°C, average particle size of 250-1000μm, CO2 flow rate of 2-10ml/s and dynamic extraction time of 30-100min. The optimal conditions to obtain the maximum extraction yield were at 240bar, 60°C, 500μm, 10ml/s and 100min. The extraction yield under the above conditions was 2.72% (w/w) which is more than two times the maximum extraction yield that has been reported for this plant in the literature using traditional extraction techniques. Results from analysis of variance (ANOVA) indicated that the CO2 flow rate and the extraction time were the most significant factors on the extraction yield by percentage contribution of 44.27 and 28.86, respectively. Finally, the chemical composition of the essential oil was evaluated by using gas chromatography-mass spectroscopy (GC-MS). Citral, p-mentha-1,3,8-triene, D-3-carene and methyl geranate were the major components identified. Copyright © 2015. Published by Elsevier B.V.

"In situ" extraction of essential oils by use of Dean-Stark glassware and a Vigreux column inside a microwave oven: a procedure for teaching green analytical chemistry.

PubMed

Chemat, Farid; Perino-Issartier, Sandrine; Petitcolas, Emmanuel; Fernandez, Xavier

2012-08-01

One of the principal objectives of sustainable and green processing development remains the dissemination and teaching of green chemistry in colleges, high schools, and academic laboratories. This paper describes simple glassware that illustrates the phenomenon of extraction in a conventional microwave oven as energy source and a process for green analytical chemistry. Simple glassware comprising a Dean-Stark apparatus (for extraction of aromatic plant material and recovery of essential oils and distilled water) and a Vigreux column (as an air-cooled condenser inside the microwave oven) was designed as an in-situ extraction vessel inside a microwave oven. The efficiency of this experiment was validated for extraction of essential oils from 30 g fresh orange peel, a by-product in the production of orange juice. Every laboratory throughout the world can use this equipment. The microwave power is 100 W and the irradiation time 15 min. The method is performed at atmospheric pressure without added solvent or water and furnishes essential oils similar to those obtained by conventional hydro or steam distillation. By use of GC-MS, 22 compounds in orange peel were separated and identified; the main compounds were limonene (72.1%), β-pinene (8.4%), and γ-terpinene (6.9%). This procedure is appropriate for the teaching laboratory, does not require any special microwave equipment, and enables the students to learn the skills of extraction, and chromatographic and spectroscopic analysis. They are also exposed to a dramatic visual example of rapid, sustainable, and green extraction of an essential oil, and are introduced to successful sustainable and green analytical chemistry.

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

PubMed Central

Aznar, Fidel; Pujol, Francisco A.; Pujol, Mar; Rizo, Ramón; Pujol, María-José

2014-01-01

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N 2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used. PMID:25415377

Learning probabilistic features for robotic navigation using laser sensors.

PubMed

Aznar, Fidel; Pujol, Francisco A; Pujol, Mar; Rizo, Ramón; Pujol, María-José

2014-01-01

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N(2)), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.

Optical coherence tomography used for internal biometrics

NASA Astrophysics Data System (ADS)

Chang, Shoude; Sherif, Sherif; Mao, Youxin; Flueraru, Costel

2007-06-01

Traditional biometric technologies used for security and person identification essentially deal with fingerprints, hand geometry and face images. However, because all these technologies use external features of human body, they can be easily fooled and tampered with by distorting, modifying or counterfeiting these features. Nowadays, internal biometrics which detects the internal ID features of an object is becoming increasingly important. Being capable of exploring under-skin structure, optical coherence tomography (OCT) system can be used as a powerful tool for internal biometrics. We have applied fiber-optic and full-field OCT systems to detect the multiple-layer 2D images and 3D profile of the fingerprints, which eventually result in a higher discrimination than the traditional 2D recognition methods. More importantly, the OCT based fingerprint recognition has the ability to easily distinguish artificial fingerprint dummies by analyzing the extracted layered surfaces. Experiments show that our OCT systems successfully detected the dummy, which was made of plasticene and was used to bypass the commercially available fingerprint scanning system with a false accept rate (FAR) of 100%.

A regularized approach for geodesic-based semisupervised multimanifold learning.

PubMed

Fan, Mingyu; Zhang, Xiaoqin; Lin, Zhouchen; Zhang, Zhongfei; Bao, Hujun

2014-05-01

Geodesic distance, as an essential measurement for data dissimilarity, has been successfully used in manifold learning. However, most geodesic distance-based manifold learning algorithms have two limitations when applied to classification: 1) class information is rarely used in computing the geodesic distances between data points on manifolds and 2) little attention has been paid to building an explicit dimension reduction mapping for extracting the discriminative information hidden in the geodesic distances. In this paper, we regard geodesic distance as a kind of kernel, which maps data from linearly inseparable space to linear separable distance space. In doing this, a new semisupervised manifold learning algorithm, namely regularized geodesic feature learning algorithm, is proposed. The method consists of three techniques: a semisupervised graph construction method, replacement of original data points with feature vectors which are built by geodesic distances, and a new semisupervised dimension reduction method for feature vectors. Experiments on the MNIST, USPS handwritten digit data sets, MIT CBCL face versus nonface data set, and an intelligent traffic data set show the effectiveness of the proposed algorithm.

EDA-gram: designing electrodermal activity fingerprints for visualization and feature extraction.

PubMed

Chaspari, Theodora; Tsiartas, Andreas; Stein Duker, Leah I; Cermak, Sharon A; Narayanan, Shrikanth S

2016-08-01

Wearable technology permeates every aspect of our daily life increasing the need of reliable and interpretable models for processing the large amount of biomedical data. We propose the EDA-Gram, a multidimensional fingerprint of the electrodermal activity (EDA) signal, inspired by the widely-used notion of spectrogram. The EDA-Gram is based on the sparse decomposition of EDA from a knowledge-driven set of dictionary atoms. The time axis reflects the analysis frames, the spectral dimension depicts the width of selected dictionary atoms, while intensity values are computed from the atom coefficients. In this way, EDA-Gram incorporates the amplitude and shape of Skin Conductance Responses (SCR), which comprise an essential part of the signal. EDA-Gram is further used as a foundation for signal-specific feature design. Our results indicate that the proposed representation can accentuate fine-grain signal fluctuations, which might not always be apparent through simple visual inspection. Statistical analysis and classification/regression experiments further suggest that the derived features can differentiate between multiple arousal levels and stress-eliciting environments for two datasets.

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.

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.

[Effects of Frankincense and Myrrh essential oil on transdermal absorption in vitro of Chuanxiong and penetration mechanism of skin blood flow].

PubMed

Zhu, Xiao-Fang; Luo, Jing; Guan, Yong-Mei; Yu, Ya-Ting; Jin, Chen; Zhu, Wei-Feng; Liu, Hong-Ning

2017-02-01

The aim of this paper was to explore the effects of Frankincense and Myrrh essential oil on transdermal absorption in vitro of Chuanxiong, and to investigate the possible penetration mechanism of their essential oil from the perspective of skin blood perfusion changes. Transdermal tests were performed in vitro with excised mice skin by improved Franz diffusion cells. The cumulative penetration amounts of ferulic acid in Chuanxiong were determined by HPLC to investigate the effects of Frankincense and Myrrh essential oil on transdermal permeation properties of Chuanxiong. Simultaneously, the skin blood flows were determined by laser flow doppler. The results showed that the cumulative penetration amount of ferulic acid in Chuanxiong was (8.13±0.76) μg•cm⁻² in 24 h, and was (48.91±4.87), (57.80±2.86), (63.34±4.56), (54.17±4.40), (62.52±7.79) μg•cm⁻² respectively in Azone group, Frankincense essential oil group, Myrrh essential oil, frankincense and myrrh singly extracted essential oil mixture group, and frankincense and myrrh mixed extraction essential oil group. The enhancement ratios of each essential oil groups were 7.68, 8.26, 7.26, 8.28, which were slightly greater than 6.55 in Azone group. In addition, as compared with the conditions before treatment, there were significant differences and obvious increasing trend in blood flow of rats in Frankincense essential oil group, Myrrh essential oil group, frankincense and myrrh singly extracted essential oil mixture group, and frankincense and myrrh mixed extraction essential oil group when were dosed at 10, 20, 30, 10 min respectively, indicating that the skin blood flows were increased under the effects of Frankincense and Myrrh essential oil to a certain extent. Thus, Frankincense and Myrrh essential oil had certain effect on promoting permeability of Chuanxiong both before and after drug combination, and may promote the elimination of drugs from epidermis to dermal capillaries through increase of skin blood flow, thus enhancing the transdermal permeation amounts of drugs. Copyright© by the Chinese Pharmaceutical Association.

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.

Comparative Study of Essential Oils Extracted from Algerian Myrtus communis L. Leaves Using Microwaves and Hydrodistillation

PubMed Central

Berka-Zougali, Baya; Ferhat, Mohamed-Amine; Hassani, Aicha; Chemat, Farid; Allaf, Karim S.

2012-01-01

Two different extraction methods were used for a comparative study of Algerian Myrtle leaf essential oils: solvent-free-microwave-extraction (SFME) and conventional hydrodistillation (HD). Essential oils analyzed by GC and GC-MS presented 51 components constituting 97.71 and 97.39% of the total oils, respectively. Solvent-Free-Microwave-Extract Essential oils SFME-EO were richer in oxygenated compounds. Their major compounds were 1,8-cineole, followed by α-pinene as against α-pinene, followed by 1,8-cineole for HD. Their antimicrobial activity was investigated on 12 microorganisms. The antioxidant activities were studied with the 2,2-diphenyl-1-picrylhydrazyl (DPPH•) radical scavenging method. Generally, both essential oils showed high antimicrobial and weak antioxidant activities. Microstructure analyses were also undertaken on the solid residue of myrtle leaves by Scanning Electronic Microscopy (SEM); it showed that the SFME-cellular structure undergoes significant modifications compared to the conventional HD residual solid. Comparison between hydrodistillation and SFME presented numerous distinctions. Several advantages with SFME were observed: faster kinetics and higher efficiency with similar yields: 0.32% dry basis, in 30 min as against 180 min for HD. PMID:22606003

Total antioxidant activity and antimicrobial potency of the essential oil and oleoresin of Zingiber officinale Roscoe

PubMed Central

Bellik, Yuva

2014-01-01

Objective To compare in vitro antioxidant and antimicrobial activities of the essential oil and oleoresin of Zingiber officinale Roscoe. Methods The antioxidant activity was evaluated based on the ability of the ginger extracts to scavenge ABTS°+ free radical. The antimicrobial activity was studied by the disc diffusion method and minimal inhibitory concentration was determined by using the agar incorporation method. Results Ginger extracts exerted significant antioxidant activity and dose-depend effect. In general, oleoresin showed higher antioxidant activity [IC50=(1.820±0.034) mg/mL] when compared to the essential oil [IC50=(110.14±8.44) mg/mL]. In terms of antimicrobial activity, ginger compounds were more effective against Escherichia coli, Bacillus subtilis and Staphylococcus aureus, and less effective against Bacillus cereus. Aspergillus niger was least, whereas, Penicillium spp. was higher sensitive to the ginger extracts; minimal inhibitory concentrations of the oleoresin and essential oil were 2 mg/mL and 869.2 mg/mL, respectively. Moreover, the studied extracts showed an important antifungal activity against Candida albicans. Conclusions The study confirms the wide application of ginger oleoresin and essential oil in the treatment of many bacterial and fungal diseases.

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.

