Extracting DNA from FFPE Tissue Biospecimens Using User-Friendly Automated Technology: Is There an Impact on Yield or Quality?

PubMed

Mathieson, William; Guljar, Nafia; Sanchez, Ignacio; Sroya, Manveer; Thomas, Gerry A

2018-05-03

DNA extracted from formalin-fixed, paraffin-embedded (FFPE) tissue blocks is amenable to analytical techniques, including sequencing. DNA extraction protocols are typically long and complex, often involving an overnight proteinase K digest. Automated platforms that shorten and simplify the process are therefore an attractive proposition for users wanting a faster turn-around or to process large numbers of biospecimens. It is, however, unclear whether automated extraction systems return poorer DNA yields or quality than manual extractions performed by experienced technicians. We extracted DNA from 42 FFPE clinical tissue biospecimens using the QiaCube (Qiagen) and ExScale (ExScale Biospecimen Solutions) automated platforms, comparing DNA yields and integrities with those from manual extractions. The QIAamp DNA FFPE Spin Column Kit was used for manual and QiaCube DNA extractions and the ExScale extractions were performed using two of the manufacturer's magnetic bead kits: one extracting DNA only and the other simultaneously extracting DNA and RNA. In all automated extraction methods, DNA yields and integrities (assayed using DNA Integrity Numbers from a 4200 TapeStation and the qPCR-based Illumina FFPE QC Assay) were poorer than in the manual method, with the QiaCube system performing better than the ExScale system. However, ExScale was fastest, offered the highest reproducibility when extracting DNA only, and required the least intervention or technician experience. Thus, the extraction methods have different strengths and weaknesses, would appeal to different users with different requirements, and therefore, we cannot recommend one method over another.

A novel image processing technique for 3D volumetric analysis of severely resorbed alveolar sockets with CBCT.

PubMed

Manavella, Valeria; Romano, Federica; Garrone, Federica; Terzini, Mara; Bignardi, Cristina; Aimetti, Mario

2017-06-01

The aim of this study was to present and validate a novel procedure for the quantitative volumetric assessment of extraction sockets that combines cone-beam computed tomography (CBCT) and image processing techniques. The CBCT dataset of 9 severely resorbed extraction sockets was analyzed by means of two image processing software, Image J and Mimics, using manual and automated segmentation techniques. They were also applied on 5-mm spherical aluminum markers of known volume and on a polyvinyl chloride model of one alveolar socket scanned with Micro-CT to test the accuracy. Statistical differences in alveolar socket volume were found between the different methods of volumetric analysis (P<0.0001). The automated segmentation using Mimics was the most reliable and accurate method with a relative error of 1.5%, considerably smaller than the error of 7% and of 10% introduced by the manual method using Mimics and by the automated method using ImageJ. The currently proposed automated segmentation protocol for the three-dimensional rendering of alveolar sockets showed more accurate results, excellent inter-observer similarity and increased user friendliness. The clinical application of this method enables a three-dimensional evaluation of extraction socket healing after the reconstructive procedures and during the follow-up visits.

Automated dispersive liquid-liquid microextraction coupled to high performance liquid chromatography - cold vapour atomic fluorescence spectroscopy for the determination of mercury species in natural water samples.

PubMed

Liu, Yao-Min; Zhang, Feng-Ping; Jiao, Bao-Yu; Rao, Jin-Yu; Leng, Geng

2017-04-14

An automated, home-constructed, and low cost dispersive liquid-liquid microextraction (DLLME) device that directly coupled to a high performance liquid chromatography (HPLC) - cold vapour atomic fluorescence spectroscopy (CVAFS) system was designed and developed for the determination of trace concentrations of methylmercury (MeHg + ), ethylmercury (EtHg + ) and inorganic mercury (Hg 2+ ) in natural waters. With a simple, miniaturized and efficient automated DLLME system, nanogram amounts of these mercury species were extracted from natural water samples and injected into a hyphenated HPLC-CVAFS for quantification. The complete analytical procedure, including chelation, extraction, phase separation, collection and injection of the extracts, as well as HPLC-CVAFS quantification, was automated. Key parameters, such as the type and volume of the chelation, extraction and dispersive solvent, aspiration speed, sample pH, salt effect and matrix effect, were thoroughly investigated. Under the optimum conditions, linear range was 10-1200ngL -1 for EtHg + and 5-450ngL -1 for MeHg + and Hg 2+ . Limits of detection were 3.0ngL -1 for EtHg + and 1.5ngL -1 for MeHg + and Hg 2+ . Reproducibility and recoveries were assessed by spiking three natural water samples with different Hg concentrations, giving recoveries from 88.4-96.1%, and relative standard deviations <5.1%. Copyright © 2017 Elsevier B.V. All rights reserved.

AUTOMATED SOLID PHASE EXTRACTION GC/MS FOR ANALYSIS OF SEMIVOLATILES IN WATER AND SEDIMENTS

EPA Science Inventory

Data is presented on the development of a new automated system combining solid phase extraction (SPE) with GC/MS spectrometry for the single-run analysis of water samples containing a broad range of organic compounds. The system uses commercially available automated in-line sampl...

Comparative evaluation of in-house manual, and commercial semi-automated and automated DNA extraction platforms in the sample preparation of human stool specimens for a Salmonella enterica 5'-nuclease assay.

PubMed

Schuurman, Tim; de Boer, Richard; Patty, Rachèl; Kooistra-Smid, Mirjam; van Zwet, Anton

2007-12-01

In the present study, three methods (NucliSens miniMAG [bioMérieux], MagNA Pure DNA Isolation Kit III Bacteria/Fungi [Roche], and a silica-guanidiniumthiocyanate {Si-GuSCN-F} procedure for extracting DNA from stool specimens were compared with regard to analytical performance (relative DNA recovery and down stream real-time PCR amplification of Salmonella enterica DNA), stability of the extracted DNA, hands-on time (HOT), total processing time (TPT), and costs. The Si-GuSCN-F procedure showed the highest analytical performance (relative recovery of 99%, S. enterica real-time PCR sensitivity of 91%) at the lowest associated costs per extraction (euro 4.28). However, this method did required the longest HOT (144 min) and subsequent TPT (176 min) when processing 24 extractions. Both miniMAG and MagNA Pure extraction showed similar performances at first (relative recoveries of 57% and 52%, S. enterica real-time PCR sensitivity of 85%). However, when difference in the observed Ct values after real-time PCR were taken into account, MagNA Pure resulted in a significant increase in Ct value compared to both miniMAG and Si-GuSCN-F (with on average +1.26 and +1.43 cycles). With regard to inhibition all methods showed relatively low inhibition rates (< 4%), with miniMAG providing the lowest rate (0.7%). Extracted DNA was stable for at least 1 year for all methods. HOT was lowest for MagNA Pure (60 min) and TPT was shortest for miniMAG (121 min). Costs, finally, were euro 4.28 for Si-GuSCN, euro 6.69 for MagNA Pure and euro 9.57 for miniMAG.

Literature mining of protein-residue associations with graph rules learned through distant supervision.

PubMed

Ravikumar, Ke; Liu, Haibin; Cohn, Judith D; Wall, Michael E; Verspoor, Karin

2012-10-05

We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model. The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods. The primary contributions of this work are to (1) demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2) show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.

Automated Extraction of Secondary Flow Features

NASA Technical Reports Server (NTRS)

Dorney, Suzanne M.; Haimes, Robert

2005-01-01

The use of Computational Fluid Dynamics (CFD) has become standard practice in the design and development of the major components used for air and space propulsion. To aid in the post-processing and analysis phase of CFD many researchers now use automated feature extraction utilities. These tools can be used to detect the existence of such features as shocks, vortex cores and separation and re-attachment lines. The existence of secondary flow is another feature of significant importance to CFD engineers. Although the concept of secondary flow is relatively understood there is no commonly accepted mathematical definition for secondary flow. This paper will present a definition for secondary flow and one approach for automatically detecting and visualizing secondary flow.

Metal-organic framework mixed-matrix disks: Versatile supports for automated solid-phase extraction prior to chromatographic separation.

PubMed

Ghani, Milad; Font Picó, Maria Francesca; Salehinia, Shima; Palomino Cabello, Carlos; Maya, Fernando; Berlier, Gloria; Saraji, Mohammad; Cerdà, Víctor; Turnes Palomino, Gemma

2017-03-10

We present for the first time the application of metal-organic framework (MOF) mixed-matrix disks (MMD) for the automated flow-through solid-phase extraction (SPE) of environmental pollutants. Zirconium terephthalate UiO-66 and UiO-66-NH 2 MOFs with different size (90, 200 and 300nm) have been incorporated into mechanically stable polyvinylidene difluoride (PVDF) disks. The performance of the MOF-MMDs for automated SPE of seven substituted phenols prior to HPLC analysis has been evaluated using the sequential injection analysis technique. MOF-MMDs enabled the simultaneous extraction of phenols with the concomitant size exclusion of molecules of larger size. The best extraction performance was obtained using a MOF-MMD containing 90nm UiO-66-NH 2 crystals. Using the selected MOF-MMD, detection limits ranging from 0.1 to 0.2μgL -1 were obtained. Relative standard deviations ranged from 3.9 to 5.3% intra-day, and 4.7-5.7% inter-day. Membrane batch-to-batch reproducibility was from 5.2 to 6.4%. Three different groundwater samples were analyzed with the proposed method using MOF-MMDs, obtaining recoveries ranging from 90 to 98% for all tested analytes. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated extraction of family history information from clinical notes.

PubMed

Bill, Robert; Pakhomov, Serguei; Chen, Elizabeth S; Winden, Tamara J; Carter, Elizabeth W; Melton, Genevieve B

2014-01-01

Despite increased functionality for obtaining family history in a structured format within electronic health record systems, clinical notes often still contain this information. We developed and evaluated an Unstructured Information Management Application (UIMA)-based natural language processing (NLP) module for automated extraction of family history information with functionality for identifying statements, observations (e.g., disease or procedure), relative or side of family with attributes (i.e., vital status, age of diagnosis, certainty, and negation), and predication ("indicator phrases"), the latter of which was used to establish relationships between observations and family member. The family history NLP system demonstrated F-scores of 66.9, 92.4, 82.9, 57.3, 97.7, and 61.9 for detection of family history statements, family member identification, observation identification, negation identification, vital status, and overall extraction of the predications between family members and observations, respectively. While the system performed well for detection of family history statements and predication constituents, further work is needed to improve extraction of certainty and temporal modifications.

Automated Extraction of Family History Information from Clinical Notes

PubMed Central

Bill, Robert; Pakhomov, Serguei; Chen, Elizabeth S.; Winden, Tamara J.; Carter, Elizabeth W.; Melton, Genevieve B.

2014-01-01

Despite increased functionality for obtaining family history in a structured format within electronic health record systems, clinical notes often still contain this information. We developed and evaluated an Unstructured Information Management Application (UIMA)-based natural language processing (NLP) module for automated extraction of family history information with functionality for identifying statements, observations (e.g., disease or procedure), relative or side of family with attributes (i.e., vital status, age of diagnosis, certainty, and negation), and predication (“indicator phrases”), the latter of which was used to establish relationships between observations and family member. The family history NLP system demonstrated F-scores of 66.9, 92.4, 82.9, 57.3, 97.7, and 61.9 for detection of family history statements, family member identification, observation identification, negation identification, vital status, and overall extraction of the predications between family members and observations, respectively. While the system performed well for detection of family history statements and predication constituents, further work is needed to improve extraction of certainty and temporal modifications. PMID:25954443

Can we replace curation with information extraction software?

PubMed

Karp, Peter D

2016-01-01

Can we use programs for automated or semi-automated information extraction from scientific texts as practical alternatives to professional curation? I show that error rates of current information extraction programs are too high to replace professional curation today. Furthermore, current IEP programs extract single narrow slivers of information, such as individual protein interactions; they cannot extract the large breadth of information extracted by professional curators for databases such as EcoCyc. They also cannot arbitrate among conflicting statements in the literature as curators can. Therefore, funding agencies should not hobble the curation efforts of existing databases on the assumption that a problem that has stymied Artificial Intelligence researchers for more than 60 years will be solved tomorrow. Semi-automated extraction techniques appear to have significantly more potential based on a review of recent tools that enhance curator productivity. But a full cost-benefit analysis for these tools is lacking. Without such analysis it is possible to expend significant effort developing information-extraction tools that automate small parts of the overall curation workflow without achieving a significant decrease in curation costs.Database URL. © The Author(s) 2016. Published by Oxford University Press.

EEG artifact elimination by extraction of ICA-component features using image processing algorithms.

PubMed

Radüntz, T; Scouten, J; Hochmuth, O; Meffert, B

2015-03-30

Artifact rejection is a central issue when dealing with electroencephalogram recordings. Although independent component analysis (ICA) separates data in linearly independent components (IC), the classification of these components as artifact or EEG signal still requires visual inspection by experts. In this paper, we achieve automated artifact elimination using linear discriminant analysis (LDA) for classification of feature vectors extracted from ICA components via image processing algorithms. We compare the performance of this automated classifier to visual classification by experts and identify range filtering as a feature extraction method with great potential for automated IC artifact recognition (accuracy rate 88%). We obtain almost the same level of recognition performance for geometric features and local binary pattern (LBP) features. Compared to the existing automated solutions the proposed method has two main advantages: First, it does not depend on direct recording of artifact signals, which then, e.g. have to be subtracted from the contaminated EEG. Second, it is not limited to a specific number or type of artifact. In summary, the present method is an automatic, reliable, real-time capable and practical tool that reduces the time intensive manual selection of ICs for artifact removal. The results are very promising despite the relatively small channel resolution of 25 electrodes. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Automated extraction of DNA from biological stains on fabric from crime cases. A comparison of a manual and three automated methods.

PubMed

Stangegaard, Michael; Hjort, Benjamin B; Hansen, Thomas N; Hoflund, Anders; Mogensen, Helle S; Hansen, Anders J; Morling, Niels

2013-05-01

The presence of PCR inhibitors in extracted DNA may interfere with the subsequent quantification and short tandem repeat (STR) reactions used in forensic genetic DNA typing. DNA extraction from fabric for forensic genetic purposes may be challenging due to the occasional presence of PCR inhibitors that may be co-extracted with the DNA. Using 120 forensic trace evidence samples consisting of various types of fabric, we compared three automated DNA extraction methods based on magnetic beads (PrepFiler Express Forensic DNA Extraction Kit on an AutoMate Express, QIAsyphony DNA Investigator kit either with the sample pre-treatment recommended by Qiagen or an in-house optimized sample pre-treatment on a QIAsymphony SP) and one manual method (Chelex) with the aim of reducing the amount of PCR inhibitors in the DNA extracts and increasing the proportion of reportable STR-profiles. A total of 480 samples were processed. The highest DNA recovery was obtained with the PrepFiler Express kit on an AutoMate Express while the lowest DNA recovery was obtained using a QIAsymphony SP with the sample pre-treatment recommended by Qiagen. Extraction using a QIAsymphony SP with the sample pre-treatment recommended by Qiagen resulted in the lowest percentage of PCR inhibition (0%) while extraction using manual Chelex resulted in the highest percentage of PCR inhibition (51%). The largest number of reportable STR-profiles was obtained with DNA from samples extracted with the PrepFiler Express kit (75%) while the lowest number was obtained with DNA from samples extracted using a QIAsymphony SP with the sample pre-treatment recommended by Qiagen (41%). Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Validation of an automated solid-phase extraction method for the analysis of 23 opioids, cocaine, and metabolites in urine with ultra-performance liquid chromatography-tandem mass spectrometry.

PubMed

Ramírez Fernández, María del Mar; Van Durme, Filip; Wille, Sarah M R; di Fazio, Vincent; Kummer, Natalie; Samyn, Nele

2014-06-01

The aim of this work was to automate a sample preparation procedure extracting morphine, hydromorphone, oxymorphone, norcodeine, codeine, dihydrocodeine, oxycodone, 6-monoacetyl-morphine, hydrocodone, ethylmorphine, benzoylecgonine, cocaine, cocaethylene, tramadol, meperidine, pentazocine, fentanyl, norfentanyl, buprenorphine, norbuprenorphine, propoxyphene, methadone and 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine from urine samples. Samples were extracted by solid-phase extraction (SPE) with cation exchange cartridges using a TECAN Freedom Evo 100 base robotic system, including a hydrolysis step previous extraction when required. Block modules were carefully selected in order to use the same consumable material as in manual procedures to reduce cost and/or manual sample transfers. Moreover, the present configuration included pressure monitoring pipetting increasing pipetting accuracy and detecting sampling errors. The compounds were then separated in a chromatographic run of 9 min using a BEH Phenyl analytical column on a ultra-performance liquid chromatography-tandem mass spectrometry system. Optimization of the SPE was performed with different wash conditions and elution solvents. Intra- and inter-day relative standard deviations (RSDs) were within ±15% and bias was within ±15% for most of the compounds. Recovery was >69% (RSD < 11%) and matrix effects ranged from 1 to 26% when compensated with the internal standard. The limits of quantification ranged from 3 to 25 ng/mL depending on the compound. No cross-contamination in the automated SPE system was observed. The extracted samples were stable for 72 h in the autosampler (4°C). This method was applied to authentic samples (from forensic and toxicology cases) and to proficiency testing schemes containing cocaine, heroin, buprenorphine and methadone, offering fast and reliable results. Automation resulted in improved precision and accuracy, and a minimum operator intervention, leading to safer sample handling and less time-consuming procedures.

Optimization-based method for automated road network extraction

DOT National Transportation Integrated Search

2001-09-18

Automated road information extraction has significant applicability in transportation. : It provides a means for creating, maintaining, and updating transportation network databases that : are needed for purposes ranging from traffic management to au...

PKDE4J: Entity and relation extraction for public knowledge discovery.

PubMed

Song, Min; Kim, Won Chul; Lee, Dahee; Heo, Go Eun; Kang, Keun Young

2015-10-01

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction. Copyright © 2015 Elsevier Inc. All rights reserved.

Bacterial and fungal DNA extraction from blood samples: automated protocols.

PubMed

Lorenz, Michael G; Disqué, Claudia; Mühl, Helge

2015-01-01

Automation in DNA isolation is a necessity for routine practice employing molecular diagnosis of infectious agents. To this end, the development of automated systems for the molecular diagnosis of microorganisms directly in blood samples is at its beginning. Important characteristics of systems demanded for routine use include high recovery of microbial DNA, DNA-free containment for the reduction of DNA contamination from exogenous sources, DNA-free reagents and consumables, ideally a walkaway system, and economical pricing of the equipment and consumables. Such full automation of DNA extraction evaluated and in use for sepsis diagnostics is yet not available. Here, we present protocols for the semiautomated isolation of microbial DNA from blood culture and low- and high-volume blood samples. The protocols include a manual pretreatment step followed by automated extraction and purification of microbial DNA.

AUTOMATED ANALYSIS OF AQUEOUS SAMPLES CONTAINING PESTICIDES, ACIDIC/BASIC/NEUTRAL SEMIVOLATILES AND VOLATILE ORGANIC COMPOUNDS BY SOLID PHASE EXTRACTION COUPLED IN-LINE TO LARGE VOLUME INJECTION GC/MS

EPA Science Inventory

Data is presented on the development of a new automated system combining solid phase extraction (SPE) with GC/MS spectrometry for the single-run analysis of water samples containing a broad range of organic compounds. The system uses commercially available automated in-line 10-m...

Disposable and removable nucleic acid extraction and purification cartridges for automated flow-through systems

DOEpatents

Regan, John Frederick

2014-09-09

Removable cartridges are used on automated flow-through systems for the purpose of extracting and purifying genetic material from complex matrices. Different types of cartridges are paired with specific automated protocols to concentrate, extract, and purifying pathogenic or human genetic material. Their flow-through nature allows large quantities sample to be processed. Matrices may be filtered using size exclusion and/or affinity filters to concentrate the pathogen of interest. Lysed material is ultimately passed through a filter to remove the insoluble material before the soluble genetic material is delivered past a silica-like membrane that binds the genetic material, where it is washed, dried, and eluted. Cartridges are inserted into the housing areas of flow-through automated instruments, which are equipped with sensors to ensure proper placement and usage of the cartridges. Properly inserted cartridges create fluid- and air-tight seals with the flow lines of an automated instrument.

Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow.

PubMed

Stockton, David B; Santamaria, Fidel

2017-10-01

We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI's feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features. Finally, to maximize automation, we extended our NeuroManager workflow automation suite to include these tools plus a separate investigation database. The extended suite allows the user to integrate ABI experimental and modeling data into an automated workflow deployed on heterogeneous computer infrastructures, from local servers, to high performance computing environments, to the cloud. Since our approach is focused on workflow procedures our tools can be modified to interact with the increasing number of neuroscience databases being developed to cover all scales and properties of the nervous system.

Automated video feature extraction : workshop summary report October 10-11 2012.

DOT National Transportation Integrated Search

2012-12-01

This report summarizes a 2-day workshop on automated video feature extraction. Discussion focused on the Naturalistic Driving : Study, funded by the second Strategic Highway Research Program, and also involved the companion roadway inventory dataset....

Automated microfluidic devices integrating solid-phase extraction, fluorescent labeling, and microchip electrophoresis for preterm birth biomarker analysis.

PubMed

Sahore, Vishal; Sonker, Mukul; Nielsen, Anna V; Knob, Radim; Kumar, Suresh; Woolley, Adam T

2018-01-01

We have developed multichannel integrated microfluidic devices for automated preconcentration, labeling, purification, and separation of preterm birth (PTB) biomarkers. We fabricated multilayer poly(dimethylsiloxane)-cyclic olefin copolymer (PDMS-COC) devices that perform solid-phase extraction (SPE) and microchip electrophoresis (μCE) for automated PTB biomarker analysis. The PDMS control layer had a peristaltic pump and pneumatic valves for flow control, while the PDMS fluidic layer had five input reservoirs connected to microchannels and a μCE system. The COC layers had a reversed-phase octyl methacrylate porous polymer monolith for SPE and fluorescent labeling of PTB biomarkers. We determined μCE conditions for two PTB biomarkers, ferritin (Fer) and corticotropin-releasing factor (CRF). We used these integrated microfluidic devices to preconcentrate and purify off-chip-labeled Fer and CRF in an automated fashion. Finally, we performed a fully automated on-chip analysis of unlabeled PTB biomarkers, involving SPE, labeling, and μCE separation with 1 h total analysis time. These integrated systems have strong potential to be combined with upstream immunoaffinity extraction, offering a compact sample-to-answer biomarker analysis platform. Graphical abstract Pressure-actuated integrated microfluidic devices have been developed for automated solid-phase extraction, fluorescent labeling, and microchip electrophoresis of preterm birth biomarkers.

Automated mini-column solid-phase extraction cleanup for high-throughput analysis of chemical contaminants in foods by low-pressure gas chromatography – tandem mass spectrometry

USDA-ARS?s Scientific Manuscript database

This study demonstrated the application of an automated high-throughput mini-cartridge solid-phase extraction (mini-SPE) cleanup for the rapid low-pressure gas chromatography – tandem mass spectrometry (LPGC-MS/MS) analysis of pesticides and environmental contaminants in QuEChERS extracts of foods. ...

How automated image analysis techniques help scientists in species identification and classification?

PubMed

Yousef Kalafi, Elham; Town, Christopher; Kaur Dhillon, Sarinder

2017-09-04

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification increased over the last two decades. Automation of data classification is primarily focussed on images, incorporating and analysing image data has recently become easier due to developments in computational technology. Research efforts in identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, categorizing and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies.

Automated PCR setup for forensic casework samples using the Normalization Wizard and PCR Setup robotic methods.

PubMed

Greenspoon, S A; Sykes, K L V; Ban, J D; Pollard, A; Baisden, M; Farr, M; Graham, N; Collins, B L; Green, M M; Christenson, C C

2006-12-20

Human genome, pharmaceutical and research laboratories have long enjoyed the application of robotics to performing repetitive laboratory tasks. However, the utilization of robotics in forensic laboratories for processing casework samples is relatively new and poses particular challenges. Since the quantity and quality (a mixture versus a single source sample, the level of degradation, the presence of PCR inhibitors) of the DNA contained within a casework sample is unknown, particular attention must be paid to procedural susceptibility to contamination, as well as DNA yield, especially as it pertains to samples with little biological material. The Virginia Department of Forensic Science (VDFS) has successfully automated forensic casework DNA extraction utilizing the DNA IQ(trade mark) System in conjunction with the Biomek 2000 Automation Workstation. Human DNA quantitation is also performed in a near complete automated fashion utilizing the AluQuant Human DNA Quantitation System and the Biomek 2000 Automation Workstation. Recently, the PCR setup for casework samples has been automated, employing the Biomek 2000 Automation Workstation and Normalization Wizard, Genetic Identity version, which utilizes the quantitation data, imported into the software, to create a customized automated method for DNA dilution, unique to that plate of DNA samples. The PCR Setup software method, used in conjunction with the Normalization Wizard method and written for the Biomek 2000, functions to mix the diluted DNA samples, transfer the PCR master mix, and transfer the diluted DNA samples to PCR amplification tubes. Once the process is complete, the DNA extracts, still on the deck of the robot in PCR amplification strip tubes, are transferred to pre-labeled 1.5 mL tubes for long-term storage using an automated method. The automation of these steps in the process of forensic DNA casework analysis has been accomplished by performing extensive optimization, validation and testing of the software methods.

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.

INVESTIGATION OF ARSENIC SPECIATION ON DRINKING WATER TREATMENT MEDIA UTILIZING AUTOMATED SEQUENTIAL CONTINUOUS FLOW EXTRACTION WITH IC-ICP-MS DETECTION

EPA Science Inventory

Three treatment media, used for the removal of arsenic from drinking water, were sequentially extracted using 10mM MgCl2 (pH 8), 10mM NaH2PO4 (pH 7) followed by 10mM (NH4)2C2O4 (pH 3). The media were extracted using an on-line automated continuous extraction system which allowed...

Evaluation of four automated protocols for extraction of DNA from FTA cards.

PubMed

Stangegaard, Michael; Børsting, Claus; Ferrero-Miliani, Laura; Frank-Hansen, Rune; Poulsen, Lena; Hansen, Anders J; Morling, Niels

2013-10-01

Extraction of DNA using magnetic bead-based techniques on automated DNA extraction instruments provides a fast, reliable, and reproducible method for DNA extraction from various matrices. Here, we have compared the yield and quality of DNA extracted from FTA cards using four automated extraction protocols on three different instruments. The extraction processes were repeated up to six times with the same pieces of FTA cards. The sample material on the FTA cards was either blood or buccal cells. With the QIAamp DNA Investigator and QIAsymphony DNA Investigator kits, it was possible to extract DNA from the FTA cards in all six rounds of extractions in sufficient amount and quality to obtain complete short tandem repeat (STR) profiles on a QIAcube and a QIAsymphony SP. With the PrepFiler Express kit, almost all the extractable DNA was extracted in the first two rounds of extractions. Furthermore, we demonstrated that it was possible to successfully extract sufficient DNA for STR profiling from previously processed FTA card pieces that had been stored at 4 °C for up to 1 year. This showed that rare or precious FTA card samples may be saved for future analyses even though some DNA was already extracted from the FTA cards.

Automated drumlin shape and volume estimation using high resolution LiDAR imagery (Curvature Based Relief Separation): A test from the Wadena Drumlin Field, Minnesota

NASA Astrophysics Data System (ADS)

Yu, Peter; Eyles, Nick; Sookhan, Shane

2015-10-01

Resolving the origin(s) of drumlins and related megaridges in areas of megascale glacial lineations (MSGL) left by paleo-ice sheets is critical to understanding how ancient ice sheets interacted with their sediment beds. MSGL is now linked with fast-flowing ice streams but there is a broad range of erosional and depositional models. Further progress is reliant on constraining fluxes of subglacial sediment at the ice sheet base which in turn is dependent on morphological data such as landform shape and elongation and most importantly landform volume. Past practice in determining shape has employed a broad range of geomorphological methods from strictly visualisation techniques to more complex semi-automated and automated drumlin extraction methods. This paper reviews and builds on currently available visualisation, semi-automated and automated extraction methods and presents a new, Curvature Based Relief Separation (CBRS) technique; for drumlin mapping. This uses curvature analysis to generate a base level from which topography can be normalized and drumlin volume can be derived. This methodology is tested using a high resolution (3 m) LiDAR elevation dataset from the Wadena Drumlin Field, Minnesota, USA, which was constructed by the Wadena Lobe of the Laurentide Ice Sheet ca. 20,000 years ago and which as a whole contains 2000 drumlins across an area of 7500 km2. This analysis demonstrates that CBRS provides an objective and robust procedure for automated drumlin extraction. There is strong agreement with manually selected landforms but the method is also capable of resolving features that were not detectable manually thereby considerably expanding the known population of streamlined landforms. CBRS provides an effective automatic method for visualisation of large areas of the streamlined beds of former ice sheets and for modelling sediment fluxes below ice sheets.

User-centered evaluation of Arizona BioPathway: an information extraction, integration, and visualization system.

PubMed

Quiñones, Karin D; Su, Hua; Marshall, Byron; Eggers, Shauna; Chen, Hsinchun

2007-09-01

Explosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This paper presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature-viewing method. Relation aggregation significantly contributes to knowledge-acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multiview, relation-based interface that supports user-controlled exploration of pathway information across multiple granularities.

RADIANCE: An automated, enterprise-wide solution for archiving and reporting CT radiation dose estimates.

PubMed

Cook, Tessa S; Zimmerman, Stefan L; Steingall, Scott R; Maidment, Andrew D A; Kim, Woojin; Boonn, William W

2011-01-01

There is growing interest in the ability to monitor, track, and report exposure to radiation from medical imaging. Historically, however, dose information has been stored on an image-based dose sheet, an arrangement that precludes widespread indexing. Although scanner manufacturers are beginning to include dose-related parameters in the Digital Imaging and Communications in Medicine (DICOM) headers of imaging studies, there remains a vast repository of retrospective computed tomographic (CT) data with image-based dose sheets. Consequently, it is difficult for imaging centers to monitor their dose estimates or participate in the American College of Radiology (ACR) Dose Index Registry. An automated extraction software pipeline known as Radiation Dose Intelligent Analytics for CT Examinations (RADIANCE) has been designed that quickly and accurately parses CT dose sheets to extract and archive dose-related parameters. Optical character recognition of information in the dose sheet leads to creation of a text file, which along with the DICOM study header is parsed to extract dose-related data. The data are then stored in a relational database that can be queried for dose monitoring and report creation. RADIANCE allows efficient dose analysis of CT examinations and more effective education of technologists, radiologists, and referring physicians regarding patient exposure to radiation at CT. RADIANCE also allows compliance with the ACR's dose reporting guidelines and greater awareness of patient radiation dose, ultimately resulting in improved patient care and treatment.

Automated extraction of DNA from blood and PCR setup using a Tecan Freedom EVO liquid handler for forensic genetic STR typing of reference samples.

PubMed

Stangegaard, Michael; Frøslev, Tobias G; Frank-Hansen, Rune; Hansen, Anders J; Morling, Niels

2011-04-01

We have implemented and validated automated protocols for DNA extraction and PCR setup using a Tecan Freedom EVO liquid handler mounted with the Te-MagS magnetic separation device (Tecan, Männedorf, Switzerland). The protocols were validated for accredited forensic genetic work according to ISO 17025 using the Qiagen MagAttract DNA Mini M48 kit (Qiagen GmbH, Hilden, Germany) from fresh whole blood and blood from deceased individuals. The workflow was simplified by returning the DNA extracts to the original tubes minimizing the risk of misplacing samples. The tubes that originally contained the samples were washed with MilliQ water before the return of the DNA extracts. The PCR was setup in 96-well microtiter plates. The methods were validated for the kits: AmpFℓSTR Identifiler, SGM Plus and Yfiler (Applied Biosystems, Foster City, CA), GenePrint FFFL and PowerPlex Y (Promega, Madison, WI). The automated protocols allowed for extraction and addition of PCR master mix of 96 samples within 3.5h. In conclusion, we demonstrated that (1) DNA extraction with magnetic beads and (2) PCR setup for accredited, forensic genetic short tandem repeat typing can be implemented on a simple automated liquid handler leading to the reduction of manual work, and increased quality and throughput. Copyright © 2011 Society for Laboratory Automation and Screening. Published by Elsevier Inc. All rights reserved.

Automated solid-phase extraction and liquid chromatography for assay of cyclosporine in whole blood.

PubMed

Kabra, P M; Wall, J H; Dimson, P

1987-12-01

In this rapid, precise, accurate, cost-effective, automated liquid-chromatographic procedure for determining cyclosporine in whole blood, the cyclosporine is extracted from 0.5 mL of whole blood together with 300 micrograms of cyclosporin D per liter, added as internal standard, by using an Advanced Automated Sample Processing unit. The on-line solid-phase extraction is performed on an octasilane sorbent cartridge, which is interfaced with a RP-8 guard column and an octyl analytical column, packed with 5-microns packing material. Both columns are eluted with a mobile phase containing acetonitrile/methanol/water (53/20/27 by vol) at a flow rate of 1.5 mL/min and column temperature of 70 degrees C. Absolute recovery of cyclosporine exceeded 85% and the standard curve was linear to 5000 micrograms/L. Within-run and day-to-day CVs were less than 8%. Correlation between automated and manual Bond-Elut extraction methods was excellent (r = 0.987). None of 18 drugs and four steroids tested interfered.

Imitating manual curation of text-mined facts in biomedicine.

PubMed

Rodriguez-Esteban, Raul; Iossifov, Ivan; Rzhetsky, Andrey

2006-09-08

Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted) of individual facts--to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations), we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95). Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

PubMed

Atkinson, Jonathan A; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E; Griffiths, Marcus; Wells, Darren M

2017-10-01

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping. © The Authors 2017. Published by Oxford University Press.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies

PubMed Central

Atkinson, Jonathan A.; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E.; Griffiths, Marcus

2017-01-01

Abstract Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping. PMID:29020748

Information Graphic Classification, Decomposition and Alternative Representation

ERIC Educational Resources Information Center

Gao, Jinglun

2012-01-01

This thesis work is mainly focused on two problems related to improving accessibility of information graphics for visually impaired users. The first problem is automated analysis of information graphics for information extraction and the second problem is multi-modal representations for accessibility. Information graphics are graphical…

Extraction of CT dose information from DICOM metadata: automated Matlab-based approach.

PubMed

Dave, Jaydev K; Gingold, Eric L

2013-01-01

The purpose of this study was to extract exposure parameters and dose-relevant indexes of CT examinations from information embedded in DICOM metadata. DICOM dose report files were identified and retrieved from a PACS. An automated software program was used to extract from these files information from the structured elements in the DICOM metadata relevant to exposure. Extracting information from DICOM metadata eliminated potential errors inherent in techniques based on optical character recognition, yielding 100% accuracy.

Comparison of QIAsymphony automated and QIAamp manual DNA extraction systems for measuring Epstein-Barr virus DNA load in whole blood using real-time PCR.

PubMed

Laus, Stella; Kingsley, Lawrence A; Green, Michael; Wadowsky, Robert M

2011-11-01

Automated and manual extraction systems have been used with real-time PCR for quantification of Epstein-Barr virus [human herpesvirus 4 (HHV-4)] DNA in whole blood, but few studies have evaluated relative performances. In the present study, the automated QIAsymphony and manual QIAamp extraction systems (Qiagen, Valencia, CA) were assessed using paired aliquots derived from clinical whole-blood specimens and an in-house, real-time PCR assay. The detection limits using the QIAsymphony and QIAamp systems were similar (270 and 560 copies/mL, respectively). For samples estimated as having ≥10,000 copies/mL, the intrarun and interrun variations were significantly lower using QIAsymphony (10.0% and 6.8%, respectively), compared with QIAamp (18.6% and 15.2%, respectively); for samples having ≤1000 copies/mL, the two variations ranged from 27.9% to 43.9% and were not significantly different between the two systems. Among 68 paired clinical samples, 48 pairs yielded viral loads ≥1000 copies/mL under both extraction systems. Although the logarithmic linear correlation from these positive samples was high (r(2) = 0.957), the values obtained using QIAsymphony were on average 0.2 log copies/mL higher than those obtained using QIAamp. Thus, the QIAsymphony and QIAamp systems provide similar EBV DNA load values in whole blood. Copyright © 2011 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

Automated extraction of Biomarker information from pathology reports.

PubMed

Lee, Jeongeun; Song, Hyun-Je; Yoon, Eunsil; Park, Seong-Bae; Park, Sung-Hye; Seo, Jeong-Wook; Park, Peom; Choi, Jinwook

2018-05-21

Pathology reports are written in free-text form, which precludes efficient data gathering. We aimed to overcome this limitation and design an automated system for extracting biomarker profiles from accumulated pathology reports. We designed a new data model for representing biomarker knowledge. The automated system parses immunohistochemistry reports based on a "slide paragraph" unit defined as a set of immunohistochemistry findings obtained for the same tissue slide. Pathology reports are parsed using context-free grammar for immunohistochemistry, and using a tree-like structure for surgical pathology. The performance of the approach was validated on manually annotated pathology reports of 100 randomly selected patients managed at Seoul National University Hospital. High F-scores were obtained for parsing biomarker name and corresponding test results (0.999 and 0.998, respectively) from the immunohistochemistry reports, compared to relatively poor performance for parsing surgical pathology findings. However, applying the proposed approach to our single-center dataset revealed information on 221 unique biomarkers, which represents a richer result than biomarker profiles obtained based on the published literature. Owing to the data representation model, the proposed approach can associate biomarker profiles extracted from an immunohistochemistry report with corresponding pathology findings listed in one or more surgical pathology reports. Term variations are resolved by normalization to corresponding preferred terms determined by expanded dictionary look-up and text similarity-based search. Our proposed approach for biomarker data extraction addresses key limitations regarding data representation and can handle reports prepared in the clinical setting, which often contain incomplete sentences, typographical errors, and inconsistent formatting.

Development of automated extraction method of biliary tract from abdominal CT volumes based on local intensity structure analysis

NASA Astrophysics Data System (ADS)

Koga, Kusuto; Hayashi, Yuichiro; Hirose, Tomoaki; Oda, Masahiro; Kitasaka, Takayuki; Igami, Tsuyoshi; Nagino, Masato; Mori, Kensaku

2014-03-01

In this paper, we propose an automated biliary tract extraction method from abdominal CT volumes. The biliary tract is the path by which bile is transported from liver to the duodenum. No extraction method have been reported for the automated extraction of the biliary tract from common contrast CT volumes. Our method consists of three steps including: (1) extraction of extrahepatic bile duct (EHBD) candidate regions, (2) extraction of intrahepatic bile duct (IHBD) candidate regions, and (3) combination of these candidate regions. The IHBD has linear structures and intensities of the IHBD are low in CT volumes. We use a dark linear structure enhancement (DLSE) filter based on a local intensity structure analysis method using the eigenvalues of the Hessian matrix for the IHBD candidate region extraction. The EHBD region is extracted using a thresholding process and a connected component analysis. In the combination process, we connect the IHBD candidate regions to each EHBD candidate region and select a bile duct region from the connected candidate regions. We applied the proposed method to 22 cases of CT volumes. An average Dice coefficient of extraction result was 66.7%.

Automated Ontology Generation Using Spatial Reasoning

NASA Astrophysics Data System (ADS)

Coalter, Alton; Leopold, Jennifer L.

Recently there has been much interest in using ontologies to facilitate knowledge representation, integration, and reasoning. Correspondingly, the extent of the information embodied by an ontology is increasing beyond the conventional is_a and part_of relationships. To address these requirements, a vast amount of digitally available information may need to be considered when building ontologies, prompting a desire for software tools to automate at least part of the process. The main efforts in this direction have involved textual information retrieval and extraction methods. For some domains extension of the basic relationships could be enhanced further by the analysis of 2D and/or 3D images. For this type of media, image processing algorithms are more appropriate than textual analysis methods. Herein we present an algorithm that, given a collection of 3D image files, utilizes Qualitative Spatial Reasoning (QSR) to automate the creation of an ontology for the objects represented by the images, relating the objects in terms of is_a and part_of relationships and also through unambiguous Relational Connection Calculus (RCC) relations.

Automated extraction of pleural effusion in three-dimensional thoracic CT images

NASA Astrophysics Data System (ADS)

Kido, Shoji; Tsunomori, Akinori

2009-02-01

It is important for diagnosis of pulmonary diseases to measure volume of accumulating pleural effusion in threedimensional thoracic CT images quantitatively. However, automated extraction of pulmonary effusion correctly is difficult. Conventional extraction algorithm using a gray-level based threshold can not extract pleural effusion from thoracic wall or mediastinum correctly, because density of pleural effusion in CT images is similar to those of thoracic wall or mediastinum. So, we have developed an automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion. Our method used a template of lung obtained from a normal lung for segmentation of lungs with pleural effusions. Registration process consisted of two steps. First step was a global matching processing between normal and abnormal lungs of organs such as bronchi, bones (ribs, sternum and vertebrae) and upper surfaces of livers which were extracted using a region-growing algorithm. Second step was a local matching processing between normal and abnormal lungs which were deformed by the parameter obtained from the global matching processing. Finally, we segmented a lung with pleural effusion by use of the template which was deformed by two parameters obtained from the global matching processing and the local matching processing. We compared our method with a conventional extraction method using a gray-level based threshold and two published methods. The extraction rates of pleural effusions obtained from our method were much higher than those obtained from other methods. Automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion is promising for diagnosis of pulmonary diseases by providing quantitative volume of accumulating pleural effusion.

A fully automated liquid–liquid extraction system utilizing interface detection

PubMed Central

Maslana, Eugene; Schmitt, Robert; Pan, Jeffrey

2000-01-01

The development of the Abbott Liquid-Liquid Extraction Station was a result of the need for an automated system to perform aqueous extraction on large sets of newly synthesized organic compounds used for drug discovery. The system utilizes a cylindrical laboratory robot to shuttle sample vials between two loading racks, two identical extraction stations, and a centrifuge. Extraction is performed by detecting the phase interface (by difference in refractive index) of the moving column of fluid drawn from the bottom of each vial containing a biphasic mixture. The integration of interface detection with fluid extraction maximizes sample throughput. Abbott-developed electronics process the detector signals. Sample mixing is performed by high-speed solvent injection. Centrifuging of the samples reduces interface emulsions. Operating software permits the user to program wash protocols with any one of six solvents per wash cycle with as many cycle repeats as necessary. Station capacity is eighty, 15 ml vials. This system has proven successful with a broad spectrum of both ethyl acetate and methylene chloride based chemistries. The development and characterization of this automated extraction system will be presented. PMID:18924693

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.

Automated Age-related Macular Degeneration screening system using fundus images.

PubMed

Kunumpol, P; Umpaipant, W; Kanchanaranya, N; Charoenpong, T; Vongkittirux, S; Kupakanjana, T; Tantibundhit, C

2017-07-01

This work proposed an automated screening system for Age-related Macular Degeneration (AMD), and distinguishing between wet or dry types of AMD using fundus images to assist ophthalmologists in eye disease screening and management. The algorithm employs contrast-limited adaptive histogram equalization (CLAHE) in image enhancement. Subsequently, discrete wavelet transform (DWT) and locality sensitivity discrimination analysis (LSDA) were used to extract features for a neural network model to classify the results. The results showed that the proposed algorithm was able to distinguish between normal eyes, dry AMD, or wet AMD with 98.63% sensitivity, 99.15% specificity, and 98.94% accuracy, suggesting promising potential as a medical support system for faster eye disease screening at lower costs.

Full-text automated detection of surgical site infections secondary to neurosurgery in Rennes, France.

PubMed

Campillo-Gimenez, Boris; Garcelon, Nicolas; Jarno, Pascal; Chapplain, Jean Marc; Cuggia, Marc

2013-01-01

The surveillance of Surgical Site Infections (SSI) contributes to the management of risk in French hospitals. Manual identification of infections is costly, time-consuming and limits the promotion of preventive procedures by the dedicated teams. The introduction of alternative methods using automated detection strategies is promising to improve this surveillance. The present study describes an automated detection strategy for SSI in neurosurgery, based on textual analysis of medical reports stored in a clinical data warehouse. The method consists firstly, of enrichment and concept extraction from full-text reports using NOMINDEX, and secondly, text similarity measurement using a vector space model. The text detection was compared to the conventional strategy based on self-declaration and to the automated detection using the diagnosis-related group database. The text-mining approach showed the best detection accuracy, with recall and precision equal to 92% and 40% respectively, and confirmed the interest of reusing full-text medical reports to perform automated detection of SSI.

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

Automated Protein Biomarker Analysis: on-line extraction of clinical samples by Molecularly Imprinted Polymers

NASA Astrophysics Data System (ADS)

Rossetti, Cecilia; Świtnicka-Plak, Magdalena A.; Grønhaug Halvorsen, Trine; Cormack, Peter A. G.; Sellergren, Börje; Reubsaet, Léon

2017-03-01

Robust biomarker quantification is essential for the accurate diagnosis of diseases and is of great value in cancer management. In this paper, an innovative diagnostic platform is presented which provides automated molecularly imprinted solid-phase extraction (MISPE) followed by liquid chromatography-mass spectrometry (LC-MS) for biomarker determination using ProGastrin Releasing Peptide (ProGRP), a highly sensitive biomarker for Small Cell Lung Cancer, as a model. Molecularly imprinted polymer microspheres were synthesized by precipitation polymerization and analytical optimization of the most promising material led to the development of an automated quantification method for ProGRP. The method enabled analysis of patient serum samples with elevated ProGRP levels. Particularly low sample volumes were permitted using the automated extraction within a method which was time-efficient, thereby demonstrating the potential of such a strategy in a clinical setting.

Automated systems to identify relevant documents in product risk management

PubMed Central

2012-01-01

Background Product risk management involves critical assessment of the risks and benefits of health products circulating in the market. One of the important sources of safety information is the primary literature, especially for newer products which regulatory authorities have relatively little experience with. Although the primary literature provides vast and diverse information, only a small proportion of which is useful for product risk assessment work. Hence, the aim of this study is to explore the possibility of using text mining to automate the identification of useful articles, which will reduce the time taken for literature search and hence improving work efficiency. In this study, term-frequency inverse document-frequency values were computed for predictors extracted from the titles and abstracts of articles related to three tumour necrosis factors-alpha blockers. A general automated system was developed using only general predictors and was tested for its generalizability using articles related to four other drug classes. Several specific automated systems were developed using both general and specific predictors and training sets of different sizes in order to determine the minimum number of articles required for developing such systems. Results The general automated system had an area under the curve value of 0.731 and was able to rank 34.6% and 46.2% of the total number of 'useful' articles among the first 10% and 20% of the articles presented to the evaluators when tested on the generalizability set. However, its use may be limited by the subjective definition of useful articles. For the specific automated system, it was found that only 20 articles were required to develop a specific automated system with a prediction performance (AUC 0.748) that was better than that of general automated system. Conclusions Specific automated systems can be developed rapidly and avoid problems caused by subjective definition of useful articles. Thus the efficiency of product risk management can be improved with the use of specific automated systems. PMID:22380483

A solvent-extraction module for cyclotron production of high-purity technetium-99m.

PubMed

Martini, Petra; Boschi, Alessandra; Cicoria, Gianfranco; Uccelli, Licia; Pasquali, Micòl; Duatti, Adriano; Pupillo, Gaia; Marengo, Mario; Loriggiola, Massimo; Esposito, Juan

2016-12-01

The design and fabrication of a fully-automated, remotely controlled module for the extraction and purification of technetium-99m (Tc-99m), produced by proton bombardment of enriched Mo-100 molybdenum metallic targets in a low-energy medical cyclotron, is here described. After dissolution of the irradiated solid target in hydrogen peroxide, Tc-99m was obtained under the chemical form of 99m TcO 4 - , in high radionuclidic and radiochemical purity, by solvent extraction with methyl ethyl ketone (MEK). The extraction process was accomplished inside a glass column-shaped vial especially designed to allow for an easy automation of the whole procedure. Recovery yields were always >90% of the loaded activity. The final pertechnetate saline solution Na 99m TcO 4 , purified using the automated module here described, is within the Pharmacopoeia quality control parameters and is therefore a valid alternative to generator-produced 99m Tc. The resulting automated module is cost-effective and easily replicable for in-house production of high-purity Tc-99m by cyclotrons. Copyright © 2016 Elsevier Ltd. All rights reserved.

System and method for automated object detection in an image

DOEpatents

Kenyon, Garrett T.; Brumby, Steven P.; George, John S.; Paiton, Dylan M.; Schultz, Peter F.

2015-10-06

A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.

MARS: bringing the automation of small-molecule bioanalytical sample preparations to a new frontier.

PubMed

Li, Ming; Chou, Judy; Jing, Jing; Xu, Hui; Costa, Aldo; Caputo, Robin; Mikkilineni, Rajesh; Flannelly-King, Shane; Rohde, Ellen; Gan, Lawrence; Klunk, Lewis; Yang, Liyu

2012-06-01

In recent years, there has been a growing interest in automating small-molecule bioanalytical sample preparations specifically using the Hamilton MicroLab(®) STAR liquid-handling platform. In the most extensive work reported thus far, multiple small-molecule sample preparation assay types (protein precipitation extraction, SPE and liquid-liquid extraction) have been integrated into a suite that is composed of graphical user interfaces and Hamilton scripts. Using that suite, bioanalytical scientists have been able to automate various sample preparation methods to a great extent. However, there are still areas that could benefit from further automation, specifically, the full integration of analytical standard and QC sample preparation with study sample extraction in one continuous run, real-time 2D barcode scanning on the Hamilton deck and direct Laboratory Information Management System database connectivity. We developed a new small-molecule sample-preparation automation system that improves in all of the aforementioned areas. The improved system presented herein further streamlines the bioanalytical workflow, simplifies batch run design, reduces analyst intervention and eliminates sample-handling error.

A new method for stable lead isotope extraction from seawater.

PubMed

Zurbrick, Cheryl M; Gallon, Céline; Flegal, A Russell

2013-10-24

A new technique for stable lead (Pb) isotope extraction from seawater is established using Toyopearl AF-Chelate 650M(®) resin (Tosoh Bioscience LLC). This new method is advantageous because it is semi-automated and relatively fast; in addition it introduces a relatively low blank by minimizing the volume of chemicals used in the extraction. Subsequent analyses by HR ICP-MS have a good relative external precision (2σ) of 3.5‰ for (206)Pb/(207)Pb, while analyses by MC-ICP-MS have a better relative external precision of 0.6‰. However, Pb sample concentrations limit MC-ICP-MS analyses to (206)Pb, (207)Pb, and (208)Pb. The method was validated by processing the common Pb isotope reference material NIST SRM-981 and several GEOTRACES intercalibration samples, followed by analyses by HR ICP-MS, all of which showed good agreement with previously reported values. Copyright © 2013 Elsevier B.V. All rights reserved.

Automated DNA extraction platforms offer solutions to challenges of assessing microbial biofouling in oil production facilities.

PubMed

Oldham, Athenia L; Drilling, Heather S; Stamps, Blake W; Stevenson, Bradley S; Duncan, Kathleen E

2012-11-20

The analysis of microbial assemblages in industrial, marine, and medical systems can inform decisions regarding quality control or mitigation. Modern molecular approaches to detect, characterize, and quantify microorganisms provide rapid and thorough measures unbiased by the need for cultivation. The requirement of timely extraction of high quality nucleic acids for molecular analysis is faced with specific challenges when used to study the influence of microorganisms on oil production. Production facilities are often ill equipped for nucleic acid extraction techniques, making the preservation and transportation of samples off-site a priority. As a potential solution, the possibility of extracting nucleic acids on-site using automated platforms was tested. The performance of two such platforms, the Fujifilm QuickGene-Mini80™ and Promega Maxwell®16 was compared to a widely used manual extraction kit, MOBIO PowerBiofilm™ DNA Isolation Kit, in terms of ease of operation, DNA quality, and microbial community composition. Three pipeline biofilm samples were chosen for these comparisons; two contained crude oil and corrosion products and the third transported seawater. Overall, the two more automated extraction platforms produced higher DNA yields than the manual approach. DNA quality was evaluated for amplification by quantitative PCR (qPCR) and end-point PCR to generate 454 pyrosequencing libraries for 16S rRNA microbial community analysis. Microbial community structure, as assessed by DGGE analysis and pyrosequencing, was comparable among the three extraction methods. Therefore, the use of automated extraction platforms should enhance the feasibility of rapidly evaluating microbial biofouling at remote locations or those with limited resources.

Automated DNA extraction platforms offer solutions to challenges of assessing microbial biofouling in oil production facilities

PubMed Central

2012-01-01

The analysis of microbial assemblages in industrial, marine, and medical systems can inform decisions regarding quality control or mitigation. Modern molecular approaches to detect, characterize, and quantify microorganisms provide rapid and thorough measures unbiased by the need for cultivation. The requirement of timely extraction of high quality nucleic acids for molecular analysis is faced with specific challenges when used to study the influence of microorganisms on oil production. Production facilities are often ill equipped for nucleic acid extraction techniques, making the preservation and transportation of samples off-site a priority. As a potential solution, the possibility of extracting nucleic acids on-site using automated platforms was tested. The performance of two such platforms, the Fujifilm QuickGene-Mini80™ and Promega Maxwell®16 was compared to a widely used manual extraction kit, MOBIO PowerBiofilm™ DNA Isolation Kit, in terms of ease of operation, DNA quality, and microbial community composition. Three pipeline biofilm samples were chosen for these comparisons; two contained crude oil and corrosion products and the third transported seawater. Overall, the two more automated extraction platforms produced higher DNA yields than the manual approach. DNA quality was evaluated for amplification by quantitative PCR (qPCR) and end-point PCR to generate 454 pyrosequencing libraries for 16S rRNA microbial community analysis. Microbial community structure, as assessed by DGGE analysis and pyrosequencing, was comparable among the three extraction methods. Therefore, the use of automated extraction platforms should enhance the feasibility of rapidly evaluating microbial biofouling at remote locations or those with limited resources. PMID:23168231

Exploitation of Digital Surface Models Generated from WORLDVIEW-2 Data for SAR Simulation Techniques

NASA Astrophysics Data System (ADS)

Ilehag, R.; Auer, S.; d'Angelo, P.

2017-05-01

GeoRaySAR, an automated SAR simulator developed at DLR, identifies buildings in high resolution SAR data by utilizing geometric knowledge extracted from digital surface models (DSMs). Hitherto, the simulator has utilized DSMs generated from LiDAR data from airborne sensors with pre-filtered vegetation. Discarding the need for pre-optimized model input, DSMs generated from high resolution optical data (acquired with WorldView-2) are used for the extraction of building-related SAR image parts in this work. An automatic preprocessing of the DSMs has been developed for separating buildings from elevated vegetation (trees, bushes) and reducing the noise level. Based on that, automated simulations are triggered considering the properties of real SAR images. Locations in three cities, Munich, London and Istanbul, were chosen as study areas to determine advantages and limitations related to WorldView-2 DSMs as input for GeoRaySAR. Beyond, the impact of the quality of the DSM in terms of building extraction is evaluated as well as evaluation of building DSM, a DSM only containing buildings. The results indicate that building extents can be detected with DSMs from optical satellite data with various success, dependent on the quality of the DSM as well as on the SAR imaging perspective.

Automated sample preparation using membrane microtiter extraction for bioanalytical mass spectrometry.

PubMed

Janiszewski, J; Schneider, P; Hoffmaster, K; Swyden, M; Wells, D; Fouda, H

1997-01-01

The development and application of membrane solid phase extraction (SPE) in 96-well microtiter plate format is described for the automated analysis of drugs in biological fluids. The small bed volume of the membrane allows elution of the analyte in a very small solvent volume, permitting direct HPLC injection and negating the need for the time consuming solvent evaporation step. A programmable liquid handling station (Quadra 96) was modified to automate all SPE steps. To avoid drying of the SPE bed and to enhance the analytical precision a novel protocol for performing the condition, load and wash steps in rapid succession was utilized. A block of 96 samples can now be extracted in 10 min., about 30 times faster than manual solvent extraction or single cartridge SPE methods. This processing speed complements the high-throughput speed of contemporary high performance liquid chromatography mass spectrometry (HPLC/MS) analysis. The quantitative analysis of a test analyte (Ziprasidone) in plasma demonstrates the utility and throughput of membrane SPE in combination with HPLC/MS. The results obtained with the current automated procedure compare favorably with those obtained using solvent and traditional solid phase extraction methods. The method has been used for the analysis of numerous drug prototypes in biological fluids to support drug discovery efforts.

Discovering Indicators of Successful Collaboration Using Tense: Automated Extraction of Patterns in Discourse

ERIC Educational Resources Information Center

Thompson, Kate; Kennedy-Clark, Shannon; Wheeler, Penny; Kelly, Nick

2014-01-01

This paper describes a technique for locating indicators of success within the data collected from complex learning environments, proposing an application of e-research to access learner processes and measure and track group progress. The technique combines automated extraction of tense and modality via parts-of-speech tagging with a visualisation…

Automated DNA extraction from genetically modified maize using aminosilane-modified bacterial magnetic particles.

PubMed

Ota, Hiroyuki; Lim, Tae-Kyu; Tanaka, Tsuyoshi; Yoshino, Tomoko; Harada, Manabu; Matsunaga, Tadashi

2006-09-18

A novel, automated system, PNE-1080, equipped with eight automated pestle units and a spectrophotometer was developed for genomic DNA extraction from maize using aminosilane-modified bacterial magnetic particles (BMPs). The use of aminosilane-modified BMPs allowed highly accurate DNA recovery. The (A(260)-A(320)):(A(280)-A(320)) ratio of the extracted DNA was 1.9+/-0.1. The DNA quality was sufficiently pure for PCR analysis. The PNE-1080 offered rapid assay completion (30 min) with high accuracy. Furthermore, the results of real-time PCR confirmed that our proposed method permitted the accurate determination of genetically modified DNA composition and correlated well with results obtained by conventional cetyltrimethylammonium bromide (CTAB)-based methods.

Automating concept identification in the electronic medical record: an experiment in extracting dosage information.

PubMed Central

Evans, D. A.; Brownlow, N. D.; Hersh, W. R.; Campbell, E. M.

1996-01-01

We discuss the development and evaluation of an automated procedure for extracting drug-dosage information from clinical narratives. The process was developed rapidly using existing technology and resources, including categories of terms from UMLS96. Evaluations over a large training and smaller test set of medical records demonstrate an approximately 80% rate of exact and partial matches' on target phrases, with few false positives and a modest rate of false negatives. The results suggest a strategy for automating general concept identification in electronic medical records. PMID:8947694

Testing the event witnessing status of micro-bloggers from evidence in their micro-blogs

PubMed Central

2017-01-01

This paper demonstrates a framework of processes for identifying potential witnesses of events from evidence they post to social media. The research defines original evidence models for micro-blog content sources, the relative uncertainty of different evidence types, and models for testing evidence by combination. Methods to filter and extract evidence using automated and semi-automated means are demonstrated using a Twitter case study event. Further, an implementation to test extracted evidence using Dempster Shafer Theory of Evidence are presented. The results indicate that the inclusion of evidence from micro-blog text and linked image content can increase the number of micro-bloggers identified at events, in comparison to the number of micro-bloggers identified from geotags alone. Additionally, the number of micro-bloggers that can be tested for evidence corroboration or conflict, is increased by incorporating evidence identified in their posting history. PMID:29232395

Information Fusion for Feature Extraction and the Development of Geospatial Information

DTIC Science & Technology

2004-07-01

of automated processing . 2. Requirements for Geospatial Information Accurate, timely geospatial information is critical for many military...this evaluation illustrates some of the difficulties in comparing manual and automated processing results (figure 5). The automated delineation of

Automated extraction of lysergic acid diethylamide (LSD) and N-demethyl-LSD from blood, serum, plasma, and urine samples using the Zymark RapidTrace with LC/MS/MS confirmation.

PubMed

de Kanel, J; Vickery, W E; Waldner, B; Monahan, R M; Diamond, F X

1998-05-01

A forensic procedure for the quantitative confirmation of lysergic acid diethylamide (LSD) and the qualitative confirmation of its metabolite, N-demethyl-LSD, in blood, serum, plasma, and urine samples is presented. The Zymark RapidTrace was used to perform fully automated solid-phase extractions of all specimen types. After extract evaporation, confirmations were performed using liquid chromatography (LC) followed by positive electrospray ionization (ESI+) mass spectrometry/mass spectrometry (MS/MS) without derivatization. Quantitation of LSD was accomplished using LSD-d3 as an internal standard. The limit of quantitation (LOQ) for LSD was 0.05 ng/mL. The limit of detection (LOD) for both LSD and N-demethyl-LSD was 0.025 ng/mL. The recovery of LSD was greater than 95% at levels of 0.1 ng/mL and 2.0 ng/mL. For LSD at 1.0 ng/mL, the within-run and between-run (different day) relative standard deviation (RSD) was 2.2% and 4.4%, respectively.

Comprehensive automation of the solid phase extraction gas chromatographic mass spectrometric analysis (SPE-GC/MS) of opioids, cocaine, and metabolites from serum and other matrices.