In vitro antibacterial, antifungal, and antioxidant activities of the essential oil and methanol extracts of herbal parts and callus cultures of Satureja hortensis L.

PubMed

Güllüce, M; Sökmen, M; Daferera, D; Ağar, G; Ozkan, H; Kartal, N; Polissiou, M; Sökmen, A; Sahin, F

2003-07-02

The present study was designated to evaluate the antimicrobial and antioxidant activities of the essential oil, obtained by using a Clevenger distillation apparatus, water soluble (polar) and water insoluble (nonpolar) subfractions of the methanol extracts from aerial parts of Satureja hortensis L. plants, and methanol extract from calli established from the seeds using Gamborg's B5 basal media supplemented with indole-3-butyric acid (1.0 ppm), 6-benzylaminopurine (N(6)-benzyladenine) (1.0 ppm), and sucrose (2.5%). The antimicrobial test results showed that the essential oil of S. hortensis had great potential antimicrobial activities against all 23 bacteria and 15 fungi and yeast species tested. In contrast, the methanol extract from callus cultures and water soluble subfraction of the methanol extract did not show antimicrobial activities, but the nonpolar subfraction had antibacterial activity against only five out of 23 bacterial species, which were Bacillus subtilis, Enterococcus fecalis, Pseudomonas aeruginosa, Salmonella enteritidis, and Streptococcus pyogenes. Antioxidant studies suggested that the polar subfractions of the methanol extract of intact plant and methanol extract of callus cultures were able to reduce the stable free radical 2,2-diphenyl-1-picrylhydrazyl to the yellow-colored diphenylpicrylhydrazine. In this assay, the strongest effect was observed for the tissue culture extract, with an IC(50) value of 23.76 +/- 0.80 microgram/mL, which could be compared with the synthetic antioxidant agent butylated hydroxytoluene. On the other hand, linoleic acid oxidation was 95% inhibited in the presence of the essential oil while the inhibition was 90% with the chloroform subfraction of the intact plant. The chemical composition of a hydrodistilled essential oil of S. hortensis was analyzed by gas chromatography (GC)/flame ionization detection (FID) and a GC-mass spectrometry system. A total 22 constituents representing 99.9% of the essential oil were identified by GC-FID analaysis. Thymol (29.0%), carvacrol (26.5%), gamma-terpinene (22.6%), and p-cymene (9.3%) were the main components.

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.

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.

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 Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

PubMed Central

Han, Jiuqi; Zhao, Yuwei; Sun, Hongji; Chen, Jiayun; Ke, Ang; Xu, Gesen; Zhang, Hualiang; Zhou, Jin; Wang, Changyong

2018-01-01

Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA) model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI) competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods. PMID:29713262

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.

Secondary metabolites of Seseli rigidum: Chemical composition plus antioxidant, antimicrobial and cholinesterase inhibition activity.

PubMed

Stankov-Jovanović, V P; Ilić, M D; Mitić, V D; Mihajilov-Krstev, T M; Simonović, S R; Nikolić Mandić, S D; Tabet, J C; Cole, R B

2015-01-01

Extracts of different polarity obtained from various plant parts (root, leaf, flower and fruit) of Seseli rigidum were studied by different antioxidant assays: DPPH and ABTS radical scavenging activity, by total reducing power method as well as via total content of flavonoids and polyphenols. Essential oils of all plant parts showed weak antioxidant characteristics. The inhibitory concentration range of the tested extracts, against bacteria Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Bacillus cereus, and fungi Candida albicans and Aspergillus niger was 0.01-1.50 mg/mL and of a microbicidal 0.02-3.00 mg/mL. In the interaction with cholinesterase, all essential oils proved effective as inhibitors. The highest percentage of inhibition versus human and horse cholinesterase was shown by root essential oil (38.20% and 48.30%, respectively) among oils, and root hexane extract (40.56% and 50.65% respectively). Essential oils and volatile components of all plant parts were identified by GC, GC-MS and headspace/GC-MS. Statistical analysis of the ensemble of results showed that the root essential oil composition differed significantly from essential oils of other parts of the plant. Taking into account all of the studied activities, the root hexane extract showed the best overall properties. By means of high performance liquid chromatography coupled to high resolution mass spectrometry, the 30 most abundant constituents were identified in extracts of different polarity. The presence of identified constituents was linked to observed specific biological activities, thus designating compounds potentially responsible for each exhibited activity. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Improvement of Fermented Fish Flour Quality Using Essential Oil Extracted From Fresh Leaves of Pimenta racemosa (Mill.) J. W. Moore.

PubMed

Adjou, Euloge S; Dègnon, René G; Dahouenon-Ahoussi, Edwige; Soumanou, Mohamed M; Sohounhloue, Dominique C K

2017-08-01

The aim of this study was to evaluate the efficacy of the essential oil extracted from fresh leaves of Pimenta racemosa in the improvement of fermented fish flour producing technology. Essential oil of Pimenta racemosa was extracted by hydrodistillation and its chemical composition was determined by GC and GC/MS. Different types of fermented fish flours from Lesser African Threadfin (Galeoides decadactylus) were produced by the modification of the traditional processing technology and the introduction of a step of essential oil adjunction during the process. Three different essential oil concentrations (0.5, 1.0 and 2.0 μL g -1 ) were investigated. Physicochemical, microbiological and nutritional analyzes were performed in order to evaluate the quality of the fermented fish flour produced. Results obtained revealed that the essential oil of Pimenta racemosa investigated has a chemical composition characterized by the presence of myrcene (25.1%), chavicol (7.5%) and eugenol (51.1%). Fermented fish flour produced have a good nutritional potential. However, on the microbiological level, only samples produced by adjunction of essential oil have a low level of microbial contamination, with an absence of pathogenic microorganisms.

Efficacy of Compounds Isolated from the Essential Oil of Artemisia lavandulaefolia in Control of the Cigarette Beetle, Lasioderma serricorne.

PubMed

Zhou, Jun; Zou, Kexing; Zhang, Wenjuan; Guo, Shanshan; Liu, Hong; Sun, Jiansheng; Li, Jigang; Huang, Dongye; Wu, Yan; Du, Shushan; Borjigidai, Almaz

2018-02-07

To develop natural product resources to control cigarette beetles ( Lasioderma serricorne ), the essential oil from Artemisia lavandulaefolia (Compositae) was investigated. Oil was extracted by hydrodistillation of the above-ground portion of A. lavandulaefolia and analyzed using gas chromatography-mass spectrometer (GC-MS). Extracted essential oil and three compounds isolated from the oil were then evaluated in laboratory assays to determine the fumigant, contact, and repellent efficacy against the stored-products' pest, L. serricorne . The bioactive constituents from the oil extracts were identified as chamazulene (40.4%), 1,8-cineole (16.0%), and β-caryophyllene (11.5%). In the insecticidal activity assay, the adults of L. serricorne were susceptible to fumigant action of the essential oil and 1,8-cineole, with LC 50 values of 31.81 and 5.18 mg/L air. The essential oil, 1,8-cineole, chamazulene, and β-caryophyllene exhibited contact toxicity with LD 50 values of 13.51, 15.58, 15.18 and 35.52 μg/adult, respectively. During the repellency test, the essential oil and chamazulene had repellency approximating the positive control. The results indicated that chamazulene was abundant in A. lavandulaefolia essential oil and was toxic to cigarette beetles.

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.

Classification of product inspection items using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, H.-W.

1998-03-01

Automated processing and 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. This approach involves two main steps: preprocessing and classification. Preprocessing locates individual items and segments ones that touch using a modified watershed algorithm. The second stage involves extraction of features that allow discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper. We use a new nonlinear feature extraction scheme called the maximum representation and discriminating feature (MRDF) extraction method to compute nonlinear features that are used as inputs to a classifier. The MRDF is shown to provide better classification and a better ROC (receiver operating characteristic) curve than other methods.

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.

Selectivity of Pinus sylvestris extract and essential oil to estrogen-insensitive breast cancer cells Pinus sylvestris against cancer cells

PubMed Central

Hoai, Nguyen Thi; Duc, Ho Viet; Thao, Do Thi; Orav, Anne; Raal, Ain

2015-01-01

Background: So far, the anticancer action of pine tree extracts has mainly been shown for the species distributed widely around the Asian countries. Objective: Therefore, this study was performed to examine the potential cytotoxicity of Scots pine (Pinus sylvestris L.) native also to the European region and growing widely in Estonia. Materials and Methods: The cytotoxic activity of methanol extract and essential oil of Scots pine needles was determined by sulforhodamine B assay in different human cancer cell lines. Results: This needle extract was found to suppress the viability of several human cancer cell lines showing some selectivity to estrogen receptor negative breast cancer cells, MDA-MB-231(half maximal inhibitory concentration [IC50] 35 μg/ml) in comparison with estrogen receptor-positive breast cancer cells, MCF-7 (IC50 86 μg/ml). It is the strongest cytotoxic effect at all measured, thus far for the needles and leaves extracts derived from various pine species, and is also the first study comparing the anticancer effects of pine tree extracts on molecularly different human breast cancer cells. The essential oil showed the stronger cytotoxic effect to both negative and positive breast cancer cell lines (both IC50 29 μg/ml) than pine extract (IC50 42 and 80 μg/ml, respectively). Conclusion: The data from this report indicate that Scots pine needles extract and essential oil exhibits some potential as chemopreventive or chemotherapeutic agent for mammary tumors unresponsive to endocrine treatment. PMID:26664017

Chemical composition and radical scavenging activity of essential oil and methanolic extract of Eremostachys azerbaijanica Rech.f. from Iran.

PubMed

Asnaashari, Solmaz; Afshar, Fariba Heshmati; Ebrahimi, Atefeh; Moghadam, Sedigheh Bamdad; Delazar, Abbas

2016-01-01

In the present study, the chemical composition of the essential oil and methanol (MeOH) extract of aerials of E. azerbaijanica were identified. Furthermore, the free radical scavenging properties of the volatile oil as well as the MeOH extract of the plant were assessed. The essential oil of the air-dried aerial parts was obtained by hydro-distillation using a Clevenger-type apparatus. The oil was then analyzed by gas chromatography-mass spectrometry and gas chromatography with flame ionization detector. Soxhlet extraction was performed on the aerial parts using n-hexane, dichloromethane and MeOH. The MeOH extract was then subjected to solid-phase extraction using a C18 Sep-Pak cartridge. Isolation and structural elucidation of the pure components was accomplished by high-performance liquid chromatography and spectroscopic methods (UV, (1)H-NMR). The free radical scavenging properties were determined by 2, 2-diphenyl-1-picrylhydrazyl (DPPH) assay. A total of 59 components representing 95.9% of the oil constituents were identified which were primarily characterized as terpenoids or aliphatic skeletons. The major components of the oil were hexahydrofarnesyl acetone (27.1%), 2-methyl-6-propyl-dodecane (16.4%) and tricosane (9.3%). One flavonoid (luteolin-7-O-rutinoside) and one phenylethanoid (verbascoside) were also isolated and identified from the MeOH extract. The results of DPPH assays showed that the essential oil of E. azerbaijanica possessed weak free radical scavenging activity whereas the MeOH extract and its pure constituents showed significant scavenging activities in comparison with positive controls.

Inhibition of melanogenesis versus antioxidant properties of essential oil extracted from leaves of Vitex negundo Linn and chemical composition analysis by GC-MS.