PubMed

Lerch, Oliver; Temme, Oliver; Daldrup, Thomas

2014-07-01

The analysis of opioids, cocaine, and metabolites from blood serum is a routine task in forensic laboratories. Commonly, the employed methods include many manual or partly automated steps like protein precipitation, dilution, solid phase extraction, evaporation, and derivatization preceding a gas chromatography (GC)/mass spectrometry (MS) or liquid chromatography (LC)/MS analysis. In this study, a comprehensively automated method was developed from a validated, partly automated routine method. This was possible by replicating method parameters on the automated system. Only marginal optimization of parameters was necessary. The automation relying on an x-y-z robot after manual protein precipitation includes the solid phase extraction, evaporation of the eluate, derivatization (silylation with N-methyl-N-trimethylsilyltrifluoroacetamide, MSTFA), and injection into a GC/MS. A quantitative analysis of almost 170 authentic serum samples and more than 50 authentic samples of other matrices like urine, different tissues, and heart blood on cocaine, benzoylecgonine, methadone, morphine, codeine, 6-monoacetylmorphine, dihydrocodeine, and 7-aminoflunitrazepam was conducted with both methods proving that the analytical results are equivalent even near the limits of quantification (low ng/ml range). To our best knowledge, this application is the first one reported in the literature employing this sample preparation system.

Automated road network extraction from high spatial resolution multi-spectral imagery

NASA Astrophysics Data System (ADS)

Zhang, Qiaoping

For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a road network. The extracted road network is evaluated against a reference dataset using a line segment matching algorithm. The entire process is unsupervised and fully automated. Based on extensive experimentation on a variety of remotely-sensed multi-spectral images, the proposed methodology achieves a moderate success in automating road network extraction from high spatial resolution multi-spectral imagery.

Application of Natural Language Processing and Network Analysis Techniques to Post-market Reports for the Evaluation of Dose-related Anti-Thymocyte Globulin Safety Patterns.

PubMed

Botsis, Taxiarchis; Foster, Matthew; Arya, Nina; Kreimeyer, Kory; Pandey, Abhishek; Arya, Deepa

2017-04-26

To evaluate the feasibility of automated dose and adverse event information retrieval in supporting the identification of safety patterns. We extracted all rabbit Anti-Thymocyte Globulin (rATG) reports submitted to the United States Food and Drug Administration Adverse Event Reporting System (FAERS) from the product's initial licensure in April 16, 1984 through February 8, 2016. We processed the narratives using the Medication Extraction (MedEx) and the Event-based Text-mining of Health Electronic Records (ETHER) systems and retrieved the appropriate medication, clinical, and temporal information. When necessary, the extracted information was manually curated. This process resulted in a high quality dataset that was analyzed with the Pattern-based and Advanced Network Analyzer for Clinical Evaluation and Assessment (PANACEA) to explore the association of rATG dosing with post-transplant lymphoproliferative disorder (PTLD). Although manual curation was necessary to improve the data quality, MedEx and ETHER supported the extraction of the appropriate information. We created a final dataset of 1,380 cases with complete information for rATG dosing and date of administration. Analysis in PANACEA found that PTLD was associated with cumulative doses of rATG >8 mg/kg, even in periods where most of the submissions to FAERS reported low doses of rATG. We demonstrated the feasibility of investigating a dose-related safety pattern for a particular product in FAERS using a set of automated tools.

RNA isolation from mammalian cells using porous polymer monoliths: an approach for high-throughput automation.

PubMed

Chatterjee, Anirban; Mirer, Paul L; Zaldivar Santamaria, Elvira; Klapperich, Catherine; Sharon, Andre; Sauer-Budge, Alexis F

2010-06-01

The life science and healthcare communities have been redefining the importance of ribonucleic acid (RNA) through the study of small molecule RNA (in RNAi/siRNA technologies), micro RNA (in cancer research and stem cell research), and mRNA (gene expression analysis for biologic drug targets). Research in this field increasingly requires efficient and high-throughput isolation techniques for RNA. Currently, several commercial kits are available for isolating RNA from cells. Although the quality and quantity of RNA yielded from these kits is sufficiently good for many purposes, limitations exist in terms of extraction efficiency from small cell populations and the ability to automate the extraction process. Traditionally, automating a process decreases the cost and personnel time while simultaneously increasing the throughput and reproducibility. As the RNA field matures, new methods for automating its extraction, especially from low cell numbers and in high throughput, are needed to achieve these improvements. The technology presented in this article is a step toward this goal. The method is based on a solid-phase extraction technology using a porous polymer monolith (PPM). A novel cell lysis approach and a larger binding surface throughout the PPM extraction column ensure a high yield from small starting samples, increasing sensitivity and reducing indirect costs in cell culture and sample storage. The method ensures a fast and simple procedure for RNA isolation from eukaryotic cells, with a high yield both in terms of quality and quantity. The technique is amenable to automation and streamlined workflow integration, with possible miniaturization of the sample handling process making it suitable for high-throughput applications.

A simplified In Situ cosmogenic 14C extraction system

USGS Publications Warehouse

Pigati, J.S.; Lifton, N.A.; Timothy, Jull A.J.; Quade, Jay

2010-01-01

We describe the design, construction, and testing of a new, simplified in situ radiocarbon extraction system at the University of Arizona. Blank levels for the new system are low ((234 ?? 11) ?? 103 atoms (1 ??; n = 7)) and stable. The precision of a given measurement depends on the concentration of 14C, but is typically <5% for concentrations of 100 ?? 103 atoms g-1 or more. The new system is relatively small and easy to construct, costs significantly less than the original in situ 14C extraction system at Arizona, and lends itself to future automation. ?? 2010 by the Arizona Board of Regents on behalf of the University of Arizona.

The LabTube - a novel microfluidic platform for assay automation in laboratory centrifuges.

PubMed

Kloke, A; Fiebach, A R; Zhang, S; Drechsel, L; Niekrawietz, S; Hoehl, M M; Kneusel, R; Panthel, K; Steigert, J; von Stetten, F; Zengerle, R; Paust, N

2014-05-07

Assay automation is the key for successful transformation of modern biotechnology into routine workflows. Yet, it requires considerable investment in processing devices and auxiliary infrastructure, which is not cost-efficient for laboratories with low or medium sample throughput or point-of-care testing. To close this gap, we present the LabTube platform, which is based on assay specific disposable cartridges for processing in laboratory centrifuges. LabTube cartridges comprise interfaces for sample loading and downstream applications and fluidic unit operations for release of prestored reagents, mixing, and solid phase extraction. Process control is achieved by a centrifugally-actuated ballpen mechanism. To demonstrate the workflow and functionality of the LabTube platform, we show two LabTube automated sample preparation assays from laboratory routines: DNA extractions from whole blood and purification of His-tagged proteins. Equal DNA and protein yields were observed compared to manual reference runs, while LabTube automation could significantly reduce the hands-on-time to one minute per extraction.

Multicenter Comparative Evaluation of Five Commercial Methods for Toxoplasma DNA Extraction from Amniotic Fluid▿

PubMed Central

Yera, H.; Filisetti, D.; Bastien, P.; Ancelle, T.; Thulliez, P.; Delhaes, L.

2009-01-01

Over the past few years, a number of new nucleic acid extraction methods and extraction platforms using chemistry combined with magnetic or silica particles have been developed, in combination with instruments to facilitate the extraction procedure. The objective of the present study was to investigate the suitability of these automated methods for the isolation of Toxoplasma gondii DNA from amniotic fluid (AF). Therefore, three automated procedures were compared to two commercialized manual extraction methods. The MagNA Pure Compact (Roche), BioRobot EZ1 (Qiagen), and easyMAG (bioMérieux) automated procedures were compared to two manual DNA extraction kits, the QIAamp DNA minikit (Qiagen) and the High Pure PCR template preparation kit (Roche). Evaluation was carried out with two specific Toxoplasma PCRs (targeting the 529-bp repeat element), inhibitor search PCRs, and human beta-globin PCRs. The samples each consisted of 4 ml of AF with or without a calibrated Toxoplasma gondii RH strain suspension (0, 1, 2.5, 5, and 25 tachyzoites/ml). All PCR assays were laboratory-developed real-time PCR assays, using either TaqMan or fluorescent resonance energy transfer probes. A total of 1,178 PCRs were performed, including 978 Toxoplasma PCRs. The automated and manual methods were similar in sensitivity for DNA extraction from T. gondii at the highest concentration (25 Toxoplasma gondii cells/ml). However, our results showed that the DNA extraction procedures led to variable efficacy in isolating low concentrations of tachyzoites in AF samples (<5 Toxoplasma gondii cells/ml), a difference that might have repercussions since low parasite concentrations in AF exist and can lead to congenital toxoplasmosis. PMID:19846633

Superpixel-Augmented Endmember Detection for Hyperspectral Images

NASA Technical Reports Server (NTRS)

Thompson, David R.; Castano, Rebecca; Gilmore, Martha

2011-01-01

Superpixels are homogeneous image regions comprised of several contiguous pixels. They are produced by shattering the image into contiguous, homogeneous regions that each cover between 20 and 100 image pixels. The segmentation aims for a many-to-one mapping from superpixels to image features; each image feature could contain several superpixels, but each superpixel occupies no more than one image feature. This conservative segmentation is relatively easy to automate in a robust fashion. Superpixel processing is related to the more general idea of improving hyperspectral analysis through spatial constraints, which can recognize subtle features at or below the level of noise by exploiting the fact that their spectral signatures are found in neighboring pixels. Recent work has explored spatial constraints for endmember extraction, showing significant advantages over techniques that ignore pixels relative positions. Methods such as AMEE (automated morphological endmember extraction) express spatial influence using fixed isometric relationships a local square window or Euclidean distance in pixel coordinates. In other words, two pixels covariances are based on their spatial proximity, but are independent of their absolute location in the scene. These isometric spatial constraints are most appropriate when spectral variation is smooth and constant over the image. Superpixels are simple to implement, efficient to compute, and are empirically effective. They can be used as a preprocessing step with any desired endmember extraction technique. Superpixels also have a solid theoretical basis in the hyperspectral linear mixing model, making them a principled approach for improving endmember extraction. Unlike existing approaches, superpixels can accommodate non-isometric covariance between image pixels (characteristic of discrete image features separated by step discontinuities). These kinds of image features are common in natural scenes. Analysts can substitute superpixels for image pixels during endmember analysis that leverages the spatial contiguity of scene features to enhance subtle spectral features. Superpixels define populations of image pixels that are independent samples from each image feature, permitting robust estimation of spectral properties, and reducing measurement noise in proportion to the area of the superpixel. This permits improved endmember extraction, and enables automated search for novel and constituent minerals in very noisy, hyperspatial images. This innovation begins with a graph-based segmentation based on the work of Felzenszwalb et al., but then expands their approach to the hyperspectral image domain with a Euclidean distance metric. Then, the mean spectrum of each segment is computed, and the resulting data cloud is used as input into sequential maximum angle convex cone (SMACC) endmember extraction.

An automated method for the analysis of phenolic acids in plasma based on ion-pairing micro-extraction coupled on-line to gas chromatography/mass spectrometry with in-liner derivatisation.

PubMed

Peters, Sonja; Kaal, Erwin; Horsting, Iwan; Janssen, Hans-Gerd

2012-02-24

A new method is presented for the analysis of phenolic acids in plasma based on ion-pairing 'Micro-extraction in packed sorbent' (MEPS) coupled on-line to in-liner derivatisation-gas chromatography-mass spectrometry (GC-MS). The ion-pairing reagent served a dual purpose. It was used both to improve extraction yields of the more polar analytes and as the methyl donor in the automated in-liner derivatisation method. In this way, a fully automated procedure for the extraction, derivatisation and injection of a wide range of phenolic acids in plasma samples has been obtained. An extensive optimisation of the extraction and derivatisation procedure has been performed. The entire method showed excellent repeatabilities of under 10% and linearities of 0.99 or better for all phenolic acids. The limits of detection of the optimised method for the majority of phenolic acids were 10ng/mL or lower with three phenolic acids having less-favourable detection limits of around 100 ng/mL. Finally, the newly developed method has been applied in a human intervention trial in which the bioavailability of polyphenols from wine and tea was studied. Forty plasma samples could be analysed within 24h in a fully automated method including sample extraction, derivatisation and gas chromatographic analysis. Copyright © 2011 Elsevier B.V. All rights reserved.

An Automated High-Throughput System to Fractionate Plant Natural Products for Drug Discovery

PubMed Central

Tu, Ying; Jeffries, Cynthia; Ruan, Hong; Nelson, Cynthia; Smithson, David; Shelat, Anang A.; Brown, Kristin M.; Li, Xing-Cong; Hester, John P.; Smillie, Troy; Khan, Ikhlas A.; Walker, Larry; Guy, Kip; Yan, Bing

2010-01-01

The development of an automated, high-throughput fractionation procedure to prepare and analyze natural product libraries for drug discovery screening is described. Natural products obtained from plant materials worldwide were extracted and first prefractionated on polyamide solid-phase extraction cartridges to remove polyphenols, followed by high-throughput automated fractionation, drying, weighing, and reformatting for screening and storage. The analysis of fractions with UPLC coupled with MS, PDA and ELSD detectors provides information that facilitates characterization of compounds in active fractions. Screening of a portion of fractions yielded multiple assay-specific hits in several high-throughput cellular screening assays. This procedure modernizes the traditional natural product fractionation paradigm by seamlessly integrating automation, informatics, and multimodal analytical interrogation capabilities. PMID:20232897

Composite Wavelet Filters for Enhanced Automated Target Recognition

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

ICECAP: an integrated, general-purpose, automation-assisted IC50/EC50 assay platform.

PubMed

Li, Ming; Chou, Judy; King, Kristopher W; Jing, Jing; Wei, Dong; Yang, Liyu

2015-02-01

IC50 and EC50 values are commonly used to evaluate drug potency. Mass spectrometry (MS)-centric bioanalytical and biomarker labs are now conducting IC50/EC50 assays, which, if done manually, are tedious and error-prone. Existing bioanalytical sample preparation automation systems cannot meet IC50/EC50 assay throughput demand. A general-purpose, automation-assisted IC50/EC50 assay platform was developed to automate the calculations of spiking solutions and the matrix solutions preparation scheme, the actual spiking and matrix solutions preparations, as well as the flexible sample extraction procedures after incubation. In addition, the platform also automates the data extraction, nonlinear regression curve fitting, computation of IC50/EC50 values, graphing, and reporting. The automation-assisted IC50/EC50 assay platform can process the whole class of assays of varying assay conditions. In each run, the system can handle up to 32 compounds and up to 10 concentration levels per compound, and it greatly improves IC50/EC50 assay experimental productivity and data processing efficiency. © 2014 Society for Laboratory Automation and Screening.

Automated solid-phase extraction of phenolic acids using layered double hydroxide-alumina-polymer disks.

PubMed

Ghani, Milad; Palomino Cabello, Carlos; Saraji, Mohammad; Manuel Estela, Jose; Cerdà, Víctor; Turnes Palomino, Gemma; Maya, Fernando

2018-01-26

The application of layered double hydroxide-Al 2 O 3 -polymer mixed-matrix disks for solid-phase extraction is reported for the first time. Al 2 O 3 is embedded in a polymer matrix followed by an in situ metal-exchange process to obtain a layered double hydroxide-Al 2 O 3 -polymer mixed-matrix disk with excellent flow-through properties. The extraction performance of the prepared disks is evaluated as a proof of concept for the automated extraction using sequential injection analysis of organic acids (p-hydroxybenzoic acid, 3,4-dihydroxybenzoic acid, gallic acid) following an anion-exchange mechanism. After the solid-phase extraction, phenolic acids were quantified by reversed-phase high-performance liquid chromatography with diode-array detection using a core-shell silica-C18 stationary phase and isocratic elution (acetonitrile/0.5% acetic acid in pure water, 5:95, v/v). High sensitivity and reproducibility were obtained with limits of detection in the range of 0.12-0.25 μg/L (sample volume, 4 mL), and relative standard deviations between 2.9 and 3.4% (10 μg/L, n = 6). Enrichment factors of 34-39 were obtained. Layered double hydroxide-Al 2 O 3 -polymer mixed-matrix disks had an average lifetime of 50 extractions. Analyte recoveries ranged from 93 to 96% for grape juice and nonalcoholic beer samples. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

ZK DrugResist 2.0: A TextMiner to extract semantic relations of drug resistance from PubMed.

PubMed

Khalid, Zoya; Sezerman, Osman Ugur

2017-05-01

Extracting useful knowledge from an unstructured textual data is a challenging task for biologists, since biomedical literature is growing exponentially on a daily basis. Building an automated method for such tasks is gaining much attention of researchers. ZK DrugResist is an online tool that automatically extracts mutations and expression changes associated with drug resistance from PubMed. In this study we have extended our tool to include semantic relations extracted from biomedical text covering drug resistance and established a server including both of these features. Our system was tested for three relations, Resistance (R), Intermediate (I) and Susceptible (S) by applying hybrid feature set. From the last few decades the focus has changed to hybrid approaches as it provides better results. In our case this approach combines rule-based methods with machine learning techniques. The results showed 97.67% accuracy with 96% precision, recall and F-measure. The results have outperformed the previously existing relation extraction systems thus can facilitate computational analysis of drug resistance against complex diseases and further can be implemented on other areas of biomedicine. Copyright © 2017 Elsevier Inc. All rights reserved.

A modified method for determining tannin-protein precipitation capacity using accelerated solvent extraction (ASE) and microplate gel filtration.

PubMed

McArt, Scott H; Spalinger, Donald E; Kennish, John M; Collins, William B

2006-06-01

The protein precipitation assay used by Robbins et al., (1987) Ecology 68:98-107 has been shown to predict successfully the reduction in protein availability to some ruminants due to tannins. The procedure, however, is expensive and laborious, which limits its utility, especially for quantitative ecological or nutritional applications where large numbers of assays may be required. We have modified the method to decrease its cost and increase laboratory efficiency by: (1) automating the extraction by using Accelerated Solvent Extraction (ASE); and (2) by scaling and automating the precipitation reaction, chromatography, and spectrometry with microplate gel filtration and an automated UV-VIS microplate spectrometer. ASE extraction is shown to be as effective at extracting tannins as the hot methanol technique. Additionally, the microplate assay is sensitive and precise. We show that the results from the new technique correspond in a nearly 1:1 relationship to the results of the previous technique. Hence, this method could reliably replace the older method with no loss in relevance to herbivore protein digestion. Moreover, the ASE extraction technique should be applicable to other tannin-protein precipitation assays and possibly other phenolic assays.

A displacement pump procedure to load extracts for automated gel permeation chromatography.

PubMed

Daft, J; Hopper, M; Hensley, D; Sisk, R

1990-01-01

Automated gel permeation chromatography (GPC) effectively separates lipids from pesticides in sample extracts that contain fat. Using a large syringe to load sample extracts manually onto GPC models having 5 mL holding loops is awkward, slow, and potentially hazardous. Loading with a small-volume displacement pump, however, is convenient and fast (ca 1 loop every 20 s). And more importantly, the analyst is not exposed to toxic organic vapors because the loading pump and its connecting lines do not leak in the way that a syringe does.

Applying Intelligent Algorithms to Automate the Identification of Error Factors.

PubMed

Jin, Haizhe; Qu, Qingxing; Munechika, Masahiko; Sano, Masataka; Kajihara, Chisato; Duffy, Vincent G; Chen, Han

2018-05-03

Medical errors are the manifestation of the defects occurring in medical processes. Extracting and identifying defects as medical error factors from these processes are an effective approach to prevent medical errors. However, it is a difficult and time-consuming task and requires an analyst with a professional medical background. The issues of identifying a method to extract medical error factors and reduce the extraction difficulty need to be resolved. In this research, a systematic methodology to extract and identify error factors in the medical administration process was proposed. The design of the error report, extraction of the error factors, and identification of the error factors were analyzed. Based on 624 medical error cases across four medical institutes in both Japan and China, 19 error-related items and their levels were extracted. After which, they were closely related to 12 error factors. The relational model between the error-related items and error factors was established based on a genetic algorithm (GA)-back-propagation neural network (BPNN) model. Additionally, compared to GA-BPNN, BPNN, partial least squares regression and support vector regression, GA-BPNN exhibited a higher overall prediction accuracy, being able to promptly identify the error factors from the error-related items. The combination of "error-related items, their different levels, and the GA-BPNN model" was proposed as an error-factor identification technology, which could automatically identify medical error factors.

Automated anatomical labeling of bronchial branches extracted from CT datasets based on machine learning and combination optimization and its application to bronchoscope guidance.

PubMed

Mori, Kensaku; Ota, Shunsuke; Deguchi, Daisuke; Kitasaka, Takayuki; Suenaga, Yasuhito; Iwano, Shingo; Hasegawa, Yosihnori; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi

2009-01-01

This paper presents a method for the automated anatomical labeling of bronchial branches extracted from 3D CT images based on machine learning and combination optimization. We also show applications of anatomical labeling on a bronchoscopy guidance system. This paper performs automated labeling by using machine learning and combination optimization. The actual procedure consists of four steps: (a) extraction of tree structures of the bronchus regions extracted from CT images, (b) construction of AdaBoost classifiers, (c) computation of candidate names for all branches by using the classifiers, (d) selection of best combination of anatomical names. We applied the proposed method to 90 cases of 3D CT datasets. The experimental results showed that the proposed method can assign correct anatomical names to 86.9% of the bronchial branches up to the sub-segmental lobe branches. Also, we overlaid the anatomical names of bronchial branches on real bronchoscopic views to guide real bronchoscopy.

Automation of static and dynamic non-dispersive liquid phase microextraction. Part 1: Approaches based on extractant drop-, plug-, film- and microflow-formation.

PubMed

Alexovič, Michal; Horstkotte, Burkhard; Solich, Petr; Sabo, Ján

2016-02-04

Simplicity, effectiveness, swiftness, and environmental friendliness - these are the typical requirements for the state of the art development of green analytical techniques. Liquid phase microextraction (LPME) stands for a family of elegant sample pretreatment and analyte preconcentration techniques preserving these principles in numerous applications. By using only fractions of solvent and sample compared to classical liquid-liquid extraction, the extraction kinetics, the preconcentration factor, and the cost efficiency can be increased. Moreover, significant improvements can be made by automation, which is still a hot topic in analytical chemistry. This review surveys comprehensively and in two parts the developments of automation of non-dispersive LPME methodologies performed in static and dynamic modes. Their advantages and limitations and the reported analytical performances are discussed and put into perspective with the corresponding manual procedures. The automation strategies, techniques, and their operation advantages as well as their potentials are further described and discussed. In this first part, an introduction to LPME and their static and dynamic operation modes as well as their automation methodologies is given. The LPME techniques are classified according to the different approaches of protection of the extraction solvent using either a tip-like (needle/tube/rod) support (drop-based approaches), a wall support (film-based approaches), or microfluidic devices. In the second part, the LPME techniques based on porous supports for the extraction solvent such as membranes and porous media are overviewed. An outlook on future demands and perspectives in this promising area of analytical chemistry is finally given. Copyright © 2015 Elsevier B.V. All rights reserved.

Automation of static and dynamic non-dispersive liquid phase microextraction. Part 2: Approaches based on impregnated membranes and porous supports.

PubMed

Alexovič, Michal; Horstkotte, Burkhard; Solich, Petr; Sabo, Ján

2016-02-11

A critical overview on automation of modern liquid phase microextraction (LPME) approaches based on the liquid impregnation of porous sorbents and membranes is presented. It is the continuation of part 1, in which non-dispersive LPME techniques based on the use of the extraction phase (EP) in the form of drop, plug, film, or microflow have been surveyed. Compared to the approaches described in part 1, porous materials provide an improved support for the EP. Simultaneously they allow to enlarge its contact surface and to reduce the risk of loss by incident flow or by components of surrounding matrix. Solvent-impregnated membranes or hollow fibres are further ideally suited for analyte extraction with simultaneous or subsequent back-extraction. Their use can therefore improve the procedure robustness and reproducibility as well as it "opens the door" to the new operation modes and fields of application. However, additional work and time are required for membrane replacement and renewed impregnation. Automation of porous support-based and membrane-based approaches plays an important role in the achievement of better reliability, rapidness, and reproducibility compared to manual assays. Automated renewal of the extraction solvent and coupling of sample pretreatment with the detection instrumentation can be named as examples. The different LPME methodologies using impregnated membranes and porous supports for the extraction phase and the different strategies of their automation, and their analytical applications are comprehensively described and discussed in this part. Finally, an outlook on future demands and perspectives of LPME techniques from both parts as a promising area in the field of sample pretreatment is given. Copyright © 2015 Elsevier B.V. All rights reserved.

Automated control of robotic camera tacheometers for measurements of industrial large scale objects

NASA Astrophysics Data System (ADS)

Heimonen, Teuvo; Leinonen, Jukka; Sipola, Jani

2013-04-01

The modern robotic tacheometers equipped with digital cameras (called also imaging total stations) and capable to measure reflectorless offer new possibilities to gather 3d data. In this paper an automated approach for the tacheometer measurements needed in the dimensional control of industrial large scale objects is proposed. There are two new contributions in the approach: the automated extraction of the vital points (i.e. the points to be measured) and the automated fine aiming of the tacheometer. The proposed approach proceeds through the following steps: First the coordinates of the vital points are automatically extracted from the computer aided design (CAD) data. The extracted design coordinates are then used to aim the tacheometer to point out to the designed location of the points, one after another. However, due to the deviations between the designed and the actual location of the points, the aiming need to be adjusted. An automated dynamic image-based look-and-move type servoing architecture is proposed to be used for this task. After a successful fine aiming, the actual coordinates of the point in question can be automatically measured by using the measuring functionalities of the tacheometer. The approach was validated experimentally and noted to be feasible. On average 97 % of the points actually measured in four different shipbuilding measurement cases were indeed proposed to be vital points by the automated extraction algorithm. The accuracy of the results obtained with the automatic control method of the tachoemeter were comparable to the results obtained with the manual control, and also the reliability of the image processing step of the method was found to be high in the laboratory experiments.

The BUME method: a new rapid and simple chloroform-free method for total lipid extraction of animal tissue

NASA Astrophysics Data System (ADS)

Löfgren, Lars; Forsberg, Gun-Britt; Ståhlman, Marcus

2016-06-01

In this study we present a simple and rapid method for tissue lipid extraction. Snap-frozen tissue (15-150 mg) is collected in 2 ml homogenization tubes. 500 μl BUME mixture (butanol:methanol [3:1]) is added and automated homogenization of up to 24 frozen samples at a time in less than 60 seconds is performed, followed by a 5-minute single-phase extraction. After the addition of 500 μl heptane:ethyl acetate (3:1) and 500 μl 1% acetic acid a 5-minute two-phase extraction is performed. Lipids are recovered from the upper phase by automated liquid handling using a standard 96-tip robot. A second two-phase extraction is performed using 500 μl heptane:ethyl acetate (3:1). Validation of the method showed that the extraction recoveries for the investigated lipids, which included sterols, glycerolipids, glycerophospholipids and sphingolipids were similar or better than for the Folch method. We also applied the method for lipid extraction of liver and heart and compared the lipid species profiles with profiles generated after Folch and MTBE extraction. We conclude that the BUME method is superior to the Folch method in terms of simplicity, through-put, automation, solvent consumption, economy, health and environment yet delivering lipid recoveries fully comparable to or better than the Folch method.

Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

PubMed

Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

2011-01-01

Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.

2011-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

Automated extraction of radiation dose information for CT examinations.

PubMed

Cook, Tessa S; Zimmerman, Stefan; Maidment, Andrew D A; Kim, Woojin; Boonn, William W

2010-11-01

Exposure to radiation as a result of medical imaging is currently in the spotlight, receiving attention from Congress as well as the lay press. Although scanner manufacturers are moving toward including effective dose information in the Digital Imaging and Communications in Medicine headers of imaging studies, there is a vast repository of retrospective CT data at every imaging center that stores dose information in an image-based dose sheet. As such, it is difficult for imaging centers to participate in the ACR's Dose Index Registry. The authors have designed an automated extraction system to query their PACS archive and parse CT examinations to extract the dose information stored in each dose sheet. First, an open-source optical character recognition program processes each dose sheet and converts the information to American Standard Code for Information Interchange (ASCII) text. Each text file is parsed, and radiation dose information is extracted and stored in a database which can be queried using an existing pathology and radiology enterprise search tool. Using this automated extraction pipeline, it is possible to perform dose analysis on the >800,000 CT examinations in the PACS archive and generate dose reports for all of these patients. It is also possible to more effectively educate technologists, radiologists, and referring physicians about exposure to radiation from CT by generating report cards for interpreted and performed studies. The automated extraction pipeline enables compliance with the ACR's reporting guidelines and greater awareness of radiation dose to patients, thus resulting in improved patient care and management. Copyright © 2010 American College of Radiology. Published by Elsevier Inc. All rights reserved.

A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head.

PubMed

Delora, Adam; Gonzales, Aaron; Medina, Christopher S; Mitchell, Adam; Mohed, Abdul Faheem; Jacobs, Russell E; Bearer, Elaine L

2016-01-15

Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. This novel timesaving automation of template-based brain extraction ("skull-stripping") is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI. Copyright © 2015 Elsevier B.V. All rights reserved.

Automated extraction of direct, reactive, and vat dyes from cellulosic fibers for forensic analysis by capillary electrophoresis.

PubMed

Dockery, C R; Stefan, A R; Nieuwland, A A; Roberson, S N; Baguley, B M; Hendrix, J E; Morgan, S L

2009-08-01

Systematic designed experiments were employed to find the optimum conditions for extraction of direct, reactive, and vat dyes from cotton fibers prior to forensic characterization. Automated microextractions were coupled with measurements of extraction efficiencies on a microplate reader UV-visible spectrophotometer to enable rapid screening of extraction efficiency as a function of solvent composition. Solvent extraction conditions were also developed to be compatible with subsequent forensic characterization of extracted dyes by capillary electrophoresis with UV-visible diode array detection. The capillary electrophoresis electrolyte successfully used in this work consists of 5 mM ammonium acetate in 40:60 acetonitrile-water at pH 9.3, with the addition of sodium dithionite reducing agent to facilitate analysis of vat dyes. The ultimate goal of these research efforts is enhanced discrimination of trace fiber evidence by analysis of extracted dyes.

Two Different Approaches to Automated Mark Up of Emotions in Text

NASA Astrophysics Data System (ADS)

Francisco, Virginia; Hervás, Raqucl; Gervás, Pablo

This paper presents two different approaches to automated marking up of texts with emotional labels. For the first approach a corpus of example texts previously annotated by human evaluators is mined for an initial assignment of emotional features to words. This results in a List of Emotional Words (LEW) which becomes a useful resource for later automated mark up. The mark up algorithm in this first approach mirrors closely the steps taken during feature extraction, employing for the actual assignment of emotional features a combination of the LEW resource and WordNet for knowledge-based expansion of words not occurring in LEW. The algorithm for automated mark up is tested against new text samples to test its coverage. The second approach mark up texts during their generation. We have a knowledge base which contains the necessary information for marking up the text. This information is related to actions and characters. The algorithm in this case employ the information of the knowledge database and decides the correct emotion for every sentence. The algorithm for automated mark up is tested against four different texts. The results of the two approaches are compared and discussed with respect to three main issues: relative adequacy of each one of the representations used, correctness and coverage of the proposed algorithms, and additional techniques and solutions that may be employed to improve the results.

Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

PubMed Central

Roos, Marco; Marshall, M Scott; Gibson, Andrew P; Schuemie, Martijn; Meij, Edgar; Katrenko, Sophia; van Hage, Willem Robert; Krommydas, Konstantinos; Adriaans, Pieter W

2009-01-01

Background Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation. PMID:19796406

Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images

PubMed Central

2015-01-01

Biological assays formatted as microarrays have become a critical tool for the generation of the comprehensive data sets required for systems-level understanding of biological processes. Manual annotation of data extracted from images of microarrays, however, remains a significant bottleneck, particularly for protein microarrays due to the sensitivity of this technology to weak artifact signal. In order to automate the extraction and curation of data from protein microarrays, we describe an algorithm called Crossword that logically combines information from multiple approaches to fully automate microarray segmentation. Automated artifact removal is also accomplished by segregating structured pixels from the background noise using iterative clustering and pixel connectivity. Correlation of the location of structured pixels across image channels is used to identify and remove artifact pixels from the image prior to data extraction. This component improves the accuracy of data sets while reducing the requirement for time-consuming visual inspection of the data. Crossword enables a fully automated protocol that is robust to significant spatial and intensity aberrations. Overall, the average amount of user intervention is reduced by an order of magnitude and the data quality is increased through artifact removal and reduced user variability. The increase in throughput should aid the further implementation of microarray technologies in clinical studies. PMID:24417579

Automated High-Throughput Permethylation for Glycosylation Analysis of Biologics Using MALDI-TOF-MS.

PubMed

Shubhakar, Archana; Kozak, Radoslaw P; Reiding, Karli R; Royle, Louise; Spencer, Daniel I R; Fernandes, Daryl L; Wuhrer, Manfred

2016-09-06

Monitoring glycoprotein therapeutics for changes in glycosylation throughout the drug's life cycle is vital, as glycans significantly modulate the stability, biological activity, serum half-life, safety, and immunogenicity. Biopharma companies are increasingly adopting Quality by Design (QbD) frameworks for measuring, optimizing, and controlling drug glycosylation. Permethylation of glycans prior to analysis by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is a valuable tool for glycan characterization and for screening of large numbers of samples in QbD drug realization. However, the existing protocols for manual permethylation and liquid-liquid extraction (LLE) steps are labor intensive and are thus not practical for high-throughput (HT) studies. Here we present a glycan permethylation protocol, based on 96-well microplates, that has been developed into a kit suitable for HT work. The workflow is largely automated using a liquid handling robot and includes N-glycan release, enrichment of N-glycans, permethylation, and LLE. The kit has been validated according to industry analytical performance guidelines and applied to characterize biopharmaceutical samples, including IgG4 monoclonal antibodies (mAbs) and recombinant human erythropoietin (rhEPO). The HT permethylation enabled glycan characterization and relative quantitation with minimal side reactions: the MALDI-TOF-MS profiles obtained were in good agreement with hydrophilic liquid interaction chromatography (HILIC) and ultrahigh performance liquid chromatography (UHPLC) data. Automated permethylation and extraction of 96 glycan samples was achieved in less than 5 h and automated data acquisition on MALDI-TOF-MS took on average less than 1 min per sample. This automated and HT glycan preparation and permethylation showed to be convenient, fast, and reliable and can be applied for drug glycan profiling and clinical glycan biomarker studies.

Queries over Unstructured Data: Probabilistic Methods to the Rescue

NASA Astrophysics Data System (ADS)

Sarawagi, Sunita

Unstructured data like emails, addresses, invoices, call transcripts, reviews, and press releases are now an integral part of any large enterprise. A challenge of modern business intelligence applications is analyzing and querying data seamlessly across structured and unstructured sources. This requires the development of automated techniques for extracting structured records from text sources and resolving entity mentions in data from various sources. The success of any automated method for extraction and integration depends on how effectively it unifies diverse clues in the unstructured source and in existing structured databases. We argue that statistical learning techniques like Conditional Random Fields (CRFs) provide a accurate, elegant and principled framework for tackling these tasks. Given the inherent noise in real-world sources, it is important to capture the uncertainty of the above operations via imprecise data models. CRFs provide a sound probability distribution over extractions but are not easy to represent and query in a relational framework. We present methods of approximating this distribution to query-friendly row and column uncertainty models. Finally, we present models for representing the uncertainty of de-duplication and algorithms for various Top-K count queries on imprecise duplicates.

An Investigation of the "e-rater"® Automated Scoring Engine's Grammar, Usage, Mechanics, and Style Microfeatures and Their Aggregation Model. Research Report. ETS RR-17-04

ERIC Educational Resources Information Center

Chen, Jing; Zhang, Mo; Bejar, Isaac I.

2017-01-01

Automated essay scoring (AES) generally computes essay scores as a function of macrofeatures derived from a set of microfeatures extracted from the text using natural language processing (NLP). In the "e-rater"® automated scoring engine, developed at "Educational Testing Service" (ETS) for the automated scoring of essays, each…

Mixed-mode isolation of triazine metabolites from soil and aquifer sediments using automated solid-phase extraction

USGS Publications Warehouse

Mills, M.S.; Thurman, E.M.

1992-01-01

Reversed-phase isolation and ion-exchange purification were combined in the automated solid-phase extraction of two polar s-triazine metabolites, 2-amino-4-chloro-6-(isopropylamino)-s-triazine (deethylatrazine) and 2-amino-4-chloro-6-(ethylamino)-s-triazine (deisopropylatrazine) from clay-loam and slit-loam soils and sandy aquifer sediments. First, methanol/ water (4/1, v/v) soil extracts were transferred to an automated workstation following evaporation of the methanol phase for the rapid reversed-phase isolation of the metabolites on an octadecylresin (C18). The retention of the triazine metabolites on C18 decreased substantially when trace methanol concentrations (1%) remained. Furthermore, the retention on C18 increased with decreasing aqueous solubility and increasing alkyl-chain length of the metabolites and parent herbicides, indicating a reversed-phase interaction. The analytes were eluted with ethyl acetate, which left much of the soil organic-matter impurities on the resin. Second, the small-volume organic eluate was purified on an anion-exchange resin (0.5 mL/min) to extract the remaining soil pigments that could foul the ion source of the GC/MS system. Recoveries of the analytes were 75%, using deuterated atrazine as a surrogate, and were comparable to recoveries by soxhlet extraction. The detection limit was 0.1 ??g/kg with a coefficient of variation of 15%. The ease and efficiency of this automated method makes it viable, practical technique for studying triazine metabolites in the environment.

Evaluation of three automated nucleic acid extraction systems for identification of respiratory viruses in clinical specimens by multiplex real-time PCR.

PubMed

Kim, Yoonjung; Han, Mi-Soon; Kim, Juwon; Kwon, Aerin; Lee, Kyung-A

2014-01-01

A total of 84 nasopharyngeal swab specimens were collected from 84 patients. Viral nucleic acid was extracted by three automated extraction systems: QIAcube (Qiagen, Germany), EZ1 Advanced XL (Qiagen), and MICROLAB Nimbus IVD (Hamilton, USA). Fourteen RNA viruses and two DNA viruses were detected using the Anyplex II RV16 Detection kit (Seegene, Republic of Korea). The EZ1 Advanced XL system demonstrated the best analytical sensitivity for all the three viral strains. The nucleic acids extracted by EZ1 Advanced XL showed higher positive rates for virus detection than the others. Meanwhile, the MICROLAB Nimbus IVD system was comprised of fully automated steps from nucleic extraction to PCR setup function that could reduce human errors. For the nucleic acids recovered from nasopharyngeal swab specimens, the QIAcube system showed the fewest false negative results and the best concordance rate, and it may be more suitable for detecting various viruses including RNA and DNA virus strains. Each system showed different sensitivity and specificity for detection of certain viral pathogens and demonstrated different characteristics such as turnaround time and sample capacity. Therefore, these factors should be considered when new nucleic acid extraction systems are introduced to the laboratory.

A METHOD FOR AUTOMATED ANALYSIS OF 10 ML WATER SAMPLES CONTAINING ACIDIC, BASIC, AND NEUTRAL SEMIVOLATILE COMPOUNDS LISTED IN USEPA METHOD 8270 BY SOLID PHASE EXTRACTION COUPLED IN-LINE TO LARGE VOLUME INJECTION GAS CHROMATOGRAPHY/MASS SPECTROMETRY

EPA Science Inventory

Data is presented showing the progress made towards the development of a new automated system combining solid phase extraction (SPE) with gas chromatography/mass spectrometry for the single run analysis of water samples containing a broad range of acid, base and neutral compounds...

Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

PubMed Central

Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D. Joshua

2011-01-01

Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi. PMID:21461364

Automated indexing for making of a newspaper article database

NASA Astrophysics Data System (ADS)

Kamio, Tatsuo

Automated indexing has been widely employed in the process of making newspaper article databases. It is essential to speed up the compiling time of the said databases for the large amount of articles come out daily, and save manpower involved in it, with the aid of computers. However, indexed terms which are extracted by the current automated indexing systems have no links with subject analysis, so that they are not considered to be keywords in a strict sense. Thus, the system of Nihon Keizai Shimbun KK enables to justify keywords to certain extent based on the two clues ; 1) at which location the extracted term occurred, and 2) whether or not subject area of the article corresponds to thesaurus class of the extracted term, by using characteristics peculiar to newspaper articles. Also the experiment of assigning keywords which are not occurred in articles was conducted. The fairly good result was obtained.

Topography-Assisted Electromagnetic Platform for Blood-to-PCR in a Droplet

PubMed Central

Chiou, Chi-Han; Shin, Dong Jin; Zhang, Yi; Wang, Tza-Huei

2013-01-01

This paper presents an electromagnetically actuated platform for automated sample preparation and detection of nucleic acids. The proposed platform integrates nucleic acid extraction using silica-coated magnetic particles with real-time polymerase chain reaction (PCR) on a single cartridge. Extraction of genomic material was automated by manipulating magnetic particles in droplets using a series of planar coil electromagnets assisted by topographical features, enabling efficient fluidic processing over a variety of buffers and reagents. The functionality of the platform was demonstrated by performing nucleic acid extraction from whole blood, followed by real-time PCR detection of KRAS oncogene. Automated sample processing from whole blood to PCR-ready droplet was performed in 15 minutes. We took a modular approach of decoupling the modules of magnetic manipulation and optical detection from the device itself, enabling a low-complexity cartridge that operates in tandem with simple external instruments. PMID:23835223

Automated concept-level information extraction to reduce the need for custom software and rules development.

PubMed

D'Avolio, Leonard W; Nguyen, Thien M; Goryachev, Sergey; Fiore, Louis D

2011-01-01

Despite at least 40 years of promising empirical performance, very few clinical natural language processing (NLP) or information extraction systems currently contribute to medical science or care. The authors address this gap by reducing the need for custom software and rules development with a graphical user interface-driven, highly generalizable approach to concept-level retrieval. A 'learn by example' approach combines features derived from open-source NLP pipelines with open-source machine learning classifiers to automatically and iteratively evaluate top-performing configurations. The Fourth i2b2/VA Shared Task Challenge's concept extraction task provided the data sets and metrics used to evaluate performance. Top F-measure scores for each of the tasks were medical problems (0.83), treatments (0.82), and tests (0.83). Recall lagged precision in all experiments. Precision was near or above 0.90 in all tasks. Discussion With no customization for the tasks and less than 5 min of end-user time to configure and launch each experiment, the average F-measure was 0.83, one point behind the mean F-measure of the 22 entrants in the competition. Strong precision scores indicate the potential of applying the approach for more specific clinical information extraction tasks. There was not one best configuration, supporting an iterative approach to model creation. Acceptable levels of performance can be achieved using fully automated and generalizable approaches to concept-level information extraction. The described implementation and related documentation is available for download.

Domotics Project Housing Block.

PubMed

Morón, Carlos; Payán, Alejandro; García, Alfonso; Bosquet, Francisco

2016-05-23

This document develops the study of an implementation project of a home automation system in a housing placed in the town of Galapagar, Madrid. This house, which is going to be occupied by a four-member family, consists of 67 constructed square meters distributed in lounge, kitchen, three bedrooms, bath, bathroom and terrace, this being a common arrangement in Spain. Thus, this study will allow extracting conclusions about the adequacy of the home automation in a wide percentage of housing in Spain. In this document, three house automation proposals are developed based on the requirements of the client and the different home automation levels that the Spanish House and Building Automation Association has established, besides two parallel proposals relating to the safety and the technical alarms. The mentioned proposed systems are described by means of product datasheets and descriptions, distribution plans, measurements, budgets and flow charts that describe the functioning of the system in every case. An evaluation of each system is included, based on other studies conclusions on this matter, where expected energy savings from each design, depending on the current cost of lighting, water and gas, as well as the expected economic amortization period is evaluated.

Domotics Project Housing Block

PubMed Central

Morón, Carlos; Payán, Alejandro; García, Alfonso; Bosquet, Francisco

2016-01-01

This document develops the study of an implementation project of a home automation system in a housing placed in the town of Galapagar, Madrid. This house, which is going to be occupied by a four-member family, consists of 67 constructed square meters distributed in lounge, kitchen, three bedrooms, bath, bathroom and terrace, this being a common arrangement in Spain. Thus, this study will allow extracting conclusions about the adequacy of the home automation in a wide percentage of housing in Spain. In this document, three house automation proposals are developed based on the requirements of the client and the different home automation levels that the Spanish House and Building Automation Association has established, besides two parallel proposals relating to the safety and the technical alarms. The mentioned proposed systems are described by means of product datasheets and descriptions, distribution plans, measurements, budgets and flow charts that describe the functioning of the system in every case. An evaluation of each system is included, based on other studies conclusions on this matter, where expected energy savings from each design, depending on the current cost of lighting, water and gas, as well as the expected economic amortization period is evaluated. PMID:27223285

AN EVALUATION OF SAMPLE DISPERSION MEDIAS USED WITH ACCELERATED SOLVENT EXTRACTION FOR THE EXTRACTION AND RECOVERY OF ARSENICALS FROM LFB AND DORM-2

EPA Science Inventory

An accelerated solvent extraction (ASE) device was evaluated as a semi-automated means for extracting arsenicals from quality control (QC) samples and DORM-2 [standard reference material (SRM)]. Unlike conventional extraction procedures, the ASE requires that the sample be dispe...

Extraction of features from ultrasound acoustic emissions: a tool to assess the hydraulic vulnerability of Norway spruce trunkwood?

PubMed Central

Rosner, Sabine; Klein, Andrea; Wimmer, Rupert; Karlsson, Bo

2011-01-01

Summary • The aim of this study was to assess the hydraulic vulnerability of Norway spruce (Picea abies) trunkwood by extraction of selected features of acoustic emissions (AEs) detected during dehydration of standard size samples. • The hydraulic method was used as the reference method to assess the hydraulic vulnerability of trunkwood of different cambial ages. Vulnerability curves were constructed by plotting the percentage loss of conductivity vs an overpressure of compressed air. • Differences in hydraulic vulnerability were very pronounced between juvenile and mature wood samples; therefore, useful AE features, such as peak amplitude, duration and relative energy, could be filtered out. The AE rates of signals clustered by amplitude and duration ranges and the AE energies differed greatly between juvenile and mature wood at identical relative water losses. • Vulnerability curves could be constructed by relating the cumulated amount of relative AE energy to the relative loss of water and to xylem tension. AE testing in combination with feature extraction offers a readily automated and easy to use alternative to the hydraulic method. PMID:16771986

Arduino-based automation of a DNA extraction system.

PubMed

Kim, Kyung-Won; Lee, Mi-So; Ryu, Mun-Ho; Kim, Jong-Won

2015-01-01

There have been many studies to detect infectious diseases with the molecular genetic method. This study presents an automation process for a DNA extraction system based on microfluidics and magnetic bead, which is part of a portable molecular genetic test system. This DNA extraction system consists of a cartridge with chambers, syringes, four linear stepper actuators, and a rotary stepper actuator. The actuators provide a sequence of steps in the DNA extraction process, such as transporting, mixing, and washing for the gene specimen, magnetic bead, and reagent solutions. The proposed automation system consists of a PC-based host application and an Arduino-based controller. The host application compiles a G code sequence file and interfaces with the controller to execute the compiled sequence. The controller executes stepper motor axis motion, time delay, and input-output manipulation. It drives the stepper motor with an open library, which provides a smooth linear acceleration profile. The controller also provides a homing sequence to establish the motor's reference position, and hard limit checking to prevent any over-travelling. The proposed system was implemented and its functionality was investigated, especially regarding positioning accuracy and velocity profile.

Automated multisyringe stir bar sorptive extraction using robust montmorillonite/epoxy-coated stir bars.

PubMed

Ghani, Milad; Saraji, Mohammad; Maya, Fernando; Cerdà, Víctor

2016-05-06

Herein we present a simple, rapid and low cost strategy for the preparation of robust stir bar coatings based on the combination of montmorillonite with epoxy resin. The composite stir bar was implemented in a novel automated multisyringe stir bar sorptive extraction system (MS-SBSE), and applied to the extraction of four chlorophenols (4-chlorophenol, 2,4-dichlorophenol, 2,4,6-trichlorophenol and pentachlorophenol) as model compounds, followed by high performance liquid chromatography-diode array detection. The different experimental parameters of the MS-SBSE, such as sample volume, selection of the desorption solvent, desorption volume, desorption time, sample solution pH, salt effect and extraction time were studied. Under the optimum conditions, the detection limits were between 0.02 and 0.34μgL(-1). Relative standard deviations (RSD) of the method for the analytes at 10μgL(-1) concentration level ranged from 3.5% to 4.1% (as intra-day RSD) and from 3.9% to 4.3% (as inter-day RSD at 50μgL(-1) concentration level). Batch-to-batch reproducibility for three different stir bars was 4.6-5.1%. The enrichment factors were between 30 and 49. In order to investigate the capability of the developed technique for real sample analysis, well water, wastewater and leachates from a solid waste treatment plant were satisfactorily analyzed. Copyright © 2016 Elsevier B.V. All rights reserved.

Gas pressure assisted microliquid-liquid extraction coupled online to direct infusion mass spectrometry: a new automated screening platform for bioanalysis.

PubMed

Raterink, Robert-Jan; Witkam, Yoeri; Vreeken, Rob J; Ramautar, Rawi; Hankemeier, Thomas

2014-10-21

In the field of bioanalysis, there is an increasing demand for miniaturized, automated, robust sample pretreatment procedures that can be easily connected to direct-infusion mass spectrometry (DI-MS) in order to allow the high-throughput screening of drugs and/or their metabolites in complex body fluids like plasma. Liquid-Liquid extraction (LLE) is a common sample pretreatment technique often used for complex aqueous samples in bioanalysis. Despite significant developments that have been made in automated and miniaturized LLE procedures, fully automated LLE techniques allowing high-throughput bioanalytical studies on small-volume samples using direct infusion mass spectrometry, have not been matured yet. Here, we introduce a new fully automated micro-LLE technique based on gas-pressure assisted mixing followed by passive phase separation, coupled online to nanoelectrospray-DI-MS. Our method was characterized by varying the gas flow and its duration through the solvent mixture. For evaluation of the analytical performance, four drugs were spiked to human plasma, resulting in highly acceptable precision (RSD down to 9%) and linearity (R(2) ranging from 0.990 to 0.998). We demonstrate that our new method does not only allow the reliable extraction of analytes from small sample volumes of a few microliters in an automated and high-throughput manner, but also performs comparable or better than conventional offline LLE, in which the handling of small volumes remains challenging. Finally, we demonstrate the applicability of our method for drug screening on dried blood spots showing excellent linearity (R(2) of 0.998) and precision (RSD of 9%). In conclusion, we present the proof of principe of a new high-throughput screening platform for bioanalysis based on a new automated microLLE method, coupled online to a commercially available nano-ESI-DI-MS.

EXTRACTION AND DETECTION OF ARSENICALS IN SEAWEED VIA ACCELERATED SOLVENT EXTRACTION WITH ION CHROMATOGRAPHIC SEPARATION AND ICP-MS DETECTION

EPA Science Inventory

An accelerated solvent extraction (ASE) device was evaluated as a semi-automated means of extracting arsenicals from ribbon kelp. Objective was to investigate effect of experimentally controllable ASE parameters (pressure, temperature, static time and solvent composition) on extr...

Development and validation of an automated liquid-liquid extraction GC/MS method for the determination of THC, 11-OH-THC, and free THC-carboxylic acid (THC-COOH) from blood serum.

PubMed

Purschke, Kirsten; Heinl, Sonja; Lerch, Oliver; Erdmann, Freidoon; Veit, Florian

2016-06-01

The analysis of Δ(9)-tetrahydrocannabinol (THC) and its metabolites 11-hydroxy-Δ(9)-tetrahydrocannabinol (11-OH-THC), and 11-nor-9-carboxy-Δ(9)-tetrahydrocannabinol (THC-COOH) from blood serum is a routine task in forensic toxicology laboratories. For examination of consumption habits, the concentration of the phase I metabolite THC-COOH is used. Recommendations for interpretation of analysis values in medical-psychological assessments (regranting of driver's licenses, Germany) include threshold values for the free, unconjugated THC-COOH. Using a fully automated two-step liquid-liquid extraction, THC, 11-OH-THC, and free, unconjugated THC-COOH were extracted from blood serum, silylated with N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), and analyzed by GC/MS. The automation was carried out by an x-y-z sample robot equipped with modules for shaking, centrifugation, and solvent evaporation. This method was based on a previously developed manual sample preparation method. Validation guidelines of the Society of Toxicological and Forensic Chemistry (GTFCh) were fulfilled for both methods, at which the focus of this article is the automated one. Limits of detection and quantification for THC were 0.3 and 0.6 μg/L, for 11-OH-THC were 0.1 and 0.8 μg/L, and for THC-COOH were 0.3 and 1.1 μg/L, when extracting only 0.5 mL of blood serum. Therefore, the required limit of quantification for THC of 1 μg/L in driving under the influence of cannabis cases in Germany (and other countries) can be reached and the method can be employed in that context. Real and external control samples were analyzed, and a round robin test was passed successfully. To date, the method is employed in the Institute of Legal Medicine in Giessen, Germany, in daily routine. Automation helps in avoiding errors during sample preparation and reduces the workload of the laboratory personnel. Due to its flexibility, the analysis system can be employed for other liquid-liquid extractions as well. To the best of our knowledge, this is the first publication on a comprehensively automated classical liquid-liquid extraction workflow in the field of forensic toxicological analysis. Graphical abstract GC/MS with MPS Dual Head at the Institute of Legal Medicine, Giessen, Germany. Modules from left to right: (quick) Mix (for LLE), wash station, tray 1 (vials for extracts), solvent reservoir, (m) VAP (for extract evaporation), Solvent Filling Station (solvent supply), cooled tray 2 (vials for serum samples), and centrifuge (for phase separation).

A simple automated instrument for DNA extraction in forensic casework.

PubMed

Montpetit, Shawn A; Fitch, Ian T; O'Donnell, Patrick T

2005-05-01

The Qiagen BioRobot EZ1 is a small, rapid, and reliable automated DNA extraction instrument capable of extracting DNA from up to six samples in as few as 20 min using magnetic bead technology. The San Diego Police Department Crime Laboratory has validated the BioRobot EZ1 for the DNA extraction of evidence and reference samples in forensic casework. The BioRobot EZ1 was evaluated for use on a variety of different evidence sample types including blood, saliva, and semen evidence. The performance of the BioRobot EZ1 with regard to DNA recovery and potential cross-contamination was also assessed. DNA yields obtained with the BioRobot EZ1 were comparable to those from organic extraction. The BioRobot EZ1 was effective at removing PCR inhibitors, which often co-purify with DNA in organic extractions. The incorporation of the BioRobot EZ1 into forensic casework has streamlined the DNA analysis process by reducing the need for labor-intensive phenol-chloroform extractions.

Comparison of manual and automated nucleic acid extraction methods from clinical specimens for microbial diagnosis purposes.

PubMed

Wozniak, Aniela; Geoffroy, Enrique; Miranda, Carolina; Castillo, Claudia; Sanhueza, Francia; García, Patricia

2016-11-01

The choice of nucleic acids (NAs) extraction method for molecular diagnosis in microbiology is of major importance because of the low microbial load, different nature of microorganisms, and clinical specimens. The NA yield of different extraction methods has been mostly studied using spiked samples. However, information from real human clinical specimens is scarce. The purpose of this study was to compare the performance of a manual low-cost extraction method (Qiagen kit or salting-out extraction method) with the automated high-cost MagNAPure Compact method. According to cycle threshold values for different pathogens, MagNAPure is as efficient as Qiagen for NA extraction from noncomplex clinical specimens (nasopharyngeal swab, skin swab, plasma, respiratory specimens). In contrast, according to cycle threshold values for RNAseP, MagNAPure method may not be an appropriate method for NA extraction from blood. We believe that MagNAPure versatility reduced risk of cross-contamination and reduced hands-on time compensates its high cost. Copyright © 2016 Elsevier Inc. All rights reserved.

Automated acoustic analysis in detection of spontaneous swallows in Parkinson's disease.

PubMed

Golabbakhsh, Marzieh; Rajaei, Ali; Derakhshan, Mahmoud; Sadri, Saeed; Taheri, Masoud; Adibi, Peyman

2014-10-01

Acoustic monitoring of swallow frequency has become important as the frequency of spontaneous swallowing can be an index for dysphagia and related complications. In addition, it can be employed as an objective quantification of ingestive behavior. Commonly, swallowing complications are manually detected using videofluoroscopy recordings, which require expensive equipment and exposure to radiation. In this study, a noninvasive automated technique is proposed that uses breath and swallowing recordings obtained via a microphone located over the laryngopharynx. Nonlinear diffusion filters were used in which a scale-space decomposition of recorded sound at different levels extract swallows from breath sounds and artifacts. This technique was compared to manual detection of swallows using acoustic signals on a sample of 34 subjects with Parkinson's disease. A speech language pathologist identified five subjects who showed aspiration during the videofluoroscopic swallowing study. The proposed automated method identified swallows with a sensitivity of 86.67 %, a specificity of 77.50 %, and an accuracy of 82.35 %. These results indicate the validity of automated acoustic recognition of swallowing as a fast and efficient approach to objectively estimate spontaneous swallow frequency.

Automated multi-plug filtration cleanup for liquid chromatographic-tandem mass spectrometric pesticide multi-residue analysis in representative crop commodities.

PubMed

Qin, Yuhong; Zhang, Jingru; Zhang, Yuan; Li, Fangbing; Han, Yongtao; Zou, Nan; Xu, Haowei; Qian, Meiyuan; Pan, Canping

2016-09-02

An automated multi-plug filtration cleanup (m-PFC) method on modified QuEChERS (quick, easy, cheap, effective, rugged, and safe) extracts was developed. The automatic device was aimed to reduce labor-consuming manual operation workload in the cleanup steps. It could control the volume and the speed of pulling and pushing cycles accurately. In this work, m-PFC was based on multi-walled carbon nanotubes (MWCNTs) mixed with other sorbents and anhydrous magnesium sulfate (MgSO4) in a packed tip for analysis of pesticide multi-residues in crop commodities followed by liquid chromatography with tandem mass spectrometric (LC-MS/MS) detection. It was validated by analyzing 25 pesticides in six representative matrices spiked at two concentration levels of 10 and 100μg/kg. Salts, sorbents, m-PFC procedure, automated pulling and pushing volume, automated pulling speed, and pushing speed for each matrix were optimized. After optimization, two general automated m-PFC methods were introduced to relatively simple (apple, citrus fruit, peanut) and relatively complex (spinach, leek, green tea) matrices. Spike recoveries were within 83 and 108% and 1-14% RSD for most analytes in the tested matrices. Matrix-matched calibrations were performed with the coefficients of determination >0.997 between concentration levels of 10 and 1000μg/kg. The developed method was successfully applied to the determination of pesticide residues in market samples. Copyright © 2016 Elsevier B.V. All rights reserved.

First Steps to Automated Interior Reconstruction from Semantically Enriched Point Clouds and Imagery

NASA Astrophysics Data System (ADS)

Obrock, L. S.; Gülch, E.

2018-05-01

The automated generation of a BIM-Model from sensor data is a huge challenge for the modeling of existing buildings. Currently the measurements and analyses are time consuming, allow little automation and require expensive equipment. We do lack an automated acquisition of semantical information of objects in a building. We are presenting first results of our approach based on imagery and derived products aiming at a more automated modeling of interior for a BIM building model. We examine the building parts and objects visible in the collected images using Deep Learning Methods based on Convolutional Neural Networks. For localization and classification of building parts we apply the FCN8s-Model for pixel-wise Semantic Segmentation. We, so far, reach a Pixel Accuracy of 77.2 % and a mean Intersection over Union of 44.2 %. We finally use the network for further reasoning on the images of the interior room. We combine the segmented images with the original images and use photogrammetric methods to produce a three-dimensional point cloud. We code the extracted object types as colours of the 3D-points. We thus are able to uniquely classify the points in three-dimensional space. We preliminary investigate a simple extraction method for colour and material of building parts. It is shown, that the combined images are very well suited to further extract more semantic information for the BIM-Model. With the presented methods we see a sound basis for further automation of acquisition and modeling of semantic and geometric information of interior rooms for a BIM-Model.

Automation of DNA and miRNA co-extraction for miRNA-based identification of human body fluids and tissues.

PubMed

Kulstein, Galina; Marienfeld, Ralf; Miltner, Erich; Wiegand, Peter

2016-10-01

In the last years, microRNA (miRNA) analysis came into focus in the field of forensic genetics. Yet, no standardized and recommendable protocols for co-isolation of miRNA and DNA from forensic relevant samples have been developed so far. Hence, this study evaluated the performance of an automated Maxwell® 16 System-based strategy (Promega) for co-extraction of DNA and miRNA from forensically relevant (blood and saliva) samples compared to (semi-)manual extraction methods. Three procedures were compared on the basis of recovered quantity of DNA and miRNA (as determined by real-time PCR and Bioanalyzer), miRNA profiling (shown by Cq values and extraction efficiency), STR profiles, duration, contamination risk and handling. All in all, the results highlight that the automated co-extraction procedure yielded the highest miRNA and DNA amounts from saliva and blood samples compared to both (semi-)manual protocols. Also, for aged and genuine samples of forensically relevant traces the miRNA and DNA yields were sufficient for subsequent downstream analysis. Furthermore, the strategy allows miRNA extraction only in cases where it is relevant to obtain additional information about the sample type. Besides, this system enables flexible sample throughput and labor-saving sample processing with reduced risk of cross-contamination. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Application of the BioMek 2000 Laboratory Automation Workstation and the DNA IQ System to the extraction of forensic casework samples.