PubMed

Huang, Huey-Chun; Chang, Tzu-Yun; Chang, Long-Zen; Wang, Hsiao-Fen; Yih, Kuang-Hway; Hsieh, Wan-Yu; Chang, Tsong-Min

2012-03-30

This study was aimed at investigating the antimelanogenic and antioxidative properties of the essential oil extracted from leaves of V. negundo Linn and the analysis of the chemical composition of this essential oil. The efficacy of the essential oil was evaluated spectrophotometrically, whereas the volatile chemical compounds in the essential oil were analyzed by gas chromatography-mass spectrometry (GC-MS). The results revealed that the essential oil effectively suppresses murine B16F10 tyrosinase activity and decreases the amount of melanin in a dose-dependent manner. Additionally, the essential oil significantly scavenged 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) radicals, and showed potent reducing power versus metal-ion chelating properties in a dose-dependent pattern. The chemical constituents in the essential oil are sesquiterpenes (44.41%), monoterpenes (19.25%), esters (14.77%), alcohols (8.53%), aromatic compound (5.90%), ketone (4.96%), ethers (0.4%) that together account for 98.22% of its chemical composition. It is predicted that the aromatic compound in the essential oil may contribute to its antioxidant activities. The results indicated that essential oil extracted from V. negundo Linn leaves decreased melanin production in B16F10 melanoma cells and showed potent antioxidant activities. The essential oil can thereby serve as an inhibitor of melanin synthesis and could also act as a natural antioxidant.

Antibacterial Activity of Cinnamaldehyde and Estragole Extracted from Plant Essential Oils against Pseudomonas syringae pv. actinidiae Causing Bacterial Canker Disease in Kiwifruit

PubMed Central

Song, Yu-Rim; Choi, Min-Seon; Choi, Geun-Won; Park, Il-Kwon; Oh, Chang-Sik

2016-01-01

Pseudomonas syringae pv. actinidiae (Psa) causes bacterial canker disease in kiwifruit. Antibacterial activity of plant essential oils (PEOs) originating from 49 plant species were tested against Psa by a vapor diffusion and a liquid culture assays. The five PEOs from Pimenta racemosa, P. dioica, Melaleuca linariifolia, M. cajuputii, and Cinnamomum cassia efficiently inhibited Psa growth by either assays. Among their major components, estragole, eugenol, and methyl eugenol showed significant antibacterial activity by only the liquid culture assay, while cinnamaldehyde exhibited antibacterial activity by both assays. The minimum inhibitory concentrations (MICs) of estragole and cinnamaldehyde by the liquid culture assay were 1,250 and 2,500 ppm, respectively. The MIC of cinnamaldehyde by the vapor diffusion assay was 5,000 ppm. Based on the formation of clear zones or the decrease of optical density caused by these compounds, they might kill the bacterial cells and this feature might be useful for managing the bacterial canker disease in kiwifruit. PMID:27493612

Extraction of basil leaves (ocimum canum) oleoresin with ethyl acetate solvent by using soxhletation method

NASA Astrophysics Data System (ADS)

Tambun, R.; Purba, R. R. H.; Ginting, H. K.

2017-09-01

The goal of this research is to produce oleoresin from basil leaves (Ocimum canum) by using soxhletation method and ethyl acetate as solvent. Basil commonly used in culinary as fresh vegetables. Basil contains essential oils and oleoresin that are used as flavouring agent in food, in cosmetic and ingredient in traditional medicine. The extraction method commonly used to obtain oleoresin is maceration. The problem of this method is many solvents necessary and need time to extract the raw material. To resolve the problem and to produce more oleoresin, we use soxhletation method with a combination of extraction time and ratio from the material with a solvent. The analysis consists of yield, density, refractive index, and essential oil content. The best treatment of basil leaves oleoresin extraction is at ratio of material and solvent 1:6 (w / v) for 6 hours extraction time. In this condition, the yield of basil oleoresin is 20.152%, 0.9688 g/cm3 of density, 1.502 of refractive index, 15.77% of essential oil content, and the colour of oleoresin product is dark-green.

[Research of the essential oil of Plumeria rubra var. actifolia from Laos by supercritical carbon dioxide extraction].

PubMed

Xiao, Xin-Yu; Cui, Long-Hai; Zhou, Xin-Xin; Wu, Yan; Ge, Fa-Huan

2011-05-01

The orthogonal test and the supercritical carbon dioxide fluid extraction were used for optimizing the extraction of the essential oil from Plumeria rubra var. actifolia for the first time. Compared with the steam distillation, the optimal operation parameter of extraction was as follows: extraction pressure 25 MPa, extraction temperature 45 degrees C; separator I pressure 12 MPa, separator I temperature 55 degrees C; separator II pressure 6 MPa, separator II temperature 30 degrees C. Under this condition the yield of the essential oil was 5.8927%. The components were separated and identified by GC-MS. 53 components of Plumeria rubra var. actifolia measured by SFE method were identified and determined by normalization method. The main components were 1, 6, 10-dodecatrien-3-ol, 3, 7, 11-trimethyl, benzoic acid, 2-hydroxy-, phenylmethyl ester, 1, 2-benzenedicarboxylic acid, bis(2-methylpropyl) ester,etc.. 1, 2-Benzenedicarboxylic acid, bis (2-methylpropyl) este. took up 66.11% of the total amount, and there was much difference of the results from SD method.

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.

Evaluation of extracts and essential oil from Callistemon viminalis leaves: antibacterial and antioxidant activities, total phenolic and flavonoid contents.

PubMed

Salem, Mohamed Z M; Ali, Hayssam M; El-Shanhorey, Nader A; Abdel-Megeed, Ahmed

2013-10-01

To investigate antioxidant and antibacterial activities of Callistemon viminalis (C. viminalis) leaves. The essential oil of C. viminalis leaves obtained by hydro-distillation was analyzed by GC/MS. Different extracts were tested for total phenolic and flavonoid contents and in vitro antioxidant (DPPH assay) and antibacterial (agar disc diffusion and 96-well micro-plates methods) actives. Fourteen components were identified in the essential oil, representing 98.94% of the total oil. The major components were 1,8-cineole (64.53%) and α-pinene (9.69%). Leaf essential oil exhibited the highest antioxidant activity of (88.60±1.51)% comparable to gallic acid, a standard compound [(80.00±2.12)%]. Additionally, the biggest zone of inhibitions against the studied bacterial strains was observed by the essential oil when compared to the standard antibiotic (tetracycline). The crude methanol extract and ethyl acetate fraction had a significant antibacterial activity against the tested bacterial strains. It can be suggested that C. viminalis is a great potential source of antibacterial and antioxidant compounds useful for new antimicrobial drugs from the natural basis. The present study revealed that the essential oil as well as the methanol extracts and ethyl acetate fraction of C. viminalis leaves exhibited highly significant antibacterial activity against the tested bacterial strains. Copyright © 2013 Hainan Medical College. Published by Elsevier B.V. All rights reserved.

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.

Scaling analysis of bilateral hand tremor movements in essential tremor patients.

PubMed

Blesic, S; Maric, J; Dragasevic, N; Milanovic, S; Kostic, V; Ljubisavljevic, Milos

2011-08-01

Recent evidence suggests that the dynamic-scaling behavior of the time-series of signals extracted from separate peaks of tremor spectra may reveal existence of multiple independent sources of tremor. Here, we have studied dynamic characteristics of the time-series of hand tremor movements in essential tremor (ET) patients using the detrended fluctuation analysis method. Hand accelerometry was recorded with (500 g) and without weight loading under postural conditions in 25 ET patients and 20 normal subjects. The time-series comprising peak-to-peak (PtP) intervals were extracted from regions around the first three main frequency components of power spectra (PwS) of the recorded tremors. The data were compared between the load and no-load condition on dominant (related to tremor severity) and non-dominant tremor side and with the normal (physiological) oscillations in healthy subjects. Our analysis shows that, in ET, the dynamic characteristics of the main frequency component of recorded tremors exhibit scaling behavior. Furthermore, they show that the two main components of ET tremor frequency spectra, otherwise indistinguishable without load, become significantly different after inertial loading and that they differ between the tremor sides (related to tremor severity). These results show that scaling, a time-domain analysis, helps revealing tremor features previously not revealed by frequency-domain analysis and suggest that distinct oscillatory central circuits may generate the tremor in ET patients.

The optimization of essential oils supercritical CO2 extraction from Lavandula hybrida through static-dynamic steps procedure and semi-continuous technique using response surface method

PubMed Central

Kamali, Hossein; Aminimoghadamfarouj, Noushin; Golmakani, Ebrahim; Nematollahi, Alireza

2015-01-01

Aim: The aim of this study was to examine and evaluate crucial variables in essential oils extraction process from Lavandula hybrida through static-dynamic and semi-continuous techniques using response surface method. Materials and Methods: Essential oil components were extracted from Lavandula hybrida (Lavandin) flowers using supercritical carbon dioxide via static-dynamic steps (SDS) procedure, and semi-continuous (SC) technique. Results: Using response surface method the optimum extraction yield (4.768%) was obtained via SDS at 108.7 bar, 48.5°C, 120 min (static: 8×15), 24 min (dynamic: 8×3 min) in contrast to the 4.620% extraction yield for the SC at 111.6 bar, 49.2°C, 14 min (static), 121.1 min (dynamic). Conclusion: The results indicated that a substantial reduction (81.56%) solvent usage (kg CO2/g oil) is observed in the SDS method versus the conventional SC method. PMID:25598636

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).

Chemical composition and biological properties of Satureja avromanica Maroofi.

PubMed

Abdali, Elham; Javadi, Shima; Akhgari, Maryam; Hosseini, Seyran; Dastan, Dara

2017-03-01

Satureja avromanica is an indigenous plant which is frequently used as a spice in Avraman-Kurdistan region of Iran. The present study aimed to investigate the chemical composition, antimicrobial and antioxidant properties of the S. avromanica . In addition, rosmarinic acid and total phenolic content of S. avromanica was assessed by spectrophotometric method and HPTLC. The essential oil and methanolic extract were isolated by hydrodistillation and maceration methods, respectively. A total of 32 compounds representing 98.6% of the essential oil were identified by GC-MS and GC-FID. The main constituents were n -pentacosane (23.8%), spathulenol (11.5%), β-bourbonen (11.3%) and n -docosane (11.0%). The antibacterial activity of samples were carried out by disc diffusion method and evaluate the minimal inhibitory concentration (MIC) essential oil and methanolic extract were found to be effective against Staphylococcus aureus , Bacillus cereus and Bacillus pumilus . The highest scavenging activity was found for methanolic extract of S. avromanica (21.58 µg/mL) and the total phenolics of methanolic extract of S. avromanica was 95.3 mg GAE/g. The rosmarinic acid content of S. avromanica methanolic extract was 0.83 mg/g plant. Antioxidant activity and rosmarininc acid content of S. avromanica suggests that the essential oil and methanolic extract of S. avromanica has great potential for application as a natural antimicrobial and antioxidant agent to preserve food.

[Comparison of chemical components of essential oils in needles of Pinus massoniana Lamb and Pinus elliottottii Engelm from Guangxi].