PubMed

Greenspoon, Susan A; Ban, Jeffrey D; Sykes, Karen; Ballard, Elizabeth J; Edler, Shelley S; Baisden, Melissa; Covington, Brian L

2004-01-01

Robotic systems are commonly utilized for the extraction of database samples. However, the application of robotic extraction to forensic casework samples is a more daunting task. Such a system must be versatile enough to accommodate a wide range of samples that may contain greatly varying amounts of DNA, but it must also pose no more risk of contamination than the manual DNA extraction methods. This study demonstrates that the BioMek 2000 Laboratory Automation Workstation, used in combination with the DNA IQ System, is versatile enough to accommodate the wide range of samples typically encountered by a crime laboratory. The use of a silica coated paramagnetic resin, as with the DNA IQ System, facilitates the adaptation of an open well, hands off, robotic system to the extraction of casework samples since no filtration or centrifugation steps are needed. Moreover, the DNA remains tightly coupled to the silica coated paramagnetic resin for the entire process until the elution step. A short pre-extraction incubation step is necessary prior to loading samples onto the robot and it is at this step that most modifications are made to accommodate the different sample types and substrates commonly encountered with forensic evidentiary samples. Sexual assault (mixed stain) samples, cigarette butts, blood stains, buccal swabs, and various tissue samples were successfully extracted with the BioMek 2000 Laboratory Automation Workstation and the DNA IQ System, with no evidence of contamination throughout the extensive validation studies reported here.

Classification of wet aged related macular degeneration using optical coherence tomographic images

NASA Astrophysics Data System (ADS)

Haq, Anam; Mir, Fouwad Jamil; Yasin, Ubaid Ullah; Khan, Shoab A.

2013-12-01

Wet Age related macular degeneration (AMD) is a type of age related macular degeneration. In order to detect Wet AMD we look for Pigment Epithelium detachment (PED) and fluid filled region caused by choroidal neovascularization (CNV). This form of AMD can cause vision loss if not treated in time. In this article we have proposed an automated system for detection of Wet AMD in Optical coherence tomographic (OCT) images. The proposed system extracts PED and CNV from OCT images using segmentation and morphological operations and then detailed feature set are extracted. These features are then passed on to the classifier for classification. Finally performance measures like accuracy, sensitivity and specificity are calculated and the classifier delivering the maximum performance is selected as a comparison measure. Our system gives higher performance using SVM as compared to other methods.

Two-dimensional thermal video analysis of offshore bird and bat flight

DOE PAGES

Matzner, Shari; Cullinan, Valerie I.; Duberstein, Corey A.

2015-09-11

Thermal infrared video can provide essential information about bird and bat presence and activity for risk assessment studies, but the analysis of recorded video can be time-consuming and may not extract all of the available information. Automated processing makes continuous monitoring over extended periods of time feasible, and maximizes the information provided by video. This is especially important for collecting data in remote locations that are difficult for human observers to access, such as proposed offshore wind turbine sites. We present guidelines for selecting an appropriate thermal camera based on environmental conditions and the physical characteristics of the target animals.more » We developed new video image processing algorithms that automate the extraction of bird and bat flight tracks from thermal video, and that characterize the extracted tracks to support animal identification and behavior inference. The algorithms use a video peak store process followed by background masking and perceptual grouping to extract flight tracks. The extracted tracks are automatically quantified in terms that could then be used to infer animal type and possibly behavior. The developed automated processing generates results that are reproducible and verifiable, and reduces the total amount of video data that must be retained and reviewed by human experts. Finally, we suggest models for interpreting thermal imaging information.« less

Two-dimensional thermal video analysis of offshore bird and bat flight

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

Matzner, Shari; Cullinan, Valerie I.; Duberstein, Corey A.

Thermal infrared video can provide essential information about bird and bat presence and activity for risk assessment studies, but the analysis of recorded video can be time-consuming and may not extract all of the available information. Automated processing makes continuous monitoring over extended periods of time feasible, and maximizes the information provided by video. This is especially important for collecting data in remote locations that are difficult for human observers to access, such as proposed offshore wind turbine sites. We present guidelines for selecting an appropriate thermal camera based on environmental conditions and the physical characteristics of the target animals.more » We developed new video image processing algorithms that automate the extraction of bird and bat flight tracks from thermal video, and that characterize the extracted tracks to support animal identification and behavior inference. The algorithms use a video peak store process followed by background masking and perceptual grouping to extract flight tracks. The extracted tracks are automatically quantified in terms that could then be used to infer animal type and possibly behavior. The developed automated processing generates results that are reproducible and verifiable, and reduces the total amount of video data that must be retained and reviewed by human experts. Finally, we suggest models for interpreting thermal imaging information.« less

Tools for automating spacecraft ground systems: The Intelligent Command and Control (ICC) approach

NASA Technical Reports Server (NTRS)

Stoffel, A. William; Mclean, David

1996-01-01

The practical application of scripting languages and World Wide Web tools to the support of spacecraft ground system automation, is reported on. The mission activities and the automation tools used at the Goddard Space Flight Center (MD) are reviewed. The use of the Tool Command Language (TCL) and the Practical Extraction and Report Language (PERL) scripting tools for automating mission operations is discussed together with the application of different tools for the Compton Gamma Ray Observatory ground system.

Automated extraction of temporal motor activity signals from video recordings of neonatal seizures based on adaptive block matching.

PubMed

Karayiannis, Nicolaos B; Sami, Abdul; Frost, James D; Wise, Merrill S; Mizrahi, Eli M

2005-04-01

This paper presents an automated procedure developed to extract quantitative information from video recordings of neonatal seizures in the form of motor activity signals. This procedure relies on optical flow computation to select anatomical sites located on the infants' body parts. Motor activity signals are extracted by tracking selected anatomical sites during the seizure using adaptive block matching. A block of pixels is tracked throughout a sequence of frames by searching for the most similar block of pixels in subsequent frames; this search is facilitated by employing various update strategies to account for the changing appearance of the block. The proposed procedure is used to extract temporal motor activity signals from video recordings of neonatal seizures and other events not associated with seizures.

Single-trial event-related potential extraction through one-unit ICA-with-reference

NASA Astrophysics Data System (ADS)

Lih Lee, Wee; Tan, Tele; Falkmer, Torbjörn; Leung, Yee Hong

2016-12-01

Objective. In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed. Approach. In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly. Main results. Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction. Significance. In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.

Single-trial event-related potential extraction through one-unit ICA-with-reference.

PubMed

Lee, Wee Lih; Tan, Tele; Falkmer, Torbjörn; Leung, Yee Hong

2016-12-01

In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed. In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly. Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction. In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.

Automated dynamic liquid-liquid-liquid microextraction followed by high-performance liquid chromatography-ultraviolet detection for the determination of phenoxy acid herbicides in environmental waters.

PubMed

Wu, Jingming; Ee, Kim Huey; Lee, Hian Kee

2005-08-05

Automated dynamic liquid-liquid-liquid microextraction (D-LLLME) controlled by a programmable syringe pump and combined with HPLC-UV was investigated for the extraction and determination of 5 phenoxy acid herbicides in aqueous samples. In the extraction procedure, the acceptor phase was repeatedly withdrawn into and discharged from the hollow fiber by the syringe pump. The repetitive movement of acceptor phase into and out of the hollow fiber channel facilitated the transfer of analytes into donor phase, from the organic phase held in the pore of the fiber. Parameters such as the organic solvent, concentrations of the donor and acceptor phases, plunger movement pattern, speed of agitation and ionic strength of donor phase were evaluated. Good linearity of analytes was achieved in the range of 0.5-500 ng/ml with coefficients of determination, r2 > 0.9994. Good repeatabilities of extraction performance were obtained with relative standard deviations lower than 7.5%. The method provided up-to 490-fold enrichment within 13 min. In addition, the limits of detection (LODs) ranged from 0.1 to 0.4 ng/mL (S/N = 3). D-LLLME was successfully applied for the analysis of phenoxy acid herbicides from real environmental water samples.

Automated processing of forensic casework samples using robotic workstations equipped with nondisposable tips: contamination prevention.

PubMed

Frégeau, Chantal J; Lett, C Marc; Elliott, Jim; Yensen, Craig; Fourney, Ron M

2008-05-01

An automated process has been developed for the analysis of forensic casework samples using TECAN Genesis RSP 150/8 or Freedom EVO liquid handling workstations equipped exclusively with nondisposable tips. Robot tip cleaning routines have been incorporated strategically within the DNA extraction process as well as at the end of each session. Alternative options were examined for cleaning the tips and different strategies were employed to verify cross-contamination. A 2% sodium hypochlorite wash (1/5th dilution of the 10.8% commercial bleach stock) proved to be the best overall approach for preventing cross-contamination of samples processed using our automated protocol. The bleach wash steps do not adversely impact the short tandem repeat (STR) profiles developed from DNA extracted robotically and allow for major cost savings through the implementation of fixed tips. We have demonstrated that robotic workstations equipped with fixed pipette tips can be used with confidence with properly designed tip washing routines to process casework samples using an adapted magnetic bead extraction protocol.

Analysis of volatiles in fire debris by combination of activated charcoal strips (ACS) and automated thermal desorption-gas chromatography-mass spectrometry (ATD/GC-MS).

PubMed

Martin Fabritius, Marie; Broillet, Alain; König, Stefan; Weinmann, Wolfgang

2018-06-04

Adsorption of volatiles in gaseous phase to activated charcoal strip (ACS) is one possibility for the extraction and concentration of ignitable liquid residues (ILRs) from fire debris in arson investigations. Besides liquid extraction using carbon dioxide or hexane, automated thermo-desorption can be used to transfer adsorbed residues to direct analysis by gas chromatography-mass spectrometry (GC-MS). We present a fire debris analysis work-flow with headspace adsorption of volatiles onto ACS and subsequent automated thermo-desorption (ATD) GC-MS analysis. Only a small portion of the ACS is inserted in the ATD tube for thermal desorption coupled to GC-MS, allowing for subsequent confirmation analysis with another portion of the same ACS. This approach is a promising alternative to the routinely used ACS method with solvent extraction of retained volatiles, and the application to fire debris analysis is demonstrated. Copyright © 2018 Elsevier B.V. All rights reserved.

SAR matrices: automated extraction of information-rich SAR tables from large compound data sets.

PubMed

Wassermann, Anne Mai; Haebel, Peter; Weskamp, Nils; Bajorath, Jürgen

2012-07-23

We introduce the SAR matrix data structure that is designed to elucidate SAR patterns produced by groups of structurally related active compounds, which are extracted from large data sets. SAR matrices are systematically generated and sorted on the basis of SAR information content. Matrix generation is computationally efficient and enables processing of large compound sets. The matrix format is reminiscent of SAR tables, and SAR patterns revealed by different categories of matrices are easily interpretable. The structural organization underlying matrix formation is more flexible than standard R-group decomposition schemes. Hence, the resulting matrices capture SAR information in a comprehensive manner.

Linear feature extraction from radar imagery: SBIR (Small Business Innovative Research), phase 2, option 2

NASA Astrophysics Data System (ADS)

Milgram, David L.; Kahn, Philip; Conner, Gary D.; Lawton, Daryl T.

1988-12-01

The goal of this effort is to develop and demonstrate prototype processing capabilities for a knowledge-based system to automatically extract and analyze features from Synthetic Aperture Radar (SAR) imagery. This effort constitutes Phase 2 funding through the Defense Small Business Innovative Research (SBIR) Program. Previous work examined the feasibility of and technology issues involved in the development of an automated linear feature extraction system. This final report documents this examination and the technologies involved in automating this image understanding task. In particular, it reports on a major software delivery containing an image processing algorithmic base, a perceptual structures manipulation package, a preliminary hypothesis management framework and an enhanced user interface.

Optimized manual and automated recovery of amplifiable DNA from tissues preserved in buffered formalin and alcohol-based fixative.

PubMed

Duval, Kristin; Aubin, Rémy A; Elliott, James; Gorn-Hondermann, Ivan; Birnboim, H Chaim; Jonker, Derek; Fourney, Ron M; Frégeau, Chantal J

2010-02-01

Archival tissue preserved in fixative constitutes an invaluable resource for histological examination, molecular diagnostic procedures and for DNA typing analysis in forensic investigations. However, available material is often limited in size and quantity. Moreover, recovery of DNA is often severely compromised by the presence of covalent DNA-protein cross-links generated by formalin, the most prevalent fixative. We describe the evaluation of buffer formulations, sample lysis regimens and DNA recovery strategies and define optimized manual and automated procedures for the extraction of high quality DNA suitable for molecular diagnostics and genotyping. Using a 3-step enzymatic digestion protocol carried out in the absence of dithiothreitol, we demonstrate that DNA can be efficiently released from cells or tissues preserved in buffered formalin or the alcohol-based fixative GenoFix. This preparatory procedure can then be integrated to traditional phenol/chloroform extraction, a modified manual DNA IQ or automated DNA IQ/Te-Shake-based extraction in order to recover DNA for downstream applications. Quantitative recovery of high quality DNA was best achieved from specimens archived in GenoFix and extracted using magnetic bead capture.

Automated Techniques for Quantification of Coastline Change Rates using Landsat Imagery along Caofeidian, China

NASA Astrophysics Data System (ADS)

Dong, Di; Li, Ziwei; Liu, Zhaoqin; Yu, Yang

2014-03-01

This paper focuses on automated extraction and monitoring of coastlines by remote sensing techniques using multi-temporal Landsat imagery along Caofeidian, China. Caofeidian, as one of the active economic regions in China, has experienced dramatic change due to enhanced human activities, such as land reclamation. These processes have caused morphological changes of the Caofeidian shoreline. In this study, shoreline extraction and change analysis are researched. An algorithm based on image texture and mathematical morphology is proposed to automate coastline extraction. We tested this approach and found that it's capable of extracting coastlines from TM and ETM+ images with little human modifications. Then, the detected coastline vectors are imported into Arcgis software, and the Digital Shoreline Analysis System (DSAS) is used to calculate the change rate (the end point rate and linear regression rate). The results show that in some parts of the research area, remarkable coastline changes are observed, especially the accretion rate. The abnormal accretion is mostly attributed to the large-scale land reclamation during 2003 and 2004 in Caofeidian. So we can conclude that various construction projects, especially the land reclamation project, have made Caofeidian shorelines change greatly, far above the normal.

Automated detection and location of indications in eddy current signals

DOEpatents

Brudnoy, David M.; Oppenlander, Jane E.; Levy, Arthur J.

2000-01-01

A computer implemented information extraction process that locates and identifies eddy current signal features in digital point-ordered signals, signals representing data from inspection of test materials, by enhancing the signal features relative to signal noise, detecting features of the signals, verifying the location of the signal features that can be known in advance, and outputting information about the identity and location of all detected signal features.

Fully automated analysis of four tobacco-specific N-nitrosamines in mainstream cigarette smoke using two-dimensional online solid phase extraction combined with liquid chromatography-tandem mass spectrometry.

PubMed

Zhang, Jie; Bai, Ruoshi; Yi, Xiaoli; Yang, Zhendong; Liu, Xingyu; Zhou, Jun; Liang, Wei

2016-01-01

A fully automated method for the detection of four tobacco-specific nitrosamines (TSNAs) in mainstream cigarette smoke (MSS) has been developed. The new developed method is based on two-dimensional online solid-phase extraction-liquid chromatography-tandem mass spectrometry (SPE/LC-MS/MS). The two dimensional SPE was performed in the method utilizing two cartridges with different extraction mechanisms to cleanup disturbances of different polarity to minimize sample matrix effects on each analyte. Chromatographic separation was achieved using a UPLC C18 reversed phase analytical column. Under the optimum online SPE/LC-MS/MS conditions, N'-nitrosonornicotine (NNN), N'-nitrosoanatabine (NAT), N'-nitrosoanabasine (NAB), and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) were baseline separated with good peak shapes. This method appears to be the most sensitive method yet reported for determination of TSNAs in mainstream cigarette smoke. The limits of quantification for NNN, NNK, NAT and NAB reached the levels of 6.0, 1.0, 3.0 and 0.6 pg/cig, respectively, which were well below the lowest levels of TSNAs in MSS of current commercial cigarettes. The accuracy of the measurement of four TSNAs was from 92.8 to 107.3%. The relative standard deviations of intra-and inter-day analysis were less than 5.4% and 7.5%, respectively. The main advantages of the method developed are fairly high sensitivity, selectivity and accuracy of results, minimum sample pre-treatment, full automation, and high throughput. As a part of the validation procedure, the developed method was applied to evaluate TSNAs yields for 27 top-selling commercial cigarettes in China. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Automated extraction of single H atoms with STM: tip state dependency

NASA Astrophysics Data System (ADS)

Møller, Morten; Jarvis, Samuel P.; Guérinet, Laurent; Sharp, Peter; Woolley, Richard; Rahe, Philipp; Moriarty, Philip

2017-02-01

The atomistic structure of the tip apex plays a crucial role in performing reliable atomic-scale surface and adsorbate manipulation using scanning probe techniques. We have developed an automated extraction routine for controlled removal of single hydrogen atoms from the H:Si(100) surface. The set of atomic extraction protocols detect a variety of desorption events during scanning tunneling microscope (STM)-induced modification of the hydrogen-passivated surface. The influence of the tip state on the probability for hydrogen removal was examined by comparing the desorption efficiency for various classifications of STM topographs (rows, dimers, atoms, etc). We find that dimer-row-resolving tip apices extract hydrogen atoms most readily and reliably (and with least spurious desorption), while tip states which provide atomic resolution counter-intuitively have a lower probability for single H atom removal.

Determination of Low Concentrations of Acetochlor in Water by Automated Solid-Phase Extraction and Gas Chromatography with Mass-Selective Detection

USGS Publications Warehouse

Lindley, C.E.; Stewart, J.T.; Sandstrom, M.W.

1996-01-01

A sensitive and reliable gas chromatographic/mass spectrometric (GC/MS) method for determining acetochlor in environmental water samples was developed. The method involves automated extraction of the herbicide from a filtered 1 L water sample through a C18 solid-phase extraction column, elution from the column with hexane-isopropyl alcohol (3 + 1), and concentration of the extract with nitrogen gas. The herbicide is quantitated by capillary/column GC/MS with selected-ion monitoring of 3 characteristic ions. The single-operator method detection limit for reagent water samples is 0.0015 ??g/L. Mean recoveries ranged from about 92 to 115% for 3 water matrixes fortified at 0.05 and 0.5 ??g/L. Average single-operator precision, over the course of 1 week, was better than 5%.

Maneuver Automation Software

NASA Technical Reports Server (NTRS)

Uffelman, Hal; Goodson, Troy; Pellegrin, Michael; Stavert, Lynn; Burk, Thomas; Beach, David; Signorelli, Joel; Jones, Jeremy; Hahn, Yungsun; Attiyah, Ahlam;

Creation of a virtual cutaneous tissue bank

NASA Astrophysics Data System (ADS)

LaFramboise, William A.; Shah, Sujal; Hoy, R. W.; Letbetter, D.; Petrosko, P.; Vennare, R.; Johnson, Peter C.

2000-04-01

Cellular and non-cellular constituents of skin contain fundamental morphometric features and structural patterns that correlate with tissue function. High resolution digital image acquisitions performed using an automated system and proprietary software to assemble adjacent images and create a contiguous, lossless, digital representation of individual microscope slide specimens. Serial extraction, evaluation and statistical analysis of cutaneous feature is performed utilizing an automated analysis system, to derive normal cutaneous parameters comprising essential structural skin components. Automated digital cutaneous analysis allows for fast extraction of microanatomic dat with accuracy approximating manual measurement. The process provides rapid assessment of feature both within individual specimens and across sample populations. The images, component data, and statistical analysis comprise a bioinformatics database to serve as an architectural blueprint for skin tissue engineering and as a diagnostic standard of comparison for pathologic specimens.

Automated labeling of bibliographic data extracted from biomedical online journals

NASA Astrophysics Data System (ADS)

Kim, Jongwoo; Le, Daniel X.; Thoma, George R.

2003-01-01

A prototype system has been designed to automate the extraction of bibliographic data (e.g., article title, authors, abstract, affiliation and others) from online biomedical journals to populate the National Library of Medicine"s MEDLINE database. This paper describes a key module in this system: the labeling module that employs statistics and fuzzy rule-based algorithms to identify segmented zones in an article"s HTML pages as specific bibliographic data. Results from experiments conducted with 1,149 medical articles from forty-seven journal issues are presented.

Developing an Automated Machine Learning Marine Oil Spill Detection System with Synthetic Aperture Radar

NASA Astrophysics Data System (ADS)

Pinales, J. C.; Graber, H. C.; Hargrove, J. T.; Caruso, M. J.

2016-02-01

Previous studies have demonstrated the ability to detect and classify marine hydrocarbon films with spaceborne synthetic aperture radar (SAR) imagery. The dampening effects of hydrocarbon discharges on small surface capillary-gravity waves renders the ocean surface "radar dark" compared with the standard wind-borne ocean surfaces. Given the scope and impact of events like the Deepwater Horizon oil spill, the need for improved, automated and expedient monitoring of hydrocarbon-related marine anomalies has become a pressing and complex issue for governments and the extraction industry. The research presented here describes the development, training, and utilization of an algorithm that detects marine oil spills in an automated, semi-supervised manner, utilizing X-, C-, or L-band SAR data as the primary input. Ancillary datasets include related radar-borne variables (incidence angle, etc.), environmental data (wind speed, etc.) and textural descriptors. Shapefiles produced by an experienced human-analyst served as targets (validation) during the training portion of the investigation. Training and testing datasets were chosen for development and assessment of algorithm effectiveness as well as optimal conditions for oil detection in SAR data. The algorithm detects oil spills by following a 3-step methodology: object detection, feature extraction, and classification. Previous oil spill detection and classification methodologies such as machine learning algorithms, artificial neural networks (ANN), and multivariate classification methods like partial least squares-discriminant analysis (PLS-DA) are evaluated and compared. Statistical, transform, and model-based image texture techniques, commonly used for object mapping directly or as inputs for more complex methodologies, are explored to determine optimal textures for an oil spill detection system. The influence of the ancillary variables is explored, with a particular focus on the role of strong vs. weak wind forcing.

Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalization

PubMed Central

Ray, Laura B.; Sockeel, Stéphane; Soon, Melissa; Bore, Arnaud; Myhr, Ayako; Stojanoski, Bobby; Cusack, Rhodri; Owen, Adrian M.; Doyon, Julien; Fogel, Stuart M.

2015-01-01

A spindle detection method was developed that: (1) extracts the signal of interest (i.e., spindle-related phasic changes in sigma) relative to ongoing “background” sigma activity using complex demodulation, (2) accounts for variations of spindle characteristics across the night, scalp derivations and between individuals, and (3) employs a minimum number of sometimes arbitrary, user-defined parameters. Complex demodulation was used to extract instantaneous power in the spindle band. To account for intra- and inter-individual differences, the signal was z-score transformed using a 60 s sliding window, per channel, over the course of the recording. Spindle events were detected with a z-score threshold corresponding to a low probability (e.g., 99th percentile). Spindle characteristics, such as amplitude, duration and oscillatory frequency, were derived for each individual spindle following detection, which permits spindles to be subsequently and flexibly categorized as slow or fast spindles from a single detection pass. Spindles were automatically detected in 15 young healthy subjects. Two experts manually identified spindles from C3 during Stage 2 sleep, from each recording; one employing conventional guidelines, and the other, identifying spindles with the aid of a sigma (11–16 Hz) filtered channel. These spindles were then compared between raters and to the automated detection to identify the presence of true positives, true negatives, false positives and false negatives. This method of automated spindle detection resolves or avoids many of the limitations that complicate automated spindle detection, and performs well compared to a group of non-experts, and importantly, has good external validity with respect to the extant literature in terms of the characteristics of automatically detected spindles. PMID:26441604

A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory

PubMed Central

Li, Jingchao; Cao, Yunpeng; Ying, Yulong; Li, Shuying

2016-01-01

Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately. PMID:28036329

A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory.

PubMed

Li, Jingchao; Cao, Yunpeng; Ying, Yulong; Li, Shuying

2016-01-01

Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately.

Automated Extraction and Classification of Cancer Stage Mentions fromUnstructured Text Fields in a Central Cancer Registry

PubMed Central

AAlAbdulsalam, Abdulrahman K.; Garvin, Jennifer H.; Redd, Andrew; Carter, Marjorie E.; Sweeny, Carol; Meystre, Stephane M.

2018-01-01

Cancer stage is one of the most important prognostic parameters in most cancer subtypes. The American Joint Com-mittee on Cancer (AJCC) specifies criteria for staging each cancer type based on tumor characteristics (T), lymph node involvement (N), and tumor metastasis (M) known as TNM staging system. Information related to cancer stage is typically recorded in clinical narrative text notes and other informal means of communication in the Electronic Health Record (EHR). As a result, human chart-abstractors (known as certified tumor registrars) have to search through volu-minous amounts of text to extract accurate stage information and resolve discordance between different data sources. This study proposes novel applications of natural language processing and machine learning to automatically extract and classify TNM stage mentions from records at the Utah Cancer Registry. Our results indicate that TNM stages can be extracted and classified automatically with high accuracy (extraction sensitivity: 95.5%–98.4% and classification sensitivity: 83.5%–87%). PMID:29888032

Automated Extraction and Classification of Cancer Stage Mentions fromUnstructured Text Fields in a Central Cancer Registry.

PubMed

AAlAbdulsalam, Abdulrahman K; Garvin, Jennifer H; Redd, Andrew; Carter, Marjorie E; Sweeny, Carol; Meystre, Stephane M

2018-01-01

Cancer stage is one of the most important prognostic parameters in most cancer subtypes. The American Joint Com-mittee on Cancer (AJCC) specifies criteria for staging each cancer type based on tumor characteristics (T), lymph node involvement (N), and tumor metastasis (M) known as TNM staging system. Information related to cancer stage is typically recorded in clinical narrative text notes and other informal means of communication in the Electronic Health Record (EHR). As a result, human chart-abstractors (known as certified tumor registrars) have to search through volu-minous amounts of text to extract accurate stage information and resolve discordance between different data sources. This study proposes novel applications of natural language processing and machine learning to automatically extract and classify TNM stage mentions from records at the Utah Cancer Registry. Our results indicate that TNM stages can be extracted and classified automatically with high accuracy (extraction sensitivity: 95.5%-98.4% and classification sensitivity: 83.5%-87%).

A System for Automated Extraction of Metadata from Scanned Documents using Layout Recognition and String Pattern Search Models.

PubMed

Misra, Dharitri; Chen, Siyuan; Thoma, George R

2009-01-01

One of the most expensive aspects of archiving digital documents is the manual acquisition of context-sensitive metadata useful for the subsequent discovery of, and access to, the archived items. For certain types of textual documents, such as journal articles, pamphlets, official government records, etc., where the metadata is contained within the body of the documents, a cost effective method is to identify and extract the metadata in an automated way, applying machine learning and string pattern search techniques.At the U. S. National Library of Medicine (NLM) we have developed an automated metadata extraction (AME) system that employs layout classification and recognition models with a metadata pattern search model for a text corpus with structured or semi-structured information. A combination of Support Vector Machine and Hidden Markov Model is used to create the layout recognition models from a training set of the corpus, following which a rule-based metadata search model is used to extract the embedded metadata by analyzing the string patterns within and surrounding each field in the recognized layouts.In this paper, we describe the design of our AME system, with focus on the metadata search model. We present the extraction results for a historic collection from the Food and Drug Administration, and outline how the system may be adapted for similar collections. Finally, we discuss some ongoing enhancements to our AME system.

A novel automated device for rapid nucleic acid extraction utilizing a zigzag motion of magnetic silica beads.

PubMed

Yamaguchi, Akemi; Matsuda, Kazuyuki; Uehara, Masayuki; Honda, Takayuki; Saito, Yasunori

2016-02-04

We report a novel automated device for nucleic acid extraction, which consists of a mechanical control system and a disposable cassette. The cassette is composed of a bottle, a capillary tube, and a chamber. After sample injection in the bottle, the sample is lysed, and nucleic acids are adsorbed on the surface of magnetic silica beads. These magnetic beads are transported and are vibrated through the washing reagents in the capillary tube under the control of the mechanical control system, and thus, the nucleic acid is purified without centrifugation. The purified nucleic acid is automatically extracted in 3 min for the polymerase chain reaction (PCR). The nucleic acid extraction is dependent on the transport speed and the vibration frequency of the magnetic beads, and optimizing these two parameters provided better PCR efficiency than the conventional manual procedure. There was no difference between the detection limits of our novel device and that of the conventional manual procedure. We have already developed the droplet-PCR machine, which can amplify and detect specific nucleic acids rapidly and automatically. Connecting the droplet-PCR machine to our novel automated extraction device enables PCR analysis within 15 min, and this system can be made available as a point-of-care testing in clinics as well as general hospitals. Copyright © 2015 Elsevier B.V. All rights reserved.

Implementation of a Flexible Tool for Automated Literature-Mining and Knowledgebase Development (DevToxMine)

EPA Science Inventory

Deriving novel relationships from the scientific literature is an important adjunct to datamining activities for complex datasets in genomics and high-throughput screening activities. Automated text-mining algorithms can be used to extract relevant content from the literature and...

Evaluation Of A Powder-Free DNA Extraction Method For Skeletal Remains.

PubMed

Harrel, Michelle; Mayes, Carrie; Gangitano, David; Hughes-Stamm, Sheree

2018-02-07

Bones are often recovered in forensic investigations, including missing persons and mass disasters. While traditional DNA extraction methods rely on grinding bone into powder prior to DNA purification, the TBone Ex buffer (DNA Chip Research Inc.) digests bone chips without powdering. In this study, six bones were extracted using the TBone Ex kit in conjunction with the PrepFiler ® BTA™ DNA extraction kit (Thermo Fisher Scientific) both manually and via an automated platform. Comparable amounts of DNA were recovered from a 50 mg bone chip using the TBone Ex kit and 50 mg of powdered bone with the PrepFiler ® BTA™ kit. However, automated DNA purification decreased DNA yield (p < 0.05). Nevertheless, short tandem repeat (STR) success was comparable across all methods tested. This study demonstrates that digestion of whole bone fragments is an efficient alternative to powdering bones for DNA extraction without compromising downstream STR profile quality. © 2018 American Academy of Forensic Sciences.

Automation for System Safety Analysis

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Fleming, Land; Throop, David; Thronesbery, Carroll; Flores, Joshua; Bennett, Ted; Wennberg, Paul

2009-01-01

This presentation describes work to integrate a set of tools to support early model-based analysis of failures and hazards due to system-software interactions. The tools perform and assist analysts in the following tasks: 1) extract model parts from text for architecture and safety/hazard models; 2) combine the parts with library information to develop the models for visualization and analysis; 3) perform graph analysis and simulation to identify and evaluate possible paths from hazard sources to vulnerable entities and functions, in nominal and anomalous system-software configurations and scenarios; and 4) identify resulting candidate scenarios for software integration testing. There has been significant technical progress in model extraction from Orion program text sources, architecture model derivation (components and connections) and documentation of extraction sources. Models have been derived from Internal Interface Requirements Documents (IIRDs) and FMEA documents. Linguistic text processing is used to extract model parts and relationships, and the Aerospace Ontology also aids automated model development from the extracted information. Visualizations of these models assist analysts in requirements overview and in checking consistency and completeness.

Isolation of Mitochondrial DNA from Single, Short Hairs without Roots Using Pressure Cycling Technology.

PubMed

Harper, Kathryn A; Meiklejohn, Kelly A; Merritt, Richard T; Walker, Jessica; Fisher, Constance L; Robertson, James M

2018-02-01

Hairs are commonly submitted as evidence to forensic laboratories, but standard nuclear DNA analysis is not always possible. Mitochondria (mt) provide another source of genetic material; however, manual isolation is laborious. In a proof-of-concept study, we assessed pressure cycling technology (PCT; an automated approach that subjects samples to varying cycles of high and low pressure) for extracting mtDNA from single, short hairs without roots. Using three microscopically similar donors, we determined the ideal PCT conditions and compared those yields to those obtained using the traditional manual micro-tissue grinder method. Higher yields were recovered from grinder extracts, but yields from PCT extracts exceeded the requirements for forensic analysis, with the DNA quality confirmed through sequencing. Automated extraction of mtDNA from hairs without roots using PCT could be useful for forensic laboratories processing numerous samples.

Quantitative Indicators for Behaviour Drift Detection from Home Automation Data.

PubMed

Veronese, Fabio; Masciadri, Andrea; Comai, Sara; Matteucci, Matteo; Salice, Fabio

2017-01-01

Smart Homes diffusion provides an opportunity to implement elderly monitoring, extending seniors' independence and avoiding unnecessary assistance costs. Information concerning the inhabitant behaviour is contained in home automation data, and can be extracted by means of quantitative indicators. The application of such approach proves it can evidence behaviour changes.

Evaluation of automated cell disruptor methods for oomycetous and ascomycetous model organisms

USDA-ARS?s Scientific Manuscript database

Two automated cell disruptor-based methods for RNA extraction; disruption of thawed cells submerged in TRIzol Reagent (method QP), and direct disruption of frozen cells on dry ice (method CP), were optimized for a model oomycete, Phytophthora capsici, and compared with grinding in a mortar and pestl...

An Automated Approach to Extracting River Bank Locations from Aerial Imagery Using Image Texture

DTIC Science & Technology

2013-01-01

Atchafalaya River, LA. Map Data: Google, United States Department of Agriculture Farm Ser- vice Agency, Europa Technologies AUTOMATED RIVER BANK...traverse morphologically smooth landscapes including rivers in sand or ice . Within these limitations, we hold that this technique rep- resents a valuable

xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data.

PubMed

Uppal, Karan; Soltow, Quinlyn A; Strobel, Frederick H; Pittard, W Stephen; Gernert, Kim M; Yu, Tianwei; Jones, Dean P

2013-01-16

Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation. xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites. xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

Automated in vivo identification of fungal infection on human scalp using optical coherence tomography and machine learning

NASA Astrophysics Data System (ADS)

Dubey, Kavita; Srivastava, Vishal; Singh Mehta, Dalip

2018-04-01

Early identification of fungal infection on the human scalp is crucial for avoiding hair loss. The diagnosis of fungal infection on the human scalp is based on a visual assessment by trained experts or doctors. Optical coherence tomography (OCT) has the ability to capture fungal infection information from the human scalp with a high resolution. In this study, we present a fully automated, non-contact, non-invasive optical method for rapid detection of fungal infections based on the extracted features from A-line and B-scan images of OCT. A multilevel ensemble machine model is designed to perform automated classification, which shows the superiority of our classifier to the best classifier based on the features extracted from OCT images. In this study, 60 samples (30 fungal, 30 normal) were imaged by OCT and eight features were extracted. The classification algorithm had an average sensitivity, specificity and accuracy of 92.30, 90.90 and 91.66%, respectively, for identifying fungal and normal human scalps. This remarkable classifying ability makes the proposed model readily applicable to classifying the human scalp.

Automated prediction of protein function and detection of functional sites from structure.

PubMed

Pazos, Florencio; Sternberg, Michael J E

2004-10-12

Current structural genomics projects are yielding structures for proteins whose functions are unknown. Accordingly, there is a pressing requirement for computational methods for function prediction. Here we present PHUNCTIONER, an automatic method for structure-based function prediction using automatically extracted functional sites (residues associated to functions). The method relates proteins with the same function through structural alignments and extracts 3D profiles of conserved residues. Functional features to train the method are extracted from the Gene Ontology (GO) database. The method extracts these features from the entire GO hierarchy and hence is applicable across the whole range of function specificity. 3D profiles associated with 121 GO annotations were extracted. We tested the power of the method both for the prediction of function and for the extraction of functional sites. The success of function prediction by our method was compared with the standard homology-based method. In the zone of low sequence similarity (approximately 15%), our method assigns the correct GO annotation in 90% of the protein structures considered, approximately 20% higher than inheritance of function from the closest homologue.

Direct analysis of textile dyes from trace fibers by automated microfluidics extraction system coupled with Q-TOF mass spectrometer for forensic applications.

PubMed

Sultana, Nadia; Gunning, Sean; Furst, Stephen J; Garrard, Kenneth P; Dow, Thomas A; Vinueza, Nelson R

2018-05-19

Textile fiber is a common form of transferable trace evidence at the crime scene. Different techniques such as microscopy or spectroscopy are currently being used for trace fiber analysis. Dye characterization in trace fiber adds an important molecular specificity during the analysis. In this study, we performed a direct trace fiber analysis method via dye characterization by a novel automated microfluidics device (MFD) dye extraction system coupled with a quadrupole-time-of-flight (Q-TOF) mass spectrometer (MS). The MFD system used an in-house made automated procedure which requires only 10μL of organic solvent for the extraction. The total extraction and identification time by the system is under 12min. A variety of sulfonated azo and anthraquinone dyes were analyzed from ∼1mm length nylon fiber samples. This methodology successfully characterized multiple dyes (≥3 dyes) from a single fiber thread. Additionally, it was possible to do dye characterization from single fibers with a diameter of ∼10μm. The MFD-MS system was used for elemental composition and isotopic distribution analysis where MFD-MS/MS was used for structural characterization of dyes on fibers. Copyright © 2018 Elsevier B.V. All rights reserved.

Status and plans of the Department of the Interior EROS program

USGS Publications Warehouse

,

1975-01-01

The Earth Resources Observation Systems (EROS) Program of the Department of the Interior has been actively participating in the LANDSAT (formerly ERTS) program and other investigations with remotely sensed data. A large number of applications have been demonstrated that can assist in the discovery of nonrenewable resources, monitoring areal extent of renewable resources, monitoring environmental change, and in providing repetitive data for planimetric revision of small-scale maps and maps showing land cover classes. A new and potentially revolutionary approach, that of "automated cartography," has been initiated through the versatile nature of the data available from LANDSAT. "Automated cartography" as used here refers to the ability to automatically extract land cover classes and relate these classes to geographic position.

Rapid non-enzymatic extraction method for isolating PCR-quality camelpox virus DNA from skin.

PubMed

Yousif, A Ausama; Al-Naeem, A Abdelmohsen; Al-Ali, M Ahmad

2010-10-01

Molecular diagnostic investigations of orthopoxvirus (OPV) infections are performed using a variety of clinical samples including skin lesions, tissues from internal organs, blood and secretions. Skin samples are particularly convenient for rapid diagnosis and molecular epidemiological investigations of camelpox virus (CMLV). Classical extraction procedures and commercial spin-column-based kits are time consuming, relatively expensive, and require multiple extraction and purification steps in addition to proteinase K digestion. A rapid non-enzymatic procedure for extracting CMLV DNA from dried scabs or pox lesions was developed to overcome some of the limitations of the available DNA extraction techniques. The procedure requires as little as 10mg of tissue and produces highly purified DNA [OD(260)/OD(280) ratios between 1.47 and 1.79] with concentrations ranging from 6.5 to 16 microg/ml. The extracted CMLV DNA was proven suitable for virus-specific qualitative and, semi-quantitative PCR applications. Compared to spin-column and conventional viral DNA extraction techniques, the two-step extraction procedure saves money and time, and retains the potential for automation without compromising CMLV PCR sensitivity. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Discovering gene annotations in biomedical text databases

PubMed Central

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-01-01

Background Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. Results In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. Conclusion GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values. PMID:18325104

Discovering gene annotations in biomedical text databases.

PubMed

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-03-06

Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.

Automated solid-phase extraction hyphenated to voltammetry for the determination of quercetin using magnetic nanoparticles and sequential injection lab-on-valve approach.

PubMed

Wang, Yang; Wang, Lu; Tian, Tian; Hu, Xiaoya; Yang, Chun; Xu, Qin

2012-05-21

In this study, an automated sequential injection lab-on-valve (SI-LOV) system was designed for the on-line matrix removal and preconcentration of quercetin. Octadecyl functionalized magnetic silica nanoparticles were prepared and packed into the microcolumn of the LOV as adsorbents. After being adsorbed through hydrophobic interaction, the analyte was eluted and subsequently introduced into the electrochemical flow cell by voltammetric quantification. The main parameters affecting the performance of solid-phase extraction, such as sample pH and flow rate, eluent solution and volume, accumulation potential and accumulation time were investigated in detail. Under the optimum experimental conditions, a linear calibration curve was obtained in the range of 1.0 × 10(-8) to 1 × 10(-5) mol L(-1) with R(2) = 0.9979. The limit of detection (LOD) and limit of quantitation (LOQ) were 1.3 × 10(-9) and 4.3 × 10(-9) mol L(-1), respectively. The relative standard deviation (RSD) for the determination of 1.0 × 10(-6) mol L(-1) quercetin was found to be 2.9% (n = 11) along with a sampling frequency of 40 h(-1). The applicability and reliability of the automated method described here had been applied to the determination of quercetin in human urine and red wine samples through recovery experiments, and the obtained results were in good agreement with those obtained by the HPLC method.

A System for Automated Extraction of Metadata from Scanned Documents using Layout Recognition and String Pattern Search Models

PubMed Central

Misra, Dharitri; Chen, Siyuan; Thoma, George R.

2010-01-01

One of the most expensive aspects of archiving digital documents is the manual acquisition of context-sensitive metadata useful for the subsequent discovery of, and access to, the archived items. For certain types of textual documents, such as journal articles, pamphlets, official government records, etc., where the metadata is contained within the body of the documents, a cost effective method is to identify and extract the metadata in an automated way, applying machine learning and string pattern search techniques. At the U. S. National Library of Medicine (NLM) we have developed an automated metadata extraction (AME) system that employs layout classification and recognition models with a metadata pattern search model for a text corpus with structured or semi-structured information. A combination of Support Vector Machine and Hidden Markov Model is used to create the layout recognition models from a training set of the corpus, following which a rule-based metadata search model is used to extract the embedded metadata by analyzing the string patterns within and surrounding each field in the recognized layouts. In this paper, we describe the design of our AME system, with focus on the metadata search model. We present the extraction results for a historic collection from the Food and Drug Administration, and outline how the system may be adapted for similar collections. Finally, we discuss some ongoing enhancements to our AME system. PMID:21179386

PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics.

PubMed

Fernandez-Ricaud, Luciano; Kourtchenko, Olga; Zackrisson, Martin; Warringer, Jonas; Blomberg, Anders

2016-06-23

Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology. To automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics. Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis. PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases.

A fully automated method for simultaneous determination of aflatoxins and ochratoxin A in dried fruits by pressurized liquid extraction and online solid-phase extraction cleanup coupled to ultra-high-pressure liquid chromatography-tandem mass spectrometry.

PubMed

Campone, Luca; Piccinelli, Anna Lisa; Celano, Rita; Russo, Mariateresa; Valdés, Alberto; Ibáñez, Clara; Rastrelli, Luca

2015-04-01

According to current demands and future perspectives in food safety, this study reports a fast and fully automated analytical method for the simultaneous analysis of the mycotoxins with high toxicity and wide spread, aflatoxins (AFs) and ochratoxin A (OTA) in dried fruits, a high-risk foodstuff. The method is based on pressurized liquid extraction (PLE), with aqueous methanol (30%) at 110 °C, of the slurried dried fruit and online solid-phase extraction (online SPE) cleanup of the PLE extracts with a C18 cartridge. The purified sample was directly analysed by ultra-high-pressure liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) for sensitive and selective determination of AFs and OTA. The proposed analytical procedure was validated for different dried fruits (vine fruit, fig and apricot), providing method detection and quantification limits much lower than the AFs and OTA maximum levels imposed by EU regulation in dried fruit for direct human consumption. Also, recoveries (83-103%) and repeatability (RSD < 8, n = 3) meet the performance criteria required by EU regulation for the determination of the levels of mycotoxins in foodstuffs. The main advantage of the proposed method is full automation of the whole analytical procedure that reduces the time and cost of the analysis, sample manipulation and solvent consumption, enabling high-throughput analysis and highly accurate and precise results.

Extraction of Urban Trees from Integrated Airborne Based Digital Image and LIDAR Point Cloud Datasets - Initial Results

NASA Astrophysics Data System (ADS)

Dogon-yaro, M. A.; Kumar, P.; Rahman, A. Abdul; Buyuksalih, G.

2016-10-01

Timely and accurate acquisition of information on the condition and structural changes of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting tree features include; ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraint, such as labour intensive field work, a lot of financial requirement, influences by weather condition and topographical covers which can be overcome by means of integrated airborne based LiDAR and very high resolution digital image datasets. This study presented a semi-automated approach for extracting urban trees from integrated airborne based LIDAR and multispectral digital image datasets over Istanbul city of Turkey. The above scheme includes detection and extraction of shadow free vegetation features based on spectral properties of digital images using shadow index and NDVI techniques and automated extraction of 3D information about vegetation features from the integrated processing of shadow free vegetation image and LiDAR point cloud datasets. The ability of the developed algorithms shows a promising result as an automated and cost effective approach to estimating and delineated 3D information of urban trees. The research also proved that integrated datasets is a suitable technology and a viable source of information for city managers to be used in urban trees management.

Object extraction in photogrammetric computer vision

NASA Astrophysics Data System (ADS)

Mayer, Helmut

This paper discusses state and promising directions of automated object extraction in photogrammetric computer vision considering also practical aspects arising for digital photogrammetric workstations (DPW). A review of the state of the art shows that there are only few practically successful systems on the market. Therefore, important issues for a practical success of automated object extraction are identified. A sound and most important powerful theoretical background is the basis. Here, we particularly point to statistical modeling. Testing makes clear which of the approaches are suited best and how useful they are for praxis. A key for commercial success of a practical system is efficient user interaction. As the means for data acquisition are changing, new promising application areas such as extremely detailed three-dimensional (3D) urban models for virtual television or mission rehearsal evolve.

Modelling and representation issues in automated feature extraction from aerial and satellite images

NASA Astrophysics Data System (ADS)

Sowmya, Arcot; Trinder, John

New digital systems for the processing of photogrammetric and remote sensing images have led to new approaches to information extraction for mapping and Geographic Information System (GIS) applications, with the expectation that data can become more readily available at a lower cost and with greater currency. Demands for mapping and GIS data are increasing as well for environmental assessment and monitoring. Hence, researchers from the fields of photogrammetry and remote sensing, as well as computer vision and artificial intelligence, are bringing together their particular skills for automating these tasks of information extraction. The paper will review some of the approaches used in knowledge representation and modelling for machine vision, and give examples of their applications in research for image understanding of aerial and satellite imagery.

Automated methods of tree boundary extraction and foliage transparency estimation from digital imagery

Treesearch

Sang-Mook Lee; Neil A. Clark; Philip A. Araman

2003-01-01

Foliage transparency in trees is an important indicator for forest health assessment. This paper helps advance transparency measurement research by presenting methods of automatic tree boundary extraction and foliage transparency estimation from digital images taken from the ground of open grown trees.Extraction of proper boundaries of tree crowns is the...

Automated data extraction from general practice records in an Australian setting: trends in influenza-like illness in sentinel general practices and emergency departments.

PubMed

Liljeqvist, Gösta T H; Staff, Michael; Puech, Michele; Blom, Hans; Torvaldsen, Siranda

2011-06-06

Influenza intelligence in New South Wales (NSW), Australia is derived mainly from emergency department (ED) presentations and hospital and intensive care admissions, which represent only a portion of influenza-like illness (ILI) in the population. A substantial amount of the remaining data lies hidden in general practice (GP) records. Previous attempts in Australia to gather ILI data from GPs have given them extra work. We explored the possibility of applying automated data extraction from GP records in sentinel surveillance in an Australian setting.The two research questions asked in designing the study were: Can syndromic ILI data be extracted automatically from routine GP data? How do ILI trends in sentinel general practice compare with ILI trends in EDs? We adapted a software program already capable of automated data extraction to identify records of patients with ILI in routine electronic GP records in two of the most commonly used commercial programs. This tool was applied in sentinel sites to gather retrospective data for May-October 2007-2009 and in real-time for the same interval in 2010. The data were compared with that provided by the Public Health Real-time Emergency Department Surveillance System (PHREDSS) and with ED data for the same periods. The GP surveillance tool identified seasonal trends in ILI both retrospectively and in near real-time. The curve of seasonal ILI was more responsive and less volatile than that of PHREDSS on a local area level. The number of weekly ILI presentations ranged from 8 to 128 at GP sites and from 0 to 18 in EDs in non-pandemic years. Automated data extraction from routine GP records offers a means to gather data without introducing any additional work for the practitioner. Adding this method to current surveillance programs will enhance their ability to monitor ILI and to detect early warning signals of new ILI events.

Automated Mini-Column Solid-Phase Extraction Cleanup for High-Throughput Analysis of Chemical Contaminants in Foods by Low-Pressure Gas Chromatography-Tandem Mass Spectrometry.

PubMed

Lehotay, Steven J; Han, Lijun; Sapozhnikova, Yelena

2016-01-01

This study demonstrated the application of an automated high-throughput mini-cartridge solid-phase extraction (mini-SPE) cleanup for the rapid low-pressure gas chromatography-tandem mass spectrometry (LPGC-MS/MS) analysis of pesticides and environmental contaminants in QuEChERS extracts of foods. Cleanup efficiencies and breakthrough volumes using different mini-SPE sorbents were compared using avocado, salmon, pork loin, and kale as representative matrices. Optimum extract load volume was 300 µL for the 45 mg mini-cartridges containing 20/12/12/1 (w/w/w/w) anh. MgSO 4 /PSA (primary secondary amine)/C 18 /CarbonX sorbents used in the final method. In method validation to demonstrate high-throughput capabilities and performance results, 230 spiked extracts of 10 different foods (apple, kiwi, carrot, kale, orange, black olive, wheat grain, dried basil, pork, and salmon) underwent automated mini-SPE cleanup and analysis over the course of 5 days. In all, 325 analyses for 54 pesticides and 43 environmental contaminants (3 analyzed together) were conducted using the 10 min LPGC-MS/MS method without changing the liner or retuning the instrument. Merely, 1 mg equivalent sample injected achieved <5 ng g -1 limits of quantification. With the use of internal standards, method validation results showed that 91 of the 94 analytes including pairs achieved satisfactory results (70-120 % recovery and RSD ≤ 25 %) in the 10 tested food matrices ( n  = 160). Matrix effects were typically less than ±20 %, mainly due to the use of analyte protectants, and minimal human review of software data processing was needed due to summation function integration of analyte peaks. This study demonstrated that the automated mini-SPE + LPGC-MS/MS method yielded accurate results in rugged, high-throughput operations with minimal labor and data review.

Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial

PubMed Central

Roelofs, Erik; Persoon, Lucas; Nijsten, Sebastiaan; Wiessler, Wolfgang; Dekker, André; Lambin, Philippe

2016-01-01

Introduction Collecting trial data in a medical environment is at present mostly performed manually and therefore time-consuming, prone to errors and often incomplete with the complex data considered. Faster and more accurate methods are needed to improve the data quality and to shorten data collection times where information is often scattered over multiple data sources. The purpose of this study is to investigate the possible benefit of modern data warehouse technology in the radiation oncology field. Material and methods In this study, a Computer Aided Theragnostics (CAT) data warehouse combined with automated tools for feature extraction was benchmarked against the regular manual data-collection processes. Two sets of clinical parameters were compiled for non-small cell lung cancer (NSCLC) and rectal cancer, using 27 patients per disease. Data collection times and inconsistencies were compared between the manual and the automated extraction method. Results The average time per case to collect the NSCLC data manually was 10.4 ± 2.1 min and 4.3 ± 1.1 min when using the automated method (p < 0.001). For rectal cancer, these times were 13.5 ± 4.1 and 6.8 ± 2.4 min, respectively (p < 0.001). In 3.2% of the data collected for NSCLC and 5.3% for rectal cancer, there was a discrepancy between the manual and automated method. Conclusions Aggregating multiple data sources in a data warehouse combined with tools for extraction of relevant parameters is beneficial for data collection times and offers the ability to improve data quality. The initial investments in digitizing the data are expected to be compensated due to the flexibility of the data analysis. Furthermore, successive investigations can easily select trial candidates and extract new parameters from the existing databases. PMID:23394741

Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial.

PubMed

Roelofs, Erik; Persoon, Lucas; Nijsten, Sebastiaan; Wiessler, Wolfgang; Dekker, André; Lambin, Philippe

2013-07-01

Collecting trial data in a medical environment is at present mostly performed manually and therefore time-consuming, prone to errors and often incomplete with the complex data considered. Faster and more accurate methods are needed to improve the data quality and to shorten data collection times where information is often scattered over multiple data sources. The purpose of this study is to investigate the possible benefit of modern data warehouse technology in the radiation oncology field. In this study, a Computer Aided Theragnostics (CAT) data warehouse combined with automated tools for feature extraction was benchmarked against the regular manual data-collection processes. Two sets of clinical parameters were compiled for non-small cell lung cancer (NSCLC) and rectal cancer, using 27 patients per disease. Data collection times and inconsistencies were compared between the manual and the automated extraction method. The average time per case to collect the NSCLC data manually was 10.4 ± 2.1 min and 4.3 ± 1.1 min when using the automated method (p<0.001). For rectal cancer, these times were 13.5 ± 4.1 and 6.8 ± 2.4 min, respectively (p<0.001). In 3.2% of the data collected for NSCLC and 5.3% for rectal cancer, there was a discrepancy between the manual and automated method. Aggregating multiple data sources in a data warehouse combined with tools for extraction of relevant parameters is beneficial for data collection times and offers the ability to improve data quality. The initial investments in digitizing the data are expected to be compensated due to the flexibility of the data analysis. Furthermore, successive investigations can easily select trial candidates and extract new parameters from the existing databases. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Complete automation of solid-phase extraction with subsequent liquid chromatography-tandem mass spectrometry for the quantification of benzoylecgonine, m-hydroxybenzoylecgonine, p-hydroxybenzoylecgonine, and norbenzoylecgonine in urine--application to a high-throughput urine analysis laboratory.

PubMed

Robandt, Paul P; Reda, Louis J; Klette, Kevin L

2008-10-01

A fully automated system utilizing a liquid handler and an online solid-phase extraction (SPE) device coupled with liquid chromatography-tandem mass spectrometry (LC-MS-MS) was designed to process, detect, and quantify benzoylecgonine (BZE), meta-hydroxybenzoylecgonine (m-OH BZE), para-hydroxybenzoylecgonine (p-OH BZE), and norbenzoylecgonine (nor-BZE) metabolites in human urine. The method was linear for BZE, m-OH BZE, and p-OH BZE from 1.2 to 10,000 ng/mL with limits of detection (LOD) and quantification (LOQ) of 1.2 ng/mL. Nor-BZE was linear from 5 to 10,000 ng/mL with an LOD and LOQ of 1.2 and 5 ng/mL, respectively. The intrarun precision measured as the coefficient of variation of 10 replicates of a 100 ng/mL control was less than 2.6%, and the interrun precision for 5 replicates of the same control across 8 batches was less than 4.8% for all analytes. No assay interference was noted from controls containing cocaine, cocaethylene, and ecgonine methyl ester. Excellent data concordance (R2 > 0.994) was found for direct comparison of the automated SPE-LC-MS-MS procedure and an existing gas chromatography-MS procedure using 94 human urine samples previously determined to be positive for BZE. The automated specimen handling and SPE procedure, when compared to the traditional extraction schema, eliminates the human factors of specimen handling, processing, extraction, and derivatization, thereby reducing labor costs and rework resulting from batch handling issues, and may reduce the number of fume hoods required in the laboratory.

Efficient quantification of water content in edible oils by headspace gas chromatography with vapour phase calibration.

PubMed

Xie, Wei-Qi; Gong, Yi-Xian; Yu, Kong-Xian

2018-06-01

An automated and accurate headspace gas chromatographic (HS-GC) technique was investigated for rapidly quantifying water content in edible oils. In this method, multiple headspace extraction (MHE) procedures were used to analyse the integrated water content from the edible oil sample. A simple vapour phase calibration technique with an external vapour standard was used to calibrate both the water content in the gas phase and the total weight of water in edible oil sample. After that the water in edible oils can be quantified. The data showed that the relative standard deviation of the present HS-GC method in the precision test was less than 1.13%, the relative differences between the new method and a reference method (i.e. the oven-drying method) were no more than 1.62%. The present HS-GC method is automated, accurate, efficient, and can be a reliable tool for quantifying water content in edible oil related products and research. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Validation of a DNA IQ-based extraction method for TECAN robotic liquid handling workstations for processing casework.

PubMed

Frégeau, Chantal J; Lett, C Marc; Fourney, Ron M

2010-10-01

A semi-automated DNA extraction process for casework samples based on the Promega DNA IQ™ system was optimized and validated on TECAN Genesis 150/8 and Freedom EVO robotic liquid handling stations configured with fixed tips and a TECAN TE-Shake™ unit. The use of an orbital shaker during the extraction process promoted efficiency with respect to DNA capture, magnetic bead/DNA complex washes and DNA elution. Validation studies determined the reliability and limitations of this shaker-based process. Reproducibility with regards to DNA yields for the tested robotic workstations proved to be excellent and not significantly different than that offered by the manual phenol/chloroform extraction. DNA extraction of animal:human blood mixtures contaminated with soil demonstrated that a human profile was detectable even in the presence of abundant animal blood. For exhibits containing small amounts of biological material, concordance studies confirmed that DNA yields for this shaker-based extraction process are equivalent or greater to those observed with phenol/chloroform extraction as well as our original validated automated magnetic bead percolation-based extraction process. Our data further supports the increasing use of robotics for the processing of casework samples. Crown Copyright © 2009. Published by Elsevier Ireland Ltd. All rights reserved.

Development and integration of block operations for data invariant automation of digital preprocessing and analysis of biological and biomedical Raman spectra.

PubMed

Schulze, H Georg; Turner, Robin F B

2015-06-01

High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: a comparison of two classification schemes with respect to the segmentation quality

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Benz, Michaela; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2015-03-01

The morphological analysis of bone marrow smears is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually with the use of bright field microscope. This is a time consuming, partly subjective and tedious process. Furthermore, repeated examinations of a slide yield intra- and inter-observer variances. For this reason an automation of morphological bone marrow analysis is pursued. This analysis comprises several steps: image acquisition and smear detection, cell localization and segmentation, feature extraction and cell classification. The automated classification of bone marrow cells is depending on the automated cell segmentation and the choice of adequate features extracted from different parts of the cell. In this work we focus on the evaluation of support vector machines (SVMs) and random forests (RFs) for the differentiation of bone marrow cells in 16 different classes, including immature and abnormal cell classes. Data sets of different segmentation quality are used to test the two approaches. Automated solutions for the morphological analysis for bone marrow smears could use such a classifier to pre-classify bone marrow cells and thereby shortening the examination duration.

Automated detection of abnormalities in paranasal sinus on dental panoramic radiographs by using contralateral subtraction technique based on mandible contour

NASA Astrophysics Data System (ADS)

Mori, Shintaro; Hara, Takeshi; Tagami, Motoki; Muramatsu, Chicako; Kaneda, Takashi; Katsumata, Akitoshi; Fujita, Hiroshi

2013-02-01

Inflammation in paranasal sinus sometimes becomes chronic to take long terms for the treatment. The finding is important for the early treatment, but general dentists may not recognize the findings because they focus on teeth treatments. The purpose of this study was to develop a computer-aided detection (CAD) system for the inflammation in paranasal sinus on dental panoramic radiographs (DPRs) by using the mandible contour and to demonstrate the potential usefulness of the CAD system by means of receiver operating characteristic analysis. The detection scheme consists of 3 steps: 1) Contour extraction of mandible, 2) Contralateral subtraction, and 3) Automated detection. The Canny operator and active contour model were applied to extract the edge at the first step. At the subtraction step, the right region of the extracted contour image was flipped to compare with the left region. Mutual information between two selected regions was obtained to estimate the shift parameters of image registration. The subtraction images were generated based on the shift parameter. Rectangle regions of left and right paranasal sinus on the subtraction image were determined based on the size of mandible. The abnormal side of the regions was determined by taking the difference between the averages of each region. Thirteen readers were responded to all cases without and with the automated results. The averaged AUC of all readers was increased from 0.69 to 0.73 with statistical significance (p=0.032) when the automated detection results were provided. In conclusion, the automated detection method based on contralateral subtraction technique improves readers' interpretation performance of inflammation in paranasal sinus on DPRs.

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.

PubMed

Yu, Sheng; Liao, Katherine P; Shaw, Stanley Y; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Cai, Tianxi

2015-09-01

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Comparative evaluation of three automated systems for DNA extraction in conjunction with three commercially available real-time PCR assays for quantitation of plasma Cytomegalovirus DNAemia in allogeneic stem cell transplant recipients.