PubMed

Shen, Changmao; Duan, Wengui; Cen, Bo; Tan, Jianhui

2006-11-01

Essential oils were extracted by steam distillation from the needles of Pinus massoniana Lamb and Pinus elliottottii Engelm grown in Guangxi. Various factors such as pine needle dosage and extraction time which may influence the oil yield were investigated. The optimum conditions were found to be as follows: pine needle dosage 700 g, extraction time 5 h. The essential oil yields from the needles of Pinus massoniana Lamb and Pinus elliottottii Engelm were 0.45% and 0.19%, respectively. Moreover, the chemical compositions of the essential oils were analyzed by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). Sixty four components in the essential oil from needle of Pinus massoniana Lamb were separated and twenty of them (98.59%) were identified while seventy three components in the essential oil from needle of Pinus elliottottii Engelm were separated and twenty nine of them (94.23%) were identified. Generally, the compositions of the essential oils from needles of the two varieties were similar but the contents of some compounds differed greatly. Especially, the content of alpha-pinene in the essential oils from Pinus massoniana Lamb needles was 2.6 times as that from Pinus elliottottii Engelm needles, but the content of beta-pinene was less than the latter. Mono- and sesquiterpenes were the main composition of the essential oils from Pinus massoniana Lamb and Pinus elliottottii Engelm needles.

Rock images classification by using deep convolution neural network

NASA Astrophysics Data System (ADS)

Cheng, Guojian; Guo, Wenhui

2017-08-01

Granularity analysis is one of the most essential issues in authenticate under microscope. To improve the efficiency and accuracy of traditional manual work, an convolutional neural network based method is proposed for granularity analysis from thin section image, which chooses and extracts features from image samples while build classifier to recognize granularity of input image samples. 4800 samples from Ordos basin are used for experiments under colour spaces of HSV, YCbCr and RGB respectively. On the test dataset, the correct rate in RGB colour space is 98.5%, and it is believable in HSV and YCbCr colour space. The results show that the convolution neural network can classify the rock images with high reliability.

Complexity-aware simple modeling.

PubMed

Gómez-Schiavon, Mariana; El-Samad, Hana

2018-02-26

Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach-complexity-aware simple modeling-that can bridge the gap between the small-scale and large-scale approaches. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors

NASA Astrophysics Data System (ADS)

Fayaz, S. M.; Rajanikant, G. K.

2014-07-01

Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.

Two-steps extraction of essential oil, polysaccharides and biphenyl cyclooctene lignans from Schisandra chinensis Baill fruits.

PubMed

Cheng, Zhenyu; Yang, Yingjie; Liu, Yan; Liu, Zhigang; Zhou, Hongli; Hu, Haobin

2014-08-05

A method for two-steps extraction of essential oil, polysaccharides and lignans from Schisandra chinensis Baill had been established. Firstly, S. chinensis was extracted by hydro-distillation, the extracted solution was separated from the water-insoluble residue and precipitated by adding dehydrated alcohol after the essential oil was collected, and then the precipitate as polysaccharide was collected. Finally, second extraction was performed to obtained lignans from the water-insoluble residue with ultrasonic-microwave assisted extraction (UMAE) method. Response surface methodology was employed to optimize the UMAE parameters, the optimal conditions were as follows: microwave power 430W, ethanol concentration 84%, particle size of sample 120-mesh sieves, ratio of water to raw material 15 and extraction time 2.1min. Under these optimized conditions, the total extraction yields of five lignans (Schisandrol A, Schisantherin A, Deoxyschisandrin, Schisandrin B and Schisandrin C) had reached 14.22±0.135mg/g. Compared with the traditional method of direct extraction of different bioactive components in respective procedure, the extraction yields of polysaccharides and the five lignans had reached 99% and 95%, respectively. The mean recoveries of the 5 lignan compounds and polysaccharides were 97.75-101.08% and their RSD value was less than 3.88%.The approach proposed in this study not only improved the extraction yield of lignans, but also elevated the utilization of Schisandra resources. Copyright © 2014 Elsevier B.V. All rights reserved.

Extracting the Information Backbone in Online System

PubMed Central

Zhang, Qian-Ming; Zeng, An; Shang, Ming-Sheng

2013-01-01

Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite networks. In this paper, we find that some information provided by the bipartite networks is not only redundant but also misleading. With such “less can be more” feature, we design some algorithms to improve the recommendation performance by eliminating some links from the original networks. Moreover, we propose a hybrid method combining the time-aware and topology-aware link removal algorithms to extract the backbone which contains the essential information for the recommender systems. From the practical point of view, our method can improve the performance and reduce the computational time of the recommendation system, thus improving both of their effectiveness and efficiency. PMID:23690946

Spatial frequency supports the emergence of categorical representations in visual cortex during natural scene perception.

PubMed

Dima, Diana C; Perry, Gavin; Singh, Krish D

2018-06-11

In navigating our environment, we rapidly process and extract meaning from visual cues. However, the relationship between visual features and categorical representations in natural scene perception is still not well understood. Here, we used natural scene stimuli from different categories and filtered at different spatial frequencies to address this question in a passive viewing paradigm. Using representational similarity analysis (RSA) and cross-decoding of magnetoencephalography (MEG) data, we show that categorical representations emerge in human visual cortex at ∼180 ms and are linked to spatial frequency processing. Furthermore, dorsal and ventral stream areas reveal temporally and spatially overlapping representations of low and high-level layer activations extracted from a feedforward neural network. Our results suggest that neural patterns from extrastriate visual cortex switch from low-level to categorical representations within 200 ms, highlighting the rapid cascade of processing stages essential in human visual perception. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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

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

Combining essential oils and olive extract for control of multi-drug resistant Salmonella enterica on organic leafy greens

USDA-ARS?s Scientific Manuscript database

We investigated the combined antimicrobial effects of plant essential oils and olive extract. Organic baby spinach, mature spinach, romaine lettuce, and iceberg lettuce were inoculated with the pathogen and then dip-treated in phosphate buffered saline (PBS) control, 3.0% hydrogen peroxide, a 0.1% ...

Combining essential oils and olive extract for control of multi-drug resistant Salmonella enterica on organic leafy greens

USDA-ARS?s Scientific Manuscript database

We investigated the combined antimicrobial effects of plant essential oils and olive extract against antibiotic resistant Salmonella enterica serovar Newport on organic leafy greens. Organic baby spinach, mature spinach, romaine lettuce, and iceberg lettuce were inoculated with S. Newport and dip-t...

[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.

Comparison of antimicrobial activity of essential oils, plant extracts and methylparaben in cosmetic emulsions: 2 months study.

PubMed

Herman, Anna

2014-09-01

The aim of the study was to compare the preservative effectiveness of plant extracts (Matricaria chamomilla, Aloe vera, Calendula officinalis) and essential oils (Lavandulla officinalis, Melaleuca alternifolia, Cinnamomum zeylanicum) with methylparaben in cosmetic emulsions against skin microflora during 2 months of application by volunteers. Cosmetic emulsions with extracts (2.5 %), essential oils (2.5 %), methylparaben (0.4 %) or placebo were tested by 40 volunteers during 2 months of treatment. In order to determine microbial purity of the emulsions, the samples were taken after 0, 2, 4, 6 and 8 weeks of application. Throughout the trial period it was revealed that only cinnamon oil completely inhibited the growth of bacteria, yeast and mould, as compared to all other essential oils, plant extracts and methylparaben in the tested emulsions. This result shows that cinnamon oil could successfully replace the use of methylparaben in cosmetics, at the same time ensuring microbiological purity of a cosmetic product under its in-use and storage conditions.

Metabolic profile and biological activities of Lavandula pedunculata subsp. lusitanica (Chaytor) Franco: studies on the essential oil and polar extracts.

PubMed

Costa, Patrícia; Gonçalves, Sandra; Valentão, Patrícia; Andrade, Paula B; Almeida, Carlos; Nogueira, José M F; Romano, Anabela

2013-12-01

We investigated the metabolic profile and biological activities of the essential oil and polar extracts of Lavandula pedunculata subsp. lusitanica (Chaytor) Franco collected in south Portugal. Gas chromatography-mass spectrometry (GC-MS) analysis revealed that oxygen-containing monoterpenes was the principal group of compounds identified in the essential oil. Camphor (40.6%) and fenchone (38.0%) were found as the major constituents. High-performance liquid chromatography with diode array detection (HPLC-DAD) analysis allowed the identification of hydroxycinnamic acids (3-O-caffeoylquinic, 4-O-caffeoylquinic, 5-O-caffeoylquinic and rosmarinic acids) and flavones (luteolin and apigenin) in the polar extracts, with rosmarinic acid being the main compound in most of them. The bioactive compounds from L. pedunculata polar extracts were the most efficient free-radical scavengers, Fe(2+) chelators and inhibitors of malondialdehyde production, while the essential oil was the most active against acetylcholinesterase. Our results reveal that the subspecies of L. pedunculata studied is a potential source of active metabolites with a positive effect on human health. Copyright © 2013 Elsevier Ltd. All rights reserved.

Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-Throughput Microscopy at Low Temporal Resolution

PubMed Central

Downey, Mike J.; Jeziorska, Danuta M.; Ott, Sascha; Tamai, T. Katherine; Koentges, Georgy; Vance, Keith W.; Bretschneider, Till

2011-01-01

The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers. PMID:22194797

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.

Physics-Based Image Segmentation Using First Order Statistical Properties and Genetic Algorithm for Inductive Thermography Imaging.

PubMed

Gao, Bin; Li, Xiaoqing; Woo, Wai Lok; Tian, Gui Yun

2018-05-01

Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

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.

Pressurized fluid extraction of essential oil from Lavandula hybrida using a modified supercritical fluid extractor and a central composite design for optimization.

PubMed

Kamali, Hossein; Jalilvand, Mohammad Reza; Aminimoghadamfarouj, Noushin

2012-06-01

Essential oil components were extracted from lavandin (Lavandula hybrida) flowers using pressurized fluid extraction. A central composite design was used to optimize the effective extraction variables. The chemical composition of extracted samples was analyzed by a gas chromatograph-flame ionization detector column. For achieving 100% extraction yield, the temperature, pressure, extraction time, and the solvent flow rate were adjusted at 90.6°C, 63 bar, 30.4 min, and 0.2 mL/min, respectively. The results showed that pressurized fluid extraction is a practical technique for separation of constituents such as 1,8-cineole (8.1%), linalool (34.1%), linalyl acetate (30.5%), and camphor (7.3%) from lavandin to be applied in the food, fragrance, pharmaceutical, and natural biocides industries. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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%).

Green and Efficient Processing of Cinnamomum cassia Bark by Using Ionic Liquids: Extraction of Essential Oil and Construction of UV-Resistant Composite Films from Residual Biomass.

PubMed

Mehta, Mohit J; Kumar, Arvind

2017-12-14

There is significant interest in the development of a sustainable and integrated process for the extraction of essential oils and separation of biopolymers by using novel and efficient solvent systems. Herein, cassia essential oil enriched in coumarin is extracted from Cinnamomum cassia bark by using a protic ionic liquid (IL), ethylammonium nitrate (EAN), through dissolution and the creation of a biphasic system with the help of diethyl ether. The process has been perfected, in terms of higher biomass dissolution ability and essential oil yield through the addition of aprotic ILs (based on the 1-butyl-3-methylimidazolium (C 4 mim) cation and chloride or acetate anions) to EAN. After extraction of oil, cellulose-rich material and free lignin were regenerated from biomass-IL solutions by using a 1:1 mixture of acetone-water. The purity of the extracted essential oil and biopolymers were ascertained by means of FTIR spectroscopy, NMR spectroscopy, and GC-MS techniques. Because lignin contains UV-blocking chromophores, the oil-free residual lignocellulosic material has been directly utilized to construct UV-light-resistant composite materials in conjunction with the biopolymer chitosan. Composite material thus obtained was processed to form biodegradable films, which were characterized for mechanical and optical properties. The films showed excellent UV-light resistance and mechanical properties, thereby making it a material suitable for packaging and light-sensitive applications. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Development of Personalized Urination Recognition Technology Using Smart Bands.

PubMed

Eun, Sung-Jong; Whangbo, Taeg-Keun; Park, Dong Kyun; Kim, Khae-Hawn

2017-04-01

This study collected and analyzed activity data sensed through smart bands worn by patients in order to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for recognizing urination activity in patients, this study aimed to explore the feasibility of urination monitoring systems. This study aimed to develop an algorithm that recognizes urination based on a patient's posture and changes in posture. Motion data was obtained from a smart band on the arm. An algorithm that recognizes the 3 stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and from tilt angle data. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the absolute value of the signals was calculated and then compared with the set threshold value to determine the occurrence of vibration signals. In feature extraction, the most essential information describing each pattern was identified after analyzing the characteristics of the data. The results of the feature extraction process were sorted using a classifier to detect urination. An experiment was carried out to assess the performance of the recognition technology proposed in this study. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 90.4%, proving the robustness of the proposed algorithm. The proposed urination recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after comparative analyses with standardized feature patterns.

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%.

Chemical composition of the essential oil from the leaves of Carapa guianensis collected from Venezuelan Guayana and the antimicrobial activity of the oil and crude extracts.

PubMed

Meccia, Gina; Quintero, Patricia; Rojas, Luis B; Usubillaga, Alfredo; Velasco, Judith; Diaz, Tulia; Diaz, Clara; Velásquez, Jesús; Toro, Maria

2013-11-01

The essential oil obtained by hydrodistillation of Carapa guianensis Aubl. (Meliaceae) leaves was analyzed by GC-FID and GC-MS. Twenty-three components were identified, which made up 93.7% of the oil. The most abundant constituents were bicyclogermacrene (28.5%), alpha-humulene (17.2%), germacrene B (11.9%), and trans-beta-caryophyllene (9.9%). Antimicrobial activity of the essential oil, as well as the crude extracts of the leaves obtained by refluxing the dried leaves with n-hexane, dichloromethane, and methanol, was determined using the disc diffusion assay. Activity against Staphylococcus aureus ATCC 29923 and Enterococcus faecalis ATCC 29212 was only found for the essential oil and the methanolic extract, at minimal inhibitory concentrations (MIC) of 400 microg/mL and 50 microg/mL.

Lippia origanoides chemotype differentiation based on essential oil GC-MS and principal component analysis.

PubMed

Stashenko, Elena E; Martínez, Jairo R; Ruíz, Carlos A; Arias, Ginna; Durán, Camilo; Salgar, William; Cala, Mónica

2010-01-01

Chromatographic (GC/flame ionization detection, GC/MS) and statistical analyses were applied to the study of essential oils and extracts obtained from flowers, leaves, and stems of Lippia origanoides plants, growing wild in different Colombian regions. Retention indices, mass spectra, and standard substances were used in the identification of 139 substances detected in these essential oils and extracts. Principal component analysis allowed L. origanoides classification into three chemotypes, characterized according to their essential oil major components. Alpha- and beta-phellandrenes, p-cymene, and limonene distinguished chemotype A; carvacrol and thymol were the distinctive major components of chemotypes B and C, respectively. Pinocembrin (5,7-dihydroxyflavanone) was found in L. origanoides chemotype A supercritical fluid (CO(2)) extract at a concentration of 0.83+/-0.03 mg/g of dry plant material, which makes this plant an interesting source of an important bioactive flavanone with diverse potential applications in cosmetic, food, and pharmaceutical products.

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.

Relevance of the Sea Sand Disruption Method (SSDM) for the biometrical differentiation of the essential-oil composition from conifers.

PubMed

Dawidowicz, Andrzej L; Czapczyńska, Natalia B; Wianowska, Dorota

2013-02-01

Sea Sand Disruption Method (SSDM) is a simple and cheap sample-preparation procedure allowing the reduction of organic solvent consumption, exclusion of sample component degradation, improvement of extraction efficiency and selectivity, and elimination of additional sample clean-up and pre-concentration step before chromatographic analysis. This article deals with the possibility of SSDM application for the differentiation of essential-oils components occurring in the Scots pine (Pinus sylvestris L.) and cypress (Cupressus sempervirens L.) needles from Madrid (Spain), Laganas (Zakhyntos, Greece), Cala Morell (Menorca, Spain), Lublin (Poland), Helsinki (Finland), and Oradea (Romania). The SSDM results are related to the analogous - obtained applying two other sample preparation methods - steam distillation and Pressurized Liquid Extraction (PLE). The results presented established that the total amount and the composition of essential-oil components revealed by SSDM are equivalent or higher than those obtained by one of the most effective extraction technique, PLE. Moreover, SSDM seems to provide the most representative profile of all essential-oil components as no heat is applied. Thus, this environmentally friendly method is suggested to be used as the main extraction procedure for the differentiation of essential-oil components in conifers for scientific and industrial purposes. Copyright © 2013 Verlag Helvetica Chimica Acta AG, Zürich.

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.

Combined analysis by GC (RI), GC-MS and 13C NMR of the supercritical fluid extract of Abies alba twigs.

PubMed

Duquesnoy, Emilie; Marongiu, Bruno; Castola, Vincent; Piras, Alessandra; Porcedda, Silvia; Casanova, Joseph

2010-12-01

Two samples (leaves and twigs) of Abies alba Miller from Corsica were extracted using supercritical CO2 and their chemical compositions were compared with those of the essential oils obtained from the same batch of plant material. In total 45 components were identified using combined analysis by GC (RI), GC-MS and 13C NMR. It was observed that the contents of monoterpenes (mainly represented by limonene, alpha-pinene and camphene) were significantly lower in the supercritical fluid extract (SFE) than in the essential oil (EO). Conversely, the proportions of sesquiterpenes were much higher in CO2 extracts than in essential oils (around 30% vs 4%). Cis-abienol, a diterpene alcohol, was identified only in SFE, and the proportions of this constituent (7.5% and 17.3%) were determined using quantitative 13C NMR since it was under estimated using the standard conditions of GC.

Antimicrobial activity of extracts from Tamarindus indica L. leaves

PubMed Central

Escalona-Arranz, Julio César; Péres-Roses, Renato; Urdaneta-Laffita, Imilci; Camacho-Pozo, Miladis Isabel; Rodríguez-Amado, Jesús; Licea-Jiménez, Irina

2010-01-01

Tamarindus indica L. leaves are reported worldwide as antibacterial and antifungal agents; however, this observation is not completely accurate in the case of Cuba. In this article, decoctions from fresh and sun dried leaves, as well as fluid extracts prepared with 30 and 70% ethanol-water and the pure essential oil from tamarind leaves were microbiologically tested against Bacillus subtilis, Enterococcus faecalis, Staphylococcus aureus, Escherichia coli, Salmonella typhimurium, Pseudomona aeruginosa and Candida albicans. Aqueous and fluid extracts were previously characterized by spectrophotometric determination of their total phenols and flavonoids, while the essential oil was chemically evaluated by gas chromatography/mass spectroscopy (GC/MS). Experimental data suggest phenols as active compounds against B. subtilis cultures, but not against other microorganisms. On the other hand, the essential oil exhibited a good antimicrobial spectrum when pure, but its relative low concentrations in common folk preparations do not allow for any good activity in these extracts. PMID:20931087

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.

Extraction of orange peel's essential oil by solvent-free microwave extraction

NASA Astrophysics Data System (ADS)

Qadariyah, Lailatul; Amelia, Prilia Dwi; Admiralia, Cininta; Bhuana, Donny S.; Mahfud, Mahfud

2017-05-01

Sweet orange peel (Citrus sinensis) is part of orange plant that contains essential oils. Generally, taking essential oil from orange peel is still using hydrodistillation and steam-hydrodistillation method which still needs solvent and takes a long time to produce high quality essential oil. Therefore, the objectives of this experiment are to study the process of orange peel's essential oil extraction using Solvent Free Microwave Extraction (SFME) and to study the operating condition that effect an optimum yield and quality of the essential oil. In this experiment, extraction process with SFME method goes for 60 minutes at atmospheric pressure. Variables for SFME are: variation of orange peel condition (fresh and dry), ratio orange peel mass to distiller volume (0,1; 0,2; 0,3; 0,4 g/mL), orange peel size (±0,5; ±2; ±3,5 cm width), and microwave power (100, 264, 400 Watt). Moisture content of fresh peel is 71,4% and for dry peel is 17,37% which is obtained by sun drying. The result of this experiment will be analyzed with GC-MS, SEM, density, and miscibility in ethanol 90%. The optimum result obtained from this experiment based on the number of the yield under condition of fresh orange peel is at peel mass/distiller volume 0,1 g/mL, orange peel size ±3,5 cm width, and microwave power 400 Watt, results 1,6738% yield. The result of GC-MS for fresh orange peel shows that the dominant compound is Limonene 54,140% and for dry orange peel is Limonene 59,705%. The density obtained is around 0,8282-0,8530 g/mL and miscibility in ethanol 90% is 1:5.

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.

Comparative effects of Cymbopogon schoenanthus essential oil and piperitone on Callosobruchus maculatus development.

PubMed

Ketoh, Guillaume K; Koumaglo, Honore K; Glitho, Isabelle A; Huignard, Jacques

2006-12-01

The insecticidal activity of crude essential oil extracted from Cymbopogon schoenanthus and of its main constituent, piperitone, was assessed on different developmental stages of Callosobruchus maculatus. Piperitone was more toxic to adults with a LC(50) value of 1.6 microl/l vs. 2.7 microl/l obtained with the crude extract. Piperitone inhibited the development of newly laid eggs and of neonate larvae, but was less toxic than the crude extract to individuals developing inside the seeds.

Anti-fungal activity of crude extracts and essential oil of Moringa oleifera Lam.

PubMed

Chuang, Ping-Hsien; Lee, Chi-Wei; Chou, Jia-Ying; Murugan, M; Shieh, Bor-Jinn; Chen, Hueih-Min

2007-01-01

Investigations were carried out to evaluate the therapeutic properties of the seeds and leaves of Moringa oleifera Lam as herbal medicines. Ethanol extracts showed anti-fungal activities in vitro against dermatophytes such as Trichophyton rubrum, Trichophyton mentagrophytes, Epidermophyton floccosum, and Microsporum canis. GC-MS analysis of the chemical composition of the essential oil from leaves showed a total of 44 compounds. Isolated extracts could be of use for the future development of anti-skin disease agents.

Determination of the volatile and semi-volatile secondary metabolites, and aristolochic acids in Aristolochia ringens Vahl.