PubMed

Bravo, Dayana; Clari, María Ángeles; Costa, Elisa; Muñoz-Cobo, Beatriz; Solano, Carlos; José Remigia, María; Navarro, David

2011-08-01

Limited data are available on the performance of different automated extraction platforms and commercially available quantitative real-time PCR (QRT-PCR) methods for the quantitation of cytomegalovirus (CMV) DNA in plasma. We compared the performance characteristics of the Abbott mSample preparation system DNA kit on the m24 SP instrument (Abbott), the High Pure viral nucleic acid kit on the COBAS AmpliPrep system (Roche), and the EZ1 Virus 2.0 kit on the BioRobot EZ1 extraction platform (Qiagen) coupled with the Abbott CMV PCR kit, the LightCycler CMV Quant kit (Roche), and the Q-CMV complete kit (Nanogen), for both plasma specimens from allogeneic stem cell transplant (Allo-SCT) recipients (n = 42) and the OptiQuant CMV DNA panel (AcroMetrix). The EZ1 system displayed the highest extraction efficiency over a wide range of CMV plasma DNA loads, followed by the m24 and the AmpliPrep methods. The Nanogen PCR assay yielded higher mean CMV plasma DNA values than the Abbott and the Roche PCR assays, regardless of the platform used for DNA extraction. Overall, the effects of the extraction method and the QRT-PCR used on CMV plasma DNA load measurements were less pronounced for specimens with high CMV DNA content (>10,000 copies/ml). The performance characteristics of the extraction methods and QRT-PCR assays evaluated herein for clinical samples were extensible at cell-based standards from AcroMetrix. In conclusion, different automated systems are not equally efficient for CMV DNA extraction from plasma specimens, and the plasma CMV DNA loads measured by commercially available QRT-PCRs can differ significantly. The above findings should be taken into consideration for the establishment of cutoff values for the initiation or cessation of preemptive antiviral therapies and for the interpretation of data from clinical studies in the Allo-SCT setting.

Automated and sensitive method for the determination of formoterol in human plasma by high-performance liquid chromatography and electrochemical detection.

PubMed

Campestrini, J; Lecaillon, J B; Godbillon, J

1997-12-19

An automated high-performance liquid chromatography (HPLC) method for the determination of formoterol in human plasma with improved sensitivity has been developed and validated. Formoterol and CGP 47086, the internal standard, were extracted from plasma (1 ml) using a cation-exchange solid-phase extraction (SPE) cartridge. The compounds were eluted with pH 6 buffer solution-methanol (70:30, v/v) and the eluate was further diluted with water. An aliquot of the extract solution was injected and analyzed by HPLC. The extraction, dilution, injection and chromatographic analysis were combined and automated using the automate (ASPEC) system. The chromatographic separations were achieved on a 5 microm, Hypersil ODS analytical column (200 mm x 3 mm I.D.), using (pH 6 phosphate buffer, 0.035 M + 20 mg/l EDTA)-MeOH-CH3CN (70:25:5, v/v/v) as the mobile phase at a flow-rate of 0.4 ml/min. The analytes were detected with electrochemical detection at an operating potential of +0.63 V. Intra-day accuracy and precision were assessed from the relative recoveries of calibration/quality control plasma samples in the concentration range of 7.14 to 238 pmol/l of formoterol base. The accuracy over the entire concentration range varied from 81 to 105%, and the precision (C.V.) ranged from 3 to 14%. Inter-day accuracy and precision were assessed in the concentration range of 11.9 to 238 pmol/l of formoterol base in plasma. The accuracy over the entire concentration range varied from 98 to 109%, and precision ranged from 8 to 19%. At the limit of quantitation (LOQ) of 11.9 pmol/l for inter-day measurements, the recovery value was 109% and C.V. was 19%. As shown from intra-day accuracy and precision results, favorable conditions (a newly used column, a newly washed detector cell and moderate residual cell current level) allowed us to reach a LOQ of 7.14 pmol/l of formoterol base (3 pg/ml of formoterol fumarate dihydrate). Improvement of the limit of detection by a factor of about 10 was reached as compared to the previously described methods. The method has been applied for quantifying formoterol in plasma after 120 microg drug inhalation to volunteers. Formoterol was still measurable at 24 h post-dosing in most subjects and a slow elimination of formoterol from plasma beyond 6-8 h after inhalation was demonstrated for the first time thanks to the sensitivity of the method.

A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods.

PubMed

Kesner, Adam Leon; Kuntner, Claudia

2010-10-01

Respiratory gating in PET is an approach used to minimize the negative effects of respiratory motion on spatial resolution. It is based on an initial determination of a patient's respiratory movements during a scan, typically using hardware based systems. In recent years, several fully automated databased algorithms have been presented for extracting a respiratory signal directly from PET data, providing a very practical strategy for implementing gating in the clinic. In this work, a new method is presented for extracting a respiratory signal from raw PET sinogram data and compared to previously presented automated techniques. The acquisition of respiratory signal from PET data in the newly proposed method is based on rebinning the sinogram data into smaller data structures and then analyzing the time activity behavior in the elements of these structures. From this analysis, a 1D respiratory trace is produced, analogous to a hardware derived respiratory trace. To assess the accuracy of this fully automated method, respiratory signal was extracted from a collection of 22 clinical FDG-PET scans using this method, and compared to signal derived from several other software based methods as well as a signal derived from a hardware system. The method presented required approximately 9 min of processing time for each 10 min scan (using a single 2.67 GHz processor), which in theory can be accomplished while the scan is being acquired and therefore allowing a real-time respiratory signal acquisition. Using the mean correlation between the software based and hardware based respiratory traces, the optimal parameters were determined for the presented algorithm. The mean/median/range of correlations for the set of scans when using the optimal parameters was found to be 0.58/0.68/0.07-0.86. The speed of this method was within the range of real-time while the accuracy surpassed the most accurate of the previously presented algorithms. PET data inherently contains information about patient motion; information that is not currently being utilized. We have shown that a respiratory signal can be extracted from raw PET data in potentially real-time and in a fully automated manner. This signal correlates well with hardware based signal for a large percentage of scans, and avoids the efforts and complications associated with hardware. The proposed method to extract a respiratory signal can be implemented on existing scanners and, if properly integrated, can be applied without changes to routine clinical procedures.

Object-oriented classification of drumlins from digital elevation models

NASA Astrophysics Data System (ADS)

Saha, Kakoli

Drumlins are common elements of glaciated landscapes which are easily identified by their distinct morphometric characteristics including shape, length/width ratio, elongation ratio, and uniform direction. To date, most researchers have mapped drumlins by tracing contours on maps, or through on-screen digitization directly on top of hillshaded digital elevation models (DEMs). This paper seeks to utilize the unique morphometric characteristics of drumlins and investigates automated extraction of the landforms as objects from DEMs by Definiens Developer software (V.7), using the 30 m United States Geological Survey National Elevation Dataset DEM as input. The Chautauqua drumlin field in Pennsylvania and upstate New York, USA was chosen as a study area. As the study area is huge (approximately covers 2500 sq.km. of area), small test areas were selected for initial testing of the method. Individual polygons representing the drumlins were extracted from the elevation data set by automated recognition, using Definiens' Multiresolution Segmentation tool, followed by rule-based classification. Subsequently parameters such as length, width and length-width ratio, perimeter and area were measured automatically. To test the accuracy of the method, a second base map was produced by manual on-screen digitization of drumlins from topographic maps and the same morphometric parameters were extracted from the mapped landforms using Definiens Developer. Statistical comparison showed a high agreement between the two methods confirming that object-oriented classification for extraction of drumlins can be used for mapping these landforms. The proposed method represents an attempt to solve the problem by providing a generalized rule-set for mass extraction of drumlins. To check that the automated extraction process was next applied to a larger area. Results showed that the proposed method is as successful for the bigger area as it was for the smaller test areas.

Competition for DNA binding sites using Promega DNA IQ™ paramagnetic beads.

PubMed

Frégeau, Chantal J; De Moors, Anick

2012-09-01

The Promega DNA IQ™ system is easily amenable to automation and has been an integral part of standard operating procedures for many forensic laboratories including those of the Royal Canadian Mounted Police (RCMP) since 2004. Due to some failure to extract DNA from samples that should have produced DNA using our validated automated DNA IQ™-based protocol, the competition for binding sites on the DNA IQ™ magnetic beads was more closely examined. Heme from heavily blooded samples interfered slightly with DNA binding. Increasing the concentration of Proteinase K during lysis of these samples did not enhance DNA recovery. However, diluting the sample lysate following lysis prior to DNA extraction overcame the reduction in DNA yield and preserved portions of the lysates for subsequent manual or automated extraction. Dye/chemicals from black denim lysates competed for binding sites on the DNA IQ™ beads and significantly reduced DNA recovery. Increasing the size or number of black denim cuttings during lysis had a direct adverse effect on DNA yield from various blood volumes. The dilution approach was successful on these samples and permitted the extraction of high DNA yields. Alternatively, shortening the incubation time for cell lysis to 30 min instead of the usual overnight at 56 °C prevented competition from black denim dye/chemicals and increased DNA yields. Crown Copyright © 2011. Published by Elsevier Ireland Ltd. All rights reserved.

Automatized image processing of bovine blastocysts produced in vitro for quantitative variable determination

NASA Astrophysics Data System (ADS)

Rocha, José Celso; Passalia, Felipe José; Matos, Felipe Delestro; Takahashi, Maria Beatriz; Maserati, Marc Peter, Jr.; Alves, Mayra Fernanda; de Almeida, Tamie Guibu; Cardoso, Bruna Lopes; Basso, Andrea Cristina; Nogueira, Marcelo Fábio Gouveia

2017-12-01

There is currently no objective, real-time and non-invasive method for evaluating the quality of mammalian embryos. In this study, we processed images of in vitro produced bovine blastocysts to obtain a deeper comprehension of the embryonic morphological aspects that are related to the standard evaluation of blastocysts. Information was extracted from 482 digital images of blastocysts. The resulting imaging data were individually evaluated by three experienced embryologists who graded their quality. To avoid evaluation bias, each image was related to the modal value of the evaluations. Automated image processing produced 36 quantitative variables for each image. The images, the modal and individual quality grades, and the variables extracted could potentially be used in the development of artificial intelligence techniques (e.g., evolutionary algorithms and artificial neural networks), multivariate modelling and the study of defined structures of the whole blastocyst.

Rapid System to Quantitatively Characterize the Airborne Microbial Community

NASA Technical Reports Server (NTRS)

Macnaughton, Sarah J.

1998-01-01

Bioaerosols have been linked to a wide range of different allergies and respiratory illnesses. Currently, microorganism culture is the most commonly used method for exposure assessment. Such culture techniques, however, generally fail to detect between 90-99% of the actual viable biomass. Consequently, an unbiased technique for detecting airborne microorganisms is essential. In this Phase II proposal, a portable air sampling device his been developed for the collection of airborne microbial biomass from indoor (and outdoor) environments. Methods were evaluated for extracting and identifying lipids that provide information on indoor air microbial biomass, and automation of these procedures was investigated. Also, techniques to automate the extraction of DNA were explored.

Automated solid-phase extraction of herbicides from water for gas chromatographic-mass spectrometric analysis

USGS Publications Warehouse

Meyer, M.T.; Mills, M.S.; Thurman, E.M.

1993-01-01

An automated solid-phase extraction (SPE) method was developed for the pre-concentration of chloroacetanilide and triazine herbicides, and two triazine metabolites from 100-ml water samples. Breakthrough experiments for the C18 SPE cartridge show that the two triazine metabolites are not fully retained and that increasing flow-rate decreases their retention. Standard curve r2 values of 0.998-1.000 for each compound were consistently obtained and a quantitation level of 0.05 ??g/l was achieved for each compound tested. More than 10,000 surface and ground water samples have been analyzed by this method.

Automated process for solvent separation of organic/inorganic substance

DOEpatents

Schweighardt, F.K.

1986-07-29

There is described an automated process for the solvent separation of organic/inorganic substances that operates continuously and unattended and eliminates potential errors resulting from subjectivity and the aging of the sample during analysis. In the process, metered amounts of one or more solvents are passed sequentially through a filter containing the sample under the direction of a microprocessor control apparatus. The mixture in the filter is agitated by ultrasonic cavitation for a timed period and the filtrate is collected. The filtrate of each solvent extraction is collected individually and the residue on the filter element is collected to complete the extraction process. 4 figs.

Automated process for solvent separation of organic/inorganic substance

DOEpatents

Schweighardt, Frank K.

1986-01-01

There is described an automated process for the solvent separation of organic/inorganic substances that operates continuously and unattended and eliminates potential errors resulting from subjectivity and the aging of the sample during analysis. In the process, metered amounts of one or more solvents are passed sequentially through a filter containing the sample under the direction of a microprocessor control apparatus. The mixture in the filter is agitated by ultrasonic cavitation for a timed period and the filtrate is collected. The filtrate of each solvent extraction is collected individually and the residue on the filter element is collected to complete the extraction process.

Automated apparatus for solvent separation of a coal liquefaction product stream

DOEpatents

Schweighardt, Frank K.

1985-01-01

An automated apparatus for the solvent separation of a coal liquefaction product stream that operates continuously and unattended and eliminates potential errors resulting from subjectivity and the aging of the sample during analysis. In use of the apparatus, metered amounts of one or more solvents are passed sequentially through a filter containing the sample under the direction of a microprocessor control means. The mixture in the filter is agitated by means of ultrasonic cavitation for a timed period and the filtrate is collected. The filtrate of each solvent extraction is collected individually and the residue on the filter element is collected to complete the extraction process.

SeqFIRE: a web application for automated extraction of indel regions and conserved blocks from protein multiple sequence alignments.

PubMed

Ajawatanawong, Pravech; Atkinson, Gemma C; Watson-Haigh, Nathan S; Mackenzie, Bryony; Baldauf, Sandra L

2012-07-01

Analyses of multiple sequence alignments generally focus on well-defined conserved sequence blocks, while the rest of the alignment is largely ignored or discarded. This is especially true in phylogenomics, where large multigene datasets are produced through automated pipelines. However, some of the most powerful phylogenetic markers have been found in the variable length regions of multiple alignments, particularly insertions/deletions (indels) in protein sequences. We have developed Sequence Feature and Indel Region Extractor (SeqFIRE) to enable the automated identification and extraction of indels from protein sequence alignments. The program can also extract conserved blocks and identify fast evolving sites using a combination of conservation and entropy. All major variables can be adjusted by the user, allowing them to identify the sets of variables most suited to a particular analysis or dataset. Thus, all major tasks in preparing an alignment for further analysis are combined in a single flexible and user-friendly program. The output includes a numbered list of indels, alignments in NEXUS format with indels annotated or removed and indel-only matrices. SeqFIRE is a user-friendly web application, freely available online at www.seqfire.org/.

Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality

NASA Astrophysics Data System (ADS)

Noh, Myoung-Jong; Howat, Ian M.

2018-02-01

The quality and efficiency of automated Digital Elevation Model (DEM) extraction from stereoscopic satellite imagery is critically dependent on the accuracy of the sensor model used for co-locating pixels between stereo-pair images. In the absence of ground control or manual tie point selection, errors in the sensor models must be compensated with increased matching search-spaces, increasing both the computation time and the likelihood of spurious matches. Here we present an algorithm for automatically determining and compensating the relative bias in Rational Polynomial Coefficients (RPCs) between stereo-pairs utilizing hierarchical, sub-pixel image matching in object space. We demonstrate the algorithm using a suite of image stereo-pairs from multiple satellites over a range stereo-photogrammetrically challenging polar terrains. Besides providing a validation of the effectiveness of the algorithm for improving DEM quality, experiments with prescribed sensor model errors yield insight into the dependence of DEM characteristics and quality on relative sensor model bias. This algorithm is included in the Surface Extraction through TIN-based Search-space Minimization (SETSM) DEM extraction software package, which is the primary software used for the U.S. National Science Foundation ArcticDEM and Reference Elevation Model of Antarctica (REMA) products.

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.

Design of automated oil sludge treatment unit

NASA Astrophysics Data System (ADS)

Chukhareva, N.; Korotchenko, T.; Yurkin, A.

2015-11-01

The article provides the feasibility study of contemporary oil sludge treatment methods. The basic parameters of a new resource-efficient oil sludge treatment unit that allows extracting as much oil as possible and disposing other components in efficient way have been outlined. Based on the calculation results, it has been revealed that in order to reduce the cost of the treatment unit and the expenses related to sludge disposal, it is essential to apply various combinations of the existing treatment methods.

n-SIFT: n-dimensional scale invariant feature transform.

PubMed

Cheung, Warren; Hamarneh, Ghassan

2009-09-01

We propose the n-dimensional scale invariant feature transform (n-SIFT) method for extracting and matching salient features from scalar images of arbitrary dimensionality, and compare this method's performance to other related features. The proposed features extend the concepts used for 2-D scalar images in the computer vision SIFT technique for extracting and matching distinctive scale invariant features. We apply the features to images of arbitrary dimensionality through the use of hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. We analyze the performance of a fully automated multimodal medical image matching technique based on these features, and successfully apply the technique to determine accurate feature point correspondence between pairs of 3-D MRI images and dynamic 3D + time CT data.

Emerging Environmental Contaminants and Soled Phase Microextraction: Janusz Pawliszyn's Legacy in the Environmental Arena

EPA Science Inventory

Solid phase microextraction (SPME) has revolutionized the way samples are extracted, enabling rapid, automated, and solventless extraction of many different sample types, including air, water, soil, and biological samples. As such, SPME is widely used for environmental, food, fo...

Dependency-based long short term memory network for drug-drug interaction extraction.

PubMed

Wang, Wei; Yang, Xi; Yang, Canqun; Guo, Xiaowei; Zhang, Xiang; Wu, Chengkun

2017-12-28

Drug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they have made significant progress in relation identification. We propose a dependency-based deep neural network model for DDI extraction. By introducing the dependency-based technique to a bi-directional long short term memory network (Bi-LSTM), we build three channels, namely, Linear channel, DFS channel and BFS channel. All of these channels are constructed with three network layers, including embedding layer, LSTM layer and max pooling layer from bottom up. In the embedding layer, we extract two types of features, one is distance-based feature and another is dependency-based feature. In the LSTM layer, a Bi-LSTM is instituted in each channel to better capture relation information. Then max pooling is used to get optimal features from the entire encoding sequential data. At last, we concatenate the outputs of all channels and then link it to the softmax layer for relation identification. To the best of our knowledge, our model achieves new state-of-the-art performance with the F-score of 72.0% on the DDIExtraction 2013 corpus. Moreover, our approach obtains much higher Recall value compared to the existing methods. The dependency-based Bi-LSTM model can learn effective relation information with less feature engineering in the task of DDI extraction. Besides, the experimental results show that our model excels at balancing the Precision and Recall values.

Automated extraction and semantic analysis of mutation impacts from the biomedical literature

PubMed Central

2012-01-01

Background Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually curating the rich and fast growing repository of biomedical literature is expensive and time-consuming. As a solution, text mining approaches have increasingly been deployed in the biomedical domain. While the detection of single-point mutations is well covered by existing systems, challenges still exist in grounding impacts to their respective mutations and recognizing the affected protein properties, in particular kinetic and stability properties together with physical quantities. Results We present an ontology model for mutation impacts, together with a comprehensive text mining system for extracting and analysing mutation impact information from full-text articles. Organisms, as sources of proteins, are extracted to help disambiguation of genes and proteins. Our system then detects mutation series to correctly ground detected impacts using novel heuristics. It also extracts the affected protein properties, in particular kinetic and stability properties, as well as the magnitude of the effects and validates these relations against the domain ontology. The output of our system can be provided in various formats, in particular by populating an OWL-DL ontology, which can then be queried to provide structured information. The performance of the system is evaluated on our manually annotated corpora. In the impact detection task, our system achieves a precision of 70.4%-71.1%, a recall of 71.3%-71.5%, and grounds the detected impacts with an accuracy of 76.5%-77%. The developed system, including resources, evaluation data and end-user and developer documentation is freely available under an open source license at http://www.semanticsoftware.info/open-mutation-miner. Conclusion We present Open Mutation Miner (OMM), the first comprehensive, fully open-source approach to automatically extract impacts and related relevant information from the biomedical literature. We assessed the performance of our work on manually annotated corpora and the results show the reliability of our approach. The representation of the extracted information into a structured format facilitates knowledge management and aids in database curation and correction. Furthermore, access to the analysis results is provided through multiple interfaces, including web services for automated data integration and desktop-based solutions for end user interactions. PMID:22759648

Automated extraction of radiation dose information from CT dose report images.

PubMed

Li, Xinhua; Zhang, Da; Liu, Bob

2011-06-01

The purpose of this article is to describe the development of an automated tool for retrieving texts from CT dose report images. Optical character recognition was adopted to perform text recognitions of CT dose report images. The developed tool is able to automate the process of analyzing multiple CT examinations, including text recognition, parsing, error correction, and exporting data to spreadsheets. The results were precise for total dose-length product (DLP) and were about 95% accurate for CT dose index and DLP of scanned series.

A Case Study of Reverse Engineering Integrated in an Automated Design Process

NASA Astrophysics Data System (ADS)

Pescaru, R.; Kyratsis, P.; Oancea, G.

2016-11-01

This paper presents a design methodology which automates the generation of curves extracted from the point clouds that have been obtained by digitizing the physical objects. The methodology is described on a product belonging to the industry of consumables, respectively a footwear type product that has a complex shape with many curves. The final result is the automated generation of wrapping curves, surfaces and solids according to the characteristics of the customer's foot, and to the preferences for the chosen model, which leads to the development of customized products.

Determination of 21 drugs in oral fluid using fully automated supported liquid extraction and UHPLC-MS/MS.

PubMed

Valen, Anja; Leere Øiestad, Åse Marit; Strand, Dag Helge; Skari, Ragnhild; Berg, Thomas

2017-05-01

Collection of oral fluid (OF) is easy and non-invasive compared to the collection of urine and blood, and interest in OF for drug screening and diagnostic purposes is increasing. A high-throughput ultra-high-performance liquid chromatography-tandem mass spectrometry method for determination of 21 drugs in OF using fully automated 96-well plate supported liquid extraction for sample preparation is presented. The method contains a selection of classic drugs of abuse, including amphetamines, cocaine, cannabis, opioids, and benzodiazepines. The method was fully validated for 200 μL OF/buffer mix using an Intercept OF sampling kit; validation included linearity, sensitivity, precision, accuracy, extraction recovery, matrix effects, stability, and carry-over. Inter-assay precision (RSD) and accuracy (relative error) were <15% and 13 to 5%, respectively, for all compounds at concentrations equal to or higher than the lower limit of quantification. Extraction recoveries were between 58 and 76% (RSD < 8%), except for tetrahydrocannabinol and three 7-amino benzodiazepine metabolites with recoveries between 23 and 33% (RSD between 51 and 52 % and 11 and 25%, respectively). Ion enhancement or ion suppression effects were observed for a few compounds; however, to a large degree they were compensated for by the internal standards used. Deuterium-labelled and 13 C-labelled internal standards were used for 8 and 11 of the compounds, respectively. In a comparison between Intercept and Quantisal OF kits, better recoveries and fewer matrix effects were observed for some compounds using Quantisal. The method is sensitive and robust for its purposes and has been used successfully since February 2015 for analysis of Intercept OF samples from 2600 cases in a 12-month period. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A natural language processing program effectively extracts key pathologic findings from radical prostatectomy reports.

PubMed

Kim, Brian J; Merchant, Madhur; Zheng, Chengyi; Thomas, Anil A; Contreras, Richard; Jacobsen, Steven J; Chien, Gary W

2014-12-01

Natural language processing (NLP) software programs have been widely developed to transform complex free text into simplified organized data. Potential applications in the field of medicine include automated report summaries, physician alerts, patient repositories, electronic medical record (EMR) billing, and quality metric reports. Despite these prospects and the recent widespread adoption of EMR, NLP has been relatively underutilized. The objective of this study was to evaluate the performance of an internally developed NLP program in extracting select pathologic findings from radical prostatectomy specimen reports in the EMR. An NLP program was generated by a software engineer to extract key variables from prostatectomy reports in the EMR within our healthcare system, which included the TNM stage, Gleason grade, presence of a tertiary Gleason pattern, histologic subtype, size of dominant tumor nodule, seminal vesicle invasion (SVI), perineural invasion (PNI), angiolymphatic invasion (ALI), extracapsular extension (ECE), and surgical margin status (SMS). The program was validated by comparing NLP results to a gold standard compiled by two blinded manual reviewers for 100 random pathology reports. NLP demonstrated 100% accuracy for identifying the Gleason grade, presence of a tertiary Gleason pattern, SVI, ALI, and ECE. It also demonstrated near-perfect accuracy for extracting histologic subtype (99.0%), PNI (98.9%), TNM stage (98.0%), SMS (97.0%), and dominant tumor size (95.7%). The overall accuracy of NLP was 98.7%. NLP generated a result in <1 second, whereas the manual reviewers averaged 3.2 minutes per report. This novel program demonstrated high accuracy and efficiency identifying key pathologic details from the prostatectomy report within an EMR system. NLP has the potential to assist urologists by summarizing and highlighting relevant information from verbose pathology reports. It may also facilitate future urologic research through the rapid and automated creation of large databases.

Rapid determination of six carcinogenic primary aromatic amines in mainstream cigarette smoke by two-dimensional online solid phase extraction combined with liquid chromatography tandem mass spectrometry.

PubMed

Bie, Zhenying; Lu, Wei; Zhu, You; Chen, Yusong; Ren, Hubo; Ji, Lishun

2017-01-27

A fully automated, rapid, and reliable method for simultaneous determination of six carcinogenic primary aromatic amines (AAs), including o-toluidine (o-TOL), 2, 6-dimethylaniline (2, 6-DMA), o-anisidine (o-ASD), 1-naphthylamine (1-ANP), 2-naphthylamine (2-ANP), and 4-aminobiphenyl (4-ABP), in mainstream cigarette smoke was established. The proposed method was based on two-dimensional online solid phase extraction combined with liquid chromatography tandem mass spectrometry (SPE/LC-MS/MS). The particulate phase of the mainstream cigarette smoke was collected on a Cambridge filter pad and pretreated via ultrasonic extraction with 2% formic acid (FA), while the gas phase was trapped by 2% FA without pretreatment for determination. The two-dimensional online SPE comprised of two cartridges with different absorption characteristics was applied for sample pretreatment. Analysis was performed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) under multiple reaction monitoring mode. Each sample required about 0.5h for solid phase extraction and analysis. The limit of detections (LODs) for six AAs ranged from 0.04 to 0.58ng/cig and recoveries were within 84.5%-122.9%. The relative standard deviations of intra- and inter-day tests for 3R4F reference cigarette were less than 6% and 7%, respectively, while no more than 7% and 8% separately for a type of Virginia cigarette. The proposed method enabled minimum sample pretreatment, full automation, and high throughput with high selectivity, sensitivity, and accuracy. As a part of the validation procedure, fifteen brands of cigarettes were tested by the designed method. Copyright © 2016 Elsevier B.V. All rights reserved.

Determination of perfluorinated chemicals in food and drinking water using high-flow solid-phase extraction and ultra-high performance liquid chromatography/tandem mass spectrometry.

PubMed

Chang, Ying-Chia; Chen, Wen-Ling; Bai, Fang-Yu; Chen, Pau-Chung; Wang, Gen-Shuh; Chen, Chia-Yang

2012-01-01

For this study, we developed methods of determining ten perfluorinated chemicals in drinking water, milk, fish, beef, and pig liver using high-flow automated solid-phase extraction (SPE) and ultra-high performance liquid chromatography/tandem mass spectrometry. The analytes were separated on a core-shell Kinetex C18 column. The mobile phase was composed of methanol and 10-mM N-methylmorpholine. Milk was digested with 0.5 N potassium hydroxide in Milli-Q water, and was extracted with an Atlantic HLB disk to perform automated SPE at a flow rate ranged from 70 to 86 mL/min. Drinking water was directly extracted by the SPE. Solid food samples were digested in alkaline methanol and their supernatants were diluted and also processed by SPE. The disks were washed with 40% methanol/60% water and then eluted with 0.1% ammonium hydroxide in methanol. Suppression of signal intensity of most analytes by matrixes was lower than 50%; it was generally lower in fish and drinking water but higher in liver. Most quantitative biases and relative standard deviations were lower than 15%. The limits of detection for most analytes were sub-nanograms per liter for drinking water and sub-nanograms per gram for solid food samples. This method greatly shortened the time and labor needed for digestion, SPE, and liquid chromatography. This method has been applied to analyze 14 types of food samples. Perfluorooctanoic acid was found to be the highest among the analytes (median at 3.2-64 ng/g wet weight), followed by perfluorodecanoic acid (0.7-25 ng/g) and perfluorododecanoic acid (0.6-15 ng/g).

Automated Design Tools for Integrated Mixed-Signal Microsystems (NeoCAD)

DTIC Science & Technology

2005-02-01

method, Model Order Reduction (MOR) tools, system-level, mixed-signal circuit synthesis and optimization tools, and parsitic extraction tools. A unique...Mission Area: Command and Control mixed signal circuit simulation parasitic extraction time-domain simulation IC design flow model order reduction... Extraction 1.2 Overall Program Milestones CHAPTER 2 FAST TIME DOMAIN MIXED-SIGNAL CIRCUIT SIMULATION 2.1 HAARSPICE Algorithms 2.1.1 Mathematical Background

Using Process Redesign and Information Technology to Improve Procurement

DTIC Science & Technology

1994-04-01

contrac- tor. Many large-volume contractors have automated order processing tied to ac- counting, manufacturing, and shipping subsystems. Currently...the contractor must receive the mailed order, analyze it, extract pertinent information, and en- ter that information into the automated order ... processing system. Almost all orders for small purchases are unilateral documents that do not require acceptance or acknowledgment by the contractor. For

CARES: Completely Automated Robust Edge Snapper for carotid ultrasound IMT measurement on a multi-institutional database of 300 images: a two stage system combining an intensity-based feature approach with first order absolute moments

NASA Astrophysics Data System (ADS)

Molinari, Filippo; Acharya, Rajendra; Zeng, Guang; Suri, Jasjit S.

2011-03-01

The carotid intima-media thickness (IMT) is the most used marker for the progression of atherosclerosis and onset of the cardiovascular diseases. Computer-aided measurements improve accuracy, but usually require user interaction. In this paper we characterized a new and completely automated technique for carotid segmentation and IMT measurement based on the merits of two previously developed techniques. We used an integrated approach of intelligent image feature extraction and line fitting for automatically locating the carotid artery in the image frame, followed by wall interfaces extraction based on Gaussian edge operator. We called our system - CARES. We validated the CARES on a multi-institutional database of 300 carotid ultrasound images. IMT measurement bias was 0.032 +/- 0.141 mm, better than other automated techniques and comparable to that of user-driven methodologies. Our novel approach of CARES processed 96% of the images leading to the figure of merit to be 95.7%. CARES ensured complete automation and high accuracy in IMT measurement; hence it could be a suitable clinical tool for processing of large datasets in multicenter studies involving atherosclerosis.pre-

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

Covington, E; Younge, K; Chen, X

Purpose: To evaluate the effectiveness of an automated plan check tool to improve first-time plan quality as well as standardize and document performance of physics plan checks. Methods: The Plan Checker Tool (PCT) uses the Eclipse Scripting API to check and compare data from the treatment planning system (TPS) and treatment management system (TMS). PCT was created to improve first-time plan quality, reduce patient delays, increase efficiency of our electronic workflow, and to standardize and partially automate plan checks in the TPS. A framework was developed which can be configured with different reference values and types of checks. One examplemore » is the prescribed dose check where PCT flags the user when the planned dose and the prescribed dose disagree. PCT includes a comprehensive checklist of automated and manual checks that are documented when performed by the user. A PDF report is created and automatically uploaded into the TMS. Prior to and during PCT development, errors caught during plan checks and also patient delays were tracked in order to prioritize which checks should be automated. The most common and significant errors were determined. Results: Nineteen of 33 checklist items were automated with data extracted with the PCT. These include checks for prescription, reference point and machine scheduling errors which are three of the top six causes of patient delays related to physics and dosimetry. Since the clinical roll-out, no delays have been due to errors that are automatically flagged by the PCT. Development continues to automate the remaining checks. Conclusion: With PCT, 57% of the physics plan checklist has been partially or fully automated. Treatment delays have declined since release of the PCT for clinical use. By tracking delays and errors, we have been able to measure the effectiveness of automating checks and are using this information to prioritize future development. This project was supported in part by P01CA059827.« less

Building Facade Reconstruction by Fusing Terrestrial Laser Points and Images

PubMed Central

Pu, Shi; Vosselman, George

2009-01-01

Laser data and optical data have a complementary nature for three dimensional feature extraction. Efficient integration of the two data sources will lead to a more reliable and automated extraction of three dimensional features. This paper presents a semiautomatic building facade reconstruction approach, which efficiently combines information from terrestrial laser point clouds and close range images. A building facade's general structure is discovered and established using the planar features from laser data. Then strong lines in images are extracted using Canny extractor and Hough transformation, and compared with current model edges for necessary improvement. Finally, textures with optimal visibility are selected and applied according to accurate image orientations. Solutions to several challenge problems throughout the collaborated reconstruction, such as referencing between laser points and multiple images and automated texturing, are described. The limitations and remaining works of this approach are also discussed. PMID:22408539

Automated Solar Flare Detection and Feature Extraction in High-Resolution and Full-Disk Hα Images

NASA Astrophysics Data System (ADS)

Yang, Meng; Tian, Yu; Liu, Yangyi; Rao, Changhui

2018-05-01

In this article, an automated solar flare detection method applied to both full-disk and local high-resolution Hα images is proposed. An adaptive gray threshold and an area threshold are used to segment the flare region. Features of each detected flare event are extracted, e.g. the start, peak, and end time, the importance class, and the brightness class. Experimental results have verified that the proposed method can obtain more stable and accurate segmentation results than previous works on full-disk images from Big Bear Solar Observatory (BBSO) and Kanzelhöhe Observatory for Solar and Environmental Research (KSO), and satisfying segmentation results on high-resolution images from the Goode Solar Telescope (GST). Moreover, the extracted flare features correlate well with the data given by KSO. The method may be able to implement a more complicated statistical analysis of Hα solar flares.

Biomorphic networks: approach to invariant feature extraction and segmentation for ATR

NASA Astrophysics Data System (ADS)

Baek, Andrew; Farhat, Nabil H.

1998-10-01

Invariant features in two dimensional binary images are extracted in a single layer network of locally coupled spiking (pulsating) model neurons with prescribed synapto-dendritic response. The feature vector for an image is represented as invariant structure in the aggregate histogram of interspike intervals obtained by computing time intervals between successive spikes produced from each neuron over a given period of time and combining such intervals from all neurons in the network into a histogram. Simulation results show that the feature vectors are more pattern-specific and invariant under translation, rotation, and change in scale or intensity than achieved in earlier work. We also describe an application of such networks to segmentation of line (edge-enhanced or silhouette) images. The biomorphic spiking network's capabilities in segmentation and invariant feature extraction may prove to be, when they are combined, valuable in Automated Target Recognition (ATR) and other automated object recognition systems.

Extraction of the number of peroxisomes in yeast cells by automated image analysis.

PubMed

Niemistö, Antti; Selinummi, Jyrki; Saleem, Ramsey; Shmulevich, Ilya; Aitchison, John; Yli-Harja, Olli

2006-01-01

An automated image analysis method for extracting the number of peroxisomes in yeast cells is presented. Two images of the cell population are required for the method: a bright field microscope image from which the yeast cells are detected and the respective fluorescent image from which the number of peroxisomes in each cell is found. The segmentation of the cells is based on clustering the local mean-variance space. The watershed transformation is thereafter employed to separate cells that are clustered together. The peroxisomes are detected by thresholding the fluorescent image. The method is tested with several images of a budding yeast Saccharomyces cerevisiae population, and the results are compared with manually obtained results.

Methods for automatically analyzing humpback song units.

PubMed

Rickwood, Peter; Taylor, Andrew

2008-03-01

This paper presents mathematical techniques for automatically extracting and analyzing bioacoustic signals. Automatic techniques are described for isolation of target signals from background noise, extraction of features from target signals and unsupervised classification (clustering) of the target signals based on these features. The only user-provided inputs, other than raw sound, is an initial set of signal processing and control parameters. Of particular note is that the number of signal categories is determined automatically. The techniques, applied to hydrophone recordings of humpback whales (Megaptera novaeangliae), produce promising initial results, suggesting that they may be of use in automated analysis of not only humpbacks, but possibly also in other bioacoustic settings where automated analysis is desirable.

Automated extraction of knowledge for model-based diagnostics

NASA Technical Reports Server (NTRS)

Gonzalez, Avelino J.; Myler, Harley R.; Towhidnejad, Massood; Mckenzie, Frederic D.; Kladke, Robin R.

1990-01-01

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.

ASE extraction method for simultaneous carbon and nitrogen stable isotope analysis in soft tissues of aquatic organisms.

PubMed

Bodin, Nathalie; Budzinski, Hélène; Le Ménach, Karyn; Tapie, Nathalie

2009-06-08

Since lipids are depleted in 13C relative to proteins and carbohydrates, variations in lipid composition among species and within individuals significantly influence delta13C and may result in misleading ecological interpretations. Whereas lipid extraction before IRMS analysis constitutes a way of stable isotope result lipid-normalisation, such a procedure was given up because of the un-controlled effects of the methods used (i.e., "Bligh & Dyer", Soxhlet, etc.) on delta15N. The aim of this work was to develop a simple, rapid and efficient lipid extraction method allowing for simultaneous C and N stable isotope analysis in the biological soft tissues of aquatic organisms. The goal was to be free from the lipid influence on delta13C values without interfering with delta15N values. For that purpose, the modern automated pressurized liquid extraction technique ASE (accelerated solvent extraction) was selected. Eel muscles representative of a broad range of fat contents were extracted via ASE by using different semi-polar solvents (100% dichloromethane and 80% n-hexane/20% acetone) and by operating at different temperature (ambient temperature and 100 degrees C) and pressure (750 and 1900 psi) conditions. The results were discussed in terms of lipid extraction efficiency as well as delta13C and delta15N variability.

Mapping Cortical Laminar Structure in the 3D BigBrain.

PubMed

Wagstyl, Konrad; Lepage, Claude; Bludau, Sebastian; Zilles, Karl; Fletcher, Paul C; Amunts, Katrin; Evans, Alan C

2018-07-01

Histological sections offer high spatial resolution to examine laminar architecture of the human cerebral cortex; however, they are restricted by being 2D, hence only regions with sufficiently optimal cutting planes can be analyzed. Conversely, noninvasive neuroimaging approaches are whole brain but have relatively low resolution. Consequently, correct 3D cross-cortical patterns of laminar architecture have never been mapped in histological sections. We developed an automated technique to identify and analyze laminar structure within the high-resolution 3D histological BigBrain. We extracted white matter and pial surfaces, from which we derived histologically verified surfaces at the layer I/II boundary and within layer IV. Layer IV depth was strongly predicted by cortical curvature but varied between areas. This fully automated 3D laminar analysis is an important requirement for bridging high-resolution 2D cytoarchitecture and in vivo 3D neuroimaging. It lays the foundation for in-depth, whole-brain analyses of cortical layering.

Simultaneous determination of dextromethorphan, dextrorphan, and guaifenesin in human plasma using semi-automated liquid/liquid extraction and gradient liquid chromatography tandem mass spectrometry.

PubMed

Eichhold, Thomas H; McCauley-Myers, David L; Khambe, Deepa A; Thompson, Gary A; Hoke, Steven H

2007-01-17

A method for the simultaneous determination of dextromethorphan (DEX), dextrorphan (DET), and guaifenesin (GG) in human plasma was developed, validated, and applied to determine plasma concentrations of these compounds in samples from six clinical pharmacokinetic (PK) studies. Semi-automated liquid handling systems were used to perform the majority of the sample manipulation including liquid/liquid extraction (LLE) of the analytes from human plasma. Stable-isotope-labeled analogues were utilized as internal standards (ISTDs) for each analyte to facilitate accurate and precise quantification. Extracts were analyzed using gradient liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Use of semi-automated LLE with LC-MS/MS proved to be a very rugged and reliable approach for analysis of more than 6200 clinical study samples. The lower limit of quantification was validated at 0.010, 0.010, and 1.0 ng/mL of plasma for DEX, DET, and GG, respectively. Accuracy and precision of quality control (QC) samples for all three analytes met FDA Guidance criteria of +/-15% for average QC accuracy with coefficients of variation less than 15%. Data from the thorough evaluation of the method during development, validation, and application are presented to characterize selectivity, linearity, over-range sample analysis, accuracy, precision, autosampler carry-over, ruggedness, extraction efficiency, ionization suppression, and stability. Pharmacokinetic data are also provided to illustrate improvements in systemic drug and metabolite concentration-time profiles that were achieved by formulation optimization.

Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection.

PubMed

Carrio, Adrian; Sampedro, Carlos; Sanchez-Lopez, Jose Luis; Pimienta, Miguel; Campoy, Pascual

2015-11-24

Lateral flow assay tests are nowadays becoming powerful, low-cost diagnostic tools. Obtaining a result is usually subject to visual interpretation of colored areas on the test by a human operator, introducing subjectivity and the possibility of errors in the extraction of the results. While automated test readers providing a result-consistent solution are widely available, they usually lack portability. In this paper, we present a smartphone-based automated reader for drug-of-abuse lateral flow assay tests, consisting of an inexpensive light box and a smartphone device. Test images captured with the smartphone camera are processed in the device using computer vision and machine learning techniques to perform automatic extraction of the results. A deep validation of the system has been carried out showing the high accuracy of the system. The proposed approach, applicable to any line-based or color-based lateral flow test in the market, effectively reduces the manufacturing costs of the reader and makes it portable and massively available while providing accurate, reliable results.

CERES: A Set of Automated Routines for Echelle Spectra

NASA Astrophysics Data System (ADS)

Brahm, Rafael; Jordán, Andrés; Espinoza, Néstor

2017-03-01

We present the Collection of Elemental Routines for Echelle Spectra (CERES). These routines were developed for the construction of automated pipelines for the reduction, extraction, and analysis of spectra acquired with different instruments, allowing the obtention of homogeneous and standardized results. This modular code includes tools for handling the different steps of the processing: CCD image reductions; identification and tracing of the echelle orders; optimal and rectangular extraction; computation of the wavelength solution; estimation of radial velocities; and rough and fast estimation of the atmospheric parameters. Currently, CERES has been used to develop automated pipelines for 13 different spectrographs, namely CORALIE, FEROS, HARPS, ESPaDOnS, FIES, PUCHEROS, FIDEOS, CAFE, DuPont/Echelle, Magellan/Mike, Keck/HIRES, Magellan/PFS, and APO/ARCES, but the routines can be easily used to deal with data coming from other spectrographs. We show the high precision in radial velocity that CERES achieves for some of these instruments, and we briefly summarize some results that have already been obtained using the CERES pipelines.

Note: An automated image analysis method for high-throughput classification of surface-bound bacterial cell motions.

PubMed

Shen, Simon; Syal, Karan; Tao, Nongjian; Wang, Shaopeng

2015-12-01

We present a Single-Cell Motion Characterization System (SiCMoCS) to automatically extract bacterial cell morphological features from microscope images and use those features to automatically classify cell motion for rod shaped motile bacterial cells. In some imaging based studies, bacteria cells need to be attached to the surface for time-lapse observation of cellular processes such as cell membrane-protein interactions and membrane elasticity. These studies often generate large volumes of images. Extracting accurate bacterial cell morphology features from these images is critical for quantitative assessment. Using SiCMoCS, we demonstrated simultaneous and automated motion tracking and classification of hundreds of individual cells in an image sequence of several hundred frames. This is a significant improvement from traditional manual and semi-automated approaches to segmenting bacterial cells based on empirical thresholds, and a first attempt to automatically classify bacterial motion types for motile rod shaped bacterial cells, which enables rapid and quantitative analysis of various types of bacterial motion.

A computer-aided system for automatic extraction of femur neck trabecular bone architecture using isotropic volume construction from clinical hip computed tomography images.

PubMed

Vivekanandhan, Sapthagirivasan; Subramaniam, Janarthanam; Mariamichael, Anburajan

2016-10-01

Hip fractures due to osteoporosis are increasing progressively across the globe. It is also difficult for those fractured patients to undergo dual-energy X-ray absorptiometry scans due to its complicated protocol and its associated cost. The utilisation of computed tomography for the fracture treatment has become common in the clinical practice. It would be helpful for orthopaedic clinicians, if they could get some additional information related to bone strength for better treatment planning. The aim of our study was to develop an automated system to segment the femoral neck region, extract the cortical and trabecular bone parameters, and assess the bone strength using an isotropic volume construction from clinical computed tomography images. The right hip computed tomography and right femur dual-energy X-ray absorptiometry measurements were taken from 50 south-Indian females aged 30-80 years. Each computed tomography image volume was re-constructed to form isotropic volumes. An automated system by incorporating active contour models was used to segment the neck region. A minimum distance boundary method was applied to isolate the cortical and trabecular bone components. The trabecular bone was enhanced and segmented using trabecular enrichment approach. The cortical and trabecular bone features were extracted and statistically compared with dual-energy X-ray absorptiometry measured femur neck bone mineral density. The extracted bone measures demonstrated a significant correlation with neck bone mineral density (r > 0.7, p < 0.001). The inclusion of cortical measures, along with the trabecular measures extracted after isotropic volume construction and trabecular enrichment approach procedures, resulted in better estimation of bone strength. The findings suggest that the proposed system using the clinical computed tomography images scanned with low dose could eventually be helpful in osteoporosis diagnosis and its treatment planning. © IMechE 2016.

Microtiter miniature shaken bioreactor system as a scale-down model for process development of production of therapeutic alpha-interferon2b by recombinant Escherichia coli.

PubMed

Tan, Joo Shun; Abbasiliasi, Sahar; Kadkhodaei, Saeid; Tam, Yew Joon; Tang, Teck-Kim; Lee, Yee-Ying; Ariff, Arbakariya B

2018-01-04

Demand for high-throughput bioprocessing has dramatically increased especially in the biopharmaceutical industry because the technologies are of vital importance to process optimization and media development. This can be efficiently boosted by using microtiter plate (MTP) cultivation setup embedded into an automated liquid-handling system. The objective of this study was to establish an automated microscale method for upstream and downstream bioprocessing of α-IFN2b production by recombinant Escherichia coli. The extraction performance of α-IFN2b by osmotic shock using two different systems, automated microscale platform and manual extraction in MTP was compared. The amount of α-IFN2b extracted using automated microscale platform (49.2 μg/L) was comparable to manual osmotic shock method (48.8 μg/L), but the standard deviation was 2 times lower as compared to manual osmotic shock method. Fermentation parameters in MTP involving inoculum size, agitation speed, working volume and induction profiling revealed that the fermentation conditions for the highest production of α-IFN2b (85.5 μg/L) was attained at inoculum size of 8%, working volume of 40% and agitation speed of 1000 rpm with induction at 4 h after the inoculation. Although the findings at MTP scale did not show perfect scalable results as compared to shake flask culture, but microscale technique development would serve as a convenient and low-cost solution in process optimization for recombinant protein.

An automated system for liquid-liquid extraction in monosegmented flow analysis

PubMed Central

Facchin, Ileana; Pasquini, Celio

1997-01-01

An automated system to perform liquid-liquid extraction in monosegmented flow analysis is described. The system is controlled by a microcomputer that can track the localization of the aqueous monosegmented sample in the manifold. Optical switches are employed to sense the gas-liquid interface of the air bubbles that define the monosegment. The logical level changes, generated by the switches, are flagged by the computer through a home-made interface that also contains the analogue-to-digital converter for signal acquisition. The sequence of operations, necessary for a single extraction or for concentration of the analyte in the organic phase, is triggered by these logical transitions. The system was evaluated for extraction of Cd(II), Cu(II) and Zn(II) and concentration of Cd(II) from aqueous solutions at pH 9.9 (NH3/NH4Cl buffer) into chloroform containing PAN (1-(2-pyridylazo)-2-naphthol) . The results show a mean repeatability of 3% (rsd) for a 2.0 mg l-1 Cd(II) solution and a linear increase of the concentration factor for a 0.5mg l-1 Cd(II) solution observed for up to nine extraction cycles. PMID:18924792

Hierarchical extraction of urban objects from mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Zhao, Gang; Dai, Wenxia

2015-01-01

Point clouds collected in urban scenes contain a huge number of points (e.g., billions), numerous objects with significant size variability, complex and incomplete structures, and variable point densities, raising great challenges for the automated extraction of urban objects in the field of photogrammetry, computer vision, and robotics. This paper addresses these challenges by proposing an automated method to extract urban objects robustly and efficiently. The proposed method generates multi-scale supervoxels from 3D point clouds using the point attributes (e.g., colors, intensities) and spatial distances between points, and then segments the supervoxels rather than individual points by combining graph based segmentation with multiple cues (e.g., principal direction, colors) of the supervoxels. The proposed method defines a set of rules for merging segments into meaningful units according to types of urban objects and forms the semantic knowledge of urban objects for the classification of objects. Finally, the proposed method extracts and classifies urban objects in a hierarchical order ranked by the saliency of the segments. Experiments show that the proposed method is efficient and robust for extracting buildings, streetlamps, trees, telegraph poles, traffic signs, cars, and enclosures from mobile laser scanning (MLS) point clouds, with an overall accuracy of 92.3%.

[Development of an automated processing method to detect coronary motion for coronary magnetic resonance angiography].

PubMed

Asou, Hiroya; Imada, N; Sato, T

2010-06-20

On coronary MR angiography (CMRA), cardiac motions worsen the image quality. To improve the image quality, detection of cardiac especially for individual coronary motion is very important. Usually, scan delay and duration were determined manually by the operator. We developed a new evaluation method to calculate static time of individual coronary artery. At first, coronary cine MRI was taken at the level of about 3 cm below the aortic valve (80 images/R-R). Chronological change of the signals were evaluated with Fourier transformation of each pixel of the images were done. Noise reduction with subtraction process and extraction process were done. To extract higher motion such as coronary arteries, morphological filter process and labeling process were added. Using these imaging processes, individual coronary motion was extracted and individual coronary static time was calculated automatically. We compared the images with ordinary manual method and new automated method in 10 healthy volunteers. Coronary static times were calculated with our method. Calculated coronary static time was shorter than that of ordinary manual method. And scan time became about 10% longer than that of ordinary method. Image qualities were improved in our method. Our automated detection method for coronary static time with chronological Fourier transformation has a potential to improve the image quality of CMRA and easy processing.

Automated Detection of Solar Loops by the Oriented Connectivity Method

NASA Technical Reports Server (NTRS)

Lee, Jong Kwan; Newman, Timothy S.; Gary, G. Allen

2004-01-01

An automated technique to segment solar coronal loops from intensity images of the Sun s corona is introduced. It exploits physical characteristics of the solar magnetic field to enable robust extraction from noisy images. The technique is a constructive curve detection approach, constrained by collections of estimates of the magnetic fields orientation. Its effectiveness is evaluated through experiments on synthetic and real coronal images.

Automated Nucleic Acid Extraction Systems for Detecting Cytomegalovirus and Epstein-Barr Virus Using Real-Time PCR: A Comparison Study Between the QIAsymphony RGQ and QIAcube Systems.

PubMed

Kim, Hanah; Hur, Mina; Kim, Ji Young; Moon, Hee Won; Yun, Yeo Min; Cho, Hyun Chan

2017-03-01

Cytomegalovirus (CMV) and Epstein-Barr virus (EBV) are increasingly important in immunocompromised patients. Nucleic acid extraction methods could affect the results of viral nucleic acid amplification tests. We compared two automated nucleic acid extraction systems for detecting CMV and EBV using real-time PCR assays. One hundred and fifty-three whole blood (WB) samples were tested for CMV detection, and 117 WB samples were tested for EBV detection. Viral nucleic acid was extracted in parallel by using QIAsymphony RGQ and QIAcube (Qiagen GmbH, Germany), and real-time PCR assays for CMV and EBV were performed with a Rotor-Gene Q real-time PCR cycler (Qiagen). Detection rates for CMV and EBV were compared, and agreements between the two systems were analyzed. The detection rate of CMV and EBV differed significantly between the QIAsymphony RGQ and QIAcube systems (CMV, 59.5% [91/153] vs 43.8% [67/153], P=0.0005; EBV, 59.0% [69/117] vs 42.7% [50/117], P=0.0008). The two systems showed moderate agreement for CMV and EBV detection (kappa=0.43 and 0.52, respectively). QIAsymphony RGQ showed a negligible correlation with QIAcube for quantitative EBV detection. QIAcube exhibited EBV PCR inhibition in 23.9% (28/117) of samples. Automated nucleic acid extraction systems have different performances and significantly affect the detection of viral pathogens. The QIAsymphony RGQ system appears to be superior to the QIAcube system for detecting CMV and EBV. A suitable sample preparation system should be considered for optimized nucleic acid amplification in clinical laboratories.

High-volume extraction of nucleic acids by magnetic bead technology for ultrasensitive detection of bacteria in blood components.

PubMed

Störmer, Melanie; Kleesiek, Knut; Dreier, Jens

2007-01-01

Nucleic acid isolation, the most technically demanding and laborious procedure performed in molecular diagnostics, harbors the potential for improvements in automation. A recent development is the use of magnetic beads covered with nucleic acid-binding matrices. We adapted this technology with a broad-range 23S rRNA real-time reverse transcription (RT)-PCR assay for fast and sensitive detection of bacterial contamination of blood products. We investigated different protocols for an automated high-volume extraction method based on magnetic-separation technology for the extraction of bacterial nucleic acids from platelet concentrates (PCs). We added 2 model bacteria, Staphylococcus epidermidis and Escherichia coli, to a single pool of apheresis-derived, single-donor platelets and assayed the PCs by real-time RT-PCR analysis with an improved primer-probe system and locked nucleic acid technology. Co-amplification of human beta(2)-microglobulin mRNA served as an internal control (IC). We used probit analysis to calculate the minimum concentration of bacteria that would be detected with 95% confidence. For automated magnetic bead-based extraction technology with the real-time RT-PCR, the 95% detection limit was 29 x 10(3) colony-forming units (CFU)/L for S. epidermidis and 22 x 10(3) CFU/L for E. coli. No false-positive results occurred, either due to nucleic acid contamination of reagents or externally during testing of 1030 PCs. High-volume nucleic acid extraction improved the detection limit of the assay. The improvement of the primer-probe system and the integration of an IC make the RT-PCR assay appropriate for bacteria screening of platelets.

Coupling solid-phase extraction and enzyme-linked immunosorbent assay for ultratrace determination of herbicides in pristine water

USGS Publications Warehouse

Aga, D.S.; Thurman, E.M.

1993-01-01

Solid-phase extraction (SPE) and enzyme-linked immunosorbent assay (ELISA) were coupled for automated trace analysis of pristine water samples containing 2-chloro-4-ethylamino-6-isopropylamine-s-triazine (atrazine) and 2-chloro-2???,6???-diethyl-N-(methoxymethyl)acetanilide (alachlor). The isolation of the two herbicides on a C18-resin involved the selection of an elution solvent that both removes interfering substances and is compatible with ELISA. Ethyl acetate was selected as the elution solvent followed by a solvent exchange with methanol/water (20/80, % v/v). The SPE-ELISA method has a detection limit of 5.0 ng/L (5 ppt), >90% recovery, and a relative standard deviation of ??10%. The performance of a microtiter plate-based ELISA and a magnetic particle-based ELISA coupled to SPE was also evaluated. Although the sensitivity of the two ELISA methods was comparable, the precision using magnetic particles was improved considerably (??10% versus ??20%) because of the faster reaction kinetics provided by the magnetic particles. Finally, SPE-ELISA and isotope dilution gas chromatography/ mass spectrometry correlated well (correlation coefficient of 0.96) for lake-water samples. The SPE-ELISA method is simple and may have broader applications for the inexpensive automated analysis of other contaminants in water at trace levels.

Image processing and machine learning techniques to automate diagnosis of Lugol's iodine cervigrams for a low-cost point-of-care digital colposcope

NASA Astrophysics Data System (ADS)

Asiedu, Mercy Nyamewaa; Simhal, Anish; Lam, Christopher T.; Mueller, Jenna; Chaudhary, Usamah; Schmitt, John W.; Sapiro, Guillermo; Ramanujam, Nimmi

2018-02-01

The world health organization recommends visual inspection with acetic acid (VIA) and/or Lugol's Iodine (VILI) for cervical cancer screening in low-resource settings. Human interpretation of diagnostic indicators for visual inspection is qualitative, subjective, and has high inter-observer discordance, which could lead both to adverse outcomes for the patient and unnecessary follow-ups. In this work, we a simple method for automatic feature extraction and classification for Lugol's Iodine cervigrams acquired with a low-cost, miniature, digital colposcope. Algorithms to preprocess expert physician-labelled cervigrams and to extract simple but powerful color-based features are introduced. The features are used to train a support vector machine model to classify cervigrams based on expert physician labels. The selected framework achieved a sensitivity, specificity, and accuracy of 89.2%, 66.7% and 80.6% with majority diagnosis of the expert physicians in discriminating cervical intraepithelial neoplasia (CIN +) relative to normal tissues. The proposed classifier also achieved an area under the curve of 84 when trained with majority diagnosis of the expert physicians. The results suggest that utilizing simple color-based features may enable unbiased automation of VILI cervigrams, opening the door to a full system of low-cost data acquisition complemented with automatic interpretation.

The Automation-by-Expertise-by-Training Interaction.

PubMed

Strauch, Barry

2017-03-01

I introduce the automation-by-expertise-by-training interaction in automated systems and discuss its influence on operator performance. Transportation accidents that, across a 30-year interval demonstrated identical automation-related operator errors, suggest a need to reexamine traditional views of automation. I review accident investigation reports, regulator studies, and literature on human computer interaction, expertise, and training and discuss how failing to attend to the interaction of automation, expertise level, and training has enabled operators to commit identical automation-related errors. Automated systems continue to provide capabilities exceeding operators' need for effective system operation and provide interfaces that can hinder, rather than enhance, operator automation-related situation awareness. Because of limitations in time and resources, training programs do not provide operators the expertise needed to effectively operate these automated systems, requiring them to obtain the expertise ad hoc during system operations. As a result, many do not acquire necessary automation-related system expertise. Integrating automation with expected operator expertise levels, and within training programs that provide operators the necessary automation expertise, can reduce opportunities for automation-related operator errors. Research to address the automation-by-expertise-by-training interaction is needed. However, such research must meet challenges inherent to examining realistic sociotechnical system automation features with representative samples of operators, perhaps by using observational and ethnographic research. Research in this domain should improve the integration of design and training and, it is hoped, enhance operator performance.

Fast and accurate determination of arsenobetaine in fish tissues using accelerated solvent extraction and HPLC-ICP-MS determination.

PubMed

Wahlen, Raimund

2004-04-01

A high-performance liquid chromatography-inductively coupled plasma-mass spectrometry (HPLC-ICP-MS) method has been developed for the fast and accurate analysis of arsenobetaine (AsB) in fish samples extracted by accelerated solvent extraction. The combined extraction and analysis approach is validated using certified reference materials for AsB in fish and during a European intercomparison exercise with a blind sample. Up to six species of arsenic (As) can be separated and quantitated in the extracts within a 10-min isocratic elution. The method is optimized so as to minimize time-consuming sample preparation steps and allow for automated extraction and analysis of large sample batches. A comparison of standard addition and external calibration show no significant difference in the results obtained, which indicates that the LC-ICP-MS method is not influenced by severe matrix effects. The extraction procedure can process up to 24 samples in an automated manner, yet the robustness of the developed HPLC-ICP-MS approach is highlighted by the capability to run more than 50 injections per sequence, which equates to a total run-time of more than 12 h. The method can therefore be used to rapidly and accurately assess the proportion of nontoxic AsB in fish samples with high total As content during toxicological screening studies.

A modular computational framework for automated peak extraction from ion mobility spectra

PubMed Central

2014-01-01

Background An ion mobility (IM) spectrometer coupled with a multi-capillary column (MCC) measures volatile organic compounds (VOCs) in the air or in exhaled breath. This technique is utilized in several biotechnological and medical applications. Each peak in an MCC/IM measurement represents a certain compound, which may be known or unknown. For clustering and classification of measurements, the raw data matrix must be reduced to a set of peaks. Each peak is described by its coordinates (retention time in the MCC and reduced inverse ion mobility) and shape (signal intensity, further shape parameters). This fundamental step is referred to as peak extraction. It is the basis for identifying discriminating peaks, and hence putative biomarkers, between two classes of measurements, such as a healthy control group and a group of patients with a confirmed disease. Current state-of-the-art peak extraction methods require human interaction, such as hand-picking approximate peak locations, assisted by a visualization of the data matrix. In a high-throughput context, however, it is preferable to have robust methods for fully automated peak extraction. Results We introduce PEAX, a modular framework for automated peak extraction. The framework consists of several steps in a pipeline architecture. Each step performs a specific sub-task and can be instantiated by different methods implemented as modules. We provide open-source software for the framework and several modules for each step. Additionally, an interface that allows easy extension by a new module is provided. Combining the modules in all reasonable ways leads to a large number of peak extraction methods. We evaluate all combinations using intrinsic error measures and by comparing the resulting peak sets with an expert-picked one. Conclusions Our software PEAX is able to automatically extract peaks from MCC/IM measurements within a few seconds. The automatically obtained results keep up with the results provided by current state-of-the-art peak extraction methods. This opens a high-throughput context for the MCC/IM application field. Our software is available at http://www.rahmannlab.de/research/ims. PMID:24450533

A modular computational framework for automated peak extraction from ion mobility spectra.

PubMed

D'Addario, Marianna; Kopczynski, Dominik; Baumbach, Jörg Ingo; Rahmann, Sven

2014-01-22

An ion mobility (IM) spectrometer coupled with a multi-capillary column (MCC) measures volatile organic compounds (VOCs) in the air or in exhaled breath. This technique is utilized in several biotechnological and medical applications. Each peak in an MCC/IM measurement represents a certain compound, which may be known or unknown. For clustering and classification of measurements, the raw data matrix must be reduced to a set of peaks. Each peak is described by its coordinates (retention time in the MCC and reduced inverse ion mobility) and shape (signal intensity, further shape parameters). This fundamental step is referred to as peak extraction. It is the basis for identifying discriminating peaks, and hence putative biomarkers, between two classes of measurements, such as a healthy control group and a group of patients with a confirmed disease. Current state-of-the-art peak extraction methods require human interaction, such as hand-picking approximate peak locations, assisted by a visualization of the data matrix. In a high-throughput context, however, it is preferable to have robust methods for fully automated peak extraction. We introduce PEAX, a modular framework for automated peak extraction. The framework consists of several steps in a pipeline architecture. Each step performs a specific sub-task and can be instantiated by different methods implemented as modules. We provide open-source software for the framework and several modules for each step. Additionally, an interface that allows easy extension by a new module is provided. Combining the modules in all reasonable ways leads to a large number of peak extraction methods. We evaluate all combinations using intrinsic error measures and by comparing the resulting peak sets with an expert-picked one. Our software PEAX is able to automatically extract peaks from MCC/IM measurements within a few seconds. The automatically obtained results keep up with the results provided by current state-of-the-art peak extraction methods. This opens a high-throughput context for the MCC/IM application field. Our software is available at http://www.rahmannlab.de/research/ims.

Sharing feelings online: studying emotional well-being via automated text analysis of Facebook posts.

PubMed

Settanni, Michele; Marengo, Davide

2015-01-01

Digital traces of activity on social network sites represent a vast source of ecological data with potential connections with individual behavioral and psychological characteristics. The present study investigates the relationship between user-generated textual content shared on Facebook and emotional well-being. Self-report measures of depression, anxiety, and stress were collected from 201 adult Facebook users from North Italy. Emotion-related textual indicators, including emoticon use, were extracted form users' Facebook posts via automated text analysis. Correlation analyses revealed that individuals with higher levels of depression, anxiety expressed negative emotions on Facebook more frequently. In addition, use of emoticons expressing positive emotions correlated negatively with stress level. When comparing age groups, younger users reported higher frequency of both emotion-related words and emoticon use in their posts. Also, the relationship between online emotional expression and self-report emotional well-being was generally stronger in the younger group. Overall, findings support the feasibility and validity of studying individual emotional well-being by means of examination of Facebook profiles. Implications for online screening purposes and future research directions are discussed.

Sharing feelings online: studying emotional well-being via automated text analysis of Facebook posts

PubMed Central

Settanni, Michele; Marengo, Davide

2015-01-01

Digital traces of activity on social network sites represent a vast source of ecological data with potential connections with individual behavioral and psychological characteristics. The present study investigates the relationship between user-generated textual content shared on Facebook and emotional well-being. Self-report measures of depression, anxiety, and stress were collected from 201 adult Facebook users from North Italy. Emotion-related textual indicators, including emoticon use, were extracted form users’ Facebook posts via automated text analysis. Correlation analyses revealed that individuals with higher levels of depression, anxiety expressed negative emotions on Facebook more frequently. In addition, use of emoticons expressing positive emotions correlated negatively with stress level. When comparing age groups, younger users reported higher frequency of both emotion-related words and emoticon use in their posts. Also, the relationship between online emotional expression and self-report emotional well-being was generally stronger in the younger group. Overall, findings support the feasibility and validity of studying individual emotional well-being by means of examination of Facebook profiles. Implications for online screening purposes and future research directions are discussed. PMID:26257692

A study on automated anatomical labeling to arteries concerning with colon from 3D abdominal CT images

NASA Astrophysics Data System (ADS)

Hoang, Bui Huy; Oda, Masahiro; Jiang, Zhengang; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Mori, Kensaku

2011-03-01

This paper presents an automated anatomical labeling method of arteries extracted from contrasted 3D CT images based on multi-class AdaBoost. In abdominal surgery, understanding of vasculature related to a target organ such as the colon is very important. Therefore, the anatomical structure of blood vessels needs to be understood by computers in a system supporting abdominal surgery. There are several researches on automated anatomical labeling, but there is no research on automated anatomical labeling to arteries concerning with the colon. The proposed method obtains a tree structure of arteries from the artery region and calculates features values of each branch. These feature values are thickness, curvature, direction, and running vectors of branch. Then, candidate arterial names are computed by classifiers that are trained to output artery names. Finally, a global optimization process is applied to the candidate arterial names to determine final names. Target arteries of this paper are nine lower abdominal arteries (AO, LCIA, RCIA, LEIA, REIA, SMA, IMA, LIIA, RIIA). We applied the proposed method to 14 cases of 3D abdominal contrasted CT images, and evaluated the results by leave-one-out scheme. The average precision and recall rates of the proposed method were 87.9% and 93.3%, respectively. The results of this method are applicable for anatomical name display of surgical simulation and computer aided surgery.

A literature search tool for intelligent extraction of disease-associated genes.

PubMed

Jung, Jae-Yoon; DeLuca, Todd F; Nelson, Tristan H; Wall, Dennis P

2014-01-01

To extract disorder-associated genes from the scientific literature in PubMed with greater sensitivity for literature-based support than existing methods. We developed a PubMed query to retrieve disorder-related, original research articles. Then we applied a rule-based text-mining algorithm with keyword matching to extract target disorders, genes with significant results, and the type of study described by the article. We compared our resulting candidate disorder genes and supporting references with existing databases. We demonstrated that our candidate gene set covers nearly all genes in manually curated databases, and that the references supporting the disorder-gene link are more extensive and accurate than other general purpose gene-to-disorder association databases. We implemented a novel publication search tool to find target articles, specifically focused on links between disorders and genotypes. Through comparison against gold-standard manually updated gene-disorder databases and comparison with automated databases of similar functionality we show that our tool can search through the entirety of PubMed to extract the main gene findings for human diseases rapidly and accurately.

Multimodal Teaching Analytics: Automated Extraction of Orchestration Graphs from Wearable Sensor Data

ERIC Educational Resources Information Center

Prieto, L. P.; Sharma, K.; Kidzinski, L.; Rodríguez-Triana, M. J.; Dillenbourg, P.

2018-01-01

The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time)…

ACCELERATED SOLVENT EXTRACTION COMBINED WITH AUTOMATED SOLID PHASE EXTRACTION-GC/MS FOR ANALYSIS OF SEMIVOLATILE COMPOUNDS IN HIGH MOISTURE CONTENT SOLID SAMPLES

EPA Science Inventory

A research project was initiated to address a recurring problem of elevated detection limits above required risk-based concentrations for the determination of semivolatile organic compounds in high moisture content solid samples. This project was initiated, in cooperation with t...

Deep SOMs for automated feature extraction and classification from big data streaming

NASA Astrophysics Data System (ADS)

Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

2017-03-01

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

Automated data mining: an innovative and efficient web-based approach to maintaining resident case logs.

PubMed

Bhattacharya, Pratik; Van Stavern, Renee; Madhavan, Ramesh

2010-12-01

Use of resident case logs has been considered by the Residency Review Committee for Neurology of the Accreditation Council for Graduate Medical Education (ACGME). This study explores the effectiveness of a data-mining program for creating resident logs and compares the results to a manual data-entry system. Other potential applications of data mining to enhancing resident education are also explored. Patient notes dictated by residents were extracted from the Hospital Information System and analyzed using an unstructured mining program. History, examination and ICD codes were obtained and compared to the existing manual log. The automated data History, examination, and ICD codes were gathered for a 30-day period and compared to manual case logs. The automated method extracted all resident dictations with the dates of encounter and transcription. The automated data-miner processed information from all 19 residents, while only 4 residents logged manually. The manual method identified only broad categories of diseases; the major categories were stroke or vascular disorder 53 (27.6%), epilepsy 28 (14.7%), and pain syndromes 26 (13.5%). In the automated method, epilepsy 114 (21.1%), cerebral atherosclerosis 114 (21.1%), and headache 105 (19.4%) were the most frequent primary diagnoses, and headache 89 (16.5%), seizures 94 (17.4%), and low back pain 47 (9%) were the most common chief complaints. More detailed patient information such as tobacco use 227 (42%), alcohol use 205 (38%), and drug use 38 (7%) were extracted by the data-mining method. Manual case logs are time-consuming, provide limited information, and may be unpopular with residents. Data mining is a time-effective tool that may aid in the assessment of resident experience or the ACGME core competencies or in resident clinical research. More study of this method in larger numbers of residency programs is needed.

Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach.

PubMed

Rinaldi, Fabio; Schneider, Gerold; Kaljurand, Kaarel; Hess, Michael; Andronis, Christos; Konstandi, Ourania; Persidis, Andreas

2007-02-01

The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.

Unsupervised automated high throughput phenotyping of RNAi time-lapse movies.

PubMed

Failmezger, Henrik; Fröhlich, Holger; Tresch, Achim

2013-10-04

Gene perturbation experiments in combination with fluorescence time-lapse cell imaging are a powerful tool in reverse genetics. High content applications require tools for the automated processing of the large amounts of data. These tools include in general several image processing steps, the extraction of morphological descriptors, and the grouping of cells into phenotype classes according to their descriptors. This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens. We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene's function. Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes.

Introducing automation to the molecular diagnosis of Trypanosoma cruzi infection: A comparative study of sample treatments, DNA extraction methods and real-time PCR assays.

PubMed

Abras, Alba; Ballart, Cristina; Llovet, Teresa; Roig, Carme; Gutiérrez, Cristina; Tebar, Silvia; Berenguer, Pere; Pinazo, María-Jesús; Posada, Elizabeth; Gascón, Joaquim; Schijman, Alejandro G; Gállego, Montserrat; Muñoz, Carmen

2018-01-01

Polymerase chain reaction (PCR) has become a useful tool for the diagnosis of Trypanosoma cruzi infection. The development of automated DNA extraction methodologies and PCR systems is an important step toward the standardization of protocols in routine diagnosis. To date, there are only two commercially available Real-Time PCR assays for the routine laboratory detection of T. cruzi DNA in clinical samples: TCRUZIDNA.CE (Diagnostic Bioprobes Srl) and RealCycler CHAG (Progenie Molecular). Our aim was to evaluate the RealCycler CHAG assay taking into account the whole process. We assessed the usefulness of an automated DNA extraction system based on magnetic particles (EZ1 Virus Mini Kit v2.0, Qiagen) combined with a commercially available Real-Time PCR assay targeting satellite DNA (SatDNA) of T. cruzi (RealCycler CHAG), a methodology used for routine diagnosis in our hospital. It was compared with a well-known strategy combining a commercial DNA isolation kit based on silica columns (High Pure PCR Template Preparation Kit, Roche Diagnostics) with an in-house Real-Time PCR targeting SatDNA. The results of the two methodologies were in almost perfect agreement, indicating they can be used interchangeably. However, when variations in protocol factors were applied (sample treatment, extraction method and Real-Time PCR), the results were less convincing. A comprehensive fine-tuning of the whole procedure is the key to successful results. Guanidine EDTA-blood (GEB) samples are not suitable for DNA extraction based on magnetic particles due to inhibition, at least when samples are not processed immediately. This is the first study to evaluate the RealCycler CHAG assay taking into account the overall process, including three variables (sample treatment, extraction method and Real-Time PCR). Our findings may contribute to the harmonization of protocols between laboratories and to a wider application of Real-Time PCR in molecular diagnostic laboratories associated with health centers.

Health care quality measures for children and adolescents in Foster Care: feasibility testing in electronic records.

PubMed

Deans, Katherine J; Minneci, Peter C; Nacion, Kristine M; Leonhart, Karen; Cooper, Jennifer N; Scholle, Sarah Hudson; Kelleher, Kelly J

2018-02-22

Preventive quality measures for the foster care population are largely untested. The objective of the study is to identify healthcare quality measures for young children and adolescents in foster care and to test whether the data required to calculate these measures can be feasibly extracted and interpreted within an electronic health records or within the Statewide Automated Child Welfare Information System. The AAP Recommendations for Preventive Pediatric Health Care served as the guideline for determining quality measures. Quality measures related to well child visits, developmental screenings, immunizations, trauma-related care, BMI measurements, sexually transmitted infections and depression were defined. Retrospective chart reviews were performed on a cohort of children in foster care from a single large pediatric institution and related county. Data available in the Ohio Statewide Automated Child Welfare Information System was compared to the same population studied in the electronic health record review. Quality measures were calculated as observed (received) to expected (recommended) ratios (O/E ratios) to describe the actual quantity of recommended health care that was received by individual children. Electronic health records and the Statewide Automated Child Welfare Information System data frequently lacked important information on foster care youth essential for calculating the measures. Although electronic health records were rich in encounter specific clinical data, they often lacked custodial information such as the dates of entry into and exit from foster care. In contrast, Statewide Automated Child Welfare Information System included robust data on custodial arrangements, but lacked detailed medical information. Despite these limitations, several quality measures were devised that attempted to accommodate these limitations. In this feasibility testing, neither the electronic health records at a single institution nor the county level Statewide Automated Child Welfare Information System was able to independently serve as a reliable source of data for health care quality measures for foster care youth. However, the ability to leverage both sources by matching them at an individual level may provide the complement of data necessary to assess the quality of healthcare.

FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining.

PubMed

Bachman, John A; Gyori, Benjamin M; Sorger, Peter K

2018-06-28

For automated reading of scientific publications to extract useful information about molecular mechanisms it is critical that genes, proteins and other entities be correctly associated with uniform identifiers, a process known as named entity linking or "grounding." Correct grounding is essential for resolving relationships among mined information, curated interaction databases, and biological datasets. The accuracy of this process is largely dependent on the availability of machine-readable resources associating synonyms and abbreviations commonly found in biomedical literature with uniform identifiers. In a task involving automated reading of ∼215,000 articles using the REACH event extraction software we found that grounding was disproportionately inaccurate for multi-protein families (e.g., "AKT") and complexes with multiple subunits (e.g."NF- κB"). To address this problem we constructed FamPlex, a manually curated resource defining protein families and complexes as they are commonly encountered in biomedical text. In FamPlex the gene-level constituents of families and complexes are defined in a flexible format allowing for multi-level, hierarchical membership. To create FamPlex, text strings corresponding to entities were identified empirically from literature and linked manually to uniform identifiers; these identifiers were also mapped to equivalent entries in multiple related databases. FamPlex also includes curated prefix and suffix patterns that improve named entity recognition and event extraction. Evaluation of REACH extractions on a test corpus of ∼54,000 articles showed that FamPlex significantly increased grounding accuracy for families and complexes (from 15 to 71%). The hierarchical organization of entities in FamPlex also made it possible to integrate otherwise unconnected mechanistic information across families, subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM reading system and the Biocreative VI Bioentity Normalization Task dataset demonstrated the utility of FamPlex in other settings. FamPlex is an effective resource for improving named entity recognition, grounding, and relationship resolution in automated reading of biomedical text. The content in FamPlex is available in both tabular and Open Biomedical Ontology formats at https://github.com/sorgerlab/famplex under the Creative Commons CC0 license and has been integrated into the TRIPS/DRUM and REACH reading systems.

Profiling of tryptophan-related plasma indoles in patients with carcinoid tumors by automated, on-line, solid-phase extraction and HPLC with fluorescence detection.

PubMed

Kema, I P; Meijer, W G; Meiborg, G; Ooms, B; Willemse, P H; de Vries, E G

2001-10-01

Profiling of the plasma indoles tryptophan, 5-hydroxytryptophan (5-HTP), serotonin, and 5-hydroxyindoleacetic acid (5-HIAA) is useful in the diagnosis and follow-up of patients with carcinoid tumors. We describe an automated method for the profiling of these indoles in protein-containing matrices as well as the plasma indole concentrations in healthy controls and patients with carcinoid tumors. Plasma, cerebrospinal fluid, and tissue homogenates were prepurified by automated on-line solid-phase extraction (SPE) in Hysphere Resin SH SPE cartridges containing strong hydrophobic polystyrene resin. Analytes were eluted from the SPE cartridge by column switching. Subsequent separation and detection were performed by reversed-phase HPLC combined with fluorometric detection in a total cycle time of 20 min. We obtained samples from 14 healthy controls and 17 patients with metastasized midgut carcinoid tumors for plasma indole analysis. In the patient group, urinary excretion of 5-HIAA and serotonin was compared with concentrations of plasma indoles. Within- and between-series CVs for indoles in platelet-rich plasma were 0.6-6.2% and 3.7-12%, respectively. Results for platelet-rich plasma serotonin compared favorably with those obtained by single-component analysis. Plasma 5-HIAA, but not 5-HTP was detectable in 8 of 17 patients with carcinoid tumors. In the patient group, platelet-rich plasma total tryptophan correlated negatively with platelet-rich plasma serotonin (P = 0.021; r = -0.56), urinary 5-HIAA (P = 0.003; r = -0.68), and urinary serotonin (P <0.0001; r = -0.80). The present chromatographic approach reduces analytical variation and time needed for analysis and gives more detailed information about metabolic deviations in indole metabolism than do manual, single-component analyses.

BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.

PubMed

Griffanti, Ludovica; Zamboni, Giovanna; Khan, Aamira; Li, Linxin; Bonifacio, Guendalina; Sundaresan, Vaanathi; Schulz, Ursula G; Kuker, Wilhelm; Battaglini, Marco; Rothwell, Peter M; Jenkinson, Mark

2016-11-01

Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a "predominantly neurodegenerative" and a "predominantly vascular" cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Automated extraction and validation of children's gait parameters with the Kinect.

PubMed

Motiian, Saeid; Pergami, Paola; Guffey, Keegan; Mancinelli, Corrie A; Doretto, Gianfranco

2015-12-02

Gait analysis for therapy regimen prescription and monitoring requires patients to physically access clinics with specialized equipment. The timely availability of such infrastructure at the right frequency is especially important for small children. Besides being very costly, this is a challenge for many children living in rural areas. This is why this work develops a low-cost, portable, and automated approach for in-home gait analysis, based on the Microsoft Kinect. A robust and efficient method for extracting gait parameters is introduced, which copes with the high variability of noisy Kinect skeleton tracking data experienced across the population of young children. This is achieved by temporally segmenting the data with an approach based on coupling a probabilistic matching of stride template models, learned offline, with the estimation of their global and local temporal scaling. A preliminary study conducted on healthy children between 2 and 4 years of age is performed to analyze the accuracy, precision, repeatability, and concurrent validity of the proposed method against the GAITRite when measuring several spatial and temporal children's gait parameters. The method has excellent accuracy and good precision, with segmenting temporal sequences of body joint locations into stride and step cycles. Also, the spatial and temporal gait parameters, estimated automatically, exhibit good concurrent validity with those provided by the GAITRite, as well as very good repeatability. In particular, on a range of nine gait parameters, the relative and absolute agreements were found to be good and excellent, and the overall agreements were found to be good and moderate. This work enables and validates the automated use of the Kinect for children's gait analysis in healthy subjects. In particular, the approach makes a step forward towards developing a low-cost, portable, parent-operated in-home tool for clinicians assisting young children.

Hematocrit-Independent Quantitation of Stimulants in Dried Blood Spots: Pipet versus Microfluidic-Based Volumetric Sampling Coupled with Automated Flow-Through Desorption and Online Solid Phase Extraction-LC-MS/MS Bioanalysis.

PubMed

Verplaetse, Ruth; Henion, Jack

2016-07-05

A workflow overcoming microsample collection issues and hematocrit (HCT)-related bias would facilitate more widespread use of dried blood spots (DBS). This report describes comparative results between the use of a pipet and a microfluidic-based sampling device for the creation of volumetric DBS. Both approaches were successfully coupled to HCT-independent, fully automated sample preparation and online liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) analysis allowing detection of five stimulants in finger prick blood. Reproducible, selective, accurate, and precise responses meeting generally accepted regulated bioanalysis guidelines were observed over the range of 5-1000 ng/mL whole blood. The applied heated flow-through solvent desorption of the entire spot and online solid phase extraction (SPE) procedure were unaffected by the blood's HCT value within the tested range of 28.0-61.5% HCT. Enhanced stability for mephedrone on DBS compared to liquid whole blood was observed. Finger prick blood samples were collected using both volumetric sampling approaches over a time course of 25 h after intake of a single oral dose of phentermine. A pharmacokinetic curve for the incurred phentermine was successfully produced using the described validated method. These results suggest that either volumetric sample collection method may be amenable to field-use followed by fully automated, HCT-independent DBS-SPE-LC-MS/MS bioanalysis for the quantitation of these representative controlled substances. Analytical data from DBS prepared with a pipet and microfluidic-based sampling devices were comparable, but the latter is easier to operate, making this approach more suitable for sample collection by unskilled persons.

Pharmacokinetic Studies of Chinese Medicinal Herbs Using an Automated Blood Sampling System and Liquid Chromatography-mass Spectrometry.

PubMed

Wu, Yu-Tse; Wu, Ming-Tsang; Lin, Chia-Chun; Chien, Chao-Feng; Tsai, Tung-Hu

2012-01-01

The safety of herbal products is one of the major concerns for the modernization of traditional Chinese medicine, and pharmacokinetic data of medicinal herbs guide us to design the rational use of the herbal formula. This article reviews the advantages of the automated blood sampling (ABS) systems for pharmacokinetic studies. In addition, three commonly used sample preparative methods, protein precipitation, liquid-liquid extraction and solid-phase extraction, are introduced. Furthermore, the definition, causes and evaluation of matrix effects in liquid chromatography-mass spectrometry (LC/MS) analysis are demonstrated. Finally, we present our previous works as practical examples of the application of ABS systems and LC/MS for the pharmacokinetic studies of Chinese medicinal herbs.

Wnt pathway curation using automated natural language processing: combining statistical methods with partial and full parse for knowledge extraction.

PubMed

Santos, Carlos; Eggle, Daniela; States, David J

2005-04-15

Wnt signaling is a very active area of research with highly relevant publications appearing at a rate of more than one per day. Building and maintaining databases describing signal transduction networks is a time-consuming and demanding task that requires careful literature analysis and extensive domain-specific knowledge. For instance, more than 50 factors involved in Wnt signal transduction have been identified as of late 2003. In this work we describe a natural language processing (NLP) system that is able to identify references to biological interaction networks in free text and automatically assembles a protein association and interaction map. A 'gold standard' set of names and assertions was derived by manual scanning of the Wnt genes website (http://www.stanford.edu/~rnusse/wntwindow.html) including 53 interactions involved in Wnt signaling. This system was used to analyze a corpus of peer-reviewed articles related to Wnt signaling including 3369 Pubmed and 1230 full text papers. Names for key Wnt-pathway associated proteins and biological entities are identified using a chi-squared analysis of noun phrases over-represented in the Wnt literature as compared to the general signal transduction literature. Interestingly, we identified several instances where generic terms were used on the website when more specific terms occur in the literature, and one typographic error on the Wnt canonical pathway. Using the named entity list and performing an exhaustive assertion extraction of the corpus, 34 of the 53 interactions in the 'gold standard' Wnt signaling set were successfully identified (64% recall). In addition, the automated extraction found several interactions involving key Wnt-related molecules which were missing or different from those in the canonical diagram, and these were confirmed by manual review of the text. These results suggest that a combination of NLP techniques for information extraction can form a useful first-pass tool for assisting human annotation and maintenance of signal pathway databases. The pipeline software components are freely available on request to the authors. dstates@umich.edu http://stateslab.bioinformatics.med.umich.edu/software.html.

Mapping Urban Ecosystem Services Using High Resolution Aerial Photography

NASA Astrophysics Data System (ADS)

Pilant, A. N.; Neale, A.; Wilhelm, D.

2010-12-01

Ecosystem services (ES) are the many life-sustaining benefits we receive from nature: e.g., clean air and water, food and fiber, cultural-aesthetic-recreational benefits, pollination and flood control. The ES concept is emerging as a means of integrating complex environmental and economic information to support informed environmental decision making. The US EPA is developing a web-based National Atlas of Ecosystem Services, with a component for urban ecosystems. Currently, the only wall-to-wall, national scale land cover data suitable for this analysis is the National Land Cover Data (NLCD) at 30 m spatial resolution with 5 and 10 year updates. However, aerial photography is acquired at higher spatial resolution (0.5-3 m) and more frequently (1-5 years, typically) for most urban areas. Land cover was mapped in Raleigh, NC using freely available USDA National Agricultural Imagery Program (NAIP) with 1 m ground sample distance to test the suitability of aerial photography for urban ES analysis. Automated feature extraction techniques were used to extract five land cover classes, and an accuracy assessment was performed using standard techniques. Results will be presented that demonstrate applications to mapping ES in urban environments: greenways, corridors, fragmentation, habitat, impervious surfaces, dark and light pavement (urban heat island). Automated feature extraction results mapped over NAIP color aerial photograph. At this scale, we can look at land cover and related ecosystem services at the 2-10 m scale. Small features such as individual trees and sidewalks are visible and mappable. Classified aerial photo of Downtown Raleigh NC Red: impervious surface Dark Green: trees Light Green: grass Tan: soil

An automated method of on-line extraction coupled with flow injection and capillary electrophoresis for phytochemical analysis.

PubMed

Chen, Hongli; Ding, Xiuping; Wang, Min; Chen, Xingguo

2010-11-01

In this study, an automated system for phytochemical analysis was successfully fabricated for the first time in our laboratory. The system included on-line decocting, filtering, cooling, sample introducing, separation, and detection, which greatly simplified the sample preparation and shortened the analysis time. Samples from the decoction extract were drawn every 5 min through an on-line filter and a condenser pipe to the sample loop from which 20-μL samples were injected into the running buffer and transported into a split-flow interface coupling the flow injection and capillary electrophoresis systems. The separation of glycyrrhetinic acid (GTA) and glycyrrhizic acid (GA) took less than 5 min by using a 10 mM borate buffer (adjusted pH to 8.8) and +10 kV voltage. Calibration curves showed good linearity with correlation coefficients (R) more than 0.9991. The intra-day repeatabilities (n = 5, expressed as relative standard deviation) of the proposed system, obtained using GTA and GA standards, were 1.1% and 0.8% for migration time and 0.7% and 0.9% for peak area, respectively. The mean recoveries of GTA and GA in the off-line extract of Glycyrrhiza uralensis Fisch root were better than 99.0%. The limits of detection (signal-to-noise ratio = 3) of the proposed method were 6.2 μg/mL and 6.9 μg/mL for GTA and GA, respectively. The dynamic changes of GTA and GA on the decoction time were obtained during the on-line decoction process of Glycyrrhiza uralensis Fisch root.

Digital immunohistochemistry platform for the staining variation monitoring based on integration of image and statistical analyses with laboratory information system.

PubMed

Laurinaviciene, Aida; Plancoulaine, Benoit; Baltrusaityte, Indra; Meskauskas, Raimundas; Besusparis, Justinas; Lesciute-Krilaviciene, Daiva; Raudeliunas, Darius; Iqbal, Yasir; Herlin, Paulette; Laurinavicius, Arvydas

2014-01-01

Digital immunohistochemistry (IHC) is one of the most promising applications brought by new generation image analysis (IA). While conventional IHC staining quality is monitored by semi-quantitative visual evaluation of tissue controls, IA may require more sensitive measurement. We designed an automated system to digitally monitor IHC multi-tissue controls, based on SQL-level integration of laboratory information system with image and statistical analysis tools. Consecutive sections of TMA containing 10 cores of breast cancer tissue were used as tissue controls in routine Ki67 IHC testing. Ventana slide label barcode ID was sent to the LIS to register the serial section sequence. The slides were stained and scanned (Aperio ScanScope XT), IA was performed by the Aperio/Leica Colocalization and Genie Classifier/Nuclear algorithms. SQL-based integration ensured automated statistical analysis of the IA data by the SAS Enterprise Guide project. Factor analysis and plot visualizations were performed to explore slide-to-slide variation of the Ki67 IHC staining results in the control tissue. Slide-to-slide intra-core IHC staining analysis revealed rather significant variation of the variables reflecting the sample size, while Brown and Blue Intensity were relatively stable. To further investigate this variation, the IA results from the 10 cores were aggregated to minimize tissue-related variance. Factor analysis revealed association between the variables reflecting the sample size detected by IA and Blue Intensity. Since the main feature to be extracted from the tissue controls was staining intensity, we further explored the variation of the intensity variables in the individual cores. MeanBrownBlue Intensity ((Brown+Blue)/2) and DiffBrownBlue Intensity (Brown-Blue) were introduced to better contrast the absolute intensity and the colour balance variation in each core; relevant factor scores were extracted. Finally, tissue-related factors of IHC staining variance were explored in the individual tissue cores. Our solution enabled to monitor staining of IHC multi-tissue controls by the means of IA, followed by automated statistical analysis, integrated into the laboratory workflow. We found that, even in consecutive serial tissue sections, tissue-related factors affected the IHC IA results; meanwhile, less intense blue counterstain was associated with less amount of tissue, detected by the IA tools.

Digital immunohistochemistry platform for the staining variation monitoring based on integration of image and statistical analyses with laboratory information system

PubMed Central

2014-01-01

Background Digital immunohistochemistry (IHC) is one of the most promising applications brought by new generation image analysis (IA). While conventional IHC staining quality is monitored by semi-quantitative visual evaluation of tissue controls, IA may require more sensitive measurement. We designed an automated system to digitally monitor IHC multi-tissue controls, based on SQL-level integration of laboratory information system with image and statistical analysis tools. Methods Consecutive sections of TMA containing 10 cores of breast cancer tissue were used as tissue controls in routine Ki67 IHC testing. Ventana slide label barcode ID was sent to the LIS to register the serial section sequence. The slides were stained and scanned (Aperio ScanScope XT), IA was performed by the Aperio/Leica Colocalization and Genie Classifier/Nuclear algorithms. SQL-based integration ensured automated statistical analysis of the IA data by the SAS Enterprise Guide project. Factor analysis and plot visualizations were performed to explore slide-to-slide variation of the Ki67 IHC staining results in the control tissue. Results Slide-to-slide intra-core IHC staining analysis revealed rather significant variation of the variables reflecting the sample size, while Brown and Blue Intensity were relatively stable. To further investigate this variation, the IA results from the 10 cores were aggregated to minimize tissue-related variance. Factor analysis revealed association between the variables reflecting the sample size detected by IA and Blue Intensity. Since the main feature to be extracted from the tissue controls was staining intensity, we further explored the variation of the intensity variables in the individual cores. MeanBrownBlue Intensity ((Brown+Blue)/2) and DiffBrownBlue Intensity (Brown-Blue) were introduced to better contrast the absolute intensity and the colour balance variation in each core; relevant factor scores were extracted. Finally, tissue-related factors of IHC staining variance were explored in the individual tissue cores. Conclusions Our solution enabled to monitor staining of IHC multi-tissue controls by the means of IA, followed by automated statistical analysis, integrated into the laboratory workflow. We found that, even in consecutive serial tissue sections, tissue-related factors affected the IHC IA results; meanwhile, less intense blue counterstain was associated with less amount of tissue, detected by the IA tools. PMID:25565007

Automated peak picking and peak integration in macromolecular NMR spectra using AUTOPSY.

PubMed

Koradi, R; Billeter, M; Engeli, M; Güntert, P; Wüthrich, K

1998-12-01

A new approach for automated peak picking of multidimensional protein NMR spectra with strong overlap is introduced, which makes use of the program AUTOPSY (automated peak picking for NMR spectroscopy). The main elements of this program are a novel function for local noise level calculation, the use of symmetry considerations, and the use of lineshapes extracted from well-separated peaks for resolving groups of strongly overlapping peaks. The algorithm generates peak lists with precise chemical shift and integral intensities, and a reliability measure for the recognition of each peak. The results of automated peak picking of NOESY spectra with AUTOPSY were tested in combination with the combined automated NOESY cross peak assignment and structure calculation routine NOAH implemented in the program DYANA. The quality of the resulting structures was found to be comparable with those from corresponding data obtained with manual peak picking. Copyright 1998 Academic Press.

Spatial resolution requirements for automated cartographic road extraction

USGS Publications Warehouse

Benjamin, S.; Gaydos, L.

1990-01-01

Ground resolution requirements for detection and extraction of road locations in a digitized large-scale photographic database were investigated. A color infrared photograph of Sunnyvale, California was scanned, registered to a map grid, and spatially degraded to 1- to 5-metre resolution pixels. Road locations in each data set were extracted using a combination of image processing and CAD programs. These locations were compared to a photointerpretation of road locations to determine a preferred pixel size for the extraction method. Based on road pixel omission error computations, a 3-metre pixel resolution appears to be the best choice for this extraction method. -Authors

Analysis of organic compounds in aqueous samples of former ammunition plants

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

Levsen, K.; Preiss, A.; Berger-Preiss, E.

1995-12-31

In Germany, a large number of sites exist where ammunition was produced before and in particular during World War II. These former production sites represent a particular threat to the environment because these plants were constructed and operated under war conditions, where production was far more important than protection of the health of the (in general forced) workers and the environment. New approaches are presented for the extraction and analysis of explosives and related compounds in aqueous samples from former ammunition production sites. Quantitative extraction of nitro aromatics but also of the polar nitroamines such as RDX and HMX ismore » achieved by solid phase extraction with styrene-divinylbenzene polymers (Lichrolut EN). Proton nuclear magnetic resonance ({sup 1}H-NMR) has been used to identify and quantify unknowns in ammunition waste water. Finally, automated multiple development (AMD) high performance thin layer chromatography was applied for the first time to the analysis of this compound class.« less

Machine Reading for Extraction of Bacteria and Habitat Taxonomies

PubMed Central

Kordjamshidi, Parisa; Massa, Wouter; Provoost, Thomas; Moens, Marie-Francine

2015-01-01

There is a vast amount of scientific literature available from various resources such as the internet. Automating the extraction of knowledge from these resources is very helpful for biologists to easily access this information. This paper presents a system to extract the bacteria and their habitats, as well as the relations between them. We investigate to what extent current techniques are suited for this task and test a variety of models in this regard. We detect entities in a biological text and map the habitats into a given taxonomy. Our model uses a linear chain Conditional Random Field (CRF). For the prediction of relations between the entities, a model based on logistic regression is built. Designing a system upon these techniques, we explore several improvements for both the generation and selection of good candidates. One contribution to this lies in the extended exibility of our ontology mapper that uses an advanced boundary detection and assigns the taxonomy elements to the detected habitats. Furthermore, we discover value in the combination of several distinct candidate generation rules. Using these techniques, we show results that are significantly improving upon the state of art for the BioNLP Bacteria Biotopes task. PMID:27077141

Long-term reproducibility of relative sensitivity factors obtained with CAMECA Wf

NASA Astrophysics Data System (ADS)

Gui, D.; Xing, Z. X.; Huang, Y. H.; Mo, Z. Q.; Hua, Y. N.; Zhao, S. P.; Cha, L. Z.

2008-12-01

As the wafer size continues to increase and the feature size of the integrated circuits (IC) continues to shrink, process control of IC manufacturing becomes ever more important to reduce the cost of failures caused by the drift of processes or equipments. Characterization tools with high precision and reproducibility are required to capture any abnormality of the process. Although Secondary ion mass spectrometry (SIMS) has been widely used in dopant profile control, it was reported that magnetic sector SIMS, compared to quadrupole SIMS, has lower short-term repeatability and long-term reproducibility due to the high extraction field applied between sample and extraction lens. In this paper, we demonstrate that CAMECA Wf can deliver high long-term reproducibility because of its high-level automation and improved design of immersion lens. The relative standard deviation (R.S.D.) of the relative sensitivity factors (RSF) of three typical elements, i.e., boron (B), phosphorous (P) and nitrogen (N), over 3 years are 3.7%, 5.5% and 4.1%, respectively. The high reproducibility results have a practical implication that deviation can be estimated without testing the standards.

Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure.

PubMed

Garvin, Jennifer H; DuVall, Scott L; South, Brett R; Bray, Bruce E; Bolton, Daniel; Heavirland, Julia; Pickard, Steve; Heidenreich, Paul; Shen, Shuying; Weir, Charlene; Samore, Matthew; Goldstein, Mary K

2012-01-01

Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets; a third reviewer adjudicated disagreements. System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). An EF value of <40% can be accurately identified in VA echocardiogram reports. An automated information extraction system can be used to accurately extract EF for quality measurement.

Evaluation of mericon E. coli O157 Screen Plus and mericon E. coli STEC O-Type Pathogen Detection Assays in Select Foods: Collaborative Study, First Action 2017.05.

PubMed

Bird, Patrick; Benzinger, M Joseph; Bastin, Benjamin; Crowley, Erin; Agin, James; Goins, David; Armstrong, Marcia

2018-05-01

QIAGEN mericon Escherichia coli O157 Screen Plus and mericon E. coli Shiga toxin-producing E. coli (STEC) O-Type Pathogen Detection Assays use Real-Time PCR technology for the rapid, accurate detection of E. coli O157 and the "big six" (O26, O45, O103, O111, O121, O145) (non-O157 STEC) in select food types. Using a paired study design, the assays were compared with the U.S. Department of Agriculture, Food Safety Inspection Service Microbiology Laboratory Guidebook Chapter 5.09 reference method for the detection of E. coli O157:H7 in raw ground beef. Both mericon assays were evaluated using the manual and an automated DNA extraction method. Thirteen technicians from five laboratories located within the continental United States participated in the collaborative study. Three levels of contamination were evaluated. Statistical analysis was conducted according to the probability of detection (POD) statistical model. Results obtained for the low-inoculum level test portions produced a difference between laboratories POD (dLPOD) value with a 95% confidence interval of 0.00 (-0.12, 0.12) for the mericon E. coli O157 Screen Plus with manual and automated extraction and mericon E. coli STEC O-Type with manual extraction and -0.01 (-0.13, 0.10) for the mericon E. coli STEC O-Type with automated extraction. The dLPOD results indicate equivalence between the candidate methods and the reference method.

THE APPLICATION OF ENGLISH-WORD MORPHOLOGY TO AUTOMATIC INDEXING AND EXTRACTING. ANNUAL SUMMARY REPORT.

ERIC Educational Resources Information Center

DOLBY, J.L.; AND OTHERS

THE STUDY IS CONCERNED WITH THE LINGUISTIC PROBLEM INVOLVED IN TEXT COMPRESSION--EXTRACTING, INDEXING, AND THE AUTOMATIC CREATION OF SPECIAL-PURPOSE CITATION DICTIONARIES. IN SPITE OF EARLY SUCCESS IN USING LARGE-SCALE COMPUTERS TO AUTOMATE CERTAIN HUMAN TASKS, THESE PROBLEMS REMAIN AMONG THE MOST DIFFICULT TO SOLVE. ESSENTIALLY, THE PROBLEM IS TO…

IMS - MS Data Extractor

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

2015-10-20

An automated drift time extraction and computed associated collision cross section software tool for small molecule analysis with ion mobility spectrometry-mass spectrometry (IMS-MS). The software automatically extracts drift times and computes associated collision cross sections for small molecules analyzed using ion mobility spectrometry-mass spectrometry (IMS-MS) based on a target list of expected ions provided by the user.

Quantification of 31 illicit and medicinal drugs and metabolites in whole blood by fully automated solid-phase extraction and ultra-performance liquid chromatography-tandem mass spectrometry.

PubMed

Bjørk, Marie Kjærgaard; Simonsen, Kirsten Wiese; Andersen, David Wederkinck; Dalsgaard, Petur Weihe; Sigurðardóttir, Stella Rögn; Linnet, Kristian; Rasmussen, Brian Schou

2013-03-01

An efficient method for analyzing illegal and medicinal drugs in whole blood using fully automated sample preparation and short ultra-high-performance liquid chromatography-tandem mass spectrometry (MS/MS) run time is presented. A selection of 31 drugs, including amphetamines, cocaine, opioids, and benzodiazepines, was used. In order to increase the efficiency of routine analysis, a robotic system based on automated liquid handling and capable of handling all unit operation for sample preparation was built on a Freedom Evo 200 platform with several add-ons from Tecan and third-party vendors. Solid-phase extraction was performed using Strata X-C plates. Extraction time for 96 samples was less than 3 h. Chromatography was performed using an ACQUITY UPLC system (Waters Corporation, Milford, USA). Analytes were separated on a 100 mm × 2.1 mm, 1.7 μm Acquity UPLC CSH C(18) column using a 6.5 min 0.1 % ammonia (25 %) in water/0.1 % ammonia (25 %) in methanol gradient and quantified by MS/MS (Waters Quattro Premier XE) in multiple-reaction monitoring mode. Full validation, including linearity, precision and trueness, matrix effect, ion suppression/enhancement of co-eluting analytes, recovery, and specificity, was performed. The method was employed successfully in the laboratory and used for routine analysis of forensic material. In combination with tetrahydrocannabinol analysis, the method covered 96 % of cases involving driving under the influence of drugs. The manual labor involved in preparing blood samples, solvents, etc., was reduced to a half an hour per batch. The automated sample preparation setup also minimized human exposure to hazardous materials, provided highly improved ergonomics, and eliminated manual pipetting.

A Database Design for a Unit Status Reporting System.

DTIC Science & Technology

1987-03-01

definitions. g. Extraction of data dictionary entries from existing programs. [Ref. 7:pp. 63-66] The third tool is used to define the logic of the...Automation of the Unit Status Reporting System is feasible, and would require: integrated files of data, some direct data extraction from those files...an extract of AR 220-1. Relevant sections of the regulation are included to provide an easy reference for the reader. The last section of the

Optimization of subculture and DNA extraction steps within the whole genome sequencing workflow for source tracking of Salmonella enterica and Listeria monocytogenes.

PubMed

Gimonet, Johan; Portmann, Anne-Catherine; Fournier, Coralie; Baert, Leen

2018-06-16

This work shows that an incubation time reduced to 4-5 h to prepare a culture for DNA extraction followed by an automated DNA extraction can shorten the hands-on time, the turnaround time by 30% and increase the throughput while maintaining the WGS quality assessed by high quality Single Nucleotide Polymorphism analysis. Copyright © 2018. Published by Elsevier B.V.

KAM (Knowledge Acquisition Module): A tool to simplify the knowledge acquisition process

NASA Technical Reports Server (NTRS)

Gettig, Gary A.

1988-01-01

Analysts, knowledge engineers and information specialists are faced with increasing volumes of time-sensitive data in text form, either as free text or highly structured text records. Rapid access to the relevant data in these sources is essential. However, due to the volume and organization of the contents, and limitations of human memory and association, frequently: (1) important information is not located in time; (2) reams of irrelevant data are searched; and (3) interesting or critical associations are missed due to physical or temporal gaps involved in working with large files. The Knowledge Acquisition Module (KAM) is a microcomputer-based expert system designed to assist knowledge engineers, analysts, and other specialists in extracting useful knowledge from large volumes of digitized text and text-based files. KAM formulates non-explicit, ambiguous, or vague relations, rules, and facts into a manageable and consistent formal code. A library of system rules or heuristics is maintained to control the extraction of rules, relations, assertions, and other patterns from the text. These heuristics can be added, deleted or customized by the user. The user can further control the extraction process with optional topic specifications. This allows the user to cluster extracts based on specific topics. Because KAM formalizes diverse knowledge, it can be used by a variety of expert systems and automated reasoning applications. KAM can also perform important roles in computer-assisted training and skill development. Current research efforts include the applicability of neural networks to aid in the extraction process and the conversion of these extracts into standard formats.

Appropriateness in using LANDSAT in development energy related data bases

NASA Technical Reports Server (NTRS)

Harnden, E.

1981-01-01

The use of automated classification systems in the field of resource management and resource inventory is discussed. Applications of LANDSAT classification are outlined and include: energy load forecasting based upon land use inventories and change analysis, impact analysis of activities related to energy extraction, capability/suitability mapping in support of generation and substation location and transmission line routing, and assessment of solar energy potential in a highly urbanized setting where land values are high. It is found that the use of LANDSAT data is adequate for general inventories where few data categories are required, where resolution of data to around 150 acres minimum is required, and where no other complete imagery set can be obtained.

Extracting Information from Narratives: An Application to Aviation Safety Reports

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

Posse, Christian; Matzke, Brett D.; Anderson, Catherine M.

2005-05-12

Aviation safety reports are the best available source of information about why a flight incident happened. However, stream of consciousness permeates the narratives making difficult the automation of the information extraction task. We propose an approach and infrastructure based on a common pattern specification language to capture relevant information via normalized template expression matching in context. Template expression matching handles variants of multi-word expressions. Normalization improves the likelihood of correct hits by standardizing and cleaning the vocabulary used in narratives. Checking for the presence of negative modifiers in the proximity of a potential hit reduces the chance of false hits.more » We present the above approach in the context of a specific application, which is the extraction of human performance factors from NASA ASRS reports. While knowledge infusion from experts plays a critical role during the learning phase, early results show that in a production mode, the automated process provides information that is consistent with analyses by human subjects.« less

Automated Fault Interpretation and Extraction using Improved Supplementary Seismic Datasets

NASA Astrophysics Data System (ADS)

Bollmann, T. A.; Shank, R.

2017-12-01

During the interpretation of seismic volumes, it is necessary to interpret faults along with horizons of interest. With the improvement of technology, the interpretation of faults can be expedited with the aid of different algorithms that create supplementary seismic attributes, such as semblance and coherency. These products highlight discontinuities, but still need a large amount of human interaction to interpret faults and are plagued by noise and stratigraphic discontinuities. Hale (2013) presents a method to improve on these datasets by creating what is referred to as a Fault Likelihood volume. In general, these volumes contain less noise and do not emphasize stratigraphic features. Instead, planar features within a specified strike and dip range are highlighted. Once a satisfactory Fault Likelihood Volume is created, extraction of fault surfaces is much easier. The extracted fault surfaces are then exported to interpretation software for QC. Numerous software packages have implemented this methodology with varying results. After investigating these platforms, we developed a preferred Automated Fault Interpretation workflow.

Automated Extraction of Substance Use Information from Clinical Texts.

PubMed

Wang, Yan; Chen, Elizabeth S; Pakhomov, Serguei; Arsoniadis, Elliot; Carter, Elizabeth W; Lindemann, Elizabeth; Sarkar, Indra Neil; Melton, Genevieve B

2015-01-01

Within clinical discourse, social history (SH) includes important information about substance use (alcohol, drug, and nicotine use) as key risk factors for disease, disability, and mortality. In this study, we developed and evaluated a natural language processing (NLP) system for automated detection of substance use statements and extraction of substance use attributes (e.g., temporal and status) based on Stanford Typed Dependencies. The developed NLP system leveraged linguistic resources and domain knowledge from a multi-site social history study, Propbank and the MiPACQ corpus. The system attained F-scores of 89.8, 84.6 and 89.4 respectively for alcohol, drug, and nicotine use statement detection, as well as average F-scores of 82.1, 90.3, 80.8, 88.7, 96.6, and 74.5 respectively for extraction of attributes. Our results suggest that NLP systems can achieve good performance when augmented with linguistic resources and domain knowledge when applied to a wide breadth of substance use free text clinical notes.

Robotic solid phase extraction and high performance liquid chromatographic analysis of ranitidine in serum or plasma.

PubMed

Lloyd, T L; Perschy, T B; Gooding, A E; Tomlinson, J J

1992-01-01

A fully automated assay for the analysis of ranitidine in serum and plasma, with and without an internal standard, was validated. It utilizes robotic solid phase extraction with on-line high performance liquid chromatographic (HPLC) analysis. The ruggedness of the assay was demonstrated over a three-year period. A Zymark Py Technology II robotic system was used for serial processing from initial aspiration of samples from original collection containers, to final direct injection onto the on-line HPLC system. Automated serial processing with on-line analysis provided uniform sample history and increased productivity by freeing the chemist to analyse data and perform other tasks. The solid phase extraction efficiency was 94% throughout the assay range of 10-250 ng/mL. The coefficients of variation for within- and between-day quality control samples ranged from 1 to 6% and 1 to 5%, respectively. Mean accuracy for between-day standards and quality control results ranged from 97 to 102% of the respective theoretical concentrations.

Automated software system for checking the structure and format of ACM SIG documents

NASA Astrophysics Data System (ADS)

Mirza, Arsalan Rahman; Sah, Melike

2017-04-01

Microsoft (MS) Office Word is one of the most commonly used software tools for creating documents. MS Word 2007 and above uses XML to represent the structure of MS Word documents. Metadata about the documents are automatically created using Office Open XML (OOXML) syntax. We develop a new framework, which is called ADFCS (Automated Document Format Checking System) that takes the advantage of the OOXML metadata, in order to extract semantic information from MS Office Word documents. In particular, we develop a new ontology for Association for Computing Machinery (ACM) Special Interested Group (SIG) documents for representing the structure and format of these documents by using OWL (Web Ontology Language). Then, the metadata is extracted automatically in RDF (Resource Description Framework) according to this ontology using the developed software. Finally, we generate extensive rules in order to infer whether the documents are formatted according to ACM SIG standards. This paper, introduces ACM SIG ontology, metadata extraction process, inference engine, ADFCS online user interface, system evaluation and user study evaluations.

Automated evaluation of electronic discharge notes to assess quality of care for cardiovascular diseases using Medical Language Extraction and Encoding System (MedLEE)

PubMed Central

Lin, Jou-Wei; Yang, Chen-Wei

2010-01-01

The objective of this study was to develop and validate an automated acquisition system to assess quality of care (QC) measures for cardiovascular diseases. This system combining searching and retrieval algorithms was designed to extract QC measures from electronic discharge notes and to estimate the attainment rates to the current standards of care. It was developed on the patients with ST-segment elevation myocardial infarction and tested on the patients with unstable angina/non-ST-segment elevation myocardial infarction, both diseases sharing almost the same QC measures. The system was able to reach a reasonable agreement (κ value) with medical experts from 0.65 (early reperfusion rate) to 0.97 (β-blockers and lipid-lowering agents before discharge) for different QC measures in the test set, and then applied to evaluate QC in the patients who underwent coronary artery bypass grafting surgery. The result has validated a new tool to reliably extract QC measures for cardiovascular diseases. PMID:20442141

All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning.

PubMed

Airola, Antti; Pyysalo, Sampo; Björne, Jari; Pahikkala, Tapio; Ginter, Filip; Salakoski, Tapio

2008-11-19

Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided.

Building an automated SOAP classifier for emergency department reports.

PubMed

Mowery, Danielle; Wiebe, Janyce; Visweswaran, Shyam; Harkema, Henk; Chapman, Wendy W

2012-02-01

Information extraction applications that extract structured event and entity information from unstructured text can leverage knowledge of clinical report structure to improve performance. The Subjective, Objective, Assessment, Plan (SOAP) framework, used to structure progress notes to facilitate problem-specific, clinical decision making by physicians, is one example of a well-known, canonical structure in the medical domain. Although its applicability to structuring data is understood, its contribution to information extraction tasks has not yet been determined. The first step to evaluating the SOAP framework's usefulness for clinical information extraction is to apply the model to clinical narratives and develop an automated SOAP classifier that classifies sentences from clinical reports. In this quantitative study, we applied the SOAP framework to sentences from emergency department reports, and trained and evaluated SOAP classifiers built with various linguistic features. We found the SOAP framework can be applied manually to emergency department reports with high agreement (Cohen's kappa coefficients over 0.70). Using a variety of features, we found classifiers for each SOAP class can be created with moderate to outstanding performance with F(1) scores of 93.9 (subjective), 94.5 (objective), 75.7 (assessment), and 77.0 (plan). We look forward to expanding the framework and applying the SOAP classification to clinical information extraction tasks. Copyright © 2011. Published by Elsevier Inc.

Demonstration and validation of automated agricultural field extraction from multi-temporal Landsat data for the majority of United States harvested cropland

NASA Astrophysics Data System (ADS)

Yan, L.; Roy, D. P.

2014-12-01

The spatial distribution of agricultural fields is a fundamental description of rural landscapes and the location and extent of fields is important to establish the area of land utilized for agricultural yield prediction, resource allocation, and for economic planning, and may be indicative of the degree of agricultural capital investment, mechanization, and labor intensity. To date, field objects have not been extracted from satellite data over large areas because of computational constraints, the complexity of the extraction task, and because consistently processed appropriate resolution data have not been available or affordable. A recently published automated methodology to extract agricultural crop fields from weekly 30 m Web Enabled Landsat data (WELD) time series was refined and applied to 14 states that cover 70% of harvested U.S. cropland (USDA 2012 Census). The methodology was applied to 2010 combined weekly Landsat 5 and 7 WELD data. The field extraction and quantitative validation results are presented for the following 14 states: Iowa, North Dakota, Illinois, Kansas, Minnesota, Nebraska, Texas, South Dakota, Missouri, Indiana, Ohio, Wisconsin, Oklahoma and Michigan (sorted by area of harvested cropland). These states include the top 11 U.S states by harvested cropland area. Implications and recommendations for systematic application to global coverage Landsat data are discussed.

Validation of high-throughput measurement system with microwave-assisted extraction, fully automated sample preparation device, and gas chromatography-electron capture detector for determination of polychlorinated biphenyls in whale blubber.

PubMed

Fujita, Hiroyuki; Honda, Katsuhisa; Hamada, Noriaki; Yasunaga, Genta; Fujise, Yoshihiro

2009-02-01

Validation of a high-throughput measurement system with microwave-assisted extraction (MAE), fully automated sample preparation device (SPD), and gas chromatography-electron capture detector (GC-ECD) for the determination of polychlorinated biphenyls (PCBs) in minke whale blubber was performed. PCB congeners accounting for > 95% of the total PCBs burden in blubber were efficiently extracted with a small volume (20 mL) of n-hexane using MAE due to simultaneous saponification and extraction. Further, the crude extract obtained by MAE was rapidly purified and automatically substituted to a small volume (1 mL) of toluene using SPD without using concentrators. Furthermore, the concentration of PCBs in the purified and concentrated solution was accurately determined by GC-ECD. Moreover, the result of accuracy test using a certified material (SRM 1588b; Cod liver oil) showed good agreement with the NIST certified concentration values. In addition, the method quantification limit of total-PCB in whale blubbers was 41 ng g(-1). This new measurement system for PCBs takes only four hours. Consequently, it indicated this method is the most suitable for the monitoring and screening of PCBs in the conservation of the marine ecosystem and safe distribution of foods.

An algorithm for automatic parameter adjustment for brain extraction in BrainSuite

NASA Astrophysics Data System (ADS)

Rajagopal, Gautham; Joshi, Anand A.; Leahy, Richard M.

2017-02-01

Brain Extraction (classification of brain and non-brain tissue) of MRI brain images is a crucial pre-processing step necessary for imaging-based anatomical studies of the human brain. Several automated methods and software tools are available for performing this task, but differences in MR image parameters (pulse sequence, resolution) and instrumentand subject-dependent noise and artefacts affect the performance of these automated methods. We describe and evaluate a method that automatically adapts the default parameters of the Brain Surface Extraction (BSE) algorithm to optimize a cost function chosen to reflect accurate brain extraction. BSE uses a combination of anisotropic filtering, Marr-Hildreth edge detection, and binary morphology for brain extraction. Our algorithm automatically adapts four parameters associated with these steps to maximize the brain surface area to volume ratio. We evaluate the method on a total of 109 brain volumes with ground truth brain masks generated by an expert user. A quantitative evaluation of the performance of the proposed algorithm showed an improvement in the mean (s.d.) Dice coefficient from 0.8969 (0.0376) for default parameters to 0.9509 (0.0504) for the optimized case. These results indicate that automatic parameter optimization can result in significant improvements in definition of the brain mask.

Development of coatings for automated 96-blade solid phase microextraction-liquid chromatography-tandem mass spectrometry system, capable of extracting a wide polarity range of analytes from biological fluids.

PubMed

Mirnaghi, Fatemeh S; Pawliszyn, Janusz

2012-10-26

This work presents the development and evaluation of biocompatible polyacrylonitrile-polystyrene-divinylbenzene (PAN-PS-DVB) and polyacrylonitrile-phenylboronic acid (PAN-PBA) coatings for automated 96-blades (thin-film) solid phase microextraction (SPME) system, using high performance liquid chromatography (HPLC) coupled with tandem mass spectrometry (MS/MS). The SPME condition was optimized for 60 min equilibrium extraction and 40 min desorption for PAN-PS-DVB, and 120 min equilibrium extraction and 60 min desorption for PAN-PBA for parallel sample preparation of up to 96 samples. The thin film geometry of the SPME blades provided good extraction efficiency due to the larger surface area of the coating, and simultaneous sample preparation provided fast and accurate analysis. The PAN-PS-DVB and PAN-PBA 96-blade SPME coatings were evaluated for extraction of analytes in a wide range of polarity (log P=2.8 to -3.7), and they demonstrated efficient extraction recovery (3.5-98.9% for PAN-PS-DVB and 4.0-74.1% for PAN-PBA) for both polar and non-polar groups of compounds. Reusability, reproducibility, and reliability of the system were evaluated. The results demonstrated that both coatings presented chemical and mechanical stability and long-lasting extraction efficiency for more than 100 usages in phosphate-buffered saline (PBS) and human plasma. Copyright © 2012 Elsevier B.V. All rights reserved.

Automated spectral and timing analysis of AGNs

NASA Astrophysics Data System (ADS)

Munz, F.; Karas, V.; Guainazzi, M.

2006-12-01

% We have developed an autonomous script that helps the user to automate the XMM-Newton data analysis for the purposes of extensive statistical investigations. We test this approach by examining X-ray spectra of bright AGNs pre-selected from the public database. The event lists extracted in this process were studied further by constructing their energy-resolved Fourier power-spectrum density. This analysis combines energy distributions, light-curves, and their power-spectra and it proves useful to assess the variability patterns present is the data. As another example, an automated search was based on the XSPEC package to reveal the emission features in 2-8 keV range.

An overview of the Progenika ID CORE XT: an automated genotyping platform based on a fluidic microarray system.

PubMed

Goldman, Mindy; Núria, Núria; Castilho, Lilian M

2015-01-01

Automated testing platforms facilitate the introduction of red cell genotyping of patients and blood donors. Fluidic microarray systems, such as Luminex XMAP (Austin, TX), are used in many clinical applications, including HLA and HPA typing. The Progenika ID CORE XT (Progenika Biopharma-Grifols, Bizkaia, Spain) uses this platform to analyze 29 polymorphisms determining 37 antigens in 10 blood group systems. Once DNA has been extracted, processing time is approximately 4 hours. The system is highly automated and includes integrated analysis software that produces a file and a report with genotype and predicted phenotype results.

Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions

PubMed Central

Maruthapillai, Vasanthan; Murugappan, Murugappan

2016-01-01

In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject’s face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject’s face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network. PMID:26859884

XML-based data model and architecture for a knowledge-based grid-enabled problem-solving environment for high-throughput biological imaging.

PubMed

Ahmed, Wamiq M; Lenz, Dominik; Liu, Jia; Paul Robinson, J; Ghafoor, Arif

2008-03-01

High-throughput biological imaging uses automated imaging devices to collect a large number of microscopic images for analysis of biological systems and validation of scientific hypotheses. Efficient manipulation of these datasets for knowledge discovery requires high-performance computational resources, efficient storage, and automated tools for extracting and sharing such knowledge among different research sites. Newly emerging grid technologies provide powerful means for exploiting the full potential of these imaging techniques. Efficient utilization of grid resources requires the development of knowledge-based tools and services that combine domain knowledge with analysis algorithms. In this paper, we first investigate how grid infrastructure can facilitate high-throughput biological imaging research, and present an architecture for providing knowledge-based grid services for this field. We identify two levels of knowledge-based services. The first level provides tools for extracting spatiotemporal knowledge from image sets and the second level provides high-level knowledge management and reasoning services. We then present cellular imaging markup language, an extensible markup language-based language for modeling of biological images and representation of spatiotemporal knowledge. This scheme can be used for spatiotemporal event composition, matching, and automated knowledge extraction and representation for large biological imaging datasets. We demonstrate the expressive power of this formalism by means of different examples and extensive experimental results.

Palliative Care Scorecard.