PubMed

Stashenko, Elena E; Andrés Ordóñez, Sergio; Marín, Néstor Armando; Martínez, Jairo René

2009-10-01

Volatile and semi-volatile secondary metabolites, as well as aristolochic acids (AA), present in leaves, stems, and flowers of Aristolochia ringens were determined by gas chromatography (GC)-mass spectrometry (MS) and high-performance liquid chromatography (HPLC) methods, respectively. Metabolite isolation was performed using different extraction techniques: microwave-assisted hydrodistillation (MWHD), supercritical fluid extraction, and headspace solid-phase microextraction (HS-SPME). The chemical composition of the extracts and oils was established by GC-MS. The determinations of AAI and AAII were conducted by methanolic extraction of different plant parts followed by HPLC analysis. Essential oil yields from leaves and stems were 0.008 +/- 0.0022% and 0.047 +/- 0.0026%, respectively. Aristolochia ringens flowers did not yield essential oil under MWHD. Sesquiterpene hydrocarbons (66%) were the main compounds in the essential oil isolated from leaves whereas monoterpene hydrocarbons (73%) predominated in the stems essential oil. Yields of extracts isolated by SFE from leaves, stems, and flowers were 4 +/- 1.8%, 1.2 +/- 0.25%, and 4 +/- 1.8%, respectively. In vivo HS-SPME of flowers isolated compounds with known unpleasant smells such as volatile aldehydes and short-chain carboxylic acids. HPLC analysis detected the presence of AAII in the flowers of Aristolochia ringens at a concentration of 610 +/- 47 mg/kg of dried flower.

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.

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.

Inhibition of the growth and development of mosquito larvae of Culex quinquefasciatus (Diptera: Culicidae) treated with extract from leaves of Pseudocalymma alliaceum (Bignonaceae).

PubMed

Granados-Echegoyen, Carlos; Pérez-Pacheco, Rafael; Soto-Hernández, Marcos; Ruiz-Vega, Jaime; Lagunez-Rivera, Luicita; Alonso-Hernandez, Nancy; Gato-Armas, Rene

2014-08-01

To determine larvicidal activity of the essential oil, hydrolat and botanical extracts derived from leaves of Pseudocalymma alliaceum on mosquito larvae of Culex quinquefasciatus. Groups of twenty larvae were used in the larvicidal assays. The mortality, relative growth rate, the larval and pupal duration and viability was estimated. The essential oil was analyzed by solid phase microextraction using gas chromatography coupled to mass spectrometry. Essential oil at 800 ppm showed larvicidal activity at 24 h with lethal values of LC50 and LC90 of 267.33 and 493.63 ppm. The hydrolat at 20% and 10% on 2nd stage larvae showed 100% effectiveness after 24 h. The aqueous extract at 10% had a relative growth index of 0.58, while the ethanolic and methanolic extract obtained values of 0.76 and 0.70 and control reached 0.99. Larvae treated with 10% of methanol, ethanol and aqueous extract showed a reduction in larval duration of 5.00, 2.20 and 4.35 days; ethanol extract at 1% provoke decrease of 2.40 days in the development and exhibited an increment of 3.30 days when treated with 0.01%. Aqueous, ethanol and methanol extracts at 10% reduced in 6.15, 3.42 and 5.57 days pupal development. The main compounds were diallyl disulfide (50.05%), diallyl sulfide (11.77%) and trisulfide di-2-propenyl (10.37%). The study demonstrated for the first time, the larvicidal activity of the essential oil and hydrolat of Pseudocalymma alliaceum; aqueous, ethanol and methanol extracts inhibited the normal growth and development of mosquito larvae, prolonging and delaying larval and pupal duration. Copyright © 2014 Hainan Medical College. Published by Elsevier B.V. All rights reserved.

75 FR 16046 - Endangered and Threatened Wildlife and Plants; Listing Casey's June Beetle as Endangered and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-31

... the physical and biological features essential to the conservation of Casey's June beetle, and what special management considerations or protections may be required to maintain or enhance the essential... with... [the Act], on which are found those physical or biological features (I) essential to the...

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.

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

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.

Chemical Compositions of the Peel Essential Oil of Citrus aurantium and Its Natural Larvicidal Activity against the Malaria Vector Anopheles stephensi (Diptera: Culicidae) in Comparison with Citrus paradisi

PubMed Central

Sanei-Dehkordi, Alireza; Sedaghat, Mohammad Mehdi; Vatandoost, Hassan; Abai, Mohammad Reza

2016-01-01

Background: Recently, essential oils and extracts derived from plants have received much interest as potential bio-active agents against mosquito vectors. Methods: The essential oils extract from fresh peel of ripe fruit of Citrus aurantium and Citrus paradisi were tested against mosquito vector Anopheles stephensi (Diptera: Culicidae) under laboratory condition. Then chemical composition of the essential oil of C. aurantium was analyzed using gas chromatography-mass spectrometry (GC–MS). Results: The essential oils obtained from C. aurantium, and C. paradisi showed good larviciding effect against An. stephensi with LC50 values 31.20 ppm and 35.71 ppm respectively. Clear dose response relationships were established with the highest dose of 80 ppm plant extract evoking almost 100% mortality. Twenty-one (98.62%) constituents in the leaf oil were identified. The main constituent of the leaf oil was Dl-limonene (94.81). Conclusion: The results obtained from this study suggest that the limonene of peel essential oil of C. aurantium is promising as larvicide against An. stephensi larvae and could be useful in the search for new natural larvicidal compounds. PMID:28032110

Chemical Compositions of the Peel Essential Oil of Citrus aurantium and Its Natural Larvicidal Activity against the Malaria Vector Anopheles stephensi (Diptera: Culicidae) in Comparison with Citrus paradisi.

PubMed

Sanei-Dehkordi, Alireza; Sedaghat, Mohammad Mehdi; Vatandoost, Hassan; Abai, Mohammad Reza

2016-12-01

Recently, essential oils and extracts derived from plants have received much interest as potential bio-active agents against mosquito vectors. The essential oils extract from fresh peel of ripe fruit of Citrus aurantium and Citrus paradisi were tested against mosquito vector Anopheles stephensi (Diptera: Culicidae) under laboratory condition. Then chemical composition of the essential oil of C. aurantium was analyzed using gas chromatography-mass spectrometry (GC-MS). The essential oils obtained from C. aurantium , and C. paradisi showed good larviciding effect against An. stephensi with LC 50 values 31.20 ppm and 35.71 ppm respectively. Clear dose response relationships were established with the highest dose of 80 ppm plant extract evoking almost 100% mortality. Twenty-one (98.62%) constituents in the leaf oil were identified. The main constituent of the leaf oil was Dl-limonene (94.81). The results obtained from this study suggest that the limonene of peel essential oil of C. aurantium is promising as larvicide against An. stephensi larvae and could be useful in the search for new natural larvicidal compounds.

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.

Effects of a blended garlic and cinnamon essential oil extract with and without monensin sodium on the performance of grazing steers

USDA-ARS?s Scientific Manuscript database

A series of stocker grazing experiments were conducted with the objective to determine the efficacy of supplementing growing calf diets with essential oils from garlic and cinnamon extracts (GCOE) in promoting growth on cool-season annuals in Arkansas (SWREC) and Oklahoma (SPRRS), or native rangelan...

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.

Atoms of recognition in human and computer vision.

PubMed

Ullman, Shimon; Assif, Liav; Fetaya, Ethan; Harari, Daniel

2016-03-08

Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkable progress and have begun to rival human performance in some challenging tasks. These models are trained on image examples and learn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models are similar to those used by the human visual system. Here we show, by introducing and using minimal recognizable images, that the human visual system uses features and processes that are not used by current models and that are critical for recognition. We found by psychophysical studies that at the level of minimal recognizable images a minute change in the image can have a drastic effect on recognition, thus identifying features that are critical for the task. Simulations then showed that current models cannot explain this sensitivity to precise feature configurations and, more generally, do not learn to recognize minimal images at a human level. The role of the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend our understanding of visual recognition and its cortical mechanisms and will enhance the capacity of computational models to learn from visual experience and to deal with recognition and detailed image interpretation.

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.

Development of pressurised hot water extraction (PHWE) for essential compounds from Moringa oleifera leaf extracts.

PubMed

Matshediso, Phatsimo G; Cukrowska, Ewa; Chimuka, Luke

2015-04-01

Pressurised hot water extraction (PHWE) is a "green" technology which can be used for the extraction of essential components in Moringa oleifera leaf extracts. The behaviour of three flavonols (myricetin, quercetin and kaempferol) and total phenolic content (TPC) in Moringa leaf powder were investigated at various temperatures using PHWE. The TPC of extracts from PHWE were investigated using two indicators. These are reducing activity and the radical scavenging activity of 2,2-diphenyl-1-picrylhydrazyl (DPPH). Flavonols content in the PHWE extracts were analysed on high performance liquid chromatography with ultra violet (HPLC-UV) detection. The concentration of kaempferol and myricetin started decreasing at 150 °C while that of quercetin remained steady with extraction temperature. Optimum extraction temperature for flavonols and DPPH radical scavenging activity was found to be 100 °C. The TPC increased with temperature until 150 °C and then decreased while the reducing activity increased. Copyright © 2014 Elsevier Ltd. All rights reserved.

Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images.

PubMed

Kimori, Yoshitaka; Baba, Norio; Morone, Nobuhiro

2010-07-08

A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods. A new method to extract closely located, small target spots from biological images is proposed. This method starts with a simple but practical operation based on the extended morphological top-hat transformation to subtract an uneven background. The core of our novel approach is the following: first, the original image is rotated in an arbitrary direction and each rotated image is opened with a single straight line-segment structuring element. Second, the opened images are unified and then subtracted from the original image. To evaluate these procedures, model images of simulated spots with closely located targets were created and the efficacy of our method was compared to that of conventional morphological filtering methods. The results showed the better performance of our method. The spots of real microscope images can be quantified to confirm that the method is applicable in a given practice. Our method achieved effective spot extraction under various image conditions, including aggregated target spots, poor signal-to-noise ratio, and large variations in the background intensity. Furthermore, it has no restrictions with respect to the shape of the extracted spots. The features of our method allow its broad application in biological and biomedical image information analysis.

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.

Single-nucleus Hi-C of mammalian oocytes and zygotes.

PubMed

Gassler, Johanna; Flyamer, Ilya M; Tachibana, Kikuë

2018-01-01

The 3D folding of the genome is linked to essential nuclear processes including gene expression, DNA repair, and replication. Chromatin conformation capture assays such as Hi-C are providing unprecedented insights into higher-order chromatin structure. Bulk Hi-C of millions of cells enables detection of average chromatin features at high resolution but is challenging to apply to rare cell types. This chapter describes our recently developed single-nucleus Hi-C (snHi-C) approach for detection of chromatin contacts in single nuclei of murine oocytes and one-cell embryos (zygotes). The step-by-step protocol includes isolation of these cells, extraction of nuclei, fixation, restriction digestion, ligation, and whole genome amplification. Contacts obtained by snHi-C allow detection of chromatin features including loops, topologically associating domains, and compartments when averaged over the genome. The combination of snHi-C with other single-cell techniques in these and other rare cell types will likely provide a comprehensive picture of how chromatin architecture shapes cell identity. © 2018 Elsevier Inc. All rights reserved.

Evaluation of vector coastline features extracted from 'structure from motion'-derived elevation data

USGS Publications Warehouse

Kinsman, Nicole; Gibbs, Ann E.; Nolan, Matt

2015-01-01

For extensive and remote coastlines, the absence of high-quality elevation models—for example, those produced with lidar—leaves some coastal populations lacking one of the essential elements for mapping shoreline positions or flood extents. Here, we compare seven different elevation products in a lowlying area in western Alaska to establish their appropriateness for coastal mapping applications that require the delineation of elevation-based vectors. We further investigate the effective use of a Structure from Motion (SfM)-derived surface model (vertical RMSE<20 cm) by generating a tidal datum-based shoreline and an inundation extent map for a 2011 flood event. Our results suggest that SfM-derived elevation products can yield elevation-based vector features that have horizontal positional uncertainties comparable to those derived from other techniques. We also provide a rule-of-thumb equation to aid in the selection of minimum elevation model specifications based on terrain slope, vertical uncertainties, and desired horizontal accuracy.