PubMed

Kittelson, Sheri; Pierce, Read; Youngwerth, Jeanie

2017-05-01

In response to poor healthcare quality outcomes and rising costs, healthcare reform triple aim has increased requirements for providers to demonstrate value to payers, partners, and the public. Electronically automating measurement of the meaningful impact of palliative care (PC) programs on clinical, operational, and financial systems over time is imperative to the success of the field and the goal of development of this automated PC scorecard. The scorecard was organized into a format of quality measures identified by the Measuring What Matters (MWM) project that are defined as important to the team, automatically extracted from the electronic health record, valid, and can be impacted over time. The scorecard was initially created using University of Florida Health (UF) data, a new PC program, and successfully applied and implemented at University of Colorado Anschutz Medical Campus (CU), a second institution with a mature PC program. Clinical metrics are organized in the scorecard based on MWM and described in terms of the metric definition, rationale for selection, measure type (structure, process, or outcome), and whether this represents a direct or proxy measure. The process of constructing the scorecard helped identify areas within both systems for potential improvement in team structure, clinical processes, and outcomes. In addition, by automating data extraction, the scorecard decreases costs associated with manual data entry and extraction, freeing clinical staff to care for patients and increasing the value of PC delivered to patients.

Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions.

PubMed

Maruthapillai, Vasanthan; Murugappan, Murugappan

2016-01-01

In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject's face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject's face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network.

Alert management for home healthcare based on home automation analysis.

PubMed

Truong, T T; de Lamotte, F; Diguet, J-Ph; Said-Hocine, F

2010-01-01

Rising healthcare for elder and disabled people can be controlled by offering people autonomy at home by means of information technology. In this paper, we present an original and sensorless alert management solution which performs multimedia and home automation service discrimination and extracts highly regular home activities as sensors for alert management. The results of simulation data, based on real context, allow us to evaluate our approach before application to real data.

Automated Cooperative Trajectories

NASA Technical Reports Server (NTRS)

Hanson, Curt; Pahle, Joseph; Brown, Nelson

2015-01-01

This presentation is an overview of the Automated Cooperative Trajectories project. An introduction to the phenomena of wake vortices is given, along with a summary of past research into the possibility of extracting energy from the wake by flying close parallel trajectories. Challenges and barriers to adoption of civilian automatic wake surfing technology are identified. A hardware-in-the-loop simulation is described that will support future research. Finally, a roadmap for future research and technology transition is proposed.

Facial recognition techniques applied to the automated registration of patients in the emergency treatment of head injuries.

PubMed

Gooroochurn, M; Kerr, D; Bouazza-Marouf, K; Ovinis, M

2011-02-01

This paper describes the development of a registration framework for image-guided solutions to the automation of certain routine neurosurgical procedures. The registration process aligns the pose of the patient in the preoperative space to that of the intraoperative space. Computerized tomography images are used in the preoperative (planning) stage, whilst white light (TV camera) images are used to capture the intraoperative pose. Craniofacial landmarks, rather than artificial markers, are used as the registration basis for the alignment. To create further synergy between the user and the image-guided system, automated methods for extraction of these landmarks have been developed. The results obtained from the application of a polynomial neural network classifier based on Gabor features for the detection and localization of the selected craniofacial landmarks, namely the ear tragus and eye corners in the white light modality are presented. The robustness of the classifier to variations in intensity and noise is analysed. The results show that such a classifier gives good performance for the extraction of craniofacial landmarks.

Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection

PubMed Central

Carrio, Adrian; Sampedro, Carlos; Sanchez-Lopez, Jose Luis; Pimienta, Miguel; Campoy, Pascual

2015-01-01

Lateral flow assay tests are nowadays becoming powerful, low-cost diagnostic tools. Obtaining a result is usually subject to visual interpretation of colored areas on the test by a human operator, introducing subjectivity and the possibility of errors in the extraction of the results. While automated test readers providing a result-consistent solution are widely available, they usually lack portability. In this paper, we present a smartphone-based automated reader for drug-of-abuse lateral flow assay tests, consisting of an inexpensive light box and a smartphone device. Test images captured with the smartphone camera are processed in the device using computer vision and machine learning techniques to perform automatic extraction of the results. A deep validation of the system has been carried out showing the high accuracy of the system. The proposed approach, applicable to any line-based or color-based lateral flow test in the market, effectively reduces the manufacturing costs of the reader and makes it portable and massively available while providing accurate, reliable results. PMID:26610513

Automated Liquid Microjunction Surface Sampling-HPLC-MS/MS Analysis of Drugs and Metabolites in Whole-Body Thin Tissue Sections

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

Kertesz, Vilmos; Van Berkel, Gary J

A fully automated liquid extraction-based surface sampling system utilizing a commercially available autosampler coupled to high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) detection is reported. Discrete spots selected for droplet-based sampling and automated sample queue generation for both the autosampler and MS were enabled by using in-house developed software. In addition, co-registration of spatially resolved sampling position and HPLC-MS information to generate heatmaps of compounds monitored for subsequent data analysis was also available in the software. The system was evaluated with whole-body thin tissue sections from propranolol dosed rat. The hands-free operation of the system was demonstrated by creating heatmapsmore » of the parent drug and its hydroxypropranolol glucuronide metabolites with 1 mm resolution in the areas of interest. The sample throughput was approximately 5 min/sample defined by the time needed for chromatographic separation. The spatial distributions of both the drug and its metabolites were consistent with previous studies employing other liquid extraction-based surface sampling methodologies.« less

Evaluation of automated urban surface water extraction from Sentinel-2A imagery using different water indices

NASA Astrophysics Data System (ADS)

Yang, Xiucheng; Chen, Li

2017-04-01

Urban surface water is characterized by complex surface continents and small size of water bodies, and the mapping of urban surface water is currently a challenging task. The moderate-resolution remote sensing satellites provide effective ways of monitoring surface water. This study conducts an exploratory evaluation on the performance of the newly available Sentinel-2A multispectral instrument (MSI) imagery for detecting urban surface water. An automatic framework that integrates pixel-level threshold adjustment and object-oriented segmentation is proposed. Based on the automated workflow, different combinations of visible, near infrared, and short-wave infrared bands in Sentinel-2 image via different water indices are first compared. Results show that object-level modified normalized difference water index (MNDWI with band 11) and automated water extraction index are feasible in urban surface water mapping for Sentinel-2 MSI imagery. Moreover, comparative results are obtained utilizing optimal MNDWI from Sentinel-2 and Landsat 8 images, respectively. Consequently, Sentinel-2 MSI achieves the kappa coefficient of 0.92, compared with that of 0.83 from Landsat 8 operational land imager.

Automated Information Extraction on Treatment and Prognosis for Non-Small Cell Lung Cancer Radiotherapy Patients: Clinical Study.

PubMed

Zheng, Shuai; Jabbour, Salma K; O'Reilly, Shannon E; Lu, James J; Dong, Lihua; Ding, Lijuan; Xiao, Ying; Yue, Ning; Wang, Fusheng; Zou, Wei

2018-02-01

In outcome studies of oncology patients undergoing radiation, researchers extract valuable information from medical records generated before, during, and after radiotherapy visits, such as survival data, toxicities, and complications. Clinical studies rely heavily on these data to correlate the treatment regimen with the prognosis to develop evidence-based radiation therapy paradigms. These data are available mainly in forms of narrative texts or table formats with heterogeneous vocabularies. Manual extraction of the related information from these data can be time consuming and labor intensive, which is not ideal for large studies. The objective of this study was to adapt the interactive information extraction platform Information and Data Extraction using Adaptive Learning (IDEAL-X) to extract treatment and prognosis data for patients with locally advanced or inoperable non-small cell lung cancer (NSCLC). We transformed patient treatment and prognosis documents into normalized structured forms using the IDEAL-X system for easy data navigation. The adaptive learning and user-customized controlled toxicity vocabularies were applied to extract categorized treatment and prognosis data, so as to generate structured output. In total, we extracted data from 261 treatment and prognosis documents relating to 50 patients, with overall precision and recall more than 93% and 83%, respectively. For toxicity information extractions, which are important to study patient posttreatment side effects and quality of life, the precision and recall achieved 95.7% and 94.5% respectively. The IDEAL-X system is capable of extracting study data regarding NSCLC chemoradiation patients with significant accuracy and effectiveness, and therefore can be used in large-scale radiotherapy clinical data studies. ©Shuai Zheng, Salma K Jabbour, Shannon E O'Reilly, James J Lu, Lihua Dong, Lijuan Ding, Ying Xiao, Ning Yue, Fusheng Wang, Wei Zou. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 01.02.2018.

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.

A Hybrid DNA Extraction Method for the Qualitative and Quantitative Assessment of Bacterial Communities from Poultry Production Samples

PubMed Central

Rothrock, Michael J.; Hiett, Kelli L.; Gamble, John; Caudill, Andrew C.; Cicconi-Hogan, Kellie M.; Caporaso, J. Gregory

2014-01-01

The efficacy of DNA extraction protocols can be highly dependent upon both the type of sample being investigated and the types of downstream analyses performed. Considering that the use of new bacterial community analysis techniques (e.g., microbiomics, metagenomics) is becoming more prevalent in the agricultural and environmental sciences and many environmental samples within these disciplines can be physiochemically and microbiologically unique (e.g., fecal and litter/bedding samples from the poultry production spectrum), appropriate and effective DNA extraction methods need to be carefully chosen. Therefore, a novel semi-automated hybrid DNA extraction method was developed specifically for use with environmental poultry production samples. This method is a combination of the two major types of DNA extraction: mechanical and enzymatic. A two-step intense mechanical homogenization step (using bead-beating specifically formulated for environmental samples) was added to the beginning of the “gold standard” enzymatic DNA extraction method for fecal samples to enhance the removal of bacteria and DNA from the sample matrix and improve the recovery of Gram-positive bacterial community members. Once the enzymatic extraction portion of the hybrid method was initiated, the remaining purification process was automated using a robotic workstation to increase sample throughput and decrease sample processing error. In comparison to the strict mechanical and enzymatic DNA extraction methods, this novel hybrid method provided the best overall combined performance when considering quantitative (using 16S rRNA qPCR) and qualitative (using microbiomics) estimates of the total bacterial communities when processing poultry feces and litter samples. PMID:25548939

Automated rule-base creation via CLIPS-Induce

NASA Technical Reports Server (NTRS)

Murphy, Patrick M.

1994-01-01

Many CLIPS rule-bases contain one or more rule groups that perform classification. In this paper we describe CLIPS-Induce, an automated system for the creation of a CLIPS classification rule-base from a set of test cases. CLIPS-Induce consists of two components, a decision tree induction component and a CLIPS production extraction component. ID3, a popular decision tree induction algorithm, is used to induce a decision tree from the test cases. CLIPS production extraction is accomplished through a top-down traversal of the decision tree. Nodes of the tree are used to construct query rules, and branches of the tree are used to construct classification rules. The learned CLIPS productions may easily be incorporated into a large CLIPS system that perform tasks such as accessing a database or displaying information.

Solid-Phase Extraction Strategies to Surmount Body Fluid Sample Complexity in High-Throughput Mass Spectrometry-Based Proteomics

PubMed Central

Bladergroen, Marco R.; van der Burgt, Yuri E. M.

2015-01-01

For large-scale and standardized applications in mass spectrometry- (MS-) based proteomics automation of each step is essential. Here we present high-throughput sample preparation solutions for balancing the speed of current MS-acquisitions and the time needed for analytical workup of body fluids. The discussed workflows reduce body fluid sample complexity and apply for both bottom-up proteomics experiments and top-down protein characterization approaches. Various sample preparation methods that involve solid-phase extraction (SPE) including affinity enrichment strategies have been automated. Obtained peptide and protein fractions can be mass analyzed by direct infusion into an electrospray ionization (ESI) source or by means of matrix-assisted laser desorption ionization (MALDI) without further need of time-consuming liquid chromatography (LC) separations. PMID:25692071

In vivo classification of human skin burns using machine learning and quantitative features captured by optical coherence tomography

NASA Astrophysics Data System (ADS)

Singla, Neeru; Srivastava, Vishal; Singh Mehta, Dalip

2018-02-01

We report the first fully automated detection of human skin burn injuries in vivo, with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Our proposed automated procedure entails building a machine-learning-based classifier by extracting quantitative features from normal and burn tissue images recorded by OCT. In this study, 56 samples (28 normal, 28 burned) were imaged by OCT and eight features were extracted. A linear model classifier was trained using 34 samples and 22 samples were used to test the model. Sensitivity of 91.6% and specificity of 90% were obtained. Our results demonstrate the capability of a computer-aided technique for accurately and automatically identifying burn tissue resection margins during surgical treatment.

Determination of Hypochlorite in Bleaching Products with Flower Extracts to Demonstrate the Principles of Flow Injection Analysis

ERIC Educational Resources Information Center

Ramos, Luiz Antonio; Prieto, Katia Roberta; Carvalheiro, Eder Tadeu Gomes; Carvalheiro, Carla Cristina Schmitt

2005-01-01

The use of crude flower extracts to the principle of analytical chemistry automation, with the flow injection analysis (FIA) procedure developed to determine hypochlorite in household bleaching products was performed. The FIA comprises a group of techniques based on injection of a liquid sample into a moving, nonsegmented carrier stream of a…

Electrically evoked compound action potentials artefact rejection by independent component analysis: procedure automation.

PubMed

Akhoun, Idrick; McKay, Colette; El-Deredy, Wael

2015-01-15

Independent-components-analysis (ICA) successfully separated electrically-evoked compound action potentials (ECAPs) from the stimulation artefact and noise (ECAP-ICA, Akhoun et al., 2013). This paper shows how to automate the ECAP-ICA artefact cancellation process. Raw-ECAPs without artefact rejection were consecutively recorded for each stimulation condition from at least 8 intra-cochlear electrodes. Firstly, amplifier-saturated recordings were discarded, and the data from different stimulus conditions (different current-levels) were concatenated temporally. The key aspect of the automation procedure was the sequential deductive source categorisation after ICA was applied with a restriction to 4 sources. The stereotypical aspect of the 4 sources enables their automatic classification as two artefact components, a noise and the sought ECAP based on theoretical and empirical considerations. The automatic procedure was tested using 8 cochlear implant (CI) users and one to four stimulus electrodes. The artefact and noise sources were successively identified and discarded, leaving the ECAP as the remaining source. The automated ECAP-ICA procedure successfully extracted the correct ECAPs compared to standard clinical forward masking paradigm in 22 out of 26 cases. ECAP-ICA does not require extracting the ECAP from a combination of distinct buffers as it is the case with regular methods. It is an alternative that does not have the possible bias of traditional artefact rejections such as alternate-polarity or forward-masking paradigms. The ECAP-ICA procedure bears clinical relevance, for example as the artefact rejection sub-module of automated ECAP-threshold detection techniques, which are common features of CI clinical fitting software. Copyright © 2014. Published by Elsevier B.V.

Automated processing of whole blood samples for the determination of immunosuppressants by liquid chromatography tandem-mass spectrometry.

PubMed

Vogeser, Michael; Spöhrer, Ute

2006-01-01

Liquid chromatography tandem-mass spectrometry (LC-MS/MS) is an efficient technology for routine determination of immunosuppressants in whole blood; however, time-consuming manual sample preparation remains a significant limitation of this technique. Using a commercially available robotic pipetting system (Tecan Freedom EVO), we developed an automated sample-preparation protocol for quantification of tacrolimus in whole blood by LC-MS/MS. Barcode reading, sample resuspension, transfer of whole blood aliquots into a deep-well plate, addition of internal standard solution, mixing, and protein precipitation by addition of an organic solvent is performed by the robotic system. After centrifugation of the plate, the deproteinized supernatants are submitted to on-line solid phase extraction, using column switching prior to LC-MS/MS analysis. The only manual actions within the entire process are decapping of the tubes, and transfer of the deep-well plate from the robotic system to a centrifuge and finally to the HPLC autosampler. Whole blood pools were used to assess the reproducibility of the entire analytical system for measuring tacrolimus concentrations. A total coefficient of variation of 1.7% was found for the entire automated analytical process (n=40; mean tacrolimus concentration, 5.3 microg/L). Close agreement between tacrolimus results obtained after manual and automated sample preparation was observed. The analytical system described here, comprising automated protein precipitation, on-line solid phase extraction and LC-MS/MS analysis, is convenient and precise, and minimizes hands-on time and the risk of mistakes in the quantification of whole blood immunosuppressant concentrations compared to conventional methods.

Detection of facilities in satellite imagery using semi-supervised image classification and auxiliary contextual observables

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

Harvey, Neal R; Ruggiero, Christy E; Pawley, Norma H

2009-01-01

Detecting complex targets, such as facilities, in commercially available satellite imagery is a difficult problem that human analysts try to solve by applying world knowledge. Often there are known observables that can be extracted by pixel-level feature detectors that can assist in the facility detection process. Individually, each of these observables is not sufficient for an accurate and reliable detection, but in combination, these auxiliary observables may provide sufficient context for detection by a machine learning algorithm. We describe an approach for automatic detection of facilities that uses an automated feature extraction algorithm to extract auxiliary observables, and a semi-supervisedmore » assisted target recognition algorithm to then identify facilities of interest. We illustrate the approach using an example of finding schools in Quickbird image data of Albuquerque, New Mexico. We use Los Alamos National Laboratory's Genie Pro automated feature extraction algorithm to find a set of auxiliary features that should be useful in the search for schools, such as parking lots, large buildings, sports fields and residential areas and then combine these features using Genie Pro's assisted target recognition algorithm to learn a classifier that finds schools in the image data.« less

Improving the automated detection of refugee/IDP dwellings using the multispectral bands of the WorldView-2 satellite

NASA Astrophysics Data System (ADS)

Kemper, Thomas; Gueguen, Lionel; Soille, Pierre

2012-06-01

The enumeration of the population remains a critical task in the management of refugee/IDP camps. Analysis of very high spatial resolution satellite data proofed to be an efficient and secure approach for the estimation of dwellings and the monitoring of the camp over time. In this paper we propose a new methodology for the automated extraction of features based on differential morphological decomposition segmentation for feature extraction and interactive training sample selection from the max-tree and min-tree structures. This feature extraction methodology is tested on a WorldView-2 scene of an IDP camp in Darfur Sudan. Special emphasis is given to the additional available bands of the WorldView-2 sensor. The results obtained show that the interactive image information tool is performing very well by tuning the feature extraction to the local conditions. The analysis of different spectral subsets shows that it is possible to obtain good results already with an RGB combination, but by increasing the number of spectral bands the detection of dwellings becomes more accurate. Best results were obtained using all eight bands of WorldView-2 satellite.

Automated extraction and classification of time-frequency contours in humpback vocalizations.

PubMed

Ou, Hui; Au, Whitlow W L; Zurk, Lisa M; Lammers, Marc O

2013-01-01

A time-frequency contour extraction and classification algorithm was created to analyze humpback whale vocalizations. The algorithm automatically extracted contours of whale vocalization units by searching for gray-level discontinuities in the spectrogram images. The unit-to-unit similarity was quantified by cross-correlating the contour lines. A library of distinctive humpback units was then generated by applying an unsupervised, cluster-based learning algorithm. The purpose of this study was to provide a fast and automated feature selection tool to describe the vocal signatures of animal groups. This approach could benefit a variety of applications such as species description, identification, and evolution of song structures. The algorithm was tested on humpback whale song data recorded at various locations in Hawaii from 2002 to 2003. Results presented in this paper showed low probability of false alarm (0%-4%) under noisy environments with small boat vessels and snapping shrimp. The classification algorithm was tested on a controlled set of 30 units forming six unit types, and all the units were correctly classified. In a case study on humpback data collected in the Auau Chanel, Hawaii, in 2002, the algorithm extracted 951 units, which were classified into 12 distinctive types.

Extracting cardiac shapes and motion of the chick embryo heart outflow tract from four-dimensional optical coherence tomography images

NASA Astrophysics Data System (ADS)

Yin, Xin; Liu, Aiping; Thornburg, Kent L.; Wang, Ruikang K.; Rugonyi, Sandra

2012-09-01

Recent advances in optical coherence tomography (OCT), and the development of image reconstruction algorithms, enabled four-dimensional (4-D) (three-dimensional imaging over time) imaging of the embryonic heart. To further analyze and quantify the dynamics of cardiac beating, segmentation procedures that can extract the shape of the heart and its motion are needed. Most previous studies analyzed cardiac image sequences using manually extracted shapes and measurements. However, this is time consuming and subject to inter-operator variability. Automated or semi-automated analyses of 4-D cardiac OCT images, although very desirable, are also extremely challenging. This work proposes a robust algorithm to semi automatically detect and track cardiac tissue layers from 4-D OCT images of early (tubular) embryonic hearts. Our algorithm uses a two-dimensional (2-D) deformable double-line model (DLM) to detect target cardiac tissues. The detection algorithm uses a maximum-likelihood estimator and was successfully applied to 4-D in vivo OCT images of the heart outflow tract of day three chicken embryos. The extracted shapes captured the dynamics of the chick embryonic heart outflow tract wall, enabling further analysis of cardiac motion.

Fast automated dual-syringe based dispersive liquid-liquid microextraction coupled with gas chromatography-mass spectrometry for the determination of polycyclic aromatic hydrocarbons in environmental water samples.

PubMed

Guo, Liang; Tan, Shufang; Li, Xiao; Lee, Hian Kee

2016-03-18

An automated procedure, combining low density solvent based solvent demulsification dispersive liquid-liquid microextraction (DLLME) with gas chromatography-mass spectrometry analysis, was developed for the determination of polycyclic aromatic hydrocarbons (PAHs) in environmental water samples. Capitalizing on a two-rail commercial autosampler, fast solvent transfer using a large volume syringe dedicated to the DLLME process, and convenient extract collection using a small volume microsyringe for better GC performance were enabled. Extraction parameters including the type and volume of extraction solvent, the type and volume of dispersive solvent and demulsification solvent, extraction and demulsification time, and the speed of solvent injection were investigated and optimized. Under the optimized conditions, the linearity ranged from 0.1 to 50 μg/L, 0.2 to 50 μg/L, and 0.5 to 50 μg/L, depending on the analytes. Limits of detection were determined to be between 0.023 and 0.058 μg/L. The method was applied to determine PAHs in environmental water samples. Copyright © 2016 Elsevier B.V. All rights reserved.

CMS-2 Reverse Engineering and ENCORE/MODEL Integration

DTIC Science & Technology

1992-05-01

Automated extraction of design information from an existing software system written in CMS-2 can be used to document that system as-built, and that I The...extracted information is provided by a commer- dally available CASE tool. * Information describing software system design is automatically extracted...the displays in Figures 1, 2, and 3. T achiev ths GE 11 b iuo w as rjcs CM-2t Aa nsltr(M2da 1 n Joia Reverse EwngiernTcnlg 5RT [2GRE] . Two xampe fD

Estimating Regional Mass Balance of Himalayan Glaciers Using Hexagon Imagery: An Automated Approach

NASA Astrophysics Data System (ADS)

Maurer, J. M.; Rupper, S.

2013-12-01

Currently there is much uncertainty regarding the present and future state of Himalayan glaciers, which supply meltwater for river systems vital to more than 1.4 billion people living throughout Asia. Previous assessments of regional glacier mass balance in the Himalayas using various remote sensing and field-based methods give inconsistent results, and most assessments are over relatively short (e.g., single decade) timescales. This study aims to quantify multi-decadal changes in volume and extent of Himalayan glaciers through efficient use of the large database of declassified 1970-80s era Hexagon stereo imagery. Automation of the DEM extraction process provides an effective workflow for many images to be processed and glacier elevation changes quantified with minimal user input. The tedious procedure of manual ground control point selection necessary for block-bundle adjustment (as ephemeral data is not available for the declassified images) is automated using the Maximally Stable Extremal Regions algorithm, which matches image elements between raw Hexagon images and georeferenced Landsat 15 meter panchromatic images. Additional automated Hexagon DEM processing, co-registration, and bias correction allow for direct comparison with modern ASTER and SRTM elevation data, thus quantifying glacier elevation and area changes over several decades across largely inaccessible mountainous regions. As consistent methodology is used for all glaciers, results will likely reveal significant spatial and temporal patterns in regional ice mass balance. Ultimately, these findings could have important implications for future water resource management in light of environmental change.

Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models

NASA Astrophysics Data System (ADS)

Neubert, A.; Fripp, J.; Engstrom, C.; Schwarz, R.; Lauer, L.; Salvado, O.; Crozier, S.

2012-12-01

Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

Semi-automated extraction and characterization of Stromal Vascular Fraction using a new medical device.

PubMed

Hanke, Alexander; Prantl, Lukas; Wenzel, Carina; Nerlich, Michael; Brockhoff, Gero; Loibl, Markus; Gehmert, Sebastian

2016-01-01

The stem cell rich Stromal Vascular Fraction (SVF) can be harvested by processing lipo-aspirate or fat tissue with an enzymatic digestion followed by centrifugation. To date neither a standardised extraction method for SVF nor a generally admitted protocol for cell application in patients exists. A novel commercially available semi-automated device for the extraction of SVF promises sterility, consistent results and usability in the clinical routine. The aim of this work was to compare the quantity and quality of the SVF between the new system and an established manual laboratory method. SVF was extracted from lipo-aspirate both by a prototype of the semi-automated UNiStation™ (NeoGenesis, Seoul, Korea) and by hand preparation with common laboratory equipment. Cell composition of the SVF was characterized by multi-parametric flow-cytometry (FACSCanto-II, BD Biosciences). The total cell number (quantity) of the SVF was determined as well the percentage of cells expressing the stem cell marker CD34, the leucocyte marker CD45 and the marker CD271 for highly proliferative stem cells (quality). Lipo-aspirate obtained from six patients was processed with both the novel device (d) and the hand preparation (h) which always resulted in a macroscopically visible SVF. However, there was a tendency of a fewer cell yield per gram of used lipo-aspirate with the device (d: 1.1×105±1.1×105 vs. h: 2.0×105±1.7×105; p = 0.06). Noteworthy, the percentage of CD34+ cells was significantly lower when using the device (d: 57.3% ±23.8% vs. h: 74.1% ±13.4%; p = 0.02) and CD45+ leukocyte counts tend to be higher when compared to the hand preparation (d: 20.7% ±15.8% vs. h: 9.8% ±7.1%; p = 0.07). The percentage of highly proliferative CD271+ cells was similar for both methods (d:12.9% ±9.6% vs. h: 13.4% ±11.6%; p = 0.74) and no differences were found for double positive cells of CD34+/CD45+ (d: 5.9% ±1.7% vs. h: 1.7% ±1.1%; p = 0.13), CD34+/CD271+ (d: 24.1% ±12.0% vs. h: 14.2% ±8.5%; p = 0.07). The semi-automated closed system provides a considerable amount of sterile SVF with high reproducibility. Furthermore, the SVF extracted by both methods showed a similar cell composition which is in accordance with the data from literature. This semi-automated device offers an opportunity to take research and application of the SVF one step further to the clinic.

Evaluation and comparison of FTA card and CTAB DNA extraction methods for non-agricultural taxa.

PubMed

Siegel, Chloe S; Stevenson, Florence O; Zimmer, Elizabeth A

2017-02-01

An efficient, effective DNA extraction method is necessary for comprehensive analysis of plant genomes. This study analyzed the quality of DNA obtained using paper FTA cards prepared directly in the field when compared to the more traditional cetyltrimethylammonium bromide (CTAB)-based extraction methods from silica-dried samples. DNA was extracted using FTA cards according to the manufacturer's protocol. In parallel, CTAB-based extractions were done using the automated AutoGen DNA isolation system. DNA quality for both methods was determined for 15 non-agricultural species collected in situ, by gel separation, spectrophotometry, fluorometry, and successful amplification and sequencing of nuclear and chloroplast gene markers. The FTA card extraction method yielded less concentrated, but also less fragmented samples than the CTAB-based technique. The card-extracted samples provided DNA that could be successfully amplified and sequenced. The FTA cards are also useful because the collected samples do not require refrigeration, extensive laboratory expertise, or as many hazardous chemicals as extractions using the CTAB-based technique. The relative success of the FTA card method in our study suggested that this method could be a valuable tool for studies in plant population genetics and conservation biology that may involve screening of hundreds of individual plants. The FTA cards, like the silica gel samples, do not contain plant material capable of propagation, and therefore do not require permits from the U.S. Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) for transportation.

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.

Pediatric Brain Extraction Using Learning-based Meta-algorithm

PubMed Central

Shi, Feng; Wang, Li; Dai, Yakang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2012-01-01

Magnetic resonance imaging of pediatric brain provides valuable information for early brain development studies. Automated brain extraction is challenging due to the small brain size and dynamic change of tissue contrast in the developing brains. In this paper, we propose a novel Learning Algorithm for Brain Extraction and Labeling (LABEL) specially for the pediatric MR brain images. The idea is to perform multiple complementary brain extractions on a given testing image by using a meta-algorithm, including BET and BSE, where the parameters of each run of the meta-algorithm are effectively learned from the training data. Also, the representative subjects are selected as exemplars and used to guide brain extraction of new subjects in different age groups. We further develop a level-set based fusion method to combine multiple brain extractions together with a closed smooth surface for obtaining the final extraction. The proposed method has been extensively evaluated in subjects of three representative age groups, such as neonate (less than 2 months), infant (1–2 years), and child (5–18 years). Experimental results show that, with 45 subjects for training (15 neonates, 15 infant, and 15 children), the proposed method can produce more accurate brain extraction results on 246 testing subjects (75 neonates, 126 infants, and 45 children), i.e., at average Jaccard Index of 0.953, compared to those by BET (0.918), BSE (0.902), ROBEX (0.901), GCUT (0.856), and other fusion methods such as Majority Voting (0.919) and STAPLE (0.941). Along with the largely-improved computational efficiency, the proposed method demonstrates its ability of automated brain extraction for pediatric MR images in a large age range. PMID:22634859

Optimizing the balance between task automation and human manual control in simulated submarine track management.

PubMed

Chen, Stephanie I; Visser, Troy A W; Huf, Samuel; Loft, Shayne

2017-09-01

Automation can improve operator performance and reduce workload, but can also degrade operator situation awareness (SA) and the ability to regain manual control. In 3 experiments, we examined the extent to which automation could be designed to benefit performance while ensuring that individuals maintained SA and could regain manual control. Participants completed a simulated submarine track management task under varying task load. The automation was designed to facilitate information acquisition and analysis, but did not make task decisions. Relative to a condition with no automation, the continuous use of automation improved performance and reduced subjective workload, but degraded SA. Automation that was engaged and disengaged by participants as required (adaptable automation) moderately improved performance and reduced workload relative to no automation, but degraded SA. Automation engaged and disengaged based on task load (adaptive automation) provided no benefit to performance or workload, and degraded SA relative to no automation. Automation never led to significant return-to-manual deficits. However, all types of automation led to degraded performance on a nonautomated task that shared information processing requirements with automated tasks. Given these outcomes, further research is urgently required to establish how to design automation to maximize performance while keeping operators cognitively engaged. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Multi-Modal Glioblastoma Segmentation: Man versus Machine

PubMed Central

Pica, Alessia; Schucht, Philippe; Beck, Jürgen; Verma, Rajeev Kumar; Slotboom, Johannes; Reyes, Mauricio; Wiest, Roland

2014-01-01

Background and Purpose Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations. Methods We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error. Results Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (ρ) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation. Conclusions In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity. PMID:24804720

Comparison of two methods for measuring γ-H2AX nuclear fluorescence as a marker of DNA damage in cultured human cells: applications for microbeam radiation therapy

NASA Astrophysics Data System (ADS)

Anderson, D.; Andrais, B.; Mirzayans, R.; Siegbahn, E. A.; Fallone, B. G.; Warkentin, B.

2013-06-01

Microbeam radiation therapy (MRT) delivers single fractions of very high doses of synchrotron x-rays using arrays of microbeams. In animal experiments, MRT has achieved higher tumour control and less normal tissue toxicity compared to single-fraction broad beam irradiations of much lower dose. The mechanism behind the normal tissue sparing of MRT has yet to be fully explained. An accurate method for evaluating DNA damage, such as the γ-H2AX immunofluorescence assay, will be important for understanding the role of cellular communication in the radiobiological response of normal and cancerous cell types to MRT. We compare two methods of quantifying γ-H2AX nuclear fluorescence for uniformly irradiated cell cultures: manual counting of γ-H2AX foci by eye, and an automated, MATLAB-based fluorescence intensity measurement. We also demonstrate the automated analysis of cell cultures irradiated with an array of microbeams. In addition to offering a relatively high dynamic range of γ-H2AX signal versus irradiation dose ( > 10 Gy), our automated method provides speed, robustness, and objectivity when examining a series of images. Our in-house analysis facilitates the automated extraction of the spatial distribution of the γ-H2AX intensity with respect to the microbeam array — for example, the intensities in the peak (high dose area) and valley (area between two microbeams) regions. The automated analysis is particularly beneficial when processing a large number of samples, as is needed to systematically study the relationship between the numerous dosimetric and geometric parameters involved with MRT (e.g., microbeam width, microbeam spacing, microbeam array dimensions, peak dose, valley dose, and geometric arrangement of multiple arrays) and the resulting DNA damage.

Differential Profiling of Volatile Organic Compound Biomarker Signatures Utilizing a Logical Statistical Filter-Set and Novel Hybrid Evolutionary Classifiers

DTIC Science & Technology

2012-04-01

for automated SPME headspace sampling and in-line with a Thermo DSQII single quadrupole mass spectrometer. Collection of organic volatiles from the...urine was accomplished using a 2cm CAR/DVB/PDMS solid phase micro extraction fiber ( SPME ), Supelco supplier, inserted by the Triplus autosampler into...automated direct injection. Volatiles gathered by the SPME fiber were analyzed through desorption of the fiber by heating to elevated temperature and

Automated sleep scoring and sleep apnea detection in children

NASA Astrophysics Data System (ADS)

Baraglia, David P.; Berryman, Matthew J.; Coussens, Scott W.; Pamula, Yvonne; Kennedy, Declan; Martin, A. James; Abbott, Derek

2005-12-01

This paper investigates the automated detection of a patient's breathing rate and heart rate from their skin conductivity as well as sleep stage scoring and breathing event detection from their EEG. The software developed for these tasks is tested on data sets obtained from the sleep disorders unit at the Adelaide Women's and Children's Hospital. The sleep scoring and breathing event detection tasks used neural networks to achieve signal classification. The Fourier transform and the Higuchi fractal dimension were used to extract features for input to the neural network. The filtered skin conductivity appeared visually to bear a similarity to the breathing and heart rate signal, but a more detailed evaluation showed the relation was not consistent. Sleep stage classification was achieved with and accuracy of around 65% with some stages being accurately scored and others poorly scored. The two breathing events hypopnea and apnea were scored with varying degrees of accuracy with the highest scores being around 75% and 30%.

Sentinel-2 ArcGIS Tool for Environmental Monitoring

NASA Astrophysics Data System (ADS)

Plesoianu, Alin; Cosmin Sandric, Ionut; Anca, Paula; Vasile, Alexandru; Calugaru, Andreea; Vasile, Cristian; Zavate, Lucian

2017-04-01

This paper addresses one of the biggest challenges regarding Sentinel-2 data, related to the need of an efficient tool to access and process the large collection of images that are available. Consequently, developing a tool for the automation of Sentinel-2 data analysis is the most immediate need. We developed a series of tools for the automation of Sentinel-2 data download and processing for vegetation health monitoring. The tools automatically perform the following operations: downloading image tiles from ESA's Scientific Hub or other venders (Amazon), pre-processing of the images to extract the 10-m bands, creating image composites, applying a series of vegetation indexes (NDVI, OSAVI, etc.) and performing change detection analyses on different temporal data sets. All of these tools run in a dynamic way in the ArcGIS Platform, without the need of creating intermediate datasets (rasters, layers), as the images are processed on-the-fly in order to avoid data duplication. Finally, they allow complete integration with the ArcGIS environment and workflows

Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images

NASA Astrophysics Data System (ADS)

Maboudi, Mehdi; Amini, Jalal; Malihi, Shirin; Hahn, Michael

2018-04-01

Updated road network as a crucial part of the transportation database plays an important role in various applications. Thus, increasing the automation of the road extraction approaches from remote sensing images has been the subject of extensive research. In this paper, we propose an object based road extraction approach from very high resolution satellite images. Based on the object based image analysis, our approach incorporates various spatial, spectral, and textural objects' descriptors, the capabilities of the fuzzy logic system for handling the uncertainties in road modelling, and the effectiveness and suitability of ant colony algorithm for optimization of network related problems. Four VHR optical satellite images which are acquired by Worldview-2 and IKONOS satellites are used in order to evaluate the proposed approach. Evaluation of the extracted road networks shows that the average completeness, correctness, and quality of the results can reach 89%, 93% and 83% respectively, indicating that the proposed approach is applicable for urban road extraction. We also analyzed the sensitivity of our algorithm to different ant colony optimization parameter values. Comparison of the achieved results with the results of four state-of-the-art algorithms and quantifying the robustness of the fuzzy rule set demonstrate that the proposed approach is both efficient and transferable to other comparable images.

Protein extraction from methanol fixed paraffin embedded tissue blocks: A new possibility using cell blocks

PubMed Central

Kokkat, Theresa J.; McGarvey, Diane; Patel, Miral S.; Tieniber, Andrew D.; LiVolsi, Virginia A.; Baloch, Zubair W.

2013-01-01

Background: Methanol fixed and paraffin embedded (MFPE) cellblocks are an essential cytology preparation. However, MFPE cellblocks often contain limited material and their relatively small size has caused them to be overlooked in biomarker discovery. Advances in the field of molecular biotechnology have made it possible to extract proteins from formalin fixed and paraffin embedded (FFPE) tissue blocks. In contrast, there are no established methods for extracting proteins from MFPE cellblocks. We investigated commonly available CHAPS (3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate) buffer, as well as two commercially available Qiagen® kits and compared their effectiveness on MFPE tissue for protein yields. Materials and Methods: MFPE blocks were made by Cellient™ automated system using human tissue specimens from normal and malignant specimens collected in ThinPrep™ Vials. Protein was extracted from Cellient-methanol fixed and paraffin embedded blocks with CHAPS buffer method as well as FFPE and Mammalian Qiagen® kits. Results: Comparison of protein yields demonstrated the effectiveness of various protein extraction methods on MFPE cellblocks. Conclusion: In the current era of minimally invasive techniques to obtain minimal amount of tissue for diagnostic and prognostic purposes, the use of commercial and lab made buffer on low weight MFPE scrapings obtained by Cellient® processor opens new possibilities for protein biomarker research. PMID:24403950

ALE: automated label extraction from GEO metadata.

PubMed

Giles, Cory B; Brown, Chase A; Ripperger, Michael; Dennis, Zane; Roopnarinesingh, Xiavan; Porter, Hunter; Perz, Aleksandra; Wren, Jonathan D

2017-12-28

NCBI's Gene Expression Omnibus (GEO) is a rich community resource containing millions of gene expression experiments from human, mouse, rat, and other model organisms. However, information about each experiment (metadata) is in the format of an open-ended, non-standardized textual description provided by the depositor. Thus, classification of experiments for meta-analysis by factors such as gender, age of the sample donor, and tissue of origin is not feasible without assigning labels to the experiments. Automated approaches are preferable for this, primarily because of the size and volume of the data to be processed, but also because it ensures standardization and consistency. While some of these labels can be extracted directly from the textual metadata, many of the data available do not contain explicit text informing the researcher about the age and gender of the subjects with the study. To bridge this gap, machine-learning methods can be trained to use the gene expression patterns associated with the text-derived labels to refine label-prediction confidence. Our analysis shows only 26% of metadata text contains information about gender and 21% about age. In order to ameliorate the lack of available labels for these data sets, we first extract labels from the textual metadata for each GEO RNA dataset and evaluate the performance against a gold standard of manually curated labels. We then use machine-learning methods to predict labels, based upon gene expression of the samples and compare this to the text-based method. Here we present an automated method to extract labels for age, gender, and tissue from textual metadata and GEO data using both a heuristic approach as well as machine learning. We show the two methods together improve accuracy of label assignment to GEO samples.

A gradient-based approach for automated crest-line detection and analysis of sand dune patterns on planetary surfaces

NASA Astrophysics Data System (ADS)

Lancaster, N.; LeBlanc, D.; Bebis, G.; Nicolescu, M.

2015-12-01

Dune-field patterns are believed to behave as self-organizing systems, but what causes the patterns to form is still poorly understood. The most obvious (and in many cases the most significant) aspect of a dune system is the pattern of dune crest lines. Extracting meaningful features such as crest length, orientation, spacing, bifurcations, and merging of crests from image data can reveal important information about the specific dune-field morphological properties, development, and response to changes in boundary conditions, but manual methods are labor-intensive and time-consuming. We are developing the capability to recognize and characterize patterns of sand dunes on planetary surfaces. Our goal is to develop a robust methodology and the necessary algorithms for automated or semi-automated extraction of dune morphometric information from image data. Our main approach uses image processing methods to extract gradient information from satellite images of dune fields. Typically, the gradients have a dominant magnitude and orientation. In many cases, the images have two major dominant gradient orientations, for the sunny and shaded side of the dunes. A histogram of the gradient orientations is used to determine the dominant orientation. A threshold is applied to the image based on gradient orientations which agree with the dominant orientation. The contours of the binary image can then be used to determine the dune crest-lines, based on pixel intensity values. Once the crest-lines have been extracted, the morphological properties can be computed. We have tested our approach on a variety of images of linear and crescentic (transverse) dunes and compared dune detection algorithms with manually-digitized dune crest lines, achieving true positive values of 0.57-0.99; and false positives values of 0.30-0.67, indicating that out approach is generally robust.

Automated extraction of chemical structure information from digital raster images

PubMed Central

Park, Jungkap; Rosania, Gus R; Shedden, Kerby A; Nguyen, Mandee; Lyu, Naesung; Saitou, Kazuhiro

2009-01-01

Background To search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated. Results This paper aims to provide critical reviews for these systems and also report our recent development of ChemReader – a fully automated tool for extracting chemical structure diagrams in research articles and converting them into standard, searchable chemical file formats. Basic algorithms for recognizing lines and letters representing bonds and atoms in chemical structure diagrams can be independently run in sequence from a graphical user interface-and the algorithm parameters can be readily changed-to facilitate additional development specifically tailored to a chemical database annotation scheme. Compared with existing software programs such as OSRA, Kekule, and CLiDE, our results indicate that ChemReader outperforms other software systems on several sets of sample images from diverse sources in terms of the rate of correct outputs and the accuracy on extracting molecular substructure patterns. Conclusion The availability of ChemReader as a cheminformatic tool for extracting chemical structure information from digital raster images allows research and development groups to enrich their chemical structure databases by annotating the entries with published research articles. Based on its stable performance and high accuracy, ChemReader may be sufficiently accurate for annotating the chemical database with links to scientific research articles. PMID:19196483

Low-Cost 3D Printers Enable High-Quality and Automated Sample Preparation and Molecular Detection

PubMed Central

Chan, Kamfai; Coen, Mauricio; Hardick, Justin; Gaydos, Charlotte A.; Wong, Kah-Yat; Smith, Clayton; Wilson, Scott A.; Vayugundla, Siva Praneeth; Wong, Season

2016-01-01

Most molecular diagnostic assays require upfront sample preparation steps to isolate the target’s nucleic acids, followed by its amplification and detection using various nucleic acid amplification techniques. Because molecular diagnostic methods are generally rather difficult to perform manually without highly trained users, automated and integrated systems are highly desirable but too costly for use at point-of-care or low-resource settings. Here, we showcase the development of a low-cost and rapid nucleic acid isolation and amplification platform by modifying entry-level 3D printers that cost between $400 and $750. Our modifications consisted of replacing the extruder with a tip-comb attachment that houses magnets to conduct magnetic particle-based nucleic acid extraction. We then programmed the 3D printer to conduct motions that can perform high-quality extraction protocols. Up to 12 samples can be processed simultaneously in under 13 minutes and the efficiency of nucleic acid isolation matches well against gold-standard spin-column-based extraction technology. Additionally, we used the 3D printer’s heated bed to supply heat to perform water bath-based polymerase chain reactions (PCRs). Using another attachment to hold PCR tubes, the 3D printer was programmed to automate the process of shuttling PCR tubes between water baths. By eliminating the temperature ramping needed in most commercial thermal cyclers, the run time of a 35-cycle PCR protocol was shortened by 33%. This article demonstrates that for applications in resource-limited settings, expensive nucleic acid extraction devices and thermal cyclers that are used in many central laboratories can be potentially replaced by a device modified from inexpensive entry-level 3D printers. PMID:27362424

Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure

PubMed Central

DuVall, Scott L; South, Brett R; Bray, Bruce E; Bolton, Daniel; Heavirland, Julia; Pickard, Steve; Heidenreich, Paul; Shen, Shuying; Weir, Charlene; Samore, Matthew; Goldstein, Mary K

2012-01-01

Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. Materials and methods We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets; a third reviewer adjudicated disagreements. Results System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). Discussion An EF value of <40% can be accurately identified in VA echocardiogram reports. Conclusions An automated information extraction system can be used to accurately extract EF for quality measurement. PMID:22437073

Fully automated determination of nicotine and its major metabolites in whole blood by means of a DBS online-SPE LC-HR-MS/MS approach for sports drug testing.

PubMed

Tretzel, Laura; Thomas, Andreas; Piper, Thomas; Hedeland, Mikael; Geyer, Hans; Schänzer, Wilhelm; Thevis, Mario

2016-05-10

Dried blood spots (DBS) represent a sample matrix collected under minimal-invasive, straightforward and robust conditions. DBS specimens have been shown to provide appropriate test material for different analytical disciplines, e.g., preclinical drug development, therapeutic drug monitoring, forensic toxicology and diagnostic analysis of metabolic disorders in newborns. However, the sample preparation has occasionally been reported as laborious and time consuming. In order to minimize the manual workload and to substantiate the suitability of DBS for high sample-throughput, the automation of sample preparation processes is of paramount interest. In the current study, the development and validation of a fully automated DBS extraction method coupled to online solid-phase extraction using the example of nicotine, its major metabolites nornicotine, cotinine and trans-3'-hydroxycotinine and the tobacco alkaloids anabasine and anatabine is presented, based on the rationale that the use of nicotine-containing products for performance-enhancing purposes has been monitored by the World Anti-Doping Agency (WADA) for several years. Automation-derived DBS sample extracts were directed online to liquid chromatography high resolution/high mass accuracy tandem mass spectrometry, and target analytes were determined with support of four deuterated internal standards. Validation of the method yielded precise (CV <7.5% for intraday and <12.3% for interday measurements) and linear (r(2)>0.998) results. The limit of detection was established at 5 ng mL(-1) for all studied compounds, the extraction recovery ranged from 25 to 44%, and no matrix effects were observed. To exemplify the applicability of the DBS online-SPE LC-MS/MS approach for sports drug testing purposes, the method was applied to authentic DBS samples obtained from smokers, snus users, and e-cigarette users. Statistical evaluation of the obtained results indicated differences in metabolic behavior depending on the route of administration (inhalative versus buccal absorption) in terms of the ratio of nicotine and nornicotine. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated Feature Extraction of Foredune Morphology from Terrestrial Lidar Data

NASA Astrophysics Data System (ADS)

Spore, N.; Brodie, K. L.; Swann, C.

2014-12-01

Foredune morphology is often described in storm impact prediction models using the elevation of the dune crest and dune toe and compared with maximum runup elevations to categorize the storm impact and predicted responses. However, these parameters do not account for other foredune features that may make them more or less erodible, such as alongshore variations in morphology, vegetation coverage, or compaction. The goal of this work is to identify other descriptive features that can be extracted from terrestrial lidar data that may affect the rate of dune erosion under wave attack. Daily, mobile-terrestrial lidar surveys were conducted during a 6-day nor'easter (Hs = 4 m in 6 m water depth) along 20km of coastline near Duck, North Carolina which encompassed a variety of foredune forms in close proximity to each other. This abstract will focus on the tools developed for the automated extraction of the morphological features from terrestrial lidar data, while the response of the dune will be presented by Brodie and Spore as an accompanying abstract. Raw point cloud data can be dense and is often under-utilized due to time and personnel constraints required for analysis, since many algorithms are not fully automated. In our approach, the point cloud is first projected into a local coordinate system aligned with the coastline, and then bare earth points are interpolated onto a rectilinear 0.5 m grid creating a high resolution digital elevation model. The surface is analyzed by identifying features along each cross-shore transect. Surface curvature is used to identify the position of the dune toe, and then beach and berm morphology is extracted shoreward of the dune toe, and foredune morphology is extracted landward of the dune toe. Changes in, and magnitudes of, cross-shore slope, curvature, and surface roughness are used to describe the foredune face and each cross-shore transect is then classified using its pre-storm morphology for storm-response analysis.

Union operation image processing of data cubes separately processed by different objective filters and its application to void analysis in an all-solid-state lithium-ion battery.

PubMed

Yamamoto, Yuta; Iriyama, Yasutoshi; Muto, Shunsuke

2016-04-01

In this article, we propose a smart image-analysis method suitable for extracting target features with hierarchical dimension from original data. The method was applied to three-dimensional volume data of an all-solid lithium-ion battery obtained by the automated sequential sample milling and imaging process using a focused ion beam/scanning electron microscope to investigate the spatial configuration of voids inside the battery. To automatically fully extract the shape and location of the voids, three types of filters were consecutively applied: a median blur filter to extract relatively larger voids, a morphological opening operation filter for small dot-shaped voids and a morphological closing operation filter for small voids with concave contrasts. Three data cubes separately processed by the above-mentioned filters were integrated by a union operation to the final unified volume data, which confirmed the correct extraction of the voids over the entire dimension contained in the original data. © The Author 2015. Published by Oxford University Press on behalf of The Japanese Society of Microscopy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Building a glaucoma interaction network using a text mining approach.

PubMed

Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

2016-01-01

The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of relations that could not be found in existing interaction databases and that were found to be new, in addition to a smaller subnetwork consisting of interconnected clusters of seven glaucoma genes. Future improvements can be applied towards obtaining a better version of this network.

Integration of Ion Mobility MSE after Fully Automated, Online, High-Resolution Liquid Extraction Surface Analysis Micro-Liquid Chromatography

PubMed Central

2017-01-01

Direct analysis by mass spectrometry (imaging) has become increasingly deployed in preclinical and clinical research due to its rapid and accurate readouts. However, when it comes to biomarker discovery or histopathological diagnostics, more sensitive and in-depth profiling from localized areas is required. We developed a comprehensive, fully automated online platform for high-resolution liquid extraction surface analysis (HR-LESA) followed by micro–liquid chromatography (LC) separation and a data-independent acquisition strategy for untargeted and low abundant analyte identification directly from tissue sections. Applied to tissue sections of rat pituitary, the platform demonstrated improved spatial resolution, allowing sample areas as small as 400 μm to be studied, a major advantage over conventional LESA. The platform integrates an online buffer exchange and washing step for removal of salts and other endogenous contamination that originates from local tissue extraction. Our carry over–free platform showed high reproducibility, with an interextraction variability below 30%. Another strength of the platform is the additional selectivity provided by a postsampling gas-phase ion mobility separation. This allowed distinguishing coeluted isobaric compounds without requiring additional separation time. Furthermore, we identified untargeted and low-abundance analytes, including neuropeptides deriving from the pro-opiomelanocortin precursor protein and localized a specific area of the pituitary gland (i.e., adenohypophysis) known to secrete neuropeptides and other small metabolites related to development, growth, and metabolism. This platform can thus be applied for the in-depth study of small samples of complex tissues with histologic features of ∼400 μm or more, including potential neuropeptide markers involved in many diseases such as neurodegenerative diseases, obesity, bulimia, and anorexia nervosa. PMID:28945354

Automating digital leaf measurement: the tooth, the whole tooth, and nothing but the tooth.

PubMed

Corney, David P A; Tang, H Lilian; Clark, Jonathan Y; Hu, Yin; Jin, Jing

2012-01-01

Many species of plants produce leaves with distinct teeth around their margins. The presence and nature of these teeth can often help botanists to identify species. Moreover, it has long been known that more species native to colder regions have teeth than species native to warmer regions. It has therefore been suggested that fossilized remains of leaves can be used as a proxy for ancient climate reconstruction. Similar studies on living plants can help our understanding of the relationships. The required analysis of leaves typically involves considerable manual effort, which in practice limits the number of leaves that are analyzed, potentially reducing the power of the results. In this work, we describe a novel algorithm to automate the marginal tooth analysis of leaves found in digital images. We demonstrate our methods on a large set of images of whole herbarium specimens collected from Tilia trees (also known as lime, linden or basswood). We chose the genus Tilia as its constituent species have toothed leaves of varied size and shape. In a previous study we extracted c.1600 leaves automatically from a set of c.1100 images. Our new algorithm locates teeth on the margins of such leaves and extracts features such as each tooth's area, perimeter and internal angles, as well as counting them. We evaluate an implementation of our algorithm's performance against a manually analyzed subset of the images. We found that the algorithm achieves an accuracy of 85% for counting teeth and 75% for estimating tooth area. We also demonstrate that the automatically extracted features are sufficient to identify different species of Tilia using a simple linear discriminant analysis, and that the features relating to teeth are the most useful.

Enhanced Automated Guidance System for Horizontal Auger Boring Based on Image Processing

PubMed Central

Wu, Lingling; Wen, Guojun; Wang, Yudan; Huang, Lei; Zhou, Jiang

2018-01-01

Horizontal auger boring (HAB) is a widely used trenchless technology for the high-accuracy installation of gravity or pressure pipelines on line and grade. Differing from other pipeline installations, HAB requires a more precise and automated guidance system for use in a practical project. This paper proposes an economic and enhanced automated optical guidance system, based on optimization research of light-emitting diode (LED) light target and five automated image processing bore-path deviation algorithms. An LED target was optimized for many qualities, including light color, filter plate color, luminous intensity, and LED layout. The image preprocessing algorithm, feature extraction algorithm, angle measurement algorithm, deflection detection algorithm, and auto-focus algorithm, compiled in MATLAB, are used to automate image processing for deflection computing and judging. After multiple indoor experiments, this guidance system is applied in a project of hot water pipeline installation, with accuracy controlled within 2 mm in 48-m distance, providing accurate line and grade controls and verifying the feasibility and reliability of the guidance system. PMID:29462855

Miniaturized Mass-Spectrometry-Based Analysis System for Fully Automated Examination of Conditioned Cell Culture Media

PubMed Central

Weber, Emanuel; Pinkse, Martijn W. H.; Bener-Aksam, Eda; Vellekoop, Michael J.; Verhaert, Peter D. E. M.

2012-01-01

We present a fully automated setup for performing in-line mass spectrometry (MS) analysis of conditioned media in cell cultures, in particular focusing on the peptides therein. The goal is to assess peptides secreted by cells in different culture conditions. The developed system is compatible with MS as analytical technique, as this is one of the most powerful analysis methods for peptide detection and identification. Proof of concept was achieved using the well-known mating-factor signaling in baker's yeast, Saccharomyces cerevisiae. Our concept system holds 1 mL of cell culture medium and allows maintaining a yeast culture for, at least, 40 hours with continuous supernatant extraction (and medium replenishing). The device's small dimensions result in reduced costs for reagents and open perspectives towards full integration on-chip. Experimental data that can be obtained are time-resolved peptide profiles in a yeast culture, including information about the appearance of mating-factor-related peptides. We emphasize that the system operates without any manual intervention or pipetting steps, which allows for an improved overall sensitivity compared to non-automated alternatives. MS data confirmed previously reported aspects of the physiology of the yeast-mating process. Moreover, matingfactor breakdown products (as well as evidence for a potentially responsible protease) were found. PMID:23091722

Automated detection and classification of dice

NASA Astrophysics Data System (ADS)

Correia, Bento A. B.; Silva, Jeronimo A.; Carvalho, Fernando D.; Guilherme, Rui; Rodrigues, Fernando C.; de Silva Ferreira, Antonio M.

1995-03-01

This paper describes a typical machine vision system in an unusual application, the automated visual inspection of a Casino's playing tables. The SORTE computer vision system was developed at INETI under a contract with the Portuguese Gaming Inspection Authorities IGJ. It aims to automate the tasks of detection and classification of the dice's scores on the playing tables of the game `Banca Francesa' (which means French Banking) in Casinos. The system is based on the on-line analysis of the images captured by a monochrome CCD camera placed over the playing tables, in order to extract relevant information concerning the score indicated by the dice. Image processing algorithms for real time automatic throwing detection and dice classification were developed and implemented.

Engineering Design and Automation in the Applied Engineering Technologies (AET) Group at Los Alamos National Laboratory.

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

Wantuck, P. J.; Hollen, R. M.

2002-01-01

This paper provides an overview of some design and automation-related projects ongoing within the Applied Engineering Technologies (AET) Group at Los Alamos National Laboratory. AET uses a diverse set of technical capabilities to develop and apply processes and technologies to applications for a variety of customers both internal and external to the Laboratory. The Advanced Recovery and Integrated Extraction System (ARIES) represents a new paradigm for the processing of nuclear material from retired weapon systems in an environment that seeks to minimize the radiation dose to workers. To achieve this goal, ARIES relies upon automation-based features to handle and processmore » the nuclear material. Our Chemical Process Development Team specializes in fuzzy logic and intelligent control systems. Neural network technology has been utilized in some advanced control systems developed by team members. Genetic algorithms and neural networks have often been applied for data analysis. Enterprise modeling, or discrete event simulation, as well as chemical process simulation has been employed for chemical process plant design. Fuel cell research and development has historically been an active effort within the AET organization. Under the principal sponsorship of the Department of Energy, the Fuel Cell Team is now focusing on technologies required to produce fuel cell compatible feed gas from reformation of a variety of conventional fuels (e.g., gasoline, natural gas), principally for automotive applications. This effort involves chemical reactor design and analysis, process modeling, catalyst analysis, as well as full scale system characterization and testing. The group's Automation and Robotics team has at its foundation many years of experience delivering automated and robotic systems for nuclear, analytical chemistry, and bioengineering applications. As an integrator of commercial systems and a developer of unique custom-made systems, the team currently supports the automation needs of many Laboratory programs.« less

Complacency and bias in human use of automation: an attentional integration.

PubMed

Parasuraman, Raja; Manzey, Dietrich H

2010-06-01

Our aim was to review empirical studies of complacency and bias in human interaction with automated and decision support systems and provide an integrated theoretical model for their explanation. Automation-related complacency and automation bias have typically been considered separately and independently. Studies on complacency and automation bias were analyzed with respect to the cognitive processes involved. Automation complacency occurs under conditions of multiple-task load, when manual tasks compete with the automated task for the operator's attention. Automation complacency is found in both naive and expert participants and cannot be overcome with simple practice. Automation bias results in making both omission and commission errors when decision aids are imperfect. Automation bias occurs in both naive and expert participants, cannot be prevented by training or instructions, and can affect decision making in individuals as well as in teams. While automation bias has been conceived of as a special case of decision bias, our analysis suggests that it also depends on attentional processes similar to those involved in automation-related complacency. Complacency and automation bias represent different manifestations of overlapping automation-induced phenomena, with attention playing a central role. An integrated model of complacency and automation bias shows that they result from the dynamic interaction of personal, situational, and automation-related characteristics. The integrated model and attentional synthesis provides a heuristic framework for further research on complacency and automation bias and design options for mitigating such effects in automated and decision support systems.

Design and Development of the Terrain Information Extraction System

DTIC Science & Technology

1990-09-04

system successfully demonstrated relief measurement and orthophoto production, automated feature extraction has remained "the major problem of today’s...the hierarchical relaxation correlation method developed by Helava Associates, Inc. and digital orthophoto production. To achieve this high accuracy...image memory transfer rates will be achieved by using data blocks or "image tiles ." Further, an image fringe loading module will be implemented which

Automated prostate cancer localization without the need for peripheral zone extraction using multiparametric MRI.

PubMed

Liu, Xin; Yetik, Imam Samil

2011-06-01

Multiparametric magnetic resonance imaging (MRI) has been shown to have higher localization accuracy than transrectal ultrasound (TRUS) for prostate cancer. Therefore, automated cancer segmentation using multiparametric MRI is receiving a growing interest, since MRI can provide both morphological and functional images for tissue of interest. However, all automated methods to this date are applicable to a single zone of the prostate, and the peripheral zone (PZ) of the prostate needs to be extracted manually, which is a tedious and time-consuming job. In this paper, our goal is to remove the need of PZ extraction by incorporating the spatial and geometric information of prostate tumors with multiparametric MRI derived from T2-weighted MRI, diffusion-weighted imaging (DWI) and dynamic contrast enhanced MRI (DCE-MRI). In order to remove the need of PZ extraction, the authors propose a new method to incorporate the spatial information of the cancer. This is done by introducing a new feature called location map. This new feature is constructed by applying a nonlinear transformation to the spatial position coordinates of each pixel, so that the location map implicitly represents the geometric position of each pixel with respect to the prostate region. Then, this new feature is combined with multiparametric MR images to perform tumor localization. The proposed algorithm is applied to multiparametric prostate MRI data obtained from 20 patients with biopsy-confirmed prostate cancer. The proposed method which does not need the masks of PZ was found to have prostate cancer detection specificity of 0.84, sensitivity of 0.80 and dice coefficient value of 0.42. The authors have found that fusing the spatial information allows us to obtain tumor outline without the need of PZ extraction with a considerable success (better or similar performance to methods that require manual PZ extraction). Our experimental results quantitatively demonstrate the effectiveness of the proposed method, depicting that the proposed method has a slightly better or similar localization performance compared to methods which require the masks of PZ.