Middle Eastern rhinoplasty.

PubMed

Azizzadeh, Babak; Mashkevich, Grigoriy

2010-02-01

The ethnic appearance of the Middle Eastern nose is defined by several unique visual features, particularly a high radix, wide overprojecting dorsum, and an amorphous hanging nasal tip. These external characteristics reflect distinct structural properties of the osseo-cartilaginous nasal framework and skin-soft tissue envelope in patients of Middle Eastern extraction. The goal, and the ultimate challenge, of rhinoplasty on Middle Eastern patients is to achieve balanced aesthetic refinement, while avoiding surgical westernization. Detailed understanding of the ethnic visual harmony in a Middle Eastern nose greatly assists in preserving native nasal-facial relationships during rhinoplasty on Middle Eastern patients. Esthetic alteration of a Middle Eastern nose follows a different set of goals and principles compared with rhinoplasties on white or other ethnic patients. This article highlights the inherent nasal features of the Middle Eastern nose and reviews pertinent concepts of rhinoplasty on Middle Eastern patients. Essential considerations in the process spanning the consultation and surgery are reviewed. Reliable operative techniques that achieve a successful aesthetic outcome are discussed in detail. Copyright 2010 Elsevier Inc. All rights reserved.

Recognition of speaker-dependent continuous speech with KEAL

NASA Astrophysics Data System (ADS)

Mercier, G.; Bigorgne, D.; Miclet, L.; Le Guennec, L.; Querre, M.

1989-04-01

A description of the speaker-dependent continuous speech recognition system KEAL is given. An unknown utterance, is recognized by means of the followng procedures: acoustic analysis, phonetic segmentation and identification, word and sentence analysis. The combination of feature-based, speaker-independent coarse phonetic segmentation with speaker-dependent statistical classification techniques is one of the main design features of the acoustic-phonetic decoder. The lexical access component is essentially based on a statistical dynamic programming technique which aims at matching a phonemic lexical entry containing various phonological forms, against a phonetic lattice. Sentence recognition is achieved by use of a context-free grammar and a parsing algorithm derived from Earley's parser. A speaker adaptation module allows some of the system parameters to be adjusted by matching known utterances with their acoustical representation. The task to be performed, described by its vocabulary and its grammar, is given as a parameter of the system. Continuously spoken sentences extracted from a 'pseudo-Logo' language are analyzed and results are presented.

Predominant information quality scheme for the essential amino acids: an information-theoretical analysis.

PubMed

Esquivel, Rodolfo O; Molina-Espíritu, Moyocoyani; López-Rosa, Sheila; Soriano-Correa, Catalina; Barrientos-Salcedo, Carolina; Kohout, Miroslav; Dehesa, Jesús S

2015-08-24

In this work we undertake a pioneer information-theoretical analysis of 18 selected amino acids extracted from a natural protein, bacteriorhodopsin (1C3W). The conformational structures of each amino acid are analyzed by use of various quantum chemistry methodologies at high levels of theory: HF, M062X and CISD(Full). The Shannon entropy, Fisher information and disequilibrium are determined to grasp the spatial spreading features of delocalizability, order and uniformity of the optimized structures. These three entropic measures uniquely characterize all amino acids through a predominant information-theoretic quality scheme (PIQS), which gathers all chemical families by means of three major spreading features: delocalization, narrowness and uniformity. This scheme recognizes four major chemical families: aliphatic (delocalized), aromatic (delocalized), electro-attractive (narrowed) and tiny (uniform). All chemical families recognized by the existing energy-based classifications are embraced by this entropic scheme. Finally, novel chemical patterns are shown in the information planes associated with the PIQS entropic measures. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Dual Bioactivities of Essential Oil Extracted from the Leaves of Artemisia argyi as an Antimelanogenic versus Antioxidant Agent and Chemical Composition Analysis by GC/MS

PubMed Central

Huang, Huey-Chun; Wang, Hsiao-Fen; Yih, Kuang-Hway; Chang, Long-Zen; Chang, Tsong-Min

2012-01-01

The study was aimed at investigating the antimelanogenic and antioxidant properties of essential oil when extracted from the leaves of Artemisia argyi, then analyzing the chemical composition of the essential oil. The inhibitory effect of the essential oil on melanogenesis was evaluated by a mushroom tyrosinase activity assay and B16F10 melanoma cell model. The antioxidant capacity of the essential oil was assayed by spectrophotometric analysis, and the volatile chemical composition of the essential oil was analyzed with gas chromatography-mass spectrometry (GC/MS). The results revealed that the essential oil significantly inhibits mushroom tyrosinase activity (IC50 = 19.16 mg/mL), down-regulates B16F10 intracellular tyrosinase activity and decreases the amount of melanin content in a dose-dependent pattern. Furthermore, the essential oil significantly scavenged 2,2-diphenyl-1-picryl-hydrazyl (DPPH) and 2,2′-azino-bis (3-ethylbenzthiazoline- 6-sulphonic acid) ABTS radicals, showed an apparent reduction power as compared with metal-ion chelating activities. The chemicals constituents in the essential oil are ether (23.66%), alcohols (16.72%), sesquiterpenes (15.21%), esters (11.78%), monoterpenes (11.63%), ketones (6.09%), aromatic compounds (5.01%), and account for a 90.10% analysis of its chemical composition. It is predicted that eucalyptol and the other constituents, except for alcohols, in the essential oil may contribute to its antioxidant activities. The results indicated that essential oil extracted from A. argyi leaves decreased melanin production in B16F10 cells and showed potent antioxidant activity. The essential oil can thereby be applied as an inhibitor of melanogenesis and could also act as a natural antioxidant in skin care products. PMID:23203088

Dual bioactivities of essential oil extracted from the leaves of Artemisia argyi as an antimelanogenic versus antioxidant agent and chemical composition analysis by GC/MS.

PubMed

Huang, Huey-Chun; Wang, Hsiao-Fen; Yih, Kuang-Hway; Chang, Long-Zen; Chang, Tsong-Min

2012-11-12

The study was aimed at investigating the antimelanogenic and antioxidant properties of essential oil when extracted from the leaves of Artemisia argyi, then analyzing the chemical composition of the essential oil. The inhibitory effect of the essential oil on melanogenesis was evaluated by a mushroom tyrosinase activity assay and B16F10 melanoma cell model. The antioxidant capacity of the essential oil was assayed by spectrophotometric analysis, and the volatile chemical composition of the essential oil was analyzed with gas chromatography-mass spectrometry (GC/MS). The results revealed that the essential oil significantly inhibits mushroom tyrosinase activity (IC(50) = 19.16 mg/mL), down-regulates B16F10 intracellular tyrosinase activity and decreases the amount of melanin content in a dose-dependent pattern. Furthermore, the essential oil significantly scavenged 2,2-diphenyl-1-picryl-hydrazyl (DPPH) and 2,2'-azino-bis (3-ethylbenzthiazoline-6-sulphonic acid) ABTS radicals, showed an apparent reduction power as compared with metal-ion chelating activities. The chemicals constituents in the essential oil are ether (23.66%), alcohols (16.72%), sesquiterpenes (15.21%), esters (11.78%), monoterpenes (11.63%), ketones (6.09%), aromatic compounds (5.01%), and account for a 90.10% analysis of its chemical composition. It is predicted that eucalyptol and the other constituents, except for alcohols, in the essential oil may contribute to its antioxidant activities. The results indicated that essential oil extracted from A. argyi leaves decreased melanin production in B16F10 cells and showed potent antioxidant activity. The essential oil can thereby be applied as an inhibitor of melanogenesis and could also act as a natural antioxidant in skin care products.

Chemical content, antibacterial and antioxidant properties of essential oil extract from Tunisian Origanum majorana L. cultivated under saline condition.

PubMed

Olfa, Baâtour; Mariem, Aouadi; Salah, Abbassi Mohamed; Mouhiba, BenNasri Ayachi

2016-11-01

Essential oils of marjoram were extracted from plants, growing under non-saline and saline condition (75mM NaCl). Their antioxidant and antibaterial activity against six bacteria (Enterococcus faecalis, Escherichia coli, Salmonella enteritidis, Listeria ivanovii, Listeria inocula, and Listeria monocytogenes) were assessed. Result showed that, (i) independently of salt treatment, marjoram essential oils inhibited the growth of most of the bacteria but in degrees. The least susceptible one was Enterococcus faecalis. (ii) Gram negative bacteria seemed more sensitive to treated essential oils than Gram positive ones. (iii) Compared to synthetic antibiotics, marjoram essential oils were more effective against E. coli, L. innocua and S. enteridis. This activity was due to their high antioxidant activity. Thus, essential oils of marjoram may be an alternative source of natural antibacterial and antioxidant agents.

Antibacterial activities of essential oils and extracts of Turkish Achillea, Satureja and Thymus species against plant pathogenic bacteria.

PubMed

Kotan, Recep; Cakir, Ahmet; Dadasoglu, Fatih; Aydin, Tuba; Cakmakci, Ramazan; Ozer, Hakan; Kordali, Saban; Mete, Ebru; Dikbas, Neslihan

2010-01-15

The aims of this study were to examine the chemical composition of the essential oils and hexane extracts of the aerial parts of Satureja spicigera (C. Koch) Boiss., Thymus fallax Fisch. & CA Mey, Achillea biebersteinii Afan, and Achillea millefolium L. by GC and GC-MS, and to test antibacterial efficacy of essential oils and n-hexane, chloroform, acetone and methanol extracts as an antibacterial and seed disinfectant against 25 agricultural plant pathogens. Thymol, carvacrol, p-cymene, thymol methyl ether and gamma-terpinene were the main constituents of S. spicigera and T. fallax oils and hexane extracts. The main components of the oil of Achillea millefolium were 1,8-cineole, delta-cadinol and caryophyllene oxide, whereas the hexane extract of this species contained mainly n-hexacosane, n-tricosane and n-heneicosane. The oils and hexane extracts of S. spicigera and T. fallax exhibited potent antibacterial activity over a broad spectrum against 25 phytopathogenic bacterial strains. Carvacrol and thymol, the major constituents of S. spicigera and T. fallax oils, also showed potent antibacterial effect against the bacteria tested. The oils of Achillea species showed weak antibacterial activity. Our results also revealed that the essential oil of S. spicigera, thymol and carvacrol could be used as potential disinfection agents against seed-borne bacteria. Our results demonstrate that S. spicigera, T. fallax oils, carvacrol and thymol could become potentials for controlling certain important agricultural plant pathogenic bacteria and seed disinfectant. Copyright (c) 2009 Society of Chemical Industry.

In-vitro propagation and antimycotic potential of extracts and essential oil of roots of Aristolochia bracteolata Linn. (Aristolochiaceae).

PubMed

Gbadamosi, I T; Egunyomi, A

2012-01-01

In spite of the therapeutic importance of Aristolochia bracteolata Linn. in Nigerian ethnomedicine, it is largely collected from the wild. Owing to the acclaimed potency of the plant and the difficulty in treating candidiasis, the anticandidal activity and in vitro propagation of the plant were investigated. Phytochemical screening and preparation of extracts of the roots were done using standard procedures. Clinical isolates of Candida albicans were screened against extracts and essential oil of Aristolochia bracteolata root using agar-well diffusion method. Minimum Inhibitory Concentration (MIC) of the ethanol extract was determined using broth dilution method. The nodal cuttings of A. bracteolata were cultured on Murashige and Skoog (MS) basal media. A. bracteolata contained alkaloids, saponins and cardenolides. The water extract was inactive on all isolates. The ethanol extract (500 mg/ml) and essential oil (undiluted) exhibited anticandidal activity on 9 out of 10 isolates at 10(1) - 10(6) cfu/ml inoculums concentration. Green growth and callus formation were observed in explants cultured on MS basal media after 30 days. A. bracteolata could be a source of anticandidal phytomedicine and the in vitro propagation confirmed its sustainability as anticandidal agent.

Research on Optimal Observation Scale for Damaged Buildings after Earthquake Based on Optimal Feature Space

NASA Astrophysics Data System (ADS)

Chen, J.; Chen, W.; Dou, A.; Li, W.; Sun, Y.

2018-04-01

A new information extraction method of damaged buildings rooted in optimal feature space is put forward on the basis of the traditional object-oriented method. In this new method, ESP (estimate of scale parameter) tool is used to optimize the segmentation of image. Then the distance matrix and minimum separation distance of all kinds of surface features are calculated through sample selection to find the optimal feature space, which is finally applied to extract the image of damaged buildings after earthquake. The overall extraction accuracy reaches 83.1 %, the kappa coefficient 0.813. The new information extraction method greatly improves the extraction accuracy and efficiency, compared with the traditional object-oriented method, and owns a good promotional value in the information extraction of damaged buildings. In addition, the new method can be used for the information extraction of different-resolution images of damaged buildings after earthquake, then to seek the optimal observation scale of damaged buildings through accuracy evaluation. It is supposed that the optimal observation scale of damaged buildings is between 1 m and 1.2 m, which provides a reference for future information extraction of damaged buildings.

A data-driven dynamics simulation framework for railway vehicles

NASA Astrophysics Data System (ADS)

Nie, Yinyu; Tang, Zhao; Liu, Fengjia; Chang, Jian; Zhang, Jianjun

2018-03-01

The finite element (FE) method is essential for simulating vehicle dynamics with fine details, especially for train crash simulations. However, factors such as the complexity of meshes and the distortion involved in a large deformation would undermine its calculation efficiency. An alternative method, the multi-body (MB) dynamics simulation provides satisfying time efficiency but limited accuracy when highly nonlinear dynamic process is involved. To maintain the advantages of both methods, this paper proposes a data-driven simulation framework for dynamics simulation of railway vehicles. This framework uses machine learning techniques to extract nonlinear features from training data generated by FE simulations so that specific mesh structures can be formulated by a surrogate element (or surrogate elements) to replace the original mechanical elements, and the dynamics simulation can be implemented by co-simulation with the surrogate element(s) embedded into a MB model. This framework consists of a series of techniques including data collection, feature extraction, training data sampling, surrogate element building, and model evaluation and selection. To verify the feasibility of this framework, we present two case studies, a vertical dynamics simulation and a longitudinal dynamics simulation, based on co-simulation with MATLAB/Simulink and Simpack, and a further comparison with a popular data-driven model (the Kriging model) is provided. The simulation result shows that using the legendre polynomial regression model in building surrogate elements can largely cut down the simulation time without sacrifice in accuracy.

Fusion of Remote Sensing Methods, UAV Photogrammetry and LiDAR Scanning products for monitoring fluvial dynamics

NASA Astrophysics Data System (ADS)

Lendzioch, Theodora; Langhammer, Jakub; Hartvich, Filip

2015-04-01

Fusion of remote sensing data is a common and rapidly developing discipline, which combines data from multiple sources with different spatial and spectral resolution, from satellite sensors, aircraft and ground platforms. Fusion data contains more detailed information than each of the source and enhances the interpretation performance and accuracy of the source data and produces a high-quality visualisation of the final data. Especially, in fluvial geomorphology it is essential to get valuable images in sub-meter resolution to obtain high quality 2D and 3D information for a detailed identification, extraction and description of channel features of different river regimes and to perform a rapid mapping of changes in river topography. In order to design, test and evaluate a new approach for detection of river morphology, we combine different research techniques from remote sensing products to drone-based photogrammetry and LiDAR products (aerial LiDAR Scanner and TLS). Topographic information (e.g. changes in river channel morphology, surface roughness, evaluation of floodplain inundation, mapping gravel bars and slope characteristics) will be extracted either from one single layer or from combined layers in accordance to detect fluvial topographic changes before and after flood events. Besides statistical approaches for predictive geomorphological mapping and the determination of errors and uncertainties of the data, we will also provide 3D modelling of small fluvial features.

High-performance computing in image registration

NASA Astrophysics Data System (ADS)

Zanin, Michele; Remondino, Fabio; Dalla Mura, Mauro

2012-10-01

Thanks to the recent technological advances, a large variety of image data is at our disposal with variable geometric, radiometric and temporal resolution. In many applications the processing of such images needs high performance computing techniques in order to deliver timely responses e.g. for rapid decisions or real-time actions. Thus, parallel or distributed computing methods, Digital Signal Processor (DSP) architectures, Graphical Processing Unit (GPU) programming and Field-Programmable Gate Array (FPGA) devices have become essential tools for the challenging issue of processing large amount of geo-data. The article focuses on the processing and registration of large datasets of terrestrial and aerial images for 3D reconstruction, diagnostic purposes and monitoring of the environment. For the image alignment procedure, sets of corresponding feature points need to be automatically extracted in order to successively compute the geometric transformation that aligns the data. The feature extraction and matching are ones of the most computationally demanding operations in the processing chain thus, a great degree of automation and speed is mandatory. The details of the implemented operations (named LARES) exploiting parallel architectures and GPU are thus presented. The innovative aspects of the implementation are (i) the effectiveness on a large variety of unorganized and complex datasets, (ii) capability to work with high-resolution images and (iii) the speed of the computations. Examples and comparisons with standard CPU processing are also reported and commented.

Diabetes, insulin, and development of acute lung injury

PubMed Central

Honiden, Shyoko; Gong, Michelle N.

2009-01-01

Objectives Recently, many studies have investigated the immunomodulatory effects of insulin and glucose control in critical illness. This review examines evidence regarding the relationship between diabetes and the development of acute lung injury/acute respiratory distress syndrome (ALI/ARDS), reviews studies of lung injury related to glycemic and nonglycemic metabolic features of diabetes, and examines the effect of diabetic therapies. Data Sources and Study Selection A MEDLINE/PubMed search from inception to August 1, 2008, was conducted using the search terms acute lung injury, acute respiratory distress syndrome, hyperglycemia, diabetes mellitus, insulin, hydroxymethylglutaryl-CoA reductase inhibitors (statins), angiotensin-converting enzyme inhibitor, and peroxisome proliferator-activated receptors, including combinations of these terms. Bibliographies of retrieved articles were manually reviewed. Data Extraction and Synthesis Available studies were critically reviewed, and data were extracted with special attention to the human and animal studies that explored a) diabetes and ALI; b) hyperglycemia and ALI; c) metabolic nonhyperglycemic features of diabetes and ALI; and d) diabetic therapies and ALI. Conclusions Clinical and experimental data indicate that diabetes is protective against the development of ALI/ARDS. The pathways involved are complex and likely include effects of hyperglycemia on the inflammatory response, metabolic abnormalities in diabetes, and the interactions of therapeutic agents given to diabetic patients. Multidisciplinary, multifaceted studies, involving both animal models and clinical and molecular epidemiology techniques, are essential. PMID:19531947

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.

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.

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.

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.

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.

Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units.

PubMed

Karakida, Ryo; Okada, Masato; Amari, Shun-Ichi

2016-07-01

The restricted Boltzmann machine (RBM) is an essential constituent of deep learning, but it is hard to train by using maximum likelihood (ML) learning, which minimizes the Kullback-Leibler (KL) divergence. Instead, contrastive divergence (CD) learning has been developed as an approximation of ML learning and widely used in practice. To clarify the performance of CD learning, in this paper, we analytically derive the fixed points where ML and CDn learning rules converge in two types of RBMs: one with Gaussian visible and Gaussian hidden units and the other with Gaussian visible and Bernoulli hidden units. In addition, we analyze the stability of the fixed points. As a result, we find that the stable points of CDn learning rule coincide with those of ML learning rule in a Gaussian-Gaussian RBM. We also reveal that larger principal components of the input data are extracted at the stable points. Moreover, in a Gaussian-Bernoulli RBM, we find that both ML and CDn learning can extract independent components at one of stable points. Our analysis demonstrates that the same feature components as those extracted by ML learning are extracted simply by performing CD1 learning. Expanding this study should elucidate the specific solutions obtained by CD learning in other types of RBMs or in deep networks. Copyright © 2016 Elsevier Ltd. All rights reserved.

Reduced-Reference Quality Assessment Based on the Entropy of DWT Coefficients of Locally Weighted Gradient Magnitudes.

PubMed

Golestaneh, S Alireza; Karam, Lina

2016-08-24

Perceptual image quality assessment (IQA) attempts to use computational models to estimate the image quality in accordance with subjective evaluations. Reduced-reference (RR) image quality assessment (IQA) methods make use of partial information or features extracted from the reference image for estimating the quality of distorted images. Finding a balance between the number of RR features and accuracy of the estimated image quality is essential and important in IQA. In this paper we propose a training-free low-cost RRIQA method that requires a very small number of RR features (6 RR features). The proposed RRIQA algorithm is based on the discrete wavelet transform (DWT) of locally weighted gradient magnitudes.We apply human visual system's contrast sensitivity and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner. The RR features are computed by measuring the entropy of each DWT subband, for each scale, and pooling the subband entropies along all orientations, resulting in L RR features (one average entropy per scale) for an L-level DWT. Extensive experiments performed on seven large-scale benchmark databases demonstrate that the proposed RRIQA method delivers highly competitive performance as compared to the state-of-the-art RRIQA models as well as full reference ones for both natural and texture images. The MATLAB source code of REDLOG and the evaluation results are publicly available online at https://http://lab.engineering.asu.edu/ivulab/software/redlog/.

Diagnosis of multiple sclerosis from EEG signals using nonlinear methods.

PubMed

Torabi, Ali; Daliri, Mohammad Reza; Sabzposhan, Seyyed Hojjat

2017-12-01

EEG signals have essential and important information about the brain and neural diseases. The main purpose of this study is classifying two groups of healthy volunteers and Multiple Sclerosis (MS) patients using nonlinear features of EEG signals while performing cognitive tasks. EEG signals were recorded when users were doing two different attentional tasks. One of the tasks was based on detecting a desired change in color luminance and the other task was based on detecting a desired change in direction of motion. EEG signals were analyzed in two ways: EEG signals analysis without rhythms decomposition and EEG sub-bands analysis. After recording and preprocessing, time delay embedding method was used for state space reconstruction; embedding parameters were determined for original signals and their sub-bands. Afterwards nonlinear methods were used in feature extraction phase. To reduce the feature dimension, scalar feature selections were done by using T-test and Bhattacharyya criteria. Then, the data were classified using linear support vector machines (SVM) and k-nearest neighbor (KNN) method. The best combination of the criteria and classifiers was determined for each task by comparing performances. For both tasks, the best results were achieved by using T-test criterion and SVM classifier. For the direction-based and the color-luminance-based tasks, maximum classification performances were 93.08 and 79.79% respectively which were reached by using optimal set of features. Our results show that the nonlinear dynamic features of EEG signals seem to be useful and effective in MS diseases diagnosis.

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.