Comparison of commercial systems for extraction of nucleic acids from DNA/RNA respiratory pathogens.

PubMed

Yang, Genyan; Erdman, Dean E; Kodani, Maja; Kools, John; Bowen, Michael D; Fields, Barry S

2011-01-01

This study compared six automated nucleic acid extraction systems and one manual kit for their ability to recover nucleic acids from human nasal wash specimens spiked with five respiratory pathogens, representing Gram-positive bacteria (Streptococcus pyogenes), Gram-negative bacteria (Legionella pneumophila), DNA viruses (adenovirus), segmented RNA viruses (human influenza virus A), and non-segmented RNA viruses (respiratory syncytial virus). The robots and kit evaluated represent major commercially available methods that are capable of simultaneous extraction of DNA and RNA from respiratory specimens, and included platforms based on magnetic-bead technology (KingFisher mL, Biorobot EZ1, easyMAG, KingFisher Flex, and MagNA Pure Compact) or glass fiber filter technology (Biorobot MDX and the manual kit Allprep). All methods yielded extracts free of cross-contamination and RT-PCR inhibition. All automated systems recovered L. pneumophila and adenovirus DNA equivalently. However, the MagNA Pure protocol demonstrated more than 4-fold higher DNA recovery from the S. pyogenes than other methods. The KingFisher mL and easyMAG protocols provided 1- to 3-log wider linearity and extracted 3- to 4-fold more RNA from the human influenza virus and respiratory syncytial virus. These findings suggest that systems differed in nucleic acid recovery, reproducibility, and linearity in a pathogen specific manner. Published by Elsevier B.V.

Development and validation of an automated unit for the extraction of radiocaesium from seawater.

PubMed

Bokor, Ilonka; Sdraulig, Sandra; Jenkinson, Peter; Madamperuma, Janaka; Martin, Paul

2016-01-01

An automated unit was developed for the in-situ extraction of radiocaesium ((137)Cs and (134)Cs) from large volumes of seawater to achieve very low detection limits. The unit was designed for monitoring of Australian ocean and coastal waters, including at ports visited by nuclear-powered warships. The unit is housed within a robust case, and is easily transported and operated. It contains four filter cartridges connected in series. The first two cartridges are used to remove any suspended material that may be present in the seawater, while the last two cartridges are coated with potassium copper hexacyanoferrate for caesium extraction. Once the extraction is completed the coated cartridges are ashed. The ash is transferred to a small petri dish for counting of (137)Cs and (134)Cs by high resolution gamma spectrometry for a minimum of 24 h. The extraction method was validated for the following criteria: selectivity, trueness, precision, linearity, limit of detection and traceability. The validation showed the unit to be fit for purpose with the method capable of achieving low detection limits required for environmental samples. The results for the environmental measurements in Australian seawater correlate well with those reported in the Worldwide Marine Radioactivity Study (WOMARS). The cost of preparation and running the system is low and waste generation is minimal. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Investigation of automated feature extraction using multiple data sources

NASA Astrophysics Data System (ADS)

Harvey, Neal R.; Perkins, Simon J.; Pope, Paul A.; Theiler, James P.; David, Nancy A.; Porter, Reid B.

2003-04-01

An increasing number and variety of platforms are now capable of collecting remote sensing data over a particular scene. For many applications, the information available from any individual sensor may be incomplete, inconsistent or imprecise. However, other sources may provide complementary and/or additional data. Thus, for an application such as image feature extraction or classification, it may be that fusing the mulitple data sources can lead to more consistent and reliable results. Unfortunately, with the increased complexity of the fused data, the search space of feature-extraction or classification algorithms also greatly increases. With a single data source, the determination of a suitable algorithm may be a significant challenge for an image analyst. With the fused data, the search for suitable algorithms can go far beyond the capabilities of a human in a realistic time frame, and becomes the realm of machine learning, where the computational power of modern computers can be harnessed to the task at hand. We describe experiments in which we investigate the ability of a suite of automated feature extraction tools developed at Los Alamos National Laboratory to make use of multiple data sources for various feature extraction tasks. We compare and contrast this software's capabilities on 1) individual data sets from different data sources 2) fused data sets from multiple data sources and 3) fusion of results from multiple individual data sources.

Automated Interpretation and Extraction of Topographic Information from Time of Flight Secondary Ion Mass Spectrometry Data

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

Ievlev, Anton V.; Belianinov, Alexei; Jesse, Stephen

Time of flight secondary ion mass spectrometry (ToF SIMS) is one of the most powerful characterization tools allowing imaging of the chemical properties of various systems and materials. It allows precise studies of the chemical composition with sub-100-nm lateral and nanometer depth spatial resolution. However, comprehensive interpretation of ToF SIMS results is challengeable, because of the data volume and its multidimensionality. Furthermore, investigation of the samples with pronounced topographical features are complicated by the spectral shift. In this work we developed approach for the comprehensive ToF SIMS data interpretation based on the data analytics and automated extraction of the samplemore » topography based on time of flight shift. We further applied this approach to investigate correlation between biological function and chemical composition in Arabidopsis roots.« less

Automated Interpretation and Extraction of Topographic Information from Time of Flight Secondary Ion Mass Spectrometry Data

DOE PAGES

Ievlev, Anton V.; Belianinov, Alexei; Jesse, Stephen; ...

2017-12-06

Time of flight secondary ion mass spectrometry (ToF SIMS) is one of the most powerful characterization tools allowing imaging of the chemical properties of various systems and materials. It allows precise studies of the chemical composition with sub-100-nm lateral and nanometer depth spatial resolution. However, comprehensive interpretation of ToF SIMS results is challengeable, because of the data volume and its multidimensionality. Furthermore, investigation of the samples with pronounced topographical features are complicated by the spectral shift. In this work we developed approach for the comprehensive ToF SIMS data interpretation based on the data analytics and automated extraction of the samplemore » topography based on time of flight shift. We further applied this approach to investigate correlation between biological function and chemical composition in Arabidopsis roots.« less

Screening for anabolic steroids in urine of forensic cases using fully automated solid phase extraction and LC-MS-MS.

PubMed

Andersen, David W; Linnet, Kristian

2014-01-01

A screening method for 18 frequently measured exogenous anabolic steroids and the testosterone/epitestosterone (T/E) ratio in forensic cases has been developed and validated. The method involves a fully automated sample preparation including enzyme treatment, addition of internal standards and solid phase extraction followed by analysis by liquid chromatography-tandem mass spectrometry (LC-MS-MS) using electrospray ionization with adduct formation for two compounds. Urine samples from 580 forensic cases were analyzed to determine the T/E ratio and occurrence of exogenous anabolic steroids. Extraction recoveries ranged from 77 to 95%, matrix effects from 48 to 78%, overall process efficiencies from 40 to 54% and the lower limit of identification ranged from 2 to 40 ng/mL. In the 580 urine samples analyzed from routine forensic cases, 17 (2.9%) were found positive for one or more anabolic steroids. Only seven different steroids including testosterone were found in the material, suggesting that only a small number of common steroids are likely to occur in a forensic context. The steroids were often in high concentrations (>100 ng/mL), and a combination of steroids and/or other drugs of abuse were seen in the majority of cases. The method presented serves as a fast and automated screening procedure, proving the suitability of LC-MS-MS for analyzing anabolic steroids. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Clinical value of protein expression of kallikrein-related peptidase 7 (KLK7) in ovarian cancer.

PubMed

Dorn, Julia; Gkazepis, Apostolos; Kotzsch, Matthias; Kremer, Marcus; Propping, Corinna; Mayer, Katharina; Mengele, Karin; Diamandis, Eleftherios P; Kiechle, Marion; Magdolen, Viktor; Schmitt, Manfred

2014-01-01

Expression of the kallikrein-related peptidase 7 (KLK7) is dysregulated in ovarian cancer. We assessed KLK7 expression by ELISA and quantitative immunohistochemistry and analyzed its association with clinicopathological parameters and patients' outcome. KLK7 antigen concentrations were determined in tumor tissue extracts of 98 ovarian cancer patients by ELISA. For analysis of KLK7 immunoexpression in ovarian cancer tissue microarrays, a manual quantitative scoring system as well as a software tool for quantitative high-throughput automated image analysis was used. In immunohistochemical analyses, expression levels of KLK7 were not associated with patients' outcome. However, in multivariate analyses, KLK7 antigen levels in tumor tissue extracts were significantly associated with both overall and progression-free survival: ovarian cancer patients with high KLK7 levels had a significantly, 2-fold lower risk of death [hazard ratio (HR)=0.51, 95% confidence interval (CI)=0.29-0.90, p=0.019] or relapse [HR=0.47, 95% CI=0.25-0.91, p=0.024), as compared with patients who displayed low KLK7 levels. Our results indicate that - in contrast to earlier findings - high KLK7 antigen levels in tumor tissue extracts may be associated with a better prognosis of ovarian cancer patients.

Drug side effect extraction from clinical narratives of psychiatry and psychology patients

PubMed Central

Kocher, Jean-Pierre A; Chute, Christopher G; Savova, Guergana K

2011-01-01

Objective To extract physician-asserted drug side effects from electronic medical record clinical narratives. Materials and methods Pattern matching rules were manually developed through examining keywords and expression patterns of side effects to discover an individual side effect and causative drug relationship. A combination of machine learning (C4.5) using side effect keyword features and pattern matching rules was used to extract sentences that contain side effect and causative drug pairs, enabling the system to discover most side effect occurrences. Our system was implemented as a module within the clinical Text Analysis and Knowledge Extraction System. Results The system was tested in the domain of psychiatry and psychology. The rule-based system extracting side effects and causative drugs produced an F score of 0.80 (0.55 excluding allergy section). The hybrid system identifying side effect sentences had an F score of 0.75 (0.56 excluding allergy section) but covered more side effect and causative drug pairs than individual side effect extraction. Discussion The rule-based system was able to identify most side effects expressed by clear indication words. More sophisticated semantic processing is required to handle complex side effect descriptions in the narrative. We demonstrated that our system can be trained to identify sentences with complex side effect descriptions that can be submitted to a human expert for further abstraction. Conclusion Our system was able to extract most physician-asserted drug side effects. It can be used in either an automated mode for side effect extraction or semi-automated mode to identify side effect sentences that can significantly simplify abstraction by a human expert. PMID:21946242

Automated extraction of natural drainage density patterns for the conterminous United States through high performance computing

USGS Publications Warehouse

Stanislawski, Larry V.; Falgout, Jeff T.; Buttenfield, Barbara P.

2015-01-01

Hydrographic networks form an important data foundation for cartographic base mapping and for hydrologic analysis. Drainage density patterns for these networks can be derived to characterize local landscape, bedrock and climate conditions, and further inform hydrologic and geomorphological analysis by indicating areas where too few headwater channels have been extracted. But natural drainage density patterns are not consistently available in existing hydrographic data for the United States because compilation and capture criteria historically varied, along with climate, during the period of data collection over the various terrain types throughout the country. This paper demonstrates an automated workflow that is being tested in a high-performance computing environment by the U.S. Geological Survey (USGS) to map natural drainage density patterns at the 1:24,000-scale (24K) for the conterminous United States. Hydrographic network drainage patterns may be extracted from elevation data to guide corrections for existing hydrographic network data. The paper describes three stages in this workflow including data pre-processing, natural channel extraction, and generation of drainage density patterns from extracted channels. The workflow is concurrently implemented by executing procedures on multiple subbasin watersheds within the U.S. National Hydrography Dataset (NHD). Pre-processing defines parameters that are needed for the extraction process. Extraction proceeds in standard fashion: filling sinks, developing flow direction and weighted flow accumulation rasters. Drainage channels with assigned Strahler stream order are extracted within a subbasin and simplified. Drainage density patterns are then estimated with 100-meter resolution and subsequently smoothed with a low-pass filter. The extraction process is found to be of better quality in higher slope terrains. Concurrent processing through the high performance computing environment is shown to facilitate and refine the choice of drainage density extraction parameters and more readily improve extraction procedures than conventional processing.

A multi-atlas based method for automated anatomical Macaca fascicularis brain MRI segmentation and PET kinetic extraction.

PubMed

Ballanger, Bénédicte; Tremblay, Léon; Sgambato-Faure, Véronique; Beaudoin-Gobert, Maude; Lavenne, Franck; Le Bars, Didier; Costes, Nicolas

2013-08-15

MRI templates and digital atlases are needed for automated and reproducible quantitative analysis of non-human primate PET studies. Segmenting brain images via multiple atlases outperforms single-atlas labelling in humans. We present a set of atlases manually delineated on brain MRI scans of the monkey Macaca fascicularis. We use this multi-atlas dataset to evaluate two automated methods in terms of accuracy, robustness and reliability in segmenting brain structures on MRI and extracting regional PET measures. Twelve individual Macaca fascicularis high-resolution 3DT1 MR images were acquired. Four individual atlases were created by manually drawing 42 anatomical structures, including cortical and sub-cortical structures, white matter regions, and ventricles. To create the MRI template, we first chose one MRI to define a reference space, and then performed a two-step iterative procedure: affine registration of individual MRIs to the reference MRI, followed by averaging of the twelve resampled MRIs. Automated segmentation in native space was obtained in two ways: 1) Maximum probability atlases were created by decision fusion of two to four individual atlases in the reference space, and transformation back into the individual native space (MAXPROB)(.) 2) One to four individual atlases were registered directly to the individual native space, and combined by decision fusion (PROPAG). Accuracy was evaluated by computing the Dice similarity index and the volume difference. The robustness and reproducibility of PET regional measurements obtained via automated segmentation was evaluated on four co-registered MRI/PET datasets, which included test-retest data. Dice indices were always over 0.7 and reached maximal values of 0.9 for PROPAG with all four individual atlases. There was no significant mean volume bias. The standard deviation of the bias decreased significantly when increasing the number of individual atlases. MAXPROB performed better when increasing the number of atlases used. When all four atlases were used for the MAXPROB creation, the accuracy of morphometric segmentation approached that of the PROPAG method. PET measures extracted either via automatic methods or via the manually defined regions were strongly correlated, with no significant regional differences between methods. Intra-class correlation coefficients for test-retest data were over 0.87. Compared to single atlas extractions, multi-atlas methods improve the accuracy of region definition. They also perform comparably to manually defined regions for PET quantification. Multiple atlases of Macaca fascicularis brains are now available and allow reproducible and simplified analyses. Copyright © 2013 Elsevier Inc. All rights reserved.

A Model-Based Analysis of Semi-Automated Data Discovery and Entry Using Automated Content Extraction

DTIC Science & Technology

2011-02-01

Accomplish Goal) to (a) visually search the contents of a file folder until the icon corresponding to the desired file is located (Choose...Item_from_set), and (b) move the mouse to that icon and double click to open it (Double_select Object). Note that Choose Item_from_set and Double_select...argument, which Open File fills with <found_item>, a working memory pointer to the file icon that Choose_item_from Set finds. Look_at, Point_to

Automated, per pixel Cloud Detection from High-Resolution VNIR Data

NASA Technical Reports Server (NTRS)

Varlyguin, Dmitry L.

2007-01-01

CASA is a fully automated software program for the per-pixel detection of clouds and cloud shadows from medium- (e.g., Landsat, SPOT, AWiFS) and high- (e.g., IKONOS, QuickBird, OrbView) resolution imagery without the use of thermal data. CASA is an object-based feature extraction program which utilizes a complex combination of spectral, spatial, and contextual information available in the imagery and the hierarchical self-learning logic for accurate detection of clouds and their shadows.

A RP-HPLC-DAD-APCI/MSD method for the characterisation of medicinal Ericaceae used by the Eeyou Istchee Cree First Nations.

PubMed

Saleem, Ammar; Harris, Cory S; Asim, Muhammad; Cuerrier, Alain; Martineau, Louis; Haddad, Pierre S; Arnason, John T

2010-01-01

Ericaceae medicinal plants are traditionally used by the Eeyou Istchee Cree and other northern peoples of North America to treat type 2 diabetic symptoms. Because of the importance of phenolics as potential cures for degenerative diseases including type 2 diabetes, an analytical method was developed to detect them in the leaf extracts of 14 Ericaceae plants. To develop an optimised method which is applicable to a relatively large number of Ericaceae plants using their leaf extracts. For this purpose phenolics with a wide range of polarity, including a glucosylated benzoquinone, two phenolic acids, three flavanols, a flavanone, a flavone and five flavonols, were included in this study. Characterisation of phytochemicals in extracts was undertaken by automated matching to the UV spectra to those of an in house library of plant secondary metabolites and the authentication of their identity was achieved by reversed phase-high-performance chromatography-diode array detection-atmospheric pressure chemical ionisation/mass selective detection. Twenty-six phenolics were characterised within 26 min of chromatographic separation in 80% ethanol extracts of 14 Ericaceae plants. The calibration curves were linear within 0.5-880 microg/g dry mass of the plant with regression values better than 0.995. The limits of detection ranged from 0.3 for microg/mL for (+)-catechin to 2.6 microg/mL for chlorogenic acid. This is a first study dealing with relatively large number of Ericaceae extracts and is applicable to other plants of same family.

DEXTER: Disease-Expression Relation Extraction from Text.

PubMed

Gupta, Samir; Dingerdissen, Hayley; Ross, Karen E; Hu, Yu; Wu, Cathy H; Mazumder, Raja; Vijay-Shanker, K

2018-01-01

Gene expression levels affect biological processes and play a key role in many diseases. Characterizing expression profiles is useful for clinical research, and diagnostics and prognostics of diseases. There are currently several high-quality databases that capture gene expression information, obtained mostly from large-scale studies, such as microarray and next-generation sequencing technologies, in the context of disease. The scientific literature is another rich source of information on gene expression-disease relationships that not only have been captured from large-scale studies but have also been observed in thousands of small-scale studies. Expression information obtained from literature through manual curation can extend expression databases. While many of the existing databases include information from literature, they are limited by the time-consuming nature of manual curation and have difficulty keeping up with the explosion of publications in the biomedical field. In this work, we describe an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER) to extract information from literature on gene and microRNA expression in the context of disease. One of the motivations in developing DEXTER was to extend the BioXpress database, a cancer-focused gene expression database that includes data derived from large-scale experiments and manual curation of publications. The literature-based portion of BioXpress lags behind significantly compared to expression information obtained from large-scale studies and can benefit from our text-mined results. We have conducted two different evaluations to measure the accuracy of our text-mining tool and achieved average F-scores of 88.51 and 81.81% for the two evaluations, respectively. Also, to demonstrate the ability to extract rich expression information in different disease-related scenarios, we used DEXTER to extract information on differential expression information for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers and 826 microRNA in 171 cancers. All extractions using DEXTER are integrated in the literature-based portion of BioXpress.Database URL: http://biotm.cis.udel.edu/DEXTER.

Microextraction by packed sorbent: an emerging, selective and high-throughput extraction technique in bioanalysis.

PubMed

Pereira, Jorge; Câmara, José S; Colmsjö, Anders; Abdel-Rehim, Mohamed

2014-06-01

Sample preparation is an important analytical step regarding the isolation and concentration of desired components from complex matrices and greatly influences their reliable and accurate analysis and data quality. It is the most labor-intensive and error-prone process in analytical methodology and, therefore, may influence the analytical performance of the target analytes quantification. Many conventional sample preparation methods are relatively complicated, involving time-consuming procedures and requiring large volumes of organic solvents. Recent trends in sample preparation include miniaturization, automation, high-throughput performance, on-line coupling with analytical instruments and low-cost operation through extremely low volume or no solvent consumption. Micro-extraction techniques, such as micro-extraction by packed sorbent (MEPS), have these advantages over the traditional techniques. This paper gives an overview of MEPS technique, including the role of sample preparation in bioanalysis, the MEPS description namely MEPS formats (on- and off-line), sorbents, experimental and protocols, factors that affect the MEPS performance, and the major advantages and limitations of MEPS compared with other sample preparation techniques. We also summarize MEPS recent applications in bioanalysis. Copyright © 2014 John Wiley & Sons, Ltd.

Quantitation of repaglinide and metabolites in mouse whole-body thin tissue sections using droplet-based liquid microjunction surface sampling-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry.

PubMed

Chen, Weiqi; Wang, Lifei; Van Berkel, Gary J; Kertesz, Vilmos; Gan, Jinping

2016-03-25

Herein, quantitation aspects of a fully automated autosampler/HPLC-MS/MS system applied for unattended droplet-based surface sampling of repaglinide dosed thin tissue sections with subsequent HPLC separation and mass spectrometric analysis of parent drug and various drug metabolites were studied. Major organs (brain, lung, liver, kidney and muscle) from whole-body thin tissue sections and corresponding organ homogenates prepared from repaglinide dosed mice were sampled by surface sampling and by bulk extraction, respectively, and analyzed by HPLC-MS/MS. A semi-quantitative agreement between data obtained by surface sampling and that by employing organ homogenate extraction was observed. Drug concentrations obtained by the two methods followed the same patterns for post-dose time points (0.25, 0.5, 1 and 2 h). Drug amounts determined in the specific tissues was typically higher when analyzing extracts from the organ homogenates. In addition, relative comparison of the levels of individual metabolites between the two analytical methods also revealed good semi-quantitative agreement. Copyright © 2015 Elsevier B.V. All rights reserved.

Offshore platform sourced pollution monitoring using space-borne fully polarimetric C and X band synthetic aperture radar.

PubMed

Singha, Suman; Ressel, Rudolf

2016-11-15

Use of polarimetric SAR data for offshore pollution monitoring is relatively new and shows great potential for operational offshore platform monitoring. This paper describes the development of an automated oil spill detection chain for operational purposes based on C-band (RADARSAT-2) and X-band (TerraSAR-X) fully polarimetric images, wherein we use polarimetric features to characterize oil spills and look-alikes. Numbers of near coincident TerraSAR-X and RADARSAT-2 images have been acquired over offshore platforms. Ten polarimetric feature parameters were extracted from different types of oil and 'look-alike' spots and divided into training and validation dataset. Extracted features were then used to develop a pixel based Artificial Neural Network classifier. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spill and look-alike spots. Polarimetric features such as Scattering Diversity, Surface Scattering Fraction and Span proved to be most suitable for operational services. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quantitation of repaglinide and metabolites in mouse whole-body thin tissue sections using droplet-based liquid microjunction surface sampling-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry

DOE PAGES

Chen, Weiqi; Wang, Lifei; Van Berkel, Gary J.; ...

2015-11-03

Herein, quantitation aspects of a fully automated autosampler/HPLC-MS/MS system applied for unattended droplet-based surface sampling of repaglinide dosed thin tissue sections with subsequent HPLC separation and mass spectrometric analysis of parent drug and various drug metabolites was studied. Major organs (brain, lung, liver, kidney, muscle) from whole-body thin tissue sections and corresponding organ homogenates prepared from repaglinide dosed mice were sampled by surface sampling and by bulk extraction, respectively, and analyzed by HPLC-MS/MS. A semi-quantitative agreement between data obtained by surface sampling and that by employing organ homogenate extraction was observed. Drug concentrations obtained by the two methods followed themore » same patterns for post-dose time points (0.25, 0.5, 1 and 2 h). Drug amounts determined in the specific tissues was typically higher when analyzing extracts from the organ homogenates. Furthermore, relative comparison of the levels of individual metabolites between the two analytical methods also revealed good semi-quantitative agreement.« less

Quantitation of repaglinide and metabolites in mouse whole-body thin tissue sections using droplet-based liquid microjunction surface sampling-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry

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

Chen, Weiqi; Wang, Lifei; Van Berkel, Gary J.

Herein, quantitation aspects of a fully automated autosampler/HPLC-MS/MS system applied for unattended droplet-based surface sampling of repaglinide dosed thin tissue sections with subsequent HPLC separation and mass spectrometric analysis of parent drug and various drug metabolites was studied. Major organs (brain, lung, liver, kidney, muscle) from whole-body thin tissue sections and corresponding organ homogenates prepared from repaglinide dosed mice were sampled by surface sampling and by bulk extraction, respectively, and analyzed by HPLC-MS/MS. A semi-quantitative agreement between data obtained by surface sampling and that by employing organ homogenate extraction was observed. Drug concentrations obtained by the two methods followed themore » same patterns for post-dose time points (0.25, 0.5, 1 and 2 h). Drug amounts determined in the specific tissues was typically higher when analyzing extracts from the organ homogenates. Furthermore, relative comparison of the levels of individual metabolites between the two analytical methods also revealed good semi-quantitative agreement.« less

Automated tracking of a figure skater by using PTZ cameras

NASA Astrophysics Data System (ADS)

Haraguchi, Tomohiko; Taki, Tsuyoshi; Hasegawa, Junichi

2009-08-01

In this paper, a system for automated real-time tracking of a figure skater moving on an ice rink by using PTZ cameras is presented. This system is intended for support in training of skating, for example, as a tool for recording and evaluation of his/her motion performances. In the processing procedure of the system, an ice rink region is extracted first from a video image by region growing method, then one of hole components in the obtained rink region is extracted as a skater region. If there exists no hole component, a skater region is estimated from horizontal and vertical intensity projections of the rink region. Each camera is automatically panned and/or tilted so as to keep the skater region on almost the center of the image, and also zoomed so as to keep the height of the skater region within an appropriate range. In the experiments using 5 practical video images of skating, it was shown that the extraction rate of the skater region was almost 90%, and tracking with camera control was successfully done for almost all of the cases used here.

Increasing productivity for the analysis of trace contaminants in food by gas chromatography-mass spectrometry using automated liner exchange, backflushing and heart-cutting.

PubMed

David, Frank; Tienpont, Bart; Devos, Christophe; Lerch, Oliver; Sandra, Pat

2013-10-25

Laboratories focusing on residue analysis in food are continuously seeking to increase sample throughput by minimizing sample preparation. Generic sample extraction methods such as QuEChERS lack selectivity and consequently extracts are not free from non-volatile material that contaminates the analytical system. Co-extracted matrix constituents interfere with target analytes, even if highly sensitive and selective GC-MS/MS is used. A number of GC approaches are described that can be used to increase laboratory productivity. These techniques include automated inlet liner exchange and column backflushing for preservation of the performance of the analytical system and heart-cutting two-dimensional GC for increasing sensitivity and selectivity. The application of these tools is illustrated by the analysis of pesticides in vegetables and fruits, PCBs in milk powder and coplanar PCBs in fish. It is demonstrated that considerable increase in productivity can be achieved by decreasing instrument down-time, while analytical performance is equal or better compared to conventional trace contaminant analysis. Copyright © 2013 Elsevier B.V. All rights reserved.

Automated anatomical labeling of bronchial branches using multiple classifiers and its application to bronchoscopy guidance based on fusion of virtual and real bronchoscopy

NASA Astrophysics Data System (ADS)

Ota, Shunsuke; Deguchi, Daisuke; Kitasaka, Takayuki; Mori, Kensaku; Suenaga, Yasuhito; Hasegawa, Yoshinori; Imaizumi, Kazuyoshi; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi

2008-03-01

This paper presents a method for automated anatomical labeling of bronchial branches (ALBB) extracted from 3D CT datasets. The proposed method constructs classifiers that output anatomical names of bronchial branches by employing the machine-learning approach. We also present its application to a bronchoscopy guidance system. Since the bronchus has a complex tree structure, bronchoscopists easily tend to get disoriented and lose the way to a target location. A bronchoscopy guidance system is strongly expected to be developed to assist bronchoscopists. In such guidance system, automated presentation of anatomical names is quite useful information for bronchoscopy. Although several methods for automated ALBB were reported, most of them constructed models taking only variations of branching patterns into account and did not consider those of running directions. Since the running directions of bronchial branches differ greatly in individuals, they could not perform ALBB accurately when running directions of bronchial branches were different from those of models. Our method tries to solve such problems by utilizing the machine-learning approach. Actual procedure consists of three steps: (a) extraction of bronchial tree structures from 3D CT datasets, (b) construction of classifiers using the multi-class AdaBoost technique, and (c) automated classification of bronchial branches by using the constructed classifiers. We applied the proposed method to 51 cases of 3D CT datasets. The constructed classifiers were evaluated by leave-one-out scheme. The experimental results showed that the proposed method could assign correct anatomical names to bronchial branches of 89.1% up to segmental lobe branches. Also, we confirmed that it was quite useful to assist the bronchoscopy by presenting anatomical names of bronchial branches on real bronchoscopic views.

Two Automated Techniques for Carotid Lumen Diameter Measurement: Regional versus Boundary Approaches.

PubMed

Araki, Tadashi; Kumar, P Krishna; Suri, Harman S; Ikeda, Nobutaka; Gupta, Ajay; Saba, Luca; Rajan, Jeny; Lavra, Francesco; Sharma, Aditya M; Shafique, Shoaib; Nicolaides, Andrew; Laird, John R; Suri, Jasjit S

2016-07-01

The degree of stenosis in the carotid artery can be predicted using automated carotid lumen diameter (LD) measured from B-mode ultrasound images. Systolic velocity-based methods for measurement of LD are subjective. With the advancement of high resolution imaging, image-based methods have started to emerge. However, they require robust image analysis for accurate LD measurement. This paper presents two different algorithms for automated segmentation of the lumen borders in carotid ultrasound images. Both algorithms are modeled as a two stage process. Stage one consists of a global-based model using scale-space framework for the extraction of the region of interest. This stage is common to both algorithms. Stage two is modeled using a local-based strategy that extracts the lumen interfaces. At this stage, the algorithm-1 is modeled as a region-based strategy using a classification framework, whereas the algorithm-2 is modeled as a boundary-based approach that uses the level set framework. Two sets of databases (DB), Japan DB (JDB) (202 patients, 404 images) and Hong Kong DB (HKDB) (50 patients, 300 images) were used in this study. Two trained neuroradiologists performed manual LD tracings. The mean automated LD measured was 6.35 ± 0.95 mm for JDB and 6.20 ± 1.35 mm for HKDB. The precision-of-merit was: 97.4 % and 98.0 % w.r.t to two manual tracings for JDB and 99.7 % and 97.9 % w.r.t to two manual tracings for HKDB. Statistical tests such as ANOVA, Chi-Squared, T-test, and Mann-Whitney test were conducted to show the stability and reliability of the automated techniques.

The Adam and Eve Robot Scientists for the Automated Discovery of Scientific Knowledge

NASA Astrophysics Data System (ADS)

King, Ross

A Robot Scientist is a physically implemented robotic system that applies techniques from artificial intelligence to execute cycles of automated scientific experimentation. A Robot Scientist can automatically execute cycles of hypothesis formation, selection of efficient experiments to discriminate between hypotheses, execution of experiments using laboratory automation equipment, and analysis of results. The motivation for developing Robot Scientists is to better understand science, and to make scientific research more efficient. The Robot Scientist `Adam' was the first machine to autonomously discover scientific knowledge: both form and experimentally confirm novel hypotheses. Adam worked in the domain of yeast functional genomics. The Robot Scientist `Eve' was originally developed to automate early-stage drug development, with specific application to neglected tropical disease such as malaria, African sleeping sickness, etc. We are now adapting Eve to work with on cancer. We are also teaching Eve to autonomously extract information from the scientific literature.

Application of the informational reference system OZhUR to the automated processing of data from satellites of the Kosmos series

NASA Technical Reports Server (NTRS)

Pokras, V. M.; Yevdokimov, V. P.; Maslov, V. D.

1978-01-01

The structure and potential of the information reference system OZhUR designed for the automated data processing systems of scientific space vehicles (SV) is considered. The system OZhUR ensures control of the extraction phase of processing with respect to a concrete SV and the exchange of data between phases.The practical application of the system OZhUR is exemplified in the construction of a data processing system for satellites of the Cosmos series. As a result of automating the operations of exchange and control, the volume of manual preparation of data is significantly reduced, and there is no longer any need for individual logs which fix the status of data processing. The system Ozhur is included in the automated data processing system Nauka which is realized in language PL-1 in a binary one-address system one-state (BOS OS) electronic computer.

Automated Analysis of Fluorescence Microscopy Images to Identify Protein-Protein Interactions

DOE PAGES

Venkatraman, S.; Doktycz, M. J.; Qi, H.; ...

2006-01-01

The identification of protein interactions is important for elucidating biological networks. One obstacle in comprehensive interaction studies is the analyses of large datasets, particularly those containing images. Development of an automated system to analyze an image-based protein interaction dataset is needed. Such an analysis system is described here, to automatically extract features from fluorescence microscopy images obtained from a bacterial protein interaction assay. These features are used to relay quantitative values that aid in the automated scoring of positive interactions. Experimental observations indicate that identifying at least 50% positive cells in an image is sufficient to detect a protein interaction.more » Based on this criterion, the automated system presents 100% accuracy in detecting positive interactions for a dataset of 16 images. Algorithms were implemented using MATLAB and the software developed is available on request from the authors.« less

Validation of an automated colony counting system for group A Streptococcus.

PubMed

Frost, H R; Tsoi, S K; Baker, C A; Laho, D; Sanderson-Smith, M L; Steer, A C; Smeesters, P R

2016-02-08

The practice of counting bacterial colony forming units on agar plates has long been used as a method to estimate the concentration of live bacteria in culture. However, due to the laborious and potentially error prone nature of this measurement technique, an alternative method is desirable. Recent technologic advancements have facilitated the development of automated colony counting systems, which reduce errors introduced during the manual counting process and recording of information. An additional benefit is the significant reduction in time taken to analyse colony counting data. Whilst automated counting procedures have been validated for a number of microorganisms, the process has not been successful for all bacteria due to the requirement for a relatively high contrast between bacterial colonies and growth medium. The purpose of this study was to validate an automated counting system for use with group A Streptococcus (GAS). Twenty-one different GAS strains, representative of major emm-types, were selected for assessment. In order to introduce the required contrast for automated counting, 2,3,5-triphenyl-2H-tetrazolium chloride (TTC) dye was added to Todd-Hewitt broth with yeast extract (THY) agar. Growth on THY agar with TTC was compared with growth on blood agar and THY agar to ensure the dye was not detrimental to bacterial growth. Automated colony counts using a ProtoCOL 3 instrument were compared with manual counting to confirm accuracy over the stages of the growth cycle (latent, mid-log and stationary phases) and in a number of different assays. The average percentage differences between plating and counting methods were analysed using the Bland-Altman method. A percentage difference of ±10 % was determined as the cut-off for a critical difference between plating and counting methods. All strains measured had an average difference of less than 10 % when plated on THY agar with TTC. This consistency was also observed over all phases of the growth cycle and when plated in blood following bactericidal assays. Agreement between these methods suggest the use of an automated colony counting technique for GAS will significantly reduce time spent counting bacteria to enable a more efficient and accurate measurement of bacteria concentration in culture.

CT colonography: automated measurement of colonic polyps compared with manual techniques--human in vitro study.

PubMed

Taylor, Stuart A; Slater, Andrew; Halligan, Steve; Honeyfield, Lesley; Roddie, Mary E; Demeshski, Jamshid; Amin, Hamdam; Burling, David

2007-01-01

To prospectively investigate the relative accuracy and reproducibility of manual and automated computer software measurements by using polyps of known size in a human colectomy specimen. Institutional review board approval was obtained for the study; written consent for use of the surgical specimen was obtained. A colectomy specimen containing 27 polyps from a 16-year-old male patient with familial adenomatous polyposis was insufflated, submerged in a container with solution, and scanned at four-section multi-detector row computed tomography (CT). A histopathologist measured the maximum dimension of all polyps in the opened specimen. Digital photographs and line drawings were produced to aid CT-histologic measurement correlation. A novice (radiographic technician) and an experienced (radiologist) observer independently estimated polyp diameter with three methods: manual two-dimensional (2D) and manual three-dimensional (3D) measurement with software calipers and automated measurement with software (automatic). Data were analyzed with paired t tests and Bland-Altman limits of agreement. Seven polyps (

Evaluation of different distortion correction methods and interpolation techniques for an automated classification of celiac disease☆

PubMed Central

Gadermayr, M.; Liedlgruber, M.; Uhl, A.; Vécsei, A.

2013-01-01

Due to the optics used in endoscopes, a typical degradation observed in endoscopic images are barrel-type distortions. In this work we investigate the impact of methods used to correct such distortions in images on the classification accuracy in the context of automated celiac disease classification. For this purpose we compare various different distortion correction methods and apply them to endoscopic images, which are subsequently classified. Since the interpolation used in such methods is also assumed to have an influence on the resulting classification accuracies, we also investigate different interpolation methods and their impact on the classification performance. In order to be able to make solid statements about the benefit of distortion correction we use various different feature extraction methods used to obtain features for the classification. Our experiments show that it is not possible to make a clear statement about the usefulness of distortion correction methods in the context of an automated diagnosis of celiac disease. This is mainly due to the fact that an eventual benefit of distortion correction highly depends on the feature extraction method used for the classification. PMID:23981585

Microfluidic cartridges for DNA purification and genotyping processed in standard laboratory instruments

NASA Astrophysics Data System (ADS)

Focke, Maximilian; Mark, Daniel; Stumpf, Fabian; Müller, Martina; Roth, Günter; Zengerle, Roland; von Stetten, Felix

2011-06-01

Two microfluidic cartridges intended for upgrading standard laboratory instruments with automated liquid handling capability by use of centrifugal forces are presented. The first microfluidic cartridge enables purification of DNA from human whole blood and is operated in a standard laboratory centrifuge. The second microfluidic catridge enables genotyping of pathogens by geometrically multiplexed real-time PCR. It is operated in a slightly modified off-the-shelf thermal cycler. Both solutions aim at smart and cost-efficient ways to automate work flows in laboratories. The DNA purification cartridge automates all liquid handling steps starting from a lysed blood sample to PCR ready DNA. The cartridge contains two manually crushable glass ampoules with liquid reagents. The DNA yield extracted from a 32 μl blood sample is 192 +/- 30 ng which corresponds to 53 +/- 8% of a reference extraction. The genotyping cartridge is applied to analyse isolates of the multi-resistant Staphyloccus aureus (MRSA) by real-time PCR. The wells contain pre-stored dry reagents such as primers and probes. Evaluation of the system with 44 genotyping assays showed a 100% specificity and agreement with the reference assays in standard tubes. The lower limit of detection was well below 10 copies of DNA per reaction.

Color features as an approach for the automated screening of Salmonella strain

NASA Astrophysics Data System (ADS)

Trujillo, Alejandra Serrano; González, Viridiana Contreras; Andrade Rincón, Saulo E.; Palafox, Luis E.

2016-11-01

We present the implementation of a feature extraction approach for the automated screening of Salmonella sp., a task visually carried out by a microbiologist, where the resulting color characteristics of the culture media plate indicate the presence of this strain. The screening of Salmonella sp. is based on the inoculation and incubation of a sample on an agar plate, allowing the isolation of this strain, if present. This process uses three media: Xylose lysine deoxycholate, Salmonella Shigella, and Brilliant Green agar plates, which exhibit specific color characteristics over the colonies and over the surrounding medium for a presumed positive interpretation. Under a controlled illumination environment, images of plates are captured and the characteristics found over each agar are processed separately. Each agar is analyzed using statistical descriptors for texture, to determine the presence of colonies, followed by the extraction of color features. A comparison among the color features seen over the three media, according to the FDA Bacteriological Analytical Manual, determines the presence of Salmonella sp. on a given sample. The implemented process proves that the task addressed can be accomplished under an image processing approach, leading to the future validation and automation of additional screening processes.

The identification of clinically important elements within medical journal abstracts: Patient-Population-Problem, Exposure-Intervention, Comparison, Outcome, Duration and Results (PECODR).

PubMed

Dawes, Martin; Pluye, Pierre; Shea, Laura; Grad, Roland; Greenberg, Arlene; Nie, Jian-Yun

2007-01-01

Information retrieval in primary care is becoming more difficult as the volume of medical information held in electronic databases expands. The lexical structure of this information might permit automatic indexing and improved retrieval. To determine the possibility of identifying the key elements of clinical studies, namely Patient-Population-Problem, Exposure-Intervention, Comparison, Outcome, Duration and Results (PECODR), from abstracts of medical journals. We used a convenience sample of 20 synopses from the journal Evidence-Based Medicine (EBM) and their matching original journal article abstracts obtained from PubMed. Three independent primary care professionals identified PECODR-related extracts of text. Rules were developed to define each PECODR element and the selection process of characters, words, phrases and sentences. From the extracts of text related to PECODR elements, potential lexical patterns that might help identify those elements were proposed and assessed using NVivo software. A total of 835 PECODR-related text extracts containing 41,263 individual text characters were identified from 20 EBM journal synopses. There were 759 extracts in the corresponding PubMed abstracts containing 31,947 characters. PECODR elements were found in nearly all abstracts and synopses with the exception of duration. There was agreement on 86.6% of the extracts from the 20 EBM synopses and 85.0% on the corresponding PubMed abstracts. After consensus this rose to 98.4% and 96.9% respectively. We found potential text patterns in the Comparison, Outcome and Results elements of both EBM synopses and PubMed abstracts. Some phrases and words are used frequently and are specific for these elements in both synopses and abstracts. Results suggest a PECODR-related structure exists in medical abstracts and that there might be lexical patterns specific to these elements. More sophisticated computer-assisted lexical-semantic analysis might refine these results, and pave the way to automating PECODR indexing, and improve information retrieval in primary care.

Analysis of drugs in human tissues by supercritical fluid extraction/immunoassay

NASA Astrophysics Data System (ADS)

Furton, Kenneth G.; Sabucedo, Alberta; Rein, Joseph; Hearn, W. L.

1997-02-01

A rapid, readily automated method has been developed for the quantitative analysis of phenobarbital from human liver tissues based on supercritical carbon dioxide extraction followed by fluorescence enzyme immunoassay. The method developed significantly reduces sample handling and utilizes the entire liver homogenate. The current method yields comparable recoveries and precision and does not require the use of an internal standard, although traditional GC/MS confirmation can still be performed on sample extracts. Additionally, the proposed method uses non-toxic, inexpensive carbon dioxide, thus eliminating the use of halogenated organic solvents.

A novel DTI-QA tool: Automated metric extraction exploiting the sphericity of an agar filled phantom.

PubMed

Chavez, Sofia; Viviano, Joseph; Zamyadi, Mojdeh; Kingsley, Peter B; Kochunov, Peter; Strother, Stephen; Voineskos, Aristotle

2018-02-01

To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics. A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting. The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~15min) and the code for automated processing has been made publicly available. A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients.

PubMed

Hosseini, Mohammad-Parsa; Nazem-Zadeh, Mohammad-Reza; Pompili, Dario; Jafari-Khouzani, Kourosh; Elisevich, Kost; Soltanian-Zadeh, Hamid

2016-01-01

Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus. A template database of 195 (81 males, 114 females; age range 32-67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark. Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, -4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than LocalInfo, FreeSurfer, and hammer, respectively. The Bland-Altman similarity analysis revealed a low bias for the ABSS and LocalInfo techniques compared to the others. The ABSS method for automated hippocampal segmentation outperformed other methods, best approximating what could be achieved by manual tracing. This study also shows that four categories of input data can cause automated segmentation methods to fail. They include incomplete studies, artifact, low signal-to-noise ratio, and inhomogeneity. Different scanner platforms and pulse sequences were considered as means by which to improve reliability of the automated methods. Other modifications were specially devised to enhance a particular method assessed in this study.

Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients

PubMed Central

Hosseini, Mohammad-Parsa; Nazem-Zadeh, Mohammad-Reza; Pompili, Dario; Jafari-Khouzani, Kourosh; Elisevich, Kost; Soltanian-Zadeh, Hamid

2016-01-01

Purpose: Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus. Methods: A template database of 195 (81 males, 114 females; age range 32–67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark. Results: Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, −4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than LocalInfo, FreeSurfer, and hammer, respectively. The Bland–Altman similarity analysis revealed a low bias for the ABSS and LocalInfo techniques compared to the others. Conclusions: The ABSS method for automated hippocampal segmentation outperformed other methods, best approximating what could be achieved by manual tracing. This study also shows that four categories of input data can cause automated segmentation methods to fail. They include incomplete studies, artifact, low signal-to-noise ratio, and inhomogeneity. Different scanner platforms and pulse sequences were considered as means by which to improve reliability of the automated methods. Other modifications were specially devised to enhance a particular method assessed in this study. PMID:26745947

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Systematic review automation technologies

PubMed Central

2014-01-01

Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects. We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time. PMID:25005128

Evaluation and comparison of FTA card and CTAB DNA extraction methods for non-agricultural taxa1

PubMed Central

Siegel, Chloe S.; Stevenson, Florence O.; Zimmer, Elizabeth A.

2017-01-01

Premise of the study: An efficient, effective DNA extraction method is necessary for comprehensive analysis of plant genomes. This study analyzed the quality of DNA obtained using paper FTA cards prepared directly in the field when compared to the more traditional cetyltrimethylammonium bromide (CTAB)–based extraction methods from silica-dried samples. Methods: DNA was extracted using FTA cards according to the manufacturer’s protocol. In parallel, CTAB-based extractions were done using the automated AutoGen DNA isolation system. DNA quality for both methods was determined for 15 non-agricultural species collected in situ, by gel separation, spectrophotometry, fluorometry, and successful amplification and sequencing of nuclear and chloroplast gene markers. Results: The FTA card extraction method yielded less concentrated, but also less fragmented samples than the CTAB-based technique. The card-extracted samples provided DNA that could be successfully amplified and sequenced. The FTA cards are also useful because the collected samples do not require refrigeration, extensive laboratory expertise, or as many hazardous chemicals as extractions using the CTAB-based technique. Discussion: The relative success of the FTA card method in our study suggested that this method could be a valuable tool for studies in plant population genetics and conservation biology that may involve screening of hundreds of individual plants. The FTA cards, like the silica gel samples, do not contain plant material capable of propagation, and therefore do not require permits from the U.S. Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) for transportation. PMID:28224056

Automated solid-phase extraction coupled online with HPLC-FLD for the quantification of zearalenone in edible oil.

PubMed

Drzymala, Sarah S; Weiz, Stefan; Heinze, Julia; Marten, Silvia; Prinz, Carsten; Zimathies, Annett; Garbe, Leif-Alexander; Koch, Matthias

2015-05-01

Established maximum levels for the mycotoxin zearalenone (ZEN) in edible oil require monitoring by reliable analytical methods. Therefore, an automated SPE-HPLC online system based on dynamic covalent hydrazine chemistry has been developed. The SPE step comprises a reversible hydrazone formation by ZEN and a hydrazine moiety covalently attached to a solid phase. Seven hydrazine materials with different properties regarding the resin backbone, pore size, particle size, specific surface area, and loading have been evaluated. As a result, a hydrazine-functionalized silica gel was chosen. The final automated online method was validated and applied to the analysis of three maize germ oil samples including a provisionally certified reference material. Important performance criteria for the recovery (70-120 %) and precision (RSDr <25 %) as set by the Commission Regulation EC 401/2006 were fulfilled: The mean recovery was 78 % and RSDr did not exceed 8 %. The results of the SPE-HPLC online method were further compared to results obtained by liquid-liquid extraction with stable isotope dilution analysis LC-MS/MS and found to be in good agreement. The developed SPE-HPLC online system with fluorescence detection allows a reliable, accurate, and sensitive quantification (limit of quantification, 30 μg/kg) of ZEN in edible oils while significantly reducing the workload. To our knowledge, this is the first report on an automated SPE-HPLC method based on a covalent SPE approach.

Unattended reaction monitoring using an automated microfluidic sampler and on-line liquid chromatography.

PubMed

Patel, Darshan C; Lyu, Yaqi Fara; Gandarilla, Jorge; Doherty, Steve

2018-04-03

In-process sampling and analysis is an important aspect of monitoring kinetic profiles and impurity formation or rejection, both in development and during commercial manufacturing. In pharmaceutical process development, the technology of choice for a substantial portion of this analysis is high-performance liquid chromatography (HPLC). Traditionally, the sample extraction and preparation for reaction characterization have been performed manually. This can be time consuming, laborious, and impractical for long processes. Depending on the complexity of the sample preparation, there can be variability introduced by different analysts, and in some cases, the integrity of the sample can be compromised during handling. While there are commercial instruments available for on-line monitoring with HPLC, they lack capabilities in many key areas. Some do not provide integration of the sampling and analysis, while others afford limited flexibility in sample preparation. The current offerings provide a limited number of unit operations available for sample processing and no option for workflow customizability. This work describes development of a microfluidic automated program (MAP) which fully automates the sample extraction, manipulation, and on-line LC analysis. The flexible system is controlled using an intuitive Microsoft Excel based user interface. The autonomous system is capable of unattended reaction monitoring that allows flexible unit operations and workflow customization to enable complex operations and on-line sample preparation. The automated system is shown to offer advantages over manual approaches in key areas while providing consistent and reproducible in-process data. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated segmentation of murine lung tumors in x-ray micro-CT images

NASA Astrophysics Data System (ADS)

Swee, Joshua K. Y.; Sheridan, Clare; de Bruin, Elza; Downward, Julian; Lassailly, Francois; Pizarro, Luis

2014-03-01

Recent years have seen micro-CT emerge as a means of providing imaging analysis in pre-clinical study, with in-vivo micro-CT having been shown to be particularly applicable to the examination of murine lung tumors. Despite this, existing studies have involved substantial human intervention during the image analysis process, with the use of fully-automated aids found to be almost non-existent. We present a new approach to automate the segmentation of murine lung tumors designed specifically for in-vivo micro-CT-based pre-clinical lung cancer studies that addresses the specific requirements of such study, as well as the limitations human-centric segmentation approaches experience when applied to such micro-CT data. Our approach consists of three distinct stages, and begins by utilizing edge enhancing and vessel enhancing non-linear anisotropic diffusion filters to extract anatomy masks (lung/vessel structure) in a pre-processing stage. Initial candidate detection is then performed through ROI reduction utilizing obtained masks and a two-step automated segmentation approach that aims to extract all disconnected objects within the ROI, and consists of Otsu thresholding, mathematical morphology and marker-driven watershed. False positive reduction is finally performed on initial candidates through random-forest-driven classification using the shape, intensity, and spatial features of candidates. We provide validation of our approach using data from an associated lung cancer study, showing favorable results both in terms of detection (sensitivity=86%, specificity=89%) and structural recovery (Dice Similarity=0.88) when compared against manual specialist annotation.

Automated identification of best-quality coronary artery segments from multiple-phase coronary CT angiography (cCTA) for vessel analysis

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Chughtai, Aamer; Wei, Jun; Kazerooni, Ella A.

2016-03-01

We are developing an automated method to identify the best quality segment among the corresponding segments in multiple-phase cCTA. The coronary artery trees are automatically extracted from different cCTA phases using our multi-scale vessel segmentation and tracking method. An automated registration method is then used to align the multiple-phase artery trees. The corresponding coronary artery segments are identified in the registered vessel trees and are straightened by curved planar reformation (CPR). Four features are extracted from each segment in each phase as quality indicators in the original CT volume and the straightened CPR volume. Each quality indicator is used as a voting classifier to vote the corresponding segments. A newly designed weighted voting ensemble (WVE) classifier is finally used to determine the best-quality coronary segment. An observer preference study is conducted with three readers to visually rate the quality of the vessels in 1 to 6 rankings. Six and 10 cCTA cases are used as training and test set in this preliminary study. For the 10 test cases, the agreement between automatically identified best-quality (AI-BQ) segments and radiologist's top 2 rankings is 79.7%, and between AI-BQ and the other two readers are 74.8% and 83.7%, respectively. The results demonstrated that the performance of our automated method was comparable to those of experienced readers for identification of the best-quality coronary segments.

Automated data mining of a proprietary database system for physician quality improvement.

PubMed

Johnstone, Peter A S; Crenshaw, Tim; Cassels, Diane G; Fox, Timothy H

2008-04-01

Physician practice quality improvement is a subject of intense national debate. This report describes using a software data acquisition program to mine an existing, commonly used proprietary radiation oncology database to assess physician performance. Between 2003 and 2004, a manual analysis was performed of electronic portal image (EPI) review records. Custom software was recently developed to mine the record-and-verify database and the review process of EPI at our institution. In late 2006, a report was developed that allowed for immediate review of physician completeness and speed of EPI review for any prescribed period. The software extracted >46,000 EPIs between 2003 and 2007, providing EPI review status and time to review by each physician. Between 2003 and 2007, the department EPI review improved from 77% to 97% (range, 85.4-100%), with a decrease in the mean time to review from 4.2 days to 2.4 days. The initial intervention in 2003 to 2004 was moderately successful in changing the EPI review patterns; it was not repeated because of the time required to perform it. However, the implementation in 2006 of the automated review tool yielded a profound change in practice. Using the software, the automated chart review required approximately 1.5 h for mining and extracting the data for the 4-year period. This study quantified the EPI review process as it evolved during a 4-year period at our institution and found that automation of data retrieval and review simplified and facilitated physician quality improvement.

Practical interpretation of CYP2D6 haplotypes: Comparison and integration of automated and expert calling.

PubMed

Ruaño, Gualberto; Kocherla, Mohan; Graydon, James S; Holford, Theodore R; Makowski, Gregory S; Goethe, John W

2016-05-01

We describe a population genetic approach to compare samples interpreted with expert calling (EC) versus automated calling (AC) for CYP2D6 haplotyping. The analysis represents 4812 haplotype calls based on signal data generated by the Luminex xMap analyzers from 2406 patients referred to a high-complexity molecular diagnostics laboratory for CYP450 testing. DNA was extracted from buccal swabs. We compared the results of expert calls (EC) and automated calls (AC) with regard to haplotype number and frequency. The ratio of EC to AC was 1:3. Haplotype frequencies from EC and AC samples were convergent across haplotypes, and their distribution was not statistically different between the groups. Most duplications required EC, as only expansions with homozygous or hemizygous haplotypes could be automatedly called. High-complexity laboratories can offer equivalent interpretation to automated calling for non-expanded CYP2D6 loci, and superior interpretation for duplications. We have validated scientific expert calling specified by scoring rules as standard operating procedure integrated with an automated calling algorithm. The integration of EC with AC is a practical strategy for CYP2D6 clinical haplotyping. Copyright © 2016 Elsevier B.V. All rights reserved.

ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data.

PubMed

Wu, Wei; Keller, Corey J; Rogasch, Nigel C; Longwell, Parker; Shpigel, Emmanuel; Rolle, Camarin E; Etkin, Amit

2018-04-01

Concurrent single-pulse TMS-EEG (spTMS-EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS-EEG data suffer from enormous stimulation-induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time-intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTMS-EEG artifact rejection. A key step of this algorithm is to decompose the spTMS-EEG data into statistically independent components (ICs), and then train a pattern classifier to automatically identify artifact components based on knowledge of the spatio-temporal profile of both neural and artefactual activities. The autocleaned and hand-cleaned data yield qualitatively similar group evoked potential waveforms. The algorithm achieves a 95% IC classification accuracy referenced to expert artifact rejection performance, and does so across a large number of spTMS-EEG data sets (n = 90 stimulation sites), retains high accuracy across stimulation sites/subjects/populations/montages, and outperforms current automated algorithms. Moreover, the algorithm was superior to the artifact rejection performance of relatively novice individuals, who would be the likely users of spTMS-EEG as the technique becomes more broadly disseminated. In summary, our algorithm provides an automated, fast, objective, and accurate method for cleaning spTMS-EEG data, which can increase the utility of TMS-EEG in both clinical and basic neuroscience settings. © 2018 Wiley Periodicals, Inc.

Automated classification of optical coherence tomography images of human atrial tissue

NASA Astrophysics Data System (ADS)

Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

2016-10-01

Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions.

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, R.; Beaudet, P.

1982-01-01

An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

22 CFR 120.30 - The Automated Export System (AES).

Code of Federal Regulations, 2012 CFR

2012-04-01

... 22 Foreign Relations 1 2012-04-01 2012-04-01 false The Automated Export System (AES). 120.30 Section 120.30 Foreign Relations DEPARTMENT OF STATE INTERNATIONAL TRAFFIC IN ARMS REGULATIONS PURPOSE AND DEFINITIONS § 120.30 The Automated Export System (AES). The Automated Export System (AES) is the Department of...

22 CFR 120.30 - The Automated Export System (AES).

Code of Federal Regulations, 2014 CFR

2014-04-01

... 22 Foreign Relations 1 2014-04-01 2014-04-01 false The Automated Export System (AES). 120.30 Section 120.30 Foreign Relations DEPARTMENT OF STATE INTERNATIONAL TRAFFIC IN ARMS REGULATIONS PURPOSE AND DEFINITIONS § 120.30 The Automated Export System (AES). The Automated Export System (AES) is the Department of...

22 CFR 120.30 - The Automated Export System (AES).

Code of Federal Regulations, 2011 CFR

2011-04-01

... 22 Foreign Relations 1 2011-04-01 2011-04-01 false The Automated Export System (AES). 120.30 Section 120.30 Foreign Relations DEPARTMENT OF STATE INTERNATIONAL TRAFFIC IN ARMS REGULATIONS PURPOSE AND DEFINITIONS § 120.30 The Automated Export System (AES). The Automated Export System (AES) is the Department of...

22 CFR 120.30 - The Automated Export System (AES).

Code of Federal Regulations, 2013 CFR

2013-04-01

... 22 Foreign Relations 1 2013-04-01 2013-04-01 false The Automated Export System (AES). 120.30 Section 120.30 Foreign Relations DEPARTMENT OF STATE INTERNATIONAL TRAFFIC IN ARMS REGULATIONS PURPOSE AND DEFINITIONS § 120.30 The Automated Export System (AES). The Automated Export System (AES) is the Department of...

Support patient search on pathology reports with interactive online learning based data extraction.

PubMed

Zheng, Shuai; Lu, James J; Appin, Christina; Brat, Daniel; Wang, Fusheng

2015-01-01

Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user's interaction with minimal human effort. We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system's data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users' corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of tests. Extracting data from pathology reports could enable more accurate knowledge to support biomedical research and clinical diagnosis. IDEAL-X provides a bridge that takes advantage of online machine learning based data extraction and the knowledge from human's feedback. By combining iterative online learning and adaptive controlled vocabularies, IDEAL-X can deliver highly adaptive and accurate data extraction to support patient search.

Automated on-line renewable solid-phase extraction-liquid chromatography exploiting multisyringe flow injection-bead injection lab-on-valve analysis.

PubMed

Quintana, José Benito; Miró, Manuel; Estela, José Manuel; Cerdà, Víctor

2006-04-15

In this paper, the third generation of flow injection analysis, also named the lab-on-valve (LOV) approach, is proposed for the first time as a front end to high-performance liquid chromatography (HPLC) for on-line solid-phase extraction (SPE) sample processing by exploiting the bead injection (BI) concept. The proposed microanalytical system based on discontinuous programmable flow features automated packing (and withdrawal after single use) of a small amount of sorbent (<5 mg) into the microconduits of the flow network and quantitative elution of sorbed species into a narrow band (150 microL of 95% MeOH). The hyphenation of multisyringe flow injection analysis (MSFIA) with BI-LOV prior to HPLC analysis is utilized for on-line postextraction treatment to ensure chemical compatibility between the eluate medium and the initial HPLC gradient conditions. This circumvents the band-broadening effect commonly observed in conventional on-line SPE-based sample processors due to the low eluting strength of the mobile phase. The potential of the novel MSFI-BI-LOV hyphenation for on-line handling of complex environmental and biological samples prior to reversed-phase chromatographic separations was assessed for the expeditious determination of five acidic pharmaceutical residues (viz., ketoprofen, naproxen, bezafibrate, diclofenac, and ibuprofen) and one metabolite (viz., salicylic acid) in surface water, urban wastewater, and urine. To this end, the copolymeric divinylbenzene-co-n-vinylpyrrolidone beads (Oasis HLB) were utilized as renewable sorptive entities in the micromachined unit. The automated analytical method features relative recovery percentages of >88%, limits of detection within the range 0.02-0.67 ng mL(-1), and coefficients of variation <11% for the column renewable mode and gives rise to a drastic reduction in operation costs ( approximately 25-fold) as compared to on-line column switching systems.

A Meta-Analysis of Factors Influencing the Development of Trust in Automation: Implications for Human-Robot Interaction

DTIC Science & Technology

2014-07-01

Submoderating factors were examined and reported for human-related (i.e., age, cognitive factors, emotive factors) and automation- related (i.e., features and...capabilities) effects. Analyses were also conducted for type of automated aid: cognitive, control, and perceptual automation aids. Automated cognitive...operator, user) action. Perceptual aids are used to assist the operator or user by providing warnings or to assist with pattern recognition. All

Improvement and automation of a real-time PCR assay for vaginal fluids.

PubMed

De Vittori, E; Giampaoli, S; Barni, F; Baldi, M; Berti, A; Ripani, L; Romano Spica, V

2016-05-01

The identification of vaginal fluids is crucial in forensic science. Several molecular protocols based on PCR amplification of mfDNA (microflora DNA) specific for vaginal bacteria are now available. Unfortunately mfDNA extraction and PCR reactions require manual optimization of several steps. The aim of present study was the verification of a partial automatization of vaginal fluids identification through two instruments widely diffused in forensic laboratories: EZ1 Advanced robot and Rotor Gene Q 5Plex HRM. Moreover, taking advantage of 5-plex thermocycler technology, the ForFluid kit performances were improved by expanding the mfDNA characterization panel with a new bacterial target for vaginal fluids and with an internal positive control (IPC) to monitor PCR inhibition. Results underlined the feasibility of a semi-automated extraction of mfDNA using a BioRobot and demonstrated the analytical improvements of the kit. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

On chip preconcentration and fluorescence labeling of model proteins by use of monolithic columns: device fabrication, optimization, and automation.

PubMed

Yang, Rui; Pagaduan, Jayson V; Yu, Ming; Woolley, Adam T

2015-01-01

Microfluidic systems with monolithic columns have been developed for preconcentration and on-chip labeling of model proteins. Monoliths were prepared in microchannels by photopolymerization, and their properties were optimized by varying the composition and concentration of the monomers to improve flow and extraction. On-chip labeling of proteins was achieved by driving solutions through the monolith by use of voltage then incubating fluorescent dye with protein retained on the monolith. Subsequently, the labeled proteins were eluted, by applying voltages to reservoirs on the microdevice, and then detected, by monitoring laser-induced fluorescence. Monoliths prepared from octyl methacrylate combine the best protein retention with the possibility of separate elution of unattached fluorescent label with 50% acetonitrile. Finally, automated on-chip extraction and fluorescence labeling of a model protein were successfully demonstrated. This method involves facile sample pretreatment, and therefore has potential for production of integrated bioanalysis microchips.

Automated Fluid Feature Extraction from Transient Simulations

NASA Technical Reports Server (NTRS)

Haimes, Robert

2000-01-01

In the past, feature extraction and identification were interesting concepts, but not required in understanding the physics of a steady flow field. This is because the results of the more traditional tools like iso-surfaces, cuts and streamlines, were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of a great deal of interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one 'snap-shot' of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for co-processing environments like pV3). And methods must be developed to abstract the feature and display it in a manner that physically makes sense.

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

PubMed

Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

Automated high-throughput flow-through real-time diagnostic system

DOEpatents

Regan, John Frederick

2012-10-30

An automated real-time flow-through system capable of processing multiple samples in an asynchronous, simultaneous, and parallel fashion for nucleic acid extraction and purification, followed by assay assembly, genetic amplification, multiplex detection, analysis, and decontamination. The system is able to hold and access an unlimited number of fluorescent reagents that may be used to screen samples for the presence of specific sequences. The apparatus works by associating extracted and purified sample with a series of reagent plugs that have been formed in a flow channel and delivered to a flow-through real-time amplification detector that has a multiplicity of optical windows, to which the sample-reagent plugs are placed in an operative position. The diagnostic apparatus includes sample multi-position valves, a master sample multi-position valve, a master reagent multi-position valve, reagent multi-position valves, and an optical amplification/detection system.

Multimodal Teaching Analytics: Automated Extraction of Orchestration Graphs from Wearable Sensor Data.

PubMed

Prieto, Luis P; Sharma, Kshitij; Kidzinski, Łukasz; Rodríguez-Triana, María Jesús; Dillenbourg, Pierre

2018-04-01

The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time), on a dataset of 12 classroom sessions enacted by two different teachers in different classroom settings. The dataset included mobile eye-tracking as well as audiovisual and accelerometry data from sensors worn by the teacher. We evaluated both time-independent and time-aware models, achieving median F1 scores of about 0.7-0.8 on leave-one-session-out k-fold cross-validation. Although these results show the feasibility of this approach, they also highlight the need for larger datasets, recorded in a wider variety of classroom settings, to provide automated tagging of classroom practice that can be used in everyday practice across multiple teachers.

Extraction and Analysis of Display Data

NASA Technical Reports Server (NTRS)

Land, Chris; Moye, Kathryn

2008-01-01

The Display Audit Suite is an integrated package of software tools that partly automates the detection of Portable Computer System (PCS) Display errors. [PCS is a lap top computer used onboard the International Space Station (ISS).] The need for automation stems from the large quantity of PCS displays (6,000+, with 1,000,000+ lines of command and telemetry data). The Display Audit Suite includes data-extraction tools, automatic error detection tools, and database tools for generating analysis spread sheets. These spread sheets allow engineers to more easily identify many different kinds of possible errors. The Suite supports over 40 independent analyses, 16 NASA Tech Briefs, November 2008 and complements formal testing by being comprehensive (all displays can be checked) and by revealing errors that are difficult to detect via test. In addition, the Suite can be run early in the development cycle to find and correct errors in advance of testing.

High-throughput DNA extraction of forensic adhesive tapes.

PubMed

Forsberg, Christina; Jansson, Linda; Ansell, Ricky; Hedman, Johannes

2016-09-01

Tape-lifting has since its introduction in the early 2000's become a well-established sampling method in forensic DNA analysis. Sampling is quick and straightforward while the following DNA extraction is more challenging due to the "stickiness", rigidity and size of the tape. We have developed, validated and implemented a simple and efficient direct lysis DNA extraction protocol for adhesive tapes that requires limited manual labour. The method uses Chelex beads and is applied with SceneSafe FAST tape. This direct lysis protocol provided higher mean DNA yields than PrepFiler Express BTA on Automate Express, although the differences were not significant when using clothes worn in a controlled fashion as reference material (p=0.13 and p=0.34 for T-shirts and button-down shirts, respectively). Through in-house validation we show that the method is fit-for-purpose for application in casework, as it provides high DNA yields and amplifiability, as well as good reproducibility and DNA extract stability. After implementation in casework, the proportion of extracts with DNA concentrations above 0.01ng/μL increased from 71% to 76%. Apart from providing higher DNA yields compared with the previous method, the introduction of the developed direct lysis protocol also reduced the amount of manual labour by half and doubled the potential throughput for tapes at the laboratory. Generally, simplified manual protocols can serve as a cost-effective alternative to sophisticated automation solutions when the aim is to enable high-throughput DNA extraction of complex crime scene samples. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Blastocyst microinjection automation.

PubMed

Mattos, Leonardo S; Grant, Edward; Thresher, Randy; Kluckman, Kimberly

2009-09-01

Blastocyst microinjections are routinely involved in the process of creating genetically modified mice for biomedical research, but their efficiency is highly dependent on the skills of the operators. As a consequence, much time and resources are required for training microinjection personnel. This situation has been aggravated by the rapid growth of genetic research, which has increased the demand for mutant animals. Therefore, increased productivity and efficiency in this area are highly desired. Here, we pursue these goals through the automation of a previously developed teleoperated blastocyst microinjection system. This included the design of a new system setup to facilitate automation, the definition of rules for automatic microinjections, the implementation of video processing algorithms to extract feedback information from microscope images, and the creation of control algorithms for process automation. Experimentation conducted with this new system and operator assistance during the cells delivery phase demonstrated a 75% microinjection success rate. In addition, implantation of the successfully injected blastocysts resulted in a 53% birth rate and a 20% yield of chimeras. These results proved that the developed system was capable of automatic blastocyst penetration and retraction, demonstrating the success of major steps toward full process automation.

Fast trace determination of nine odorant and estrogenic chloro- and bromo-phenolic compounds in real water samples through automated solid-phase extraction coupled with liquid chromatography tandem mass spectrometry.

PubMed

Yuan, Su-Fen; Liu, Ze-Hua; Lian, Hai-Xian; Yang, Chuang-Tao; Lin, Qing; Yin, Hua; Lin, Zhang; Dang, Zhi

2018-02-01

A fast and reliable method was developed for simultaneous trace determination of nine odorous and estrogenic chloro- and bromo-phenolic compounds (CPs and BPs) in water samples using solid-phase extraction (SPE) coupled with liquid chromatography tandem mass spectrometry (LC-MS/MS). For sample preparation, the extraction efficiencies of two widely applied cartridges Oasis HLB and Sep-Pak C18 were compared, and the Oasis HLB cartridge showed much better extraction performance; pH of water sample also plays important role on extraction, and pH = 2-3 was found to be most appropriate. For separation of the target compounds, small addition of ammonium hydroxide can obviously improve the detection sensitivity, and the optimized addition concentration was determined as 0.2%. The developed efficient method was validated and showed excellent linearity (R 2  > 0.995), low limit of detection (LOD, 1.9-6.2 ng/L), and good recovery efficiencies of 57-95% in surface and tap water with low relative standard deviation (RSD, 1.3-17.4%). The developed method was finally applied to one tap and one surface water samples and most of these nine targets were detected, but all of them were below their odor thresholds, and their estrogen equivalent (EEQ) were also very low.

Toward designing for trust in database automation

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

Duez, P. P.; Jamieson, G. A.

Appropriate reliance on system automation is imperative for safe and productive work, especially in safety-critical systems. It is unsafe to rely on automation beyond its designed use; conversely, it can be both unproductive and unsafe to manually perform tasks that are better relegated to automated tools. Operator trust in automated tools mediates reliance, and trust appears to affect how operators use technology. As automated agents become more complex, the question of trust in automation is increasingly important. In order to achieve proper use of automation, we must engender an appropriate degree of trust that is sensitive to changes in operatingmore » functions and context. In this paper, we present research concerning trust in automation in the domain of automated tools for relational databases. Lee and See have provided models of trust in automation. One model developed by Lee and See identifies three key categories of information about the automation that lie along a continuum of attributional abstraction. Purpose-, process-and performance-related information serve, both individually and through inferences between them, to describe automation in such a way as to engender r properly-calibrated trust. Thus, one can look at information from different levels of attributional abstraction as a general requirements analysis for information key to appropriate trust in automation. The model of information necessary to engender appropriate trust in automation [1] is a general one. Although it describes categories of information, it does not provide insight on how to determine the specific information elements required for a given automated tool. We have applied the Abstraction Hierarchy (AH) to this problem in the domain of relational databases. The AH serves as a formal description of the automation at several levels of abstraction, ranging from a very abstract purpose-oriented description to a more concrete description of the resources involved in the automated process. The connection between an AH for an automated tool and a list of information elements at the three levels of attributional abstraction is then direct, providing a method for satisfying information requirements for appropriate trust in automation. In this paper, we will present our method for developing specific information requirements for an automated tool, based on a formal analysis of that tool and the models presented by Lee and See. We will show an example of the application of the AH to automation, in the domain of relational database automation, and the resulting set of specific information elements for appropriate trust in the automated tool. Finally, we will comment on the applicability of this approach to the domain of nuclear plant instrumentation. (authors)« less

Automated dynamic hollow fiber liquid-liquid-liquid microextraction combined with capillary electrophoresis for speciation of mercury in biological and environmental samples.

PubMed

Li, Pingjing; He, Man; Chen, Beibei; Hu, Bin

2015-10-09

A simple home-made automatic dynamic hollow fiber based liquid-liquid-liquid microextraction (AD-HF-LLLME) device was designed and constructed for the simultaneous extraction of organomercury and inorganic mercury species with the assistant of a programmable flow injection analyzer. With 18-crown-6 as the complexing reagent, mercury species including methyl-, ethyl-, phenyl- and inorganic mercury were extracted into the organic phase (chlorobenzene), and then back-extracted into the acceptor phase of 0.1% (m/v) 3-mercapto-1-propanesulfonic acid (MPS) aqueous solution. Compared with automatic static (AS)-HF-LLLME system, the extraction equilibrium of target mercury species was obtained in shorter time with higher extraction efficiency in AD-HF-LLLME system. Based on it, a new method of AD-HF-LLLME coupled with large volume sample stacking (LVSS)-capillary electrophoresis (CE)/UV detection was developed for the simultaneous analysis of methyl-, phenyl- and inorganic mercury species in biological samples and environmental water. Under the optimized conditions, AD-HF-LLLME provided high enrichment factors (EFs) of 149-253-fold within relatively short extraction equilibrium time (25min) and good precision with RSD between 3.8 and 8.1%. By combining AD-HF-LLLME with LVSS-CE/UV, EFs were magnified up to 2195-fold and the limits of detection (at S/N=3) for target mercury species were improved to be sub ppb level. Copyright © 2015 Elsevier B.V. All rights reserved.

Thoughts in flight: automation use and pilots' task-related and task-unrelated thought.

PubMed

Casner, Stephen M; Schooler, Jonathan W

2014-05-01

The objective was to examine the relationship between cockpit automation use and task-related and task-unrelated thought among airline pilots. Studies find that cockpit automation can sometimes relieve pilots of tedious control tasks and afford them more time to think ahead. Paradoxically, automation has also been shown to lead to lesser awareness. These results prompt the question of what pilots think about while using automation. A total of 18 airline pilots flew a Boeing 747-400 simulator while we recorded which of two levels of automation they used. As they worked, pilots were verbally probed about what they were thinking. Pilots were asked to categorize their thoughts as pertaining to (a) a specific task at hand, (b) higher-level flight-related thoughts (e.g.,planning ahead), or (c) thoughts unrelated to the flight. Pilots' performance was also measured. Pilots reported a smaller percentage of task-at-hand thoughts (27% vs. 50%) and a greater percentage of higher-level flight-related thoughts (56% vs. 29%) when using the higher level of automation. However, when all was going according to plan, using either level of automation, pilots also reported a higher percentage of task-unrelated thoughts (21%) than they did when in the midst of an unsuccessful performance (7%). Task-unrelated thoughts peaked at 25% when pilots were not interacting with the automation. Although cockpit automation may provide pilots with more time to think, it may encourage pilots to reinvest only some of this mental free time in thinking flight-related thoughts. This research informs the design of human-automation systems that more meaningfully engage the human operator.

Determination of phosphonoformate (foscarnet) in calf and human serum by automated solid-phase extraction and high-performance liquid chromatography with amperometric detection.

PubMed

Ba, B B; Corniot, A G; Ducint, D; Breilh, D; Grellet, J; Saux, M C

1999-03-05

An isocratic high-performance liquid chromatographic method with automated solid-phase extraction has been developed to determine foscarnet in calf and human serums. Extraction was performed with an anion exchanger, SAX, from which the analyte was eluted with a 50 mM potassium pyrophosphate buffer, pH 8.4. The mobile phase consisted of methanol-40 mM disodium hydrogenphosphate, pH 7.6 containing 0.25 mM tetrahexylammonium hydrogensulphate (25:75, v/v). The analyte was separated on a polyether ether ketone (PEEK) column 150x4.6 mm I.D. packed with Kromasil 100 C18, 5 microm. Amperometric detection allowed a quantification limit of 15 microM. The assay was linear from 15 to 240 microM. The recovery of foscarnet from calf serum ranged from 60.65+/-1.89% for 15 microM to 67.45+/-1.24% for 200 microM. The coefficient of variation was < or = 3.73% for intra-assay precision and < or =7.24% for inter-assay precision for calf serum concentrations ranged from 15 to 800 microM. For the same samples, the deviation from the nominal value ranged from -8.97% to +5.40% for same day accuracy and from -4.50% to +2.77% for day-to-day accuracy. Selectivity was satisfactory towards potential co-medications. Replacement of human serum by calf serum for calibration standards and quality control samples was validated. Automation brought more protection against biohazards and increase in productivity for routine monitoring and pharmacokinetic studies.

Automated extraction method for the center line of spinal canal and its application to the spinal curvature quantification in torso X-ray CT images

NASA Astrophysics Data System (ADS)

Hayashi, Tatsuro; Zhou, Xiangrong; Chen, Huayue; Hara, Takeshi; Miyamoto, Kei; Kobayashi, Tatsunori; Yokoyama, Ryujiro; Kanematsu, Masayuki; Hoshi, Hiroaki; Fujita, Hiroshi

2010-03-01

X-ray CT images have been widely used in clinical routine in recent years. CT images scanned by a modern CT scanner can show the details of various organs and tissues. This means various organs and tissues can be simultaneously interpreted on CT images. However, CT image interpretation requires a lot of time and energy. Therefore, support for interpreting CT images based on image-processing techniques is expected. The interpretation of the spinal curvature is important for clinicians because spinal curvature is associated with various spinal disorders. We propose a quantification scheme of the spinal curvature based on the center line of spinal canal on CT images. The proposed scheme consists of four steps: (1) Automated extraction of the skeletal region based on CT number thresholding. (2) Automated extraction of the center line of spinal canal. (3) Generation of the median plane image of spine, which is reformatted based on the spinal canal. (4) Quantification of the spinal curvature. The proposed scheme was applied to 10 cases, and compared with the Cobb angle that is commonly used by clinicians. We found that a high-correlation (for the 95% confidence interval, lumbar lordosis: 0.81-0.99) between values obtained by the proposed (vector) method and Cobb angle. Also, the proposed method can provide the reproducible result (inter- and intra-observer variability: within 2°). These experimental results suggested a possibility that the proposed method was efficient for quantifying the spinal curvature on CT images.

Protocol: high throughput silica-based purification of RNA from Arabidopsis seedlings in a 96-well format

PubMed Central

2011-01-01

The increasing popularity of systems-based approaches to plant research has resulted in a demand for high throughput (HTP) methods to be developed. RNA extraction from multiple samples in an experiment is a significant bottleneck in performing systems-level genomic studies. Therefore we have established a high throughput method of RNA extraction from Arabidopsis thaliana to facilitate gene expression studies in this widely used plant model. We present optimised manual and automated protocols for the extraction of total RNA from 9-day-old Arabidopsis seedlings in a 96 well plate format using silica membrane-based methodology. Consistent and reproducible yields of high quality RNA are isolated averaging 8.9 μg total RNA per sample (~20 mg plant tissue). The purified RNA is suitable for subsequent qPCR analysis of the expression of over 500 genes in triplicate from each sample. Using the automated procedure, 192 samples (2 × 96 well plates) can easily be fully processed (samples homogenised, RNA purified and quantified) in less than half a day. Additionally we demonstrate that plant samples can be stored in RNAlater at -20°C (but not 4°C) for 10 months prior to extraction with no significant effect on RNA yield or quality. Additionally, disrupted samples can be stored in the lysis buffer at -20°C for at least 6 months prior to completion of the extraction procedure providing a flexible sampling and storage scheme to facilitate complex time series experiments. PMID:22136293

Protocol: high throughput silica-based purification of RNA from Arabidopsis seedlings in a 96-well format.

PubMed

Salvo-Chirnside, Eliane; Kane, Steven; Kerr, Lorraine E

2011-12-02

The increasing popularity of systems-based approaches to plant research has resulted in a demand for high throughput (HTP) methods to be developed. RNA extraction from multiple samples in an experiment is a significant bottleneck in performing systems-level genomic studies. Therefore we have established a high throughput method of RNA extraction from Arabidopsis thaliana to facilitate gene expression studies in this widely used plant model. We present optimised manual and automated protocols for the extraction of total RNA from 9-day-old Arabidopsis seedlings in a 96 well plate format using silica membrane-based methodology. Consistent and reproducible yields of high quality RNA are isolated averaging 8.9 μg total RNA per sample (~20 mg plant tissue). The purified RNA is suitable for subsequent qPCR analysis of the expression of over 500 genes in triplicate from each sample. Using the automated procedure, 192 samples (2 × 96 well plates) can easily be fully processed (samples homogenised, RNA purified and quantified) in less than half a day. Additionally we demonstrate that plant samples can be stored in RNAlater at -20°C (but not 4°C) for 10 months prior to extraction with no significant effect on RNA yield or quality. Additionally, disrupted samples can be stored in the lysis buffer at -20°C for at least 6 months prior to completion of the extraction procedure providing a flexible sampling and storage scheme to facilitate complex time series experiments.

Automated Fluid Feature Extraction from Transient Simulations

NASA Technical Reports Server (NTRS)

Haimes, Robert; Lovely, David

1999-01-01

In the past, feature extraction and identification were interesting concepts, but not required to understand the underlying physics of a steady flow field. This is because the results of the more traditional tools like iso-surfaces, cuts and streamlines were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of much interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snap-shot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for co-processing environments like pV3). And methods must be developed to abstract the feature and display it in a manner that physically makes sense. The following is a list of the important physical phenomena found in transient (and steady-state) fluid flow: (1) Shocks, (2) Vortex cores, (3) Regions of recirculation, (4) Boundary layers, (5) Wakes. Three papers and an initial specification for the (The Fluid eXtraction tool kit) FX Programmer's guide were included. The papers, submitted to the AIAA Computational Fluid Dynamics Conference, are entitled : (1) Using Residence Time for the Extraction of Recirculation Regions, (2) Shock Detection from Computational Fluid Dynamics results and (3) On the Velocity Gradient Tensor and Fluid Feature Extraction.

Development of a Novel and Rapid Fully Automated Genetic Testing System.

PubMed

Uehara, Masayuki

2016-01-01

We have developed a rapid genetic testing system integrating nucleic acid extraction, purification, amplification, and detection in a single cartridge. The system performs real-time polymerase chain reaction (PCR) after nucleic acid purification in a fully automated manner. RNase P, a housekeeping gene, was purified from human nasal epithelial cells using silica-coated magnetic beads and subjected to real-time PCR using a novel droplet-real-time-PCR machine. The process was completed within 13 min. This system will be widely applicable for research and diagnostic uses.

Automated measurement of retinal vascular tortuosity.

PubMed Central

Hart, W. E.; Goldbaum, M.; Côté, B.; Kube, P.; Nelson, M. R.

1997-01-01

Automatic measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diagnostic tools. We describe a suite of automated tortuosity measures for blood vessel segments extracted from RGB retinal images. The tortuosity measures were evaluated in two classification tasks: (1) classifying the tortuosity of blood vessel segments and (2) classifying the tortuosity of blood vessel networks. These tortuosity measures were able to achieve a classification rate of 91% for the first problem and 95% on the second problem, which confirms that they capture much of the ophthalmologists' notion of tortuosity. Images Figure 1 PMID:9357668

Artificial intelligence in cardiology.

PubMed

Bonderman, Diana

2017-12-01

Decision-making is complex in modern medicine and should ideally be based on available data, structured knowledge and proper interpretation in the context of an individual patient. Automated algorithms, also termed artificial intelligence that are able to extract meaningful patterns from data collections and build decisions upon identified patterns may be useful assistants in clinical decision-making processes. In this article, artificial intelligence-based studies in clinical cardiology are reviewed. The text also touches on the ethical issues and speculates on the future roles of automated algorithms versus clinicians in cardiology and medicine in general.

What is a Dune: Developing AN Automated Approach to Extracting Dunes from Digital Elevation Models

NASA Astrophysics Data System (ADS)

Taylor, H.; DeCuir, C.; Wernette, P. A.; Taube, C.; Eyler, R.; Thopson, S.

2016-12-01

Coastal dunes can absorb storm surge and mitigate inland erosion caused by elevated water levels during a storm. In order to understand how a dune responds to and recovers from a storm, it is important that we can first identify and differentiate the beach and dune from the rest of the landscape. Current literature does not provide a consistent definition of what the dune features (e.g. dune toe, dune crest) are or how they can be extracted. The purpose of this research is to develop enhanced approaches to extracting dunes from a digital elevation model (DEM). Manual delineation, convergence index, least-cost path, relative relief, and vegetation abundance were compared and contrasted on a small area of Padre Island National Seashore (PAIS), Preliminary results indicate that the method used to extract the dune greatly affects our interpretation of how the dune changes. The manual delineation method was time intensive and subjective, while the convergence index approach was useful to easily identify the dune crest through maximum and minimum values. The least-cost path method proved to be time intensive due to data clipping; however, this approach resulted in continuous geomorphic landscape features (e.g. dune toe, dune crest). While the relative relief approach shows the most features in multi resolution, it is difficult to assess the accuracy of the extracted features because extracted features appear as points that can vary widely in their location from one meter to the next. The vegetation approach was greatly impacted by the seasonal and annual fluctuations of growth but is advantageous in historical change studies because it can be used to extract consistent dune formation from historical aerial imagery. Improving our ability to more accurately assess dune response and recovery to a storm will enable coastal managers to more accurately predict how dunes may respond to future climate change scenarios.

Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review.

PubMed

Tricco, Andrea C; Zarin, Wasifa; Lillie, Erin; Jeblee, Serena; Warren, Rachel; Khan, Paul A; Robson, Reid; Pham, Ba'; Hirst, Graeme; Straus, Sharon E

2018-06-14

A scoping review to characterize the literature on the use of conversations in social media as a potential source of data for detecting adverse events (AEs) related to health products. Our specific research questions were (1) What social media listening platforms exist to detect adverse events related to health products, and what are their capabilities and characteristics? (2) What is the validity and reliability of data from social media for detecting these adverse events? MEDLINE, EMBASE, Cochrane Library, and relevant websites were searched from inception to May 2016. Any type of document (e.g., manuscripts, reports) that described the use of social media data for detecting health product AEs was included. Two reviewers independently screened citations and full-texts, and one reviewer and one verifier performed data abstraction. Descriptive synthesis was conducted. After screening 3631 citations and 321 full-texts, 70 unique documents with 7 companion reports available from 2001 to 2016 were included. Forty-six documents (66%) described an automated or semi-automated information extraction system to detect health product AEs from social media conversations (in the developmental phase). Seven pre-existing information extraction systems to mine social media data were identified in eight documents. Nineteen documents compared AEs reported in social media data with validated data and found consistent AE discovery in all except two documents. None of the documents reported the validity and reliability of the overall system, but some reported on the performance of individual steps in processing the data. The validity and reliability results were found for the following steps in the data processing pipeline: data de-identification (n = 1), concept identification (n = 3), concept normalization (n = 2), and relation extraction (n = 8). The methods varied widely, and some approaches yielded better results than others. Our results suggest that the use of social media conversations for pharmacovigilance is in its infancy. Although social media data has the potential to supplement data from regulatory agency databases; is able to capture less frequently reported AEs; and can identify AEs earlier than official alerts or regulatory changes, the utility and validity of the data source remains under-studied. Open Science Framework ( https://osf.io/kv9hu/ ).

Neural network approach in multichannel auditory event-related potential analysis.

PubMed

Wu, F Y; Slater, J D; Ramsay, R E

1994-04-01

Even though there are presently no clearly defined criteria for the assessment of P300 event-related potential (ERP) abnormality, it is strongly indicated through statistical analysis that such criteria exist for classifying control subjects and patients with diseases resulting in neuropsychological impairment such as multiple sclerosis (MS). We have demonstrated the feasibility of artificial neural network (ANN) methods in classifying ERP waveforms measured at a single channel (Cz) from control subjects and MS patients. In this paper, we report the results of multichannel ERP analysis and a modified network analysis methodology to enhance automation of the classification rule extraction process. The proposed methodology significantly reduces the work of statistical analysis. It also helps to standardize the criteria of P300 ERP assessment and facilitate the computer-aided analysis on neuropsychological functions.

Design Fragments

DTIC Science & Technology

2007-04-19

define the patterns and are better at analyzing behavior. SPQR (System for Pattern Query and Recognition) [18, 58] can recognize pattern vari- ants...Stotts. SPQR : Flexible automated design pattern extraction from source code. ase, 00:215, 2003. ISSN 1527-1366. doi: http://doi.ieeecomputersociety. org

HTAPP: High-Throughput Autonomous Proteomic Pipeline

PubMed Central

Yu, Kebing; Salomon, Arthur R.

2011-01-01

Recent advances in the speed and sensitivity of mass spectrometers and in analytical methods, the exponential acceleration of computer processing speeds, and the availability of genomic databases from an array of species and protein information databases have led to a deluge of proteomic data. The development of a lab-based automated proteomic software platform for the automated collection, processing, storage, and visualization of expansive proteomic datasets is critically important. The high-throughput autonomous proteomic pipeline (HTAPP) described here is designed from the ground up to provide critically important flexibility for diverse proteomic workflows and to streamline the total analysis of a complex proteomic sample. This tool is comprised of software that controls the acquisition of mass spectral data along with automation of post-acquisition tasks such as peptide quantification, clustered MS/MS spectral database searching, statistical validation, and data exploration within a user-configurable lab-based relational database. The software design of HTAPP focuses on accommodating diverse workflows and providing missing software functionality to a wide range of proteomic researchers to accelerate the extraction of biological meaning from immense proteomic data sets. Although individual software modules in our integrated technology platform may have some similarities to existing tools, the true novelty of the approach described here is in the synergistic and flexible combination of these tools to provide an integrated and efficient analysis of proteomic samples. PMID:20336676

Silica-based ionic liquid coating for 96-blade system for extraction of aminoacids from complex matrixes.

PubMed

Mousavi, Fatemeh; Pawliszyn, Janusz

2013-11-25

1-Vinyl-3-octadecylimidazolium bromide ionic liquid [C18VIm]Br was prepared and used for the modification of mercaptopropyl-functionalized silica (Si-MPS) through surface radical chain-transfer addition. The synthesized octadecylimidazolium-modified silica (SiImC18) was characterized by thermogravimetric analysis (TGA), infrared spectroscopy (IR), (13)C NMR and (29)Si NMR spectroscopy and used as an extraction phase for the automated 96-blade solid phase microextraction (SPME) system with thin-film geometry using polyacrylonitrile (PAN) glue. The new proposed extraction phase was applied for extraction of aminoacids from grape pulp, and LC-MS-MS method was developed for separation of model compounds. Extraction efficiency, reusability, linearity, limit of detection, limit of quantitation and matrix effect were evaluated. The whole process of sample preparation for the proposed method requires 270min for 96 samples simultaneously (60min preconditioning, 90min extraction, 60min desorption and 60min for carryover step) using 96-blade SPME system. Inter-blade and intra-blade reproducibility were in the respective ranges of 5-13 and 3-10% relative standard deviation (RSD) for all model compounds. Limits of detection and quantitation of the proposed SPME-LC-MS/MS system for analysis of analytes were found to range from 0.1 to 1.0 and 0.5 to 3.0μgL(-1), respectively. Standard addition calibration was applied for quantitative analysis of aminoacids from grape juice and the results were validated with solvent extraction (SE) technique. Copyright © 2013 Elsevier B.V. All rights reserved.

Algorithms and semantic infrastructure for mutation impact extraction and grounding.

PubMed

Laurila, Jonas B; Naderi, Nona; Witte, René; Riazanov, Alexandre; Kouznetsov, Alexandre; Baker, Christopher J O

2010-12-02

Mutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases. We present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework. We address the problem of access to legacy mutation data in unstructured form through the creation of novel mutation impact extraction methods which are evaluated on a corpus of full-text articles on haloalkane dehalogenases, tagged by domain experts. Our approaches show state of the art levels of precision and recall for Mutation Grounding and respectable level of precision but lower recall for the task of Mutant-Impact relation extraction. The system is deployed using text mining and semantic web technologies with the goal of publishing to a broad spectrum of consumers.

Validated Automatic Brain Extraction of Head CT Images

PubMed Central

Muschelli, John; Ullman, Natalie L.; Mould, W. Andrew; Vespa, Paul; Hanley, Daniel F.; Crainiceanu, Ciprian M.

2015-01-01

Background X-ray Computed Tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction – the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated. Aim To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported. Methods All images were thresholded using a 0 – 100 Hounsfield units (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ = 1mm3) and re-thresholded to 0 – 100 HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing manual and automated segmentation: ICVautomatedICVmanual. To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV of each scan was estimated and and an intraclass correlation (ICC) was estimated using a one-way ANOVA. Results Smoothing images improves brain extraction results using BET for all measures except specificity (all p < 0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35. Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014, p < 0.001), accuracy (0.9971 vs. 0.9971; median difference: 0.0001, p < 0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p < 0.001) and lower specificity (0.9981 vs. 0.9982; median difference: −0.0001, p < 0.001). These measures are all very high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using smoothed data and an FI of 0.01, the mean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates the ICV estimates are similar for automated and manual segmentation. In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929, 95% CI: 0.91, 0.945) for successfully extracted brains. Conclusion BET performs well at brain extraction on thresholded, 1mm3 smoothed CT images with an FI of 0.01 or 0.1. Smoothing before applying BET is an important step not previously discussed in the literature. Analysis code is provided. PMID:25862260

Automated generation of individually customized visualizations of diagnosis-specific medical information using novel techniques of information extraction

NASA Astrophysics Data System (ADS)

Chen, Andrew A.; Meng, Frank; Morioka, Craig A.; Churchill, Bernard M.; Kangarloo, Hooshang

2005-04-01

Managing pediatric patients with neurogenic bladder (NGB) involves regular laboratory, imaging, and physiologic testing. Using input from domain experts and current literature, we identified specific data points from these tests to develop the concept of an electronic disease vector for NGB. An information extraction engine was used to extract the desired data elements from free-text and semi-structured documents retrieved from the patient"s medical record. Finally, a Java-based presentation engine created graphical visualizations of the extracted data. After precision, recall, and timing evaluation, we conclude that these tools may enable clinically useful, automatically generated, and diagnosis-specific visualizations of patient data, potentially improving compliance and ultimately, outcomes.

Terrain-driven unstructured mesh development through semi-automatic vertical feature extraction

NASA Astrophysics Data System (ADS)

Bilskie, Matthew V.; Coggin, David; Hagen, Scott C.; Medeiros, Stephen C.

2015-12-01

A semi-automated vertical feature terrain extraction algorithm is described and applied to a two-dimensional, depth-integrated, shallow water equation inundation model. The extracted features describe what are commonly sub-mesh scale elevation details (ridge and valleys), which may be ignored in standard practice because adequate mesh resolution cannot be afforded. The extraction algorithm is semi-automated, requires minimal human intervention, and is reproducible. A lidar-derived digital elevation model (DEM) of coastal Mississippi and Alabama serves as the source data for the vertical feature extraction. Unstructured mesh nodes and element edges are aligned to the vertical features and an interpolation algorithm aimed at minimizing topographic elevation error assigns elevations to mesh nodes via the DEM. The end result is a mesh that accurately represents the bare earth surface as derived from lidar with element resolution in the floodplain ranging from 15 m to 200 m. To examine the influence of the inclusion of vertical features on overland flooding, two additional meshes were developed, one without crest elevations of the features and another with vertical features withheld. All three meshes were incorporated into a SWAN+ADCIRC model simulation of Hurricane Katrina. Each of the three models resulted in similar validation statistics when compared to observed time-series water levels at gages and post-storm collected high water marks. Simulated water level peaks yielded an R2 of 0.97 and upper and lower 95% confidence interval of ∼ ± 0.60 m. From the validation at the gages and HWM locations, it was not clear which of the three model experiments performed best in terms of accuracy. Examination of inundation extent among the three model results were compared to debris lines derived from NOAA post-event aerial imagery, and the mesh including vertical features showed higher accuracy. The comparison of model results to debris lines demonstrates that additional validation techniques are necessary for state-of-the-art flood inundation models. In addition, the semi-automated, unstructured mesh generation process presented herein increases the overall accuracy of simulated storm surge across the floodplain without reliance on hand digitization or sacrificing computational cost.

Iterative normalization method for improved prostate cancer localization with multispectral magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Liu, Xin; Samil Yetik, Imam

2012-04-01

Use of multispectral magnetic resonance imaging has received a great interest for prostate cancer localization in research and clinical studies. Manual extraction of prostate tumors from multispectral magnetic resonance imaging is inefficient and subjective, while automated segmentation is objective and reproducible. For supervised, automated segmentation approaches, learning is essential to obtain the information from training dataset. However, in this procedure, all patients are assumed to have similar properties for the tumor and normal tissues, and the segmentation performance suffers since the variations across patients are ignored. To conquer this difficulty, we propose a new iterative normalization method based on relative intensity values of tumor and normal tissues to normalize multispectral magnetic resonance images and improve segmentation performance. The idea of relative intensity mimics the manual segmentation performed by human readers, who compare the contrast between regions without knowing the actual intensity values. We compare the segmentation performance of the proposed method with that of z-score normalization followed by support vector machine, local active contours, and fuzzy Markov random field. Our experimental results demonstrate that our method outperforms the three other state-of-the-art algorithms, and was found to have specificity of 0.73, sensitivity of 0.69, and accuracy of 0.79, significantly better than alternative methods.

Image classification of unlabeled malaria parasites in red blood cells.

PubMed

Zheng Zhang; Ong, L L Sharon; Kong Fang; Matthew, Athul; Dauwels, Justin; Ming Dao; Asada, Harry

2016-08-01

This paper presents a method to detect unlabeled malaria parasites in red blood cells. The current "gold standard" for malaria diagnosis is microscopic examination of thick blood smear, a time consuming process requiring extensive training. Our goal is to develop an automate process to identify malaria infected red blood cells. Major issues in automated analysis of microscopy images of unstained blood smears include overlapping cells and oddly shaped cells. Our approach creates robust templates to detect infected and uninfected red cells. Histogram of Oriented Gradients (HOGs) features are extracted from templates and used to train a classifier offline. Next, the ViolaJones object detection framework is applied to detect infected and uninfected red cells and the image background. Results show our approach out-performs classification approaches with PCA features by 50% and cell detection algorithms applying Hough transforms by 24%. Majority of related work are designed to automatically detect stained parasites in blood smears where the cells are fixed. Although it is more challenging to design algorithms for unstained parasites, our methods will allow analysis of parasite progression in live cells under different drug treatments.

Automated multi-radionuclide separation and analysis with combined detection capability

NASA Astrophysics Data System (ADS)

Plionis, Alexander Asterios

The radiological dispersal device (RDD) is a weapon of great concern to those agencies responsible for protecting the public from the modern age of terrorism. In order to effectively respond to an RDD event, these agencies need to possess the capability to rapidly identify the radiological agents involved in the incident and assess the uptake of each individual victim. Since medical treatment for internal radiation poisoning is radionuclide-specific, it is critical to identify and quantify the radiological uptake of each individual victim. This dissertation describes the development of automated analytical components that could be used to determine and quantify multiple radionuclides in human urine bioassays. This is accomplished through the use of extraction chromatography that is plumbed in-line with one of a variety of detection instruments. Flow scintillation analysis is used for 90Sr and 210Po determination, flow gamma analysis is used assess 60 Co and 137Cs, and inductively coupled plasma mass spectrometry is used to determine actinides. Detection limits for these analytes were determined for the appropriate technique and related to their implications for health physics.

A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images

PubMed Central

Hoffman, R.A.; Kothari, S.; Phan, J.H.; Wang, M.D.

2016-01-01

Computational analysis of histopathological whole slide images (WSIs) has emerged as a potential means for improving cancer diagnosis and prognosis. However, an open issue relating to the automated processing of WSIs is the identification of biological regions such as tumor, stroma, and necrotic tissue on the slide. We develop a method for classifying WSI portions (512x512-pixel tiles) into biological regions by (1) extracting a set of 461 image features from each WSI tile, (2) optimizing tile-level prediction models using nested cross-validation on a small (600 tile) manually annotated tile-level training set, and (3) validating the models against a much larger (1.7x106 tile) data set for which ground truth was available on the whole-slide level. We calculated the predicted prevalence of each tissue region and compared this prevalence to the ground truth prevalence for each image in an independent validation set. Results show significant correlation between the predicted (using automated system) and reported biological region prevalences with p < 0.001 for eight of nine cases considered. PMID:27532012

A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images.

PubMed

Hoffman, R A; Kothari, S; Phan, J H; Wang, M D

Computational analysis of histopathological whole slide images (WSIs) has emerged as a potential means for improving cancer diagnosis and prognosis. However, an open issue relating to the automated processing of WSIs is the identification of biological regions such as tumor, stroma, and necrotic tissue on the slide. We develop a method for classifying WSI portions (512x512-pixel tiles) into biological regions by (1) extracting a set of 461 image features from each WSI tile, (2) optimizing tile-level prediction models using nested cross-validation on a small (600 tile) manually annotated tile-level training set, and (3) validating the models against a much larger (1.7x10 6 tile) data set for which ground truth was available on the whole-slide level. We calculated the predicted prevalence of each tissue region and compared this prevalence to the ground truth prevalence for each image in an independent validation set. Results show significant correlation between the predicted (using automated system) and reported biological region prevalences with p < 0.001 for eight of nine cases considered.

45 CFR 310.5 - What options are available for Computerized Tribal IV-D Systems and office automation?

Code of Federal Regulations, 2010 CFR

2010-10-01

... to conduct automated data processing and recordkeeping activities through Office Automation... IV-D Systems and office automation? 310.5 Section 310.5 Public Welfare Regulations Relating to Public... AUTOMATION Requirements for Computerized Tribal IV-D Systems and Office Automation § 310.5 What options are...

Semiautomated Device for Batch Extraction of Metabolites from Tissue Samples

PubMed Central

2012-01-01

Metabolomics has become a mainstream analytical strategy for investigating metabolism. The quality of data derived from these studies is proportional to the consistency of the sample preparation. Although considerable research has been devoted to finding optimal extraction protocols, most of the established methods require extensive sample handling. Manual sample preparation can be highly effective in the hands of skilled technicians, but an automated tool for purifying metabolites from complex biological tissues would be of obvious utility to the field. Here, we introduce the semiautomated metabolite batch extraction device (SAMBED), a new tool designed to simplify metabolomics sample preparation. We discuss SAMBED’s design and show that SAMBED-based extractions are of comparable quality to extracts produced through traditional methods (13% mean coefficient of variation from SAMBED versus 16% from manual extractions). Moreover, we show that aqueous SAMBED-based methods can be completed in less than a quarter of the time required for manual extractions. PMID:22292466

The use of tannin from chestnut (Castanea vesca).

PubMed

Krisper, P; Tisler, V; Skubic, V; Rupnik, I; Kobal, S

1992-01-01

After mimosa and quebracho extracts, chestnut extract is the third most important vegetable tannin used for leather production. It is produced only in Europe on the northern side of the Mediterranean sea. The extract is prepared by hot water extraction of the bark and timber, followed by spray-drying of the solution. Analysis shows that there are insignificant variations in extract quality between batches, so the extract can be used with modern automated leather production systems. The extract contains approximately 75 percent active tanning substances. The primary component is castalagin, along with smaller amounts of vescalagin, castalin, and vescalin. A castalagin-based pharmaceutical product is currently in use for prevention and treatment of diarrhea in pigs and cattle that is caused by changes in diet. The beneficial effect is due to prevention of water losses through mucous membranes. The castalagin may also form chelates with iron, which influences the reabsorption of the metal in the animal digestive tract.

In vivo automated quantification of quality of apples during storage using optical coherence tomography images

NASA Astrophysics Data System (ADS)

Srivastava, Vishal; Dalal, Devjyoti; Kumar, Anuj; Prakash, Surya; Dalal, Krishna

2018-06-01

Moisture content is an important feature of fruits and vegetables. As 80% of apple content is water, so decreasing the moisture content will degrade the quality of apples (Golden Delicious). The computational and texture features of the apples were extracted from optical coherence tomography (OCT) images. A support vector machine with a Gaussian kernel model was used to perform automated classification. To evaluate the quality of wax coated apples during storage in vivo, our proposed method opens up the possibility of fully automated quantitative analysis based on the morphological features of apples. Our results demonstrate that the analysis of the computational and texture features of OCT images may be a good non-destructive method for the assessment of the quality of apples.

Streamlined structure elucidation of an unknown compound in a pigment formulation.

PubMed

Yüce, Imanuel; Morlock, Gertrud E

2016-10-21

A fast and reliable quality control is important for ink manufacturers to ensure a constant production grade of mixtures and chemical formulations, and unknown components attract their attention. Structure elucidating techniques seem time-consuming in combination with column-based methods, but especially the low solubility of pigment formulations is challenging the analysis. In contrast, layer chromatography is more tolerant with regard to pigment particles. One PLC plate for NMR and FTIR analyses and one HPTLC plate for recording of high resolution mass spectra, MS/MS spectra and for gathering information on polarity and spectral properties were needed to characterize a structure, exemplarily shown for an unknown component in pigment Red 57:1 to be 3-hydroxy-2-naphtoic acid. A preparative layer chromatography (PLC) workflow was developed that used an Automated Multiple Development 2 (AMD 2) system. The 0.5-mm PLC plate could still be operated in the AMD 2 system and allowed a smooth switch from the analytical to the preparative gradient separation. Through automated gradient development and the resulting focusing of bands, the sharpness of the PLC bands was improved. For NMR, the necessary high load of the target compound on the PLC plate was achieved via a selective solvent extraction that discriminated the polar sample matrix and thus increased the application volume of the extract that could maximally be applied without overloading. By doing so, the yield for NMR analysis was improved by a factor of 9. The effectivity gain through a simple, but thoroughly chosen extraction solvent is often overlooked, and for educational purpose, it was clearly illustrated and demonstrated by an extended solvent screening. Thus, PLC using an automated gradient development after a selective extraction was proven to be a new powerful combination for structural elucidation by NMR. Copyright © 2016 Elsevier B.V. All rights reserved.

Ingenious Snake: An Adaptive Multi-Class Contours Extraction

NASA Astrophysics Data System (ADS)

Li, Baolin; Zhou, Shoujun

2018-04-01

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

Automated lung tumor segmentation for whole body PET volume based on novel downhill region growing

NASA Astrophysics Data System (ADS)

Ballangan, Cherry; Wang, Xiuying; Eberl, Stefan; Fulham, Michael; Feng, Dagan

2010-03-01

We propose an automated lung tumor segmentation method for whole body PET images based on a novel downhill region growing (DRG) technique, which regards homogeneous tumor hotspots as 3D monotonically decreasing functions. The method has three major steps: thoracic slice extraction with K-means clustering of the slice features; hotspot segmentation with DRG; and decision tree analysis based hotspot classification. To overcome the common problem of leakage into adjacent hotspots in automated lung tumor segmentation, DRG employs the tumors' SUV monotonicity features. DRG also uses gradient magnitude of tumors' SUV to improve tumor boundary definition. We used 14 PET volumes from patients with primary NSCLC for validation. The thoracic region extraction step achieved good and consistent results for all patients despite marked differences in size and shape of the lungs and the presence of large tumors. The DRG technique was able to avoid the problem of leakage into adjacent hotspots and produced a volumetric overlap fraction of 0.61 +/- 0.13 which outperformed four other methods where the overlap fraction varied from 0.40 +/- 0.24 to 0.59 +/- 0.14. Of the 18 tumors in 14 NSCLC studies, 15 lesions were classified correctly, 2 were false negative and 15 were false positive.

Automated Control of the Organic and Inorganic Composition of Aloe vera Extracts Using (1)H NMR Spectroscopy.

PubMed

Monakhova, Yulia B; Randel, Gabriele; Diehl, Bernd W K

2016-09-01

Recent classification of Aloe vera whole-leaf extract by the International Agency for Research and Cancer as a possible carcinogen to humans as well as the continuous adulteration of A. vera's authentic material have generated renewed interest in controlling A. vera. The existing NMR spectroscopic method for the analysis of A. vera, which is based on a routine developed at Spectral Service, was extended. Apart from aloverose, glucose, malic acid, lactic acid, citric acid, whole-leaf material (WLM), acetic acid, fumaric acid, sodium benzoate, and potassium sorbate, the quantification of Mg(2+), Ca(2+), and fructose is possible with the addition of a Cs-EDTA solution to sample. The proposed methodology was automated, which includes phasing, baseline-correction, deconvolution (based on the Lorentzian function), integration, quantification, and reporting. The NMR method was applied to 41 A. vera preparations in the form of liquid A. vera juice and solid A. vera powder. The advantages of the new NMR methodology over the previous method were discussed. Correlation between the new and standard NMR methodologies was significant for aloverose, glucose, malic acid, lactic acid, citric acid, and WLM (P < 0.0001, R(2) = 0.99). NMR was found to be suitable for the automated simultaneous quantitative determination of 13 parameters in A. vera.

DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures

PubMed Central

Yin, Xu-Cheng; Yang, Chun; Pei, Wei-Yi; Man, Haixia; Zhang, Jun; Learned-Miller, Erik; Yu, Hong

2015-01-01

Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information. A high-quality ground truth standard can greatly facilitate the development of an automated system. This article describes DeTEXT: A database for evaluating text extraction from biomedical literature figures. It is the first publicly available, human-annotated, high quality, and large-scale figure-text dataset with 288 full-text articles, 500 biomedical figures, and 9308 text regions. This article describes how figures were selected from open-access full-text biomedical articles and how annotation guidelines and annotation tools were developed. We also discuss the inter-annotator agreement and the reliability of the annotations. We summarize the statistics of the DeTEXT data and make available evaluation protocols for DeTEXT. Finally we lay out challenges we observed in the automated detection and recognition of figure text and discuss research directions in this area. DeTEXT is publicly available for downloading at http://prir.ustb.edu.cn/DeTEXT/. PMID:25951377

Literature classification for semi-automated updating of biological knowledgebases

PubMed Central

2013-01-01

Background As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. Results We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. Conclusion We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases. PMID:24564403

High-throughput, automated extraction of DNA and RNA from clinical samples using TruTip technology on common liquid handling robots.

PubMed

Holmberg, Rebecca C; Gindlesperger, Alissa; Stokes, Tinsley; Brady, Dane; Thakore, Nitu; Belgrader, Philip; Cooney, Christopher G; Chandler, Darrell P

2013-06-11

TruTip is a simple nucleic acid extraction technology whereby a porous, monolithic binding matrix is inserted into a pipette tip. The geometry of the monolith can be adapted for specific pipette tips ranging in volume from 1.0 to 5.0 ml. The large porosity of the monolith enables viscous or complex samples to readily pass through it with minimal fluidic backpressure. Bi-directional flow maximizes residence time between the monolith and sample, and enables large sample volumes to be processed within a single TruTip. The fundamental steps, irrespective of sample volume or TruTip geometry, include cell lysis, nucleic acid binding to the inner pores of the TruTip monolith, washing away unbound sample components and lysis buffers, and eluting purified and concentrated nucleic acids into an appropriate buffer. The attributes and adaptability of TruTip are demonstrated in three automated clinical sample processing protocols using an Eppendorf epMotion 5070, Hamilton STAR and STARplus liquid handling robots, including RNA isolation from nasopharyngeal aspirate, genomic DNA isolation from whole blood, and fetal DNA extraction and enrichment from large volumes of maternal plasma (respectively).

Development of Automated Tracking System with Active Cameras for Figure Skating

NASA Astrophysics Data System (ADS)

Haraguchi, Tomohiko; Taki, Tsuyoshi; Hasegawa, Junichi

This paper presents a system based on the control of PTZ cameras for automated real-time tracking of individual figure skaters moving on an ice rink. In the video images of figure skating, irregular trajectories, various postures, rapid movements, and various costume colors are included. Therefore, it is difficult to determine some features useful for image tracking. On the other hand, an ice rink has a limited area and uniform high intensity, and skating is always performed on ice. In the proposed system, an ice rink region is first extracted from a video image by the region growing method, and then, a skater region is extracted using the rink shape information. In the camera control process, each camera is automatically panned and/or tilted so that the skater region is as close to the center of the image as possible; further, the camera is zoomed to maintain the skater image at an appropriate scale. The results of experiments performed for 10 training scenes show that the skater extraction rate is approximately 98%. Thus, it was concluded that tracking with camera control was successful for almost all the cases considered in the study.

Regenerable immuno-biochip for screening ochratoxin A in green coffee extract using an automated microarray chip reader with chemiluminescence detection.

PubMed

Sauceda-Friebe, Jimena C; Karsunke, Xaver Y Z; Vazac, Susanna; Biselli, Scarlett; Niessner, Reinhard; Knopp, Dietmar

2011-03-18

Ochratoxin A (OTA) can contaminate foodstuffs in the ppb to ppm range and once formed, it is difficult to remove. Because of its toxicity and potential risks to human health, the need exists for rapid, efficient detection methods that comply with legal maximum residual limits. In this work we have synthesized an OTA conjugate functionalized with a water-soluble peptide for covalent immobilization on a glass biochip by means of contact spotting. The chip was used for OTA determination with an indirect competitive immunoassay format with flow-through reagent addition and chemiluminescence detection, carried out with the stand-alone automated Munich Chip Reader 3 (MCR 3) platform. A buffer model and real green coffee extracts were used for this purpose. At the present, covalent conjugate immobilization allowed for at least 20 assay-regeneration cycles of the biochip surface. The total analysis time for a single sample, including measurement and surface regeneration, was 12 min and the LOQ of OTA in green coffee extract was 0.3 μg L(-1) which corresponds to 7 μg kg(-1). Copyright © 2011 Elsevier B.V. All rights reserved.

Informatics in radiology: automated Web-based graphical dashboard for radiology operational business intelligence.

PubMed

Nagy, Paul G; Warnock, Max J; Daly, Mark; Toland, Christopher; Meenan, Christopher D; Mezrich, Reuben S

2009-11-01

Radiology departments today are faced with many challenges to improve operational efficiency, performance, and quality. Many organizations rely on antiquated, paper-based methods to review their historical performance and understand their operations. With increased workloads, geographically dispersed image acquisition and reading sites, and rapidly changing technologies, this approach is increasingly untenable. A Web-based dashboard was constructed to automate the extraction, processing, and display of indicators and thereby provide useful and current data for twice-monthly departmental operational meetings. The feasibility of extracting specific metrics from clinical information systems was evaluated as part of a longer-term effort to build a radiology business intelligence architecture. Operational data were extracted from clinical information systems and stored in a centralized data warehouse. Higher-level analytics were performed on the centralized data, a process that generated indicators in a dynamic Web-based graphical environment that proved valuable in discussion and root cause analysis. Results aggregated over a 24-month period since implementation suggest that this operational business intelligence reporting system has provided significant data for driving more effective management decisions to improve productivity, performance, and quality of service in the department.

Research in satellite-aided crop inventory and monitoring

NASA Technical Reports Server (NTRS)

Erickson, J. D.; Dragg, J. L.; Bizzell, R. M.; Trichel, M. C. (Principal Investigator)

1982-01-01

Automated information extraction procedures for analysis of multitemporal LANDSAT data in non-U.S. crop inventory and monitoring are reviewed. Experiments to develope and evaluate crop area estimation technologies for spring small grains, summer crops, corn, and soybeans are discussed.

Automated detection system of single nucleotide polymorphisms using two kinds of functional magnetic nanoparticles

NASA Astrophysics Data System (ADS)

Liu, Hongna; Li, Song; Wang, Zhifei; Li, Zhiyang; Deng, Yan; Wang, Hua; Shi, Zhiyang; He, Nongyue

2008-11-01

Single nucleotide polymorphisms (SNPs) comprise the most abundant source of genetic variation in the human genome wide codominant SNPs identification. Therefore, large-scale codominant SNPs identification, especially for those associated with complex diseases, has induced the need for completely high-throughput and automated SNP genotyping method. Herein, we present an automated detection system of SNPs based on two kinds of functional magnetic nanoparticles (MNPs) and dual-color hybridization. The amido-modified MNPs (NH 2-MNPs) modified with APTES were used for DNA extraction from whole blood directly by electrostatic reaction, and followed by PCR, was successfully performed. Furthermore, biotinylated PCR products were captured on the streptavidin-coated MNPs (SA-MNPs) and interrogated by hybridization with a pair of dual-color probes to determine SNP, then the genotype of each sample can be simultaneously identified by scanning the microarray printed with the denatured fluorescent probes. This system provided a rapid, sensitive and highly versatile automated procedure that will greatly facilitate the analysis of different known SNPs in human genome.

A time-series method for automated measurement of changes in mitotic and interphase duration from time-lapse movies.

PubMed

Sigoillot, Frederic D; Huckins, Jeremy F; Li, Fuhai; Zhou, Xiaobo; Wong, Stephen T C; King, Randall W

2011-01-01

Automated time-lapse microscopy can visualize proliferation of large numbers of individual cells, enabling accurate measurement of the frequency of cell division and the duration of interphase and mitosis. However, extraction of quantitative information by manual inspection of time-lapse movies is too time-consuming to be useful for analysis of large experiments. Here we present an automated time-series approach that can measure changes in the duration of mitosis and interphase in individual cells expressing fluorescent histone 2B. The approach requires analysis of only 2 features, nuclear area and average intensity. Compared to supervised learning approaches, this method reduces processing time and does not require generation of training data sets. We demonstrate that this method is as sensitive as manual analysis in identifying small changes in interphase or mitotic duration induced by drug or siRNA treatment. This approach should facilitate automated analysis of high-throughput time-lapse data sets to identify small molecules or gene products that influence timing of cell division.

On the Automation of the MarkIII Data Analysis System.

NASA Astrophysics Data System (ADS)

Schwegmann, W.; Schuh, H.

1999-03-01

A faster and semiautomatic data analysis is an important contribution to the acceleration of the VLBI procedure. A concept for the automation of one of the most widely used VLBI software packages the MarkIII Data Analysis System was developed. Then, the program PWXCB, which extracts weather and cable calibration data from the station log-files, was automated supplementing the existing Fortran77 program-code. The new program XLOG and its results will be presented. Most of the tasks in the VLBI data analysis are very complex and their automation requires typical knowledge-based techniques. Thus, a knowledge-based system (KBS) for support and guidance of the analyst is being developed using the AI-workbench BABYLON, which is based on methods of artificial intelligence (AI). The advantages of a KBS for the MarkIII Data Analysis System and the required steps to build a KBS will be demonstrated. Examples about the current status of the project will be given, too.

An Automated Solar Synoptic Analysis Software System

NASA Astrophysics Data System (ADS)

Hong, S.; Lee, S.; Oh, S.; Kim, J.; Lee, J.; Kim, Y.; Lee, J.; Moon, Y.; Lee, D.

2012-12-01

We have developed an automated software system of identifying solar active regions, filament channels, and coronal holes, those are three major solar sources causing the space weather. Space weather forecasters of NOAA Space Weather Prediction Center produce the solar synoptic drawings as a daily basis to predict solar activities, i.e., solar flares, filament eruptions, high speed solar wind streams, and co-rotating interaction regions as well as their possible effects to the Earth. As an attempt to emulate this process with a fully automated and consistent way, we developed a software system named ASSA(Automated Solar Synoptic Analysis). When identifying solar active regions, ASSA uses high-resolution SDO HMI intensitygram and magnetogram as inputs and providing McIntosh classification and Mt. Wilson magnetic classification of each active region by applying appropriate image processing techniques such as thresholding, morphology extraction, and region growing. At the same time, it also extracts morphological and physical properties of active regions in a quantitative way for the short-term prediction of flares and CMEs. When identifying filament channels and coronal holes, images of global H-alpha network and SDO AIA 193 are used for morphological identification and also SDO HMI magnetograms for quantitative verification. The output results of ASSA are routinely checked and validated against NOAA's daily SRS(Solar Region Summary) and UCOHO(URSIgram code for coronal hole information). A couple of preliminary scientific results are to be presented using available output results. ASSA will be deployed at the Korean Space Weather Center and serve its customers in an operational status by the end of 2012.

Improving treatment plan evaluation with automation.

PubMed

Covington, Elizabeth L; Chen, Xiaoping; Younge, Kelly C; Lee, Choonik; Matuszak, Martha M; Kessler, Marc L; Keranen, Wayne; Acosta, Eduardo; Dougherty, Ashley M; Filpansick, Stephanie E; Moran, Jean M

2016-11-08

The goal of this work is to evaluate the effectiveness of Plan-Checker Tool (PCT) which was created to improve first-time plan quality, reduce patient delays, increase the efficiency of our electronic workflow, and standardize and automate the phys-ics plan review in the treatment planning system (TPS). PCT uses an application programming interface to check and compare data from the TPS and treatment management system (TMS). PCT includes a comprehensive checklist of automated and manual checks that are documented when performed by the user as part of a plan readiness check for treatment. Prior to and during PCT development, errors identified during the physics review and causes of patient treatment start delays were tracked to prioritize which checks should be automated. Nineteen of 33checklist items were automated, with data extracted with PCT. There was a 60% reduction in the number of patient delays in the six months after PCT release. PCT was suc-cessfully implemented for use on all external beam treatment plans in our clinic. While the number of errors found during the physics check did not decrease, automation of checks increased visibility of errors during the physics check, which led to decreased patient delays. The methods used here can be applied to any TMS and TPS that allows queries of the database. © 2016 The Authors.

High-throughput analysis of sulfatides in cerebrospinal fluid using automated extraction and UPLC-MS/MS.

PubMed

Blomqvist, Maria; Borén, Jan; Zetterberg, Henrik; Blennow, Kaj; Månsson, Jan-Eric; Ståhlman, Marcus

2017-07-01

Sulfatides (STs) are a group of glycosphingolipids that are highly expressed in brain. Due to their importance for normal brain function and their potential involvement in neurological diseases, development of accurate and sensitive methods for their determination is needed. Here we describe a high-throughput oriented and quantitative method for the determination of STs in cerebrospinal fluid (CSF). The STs were extracted using a fully automated liquid/liquid extraction method and quantified using ultra-performance liquid chromatography coupled to tandem mass spectrometry. With the high sensitivity of the developed method, quantification of 20 ST species from only 100 μl of CSF was performed. Validation of the method showed that the STs were extracted with high recovery (90%) and could be determined with low inter- and intra-day variation. Our method was applied to a patient cohort of subjects with an Alzheimer's disease biomarker profile. Although the total ST levels were unaltered compared with an age-matched control group, we show that the ratio of hydroxylated/nonhydroxylated STs was increased in the patient cohort. In conclusion, we believe that the fast, sensitive, and accurate method described in this study is a powerful new tool for the determination of STs in clinical as well as preclinical settings. Copyright © 2017 by the American Society for Biochemistry and Molecular Biology, Inc.

LC-HR-MS/MS standard urine screening approach: Pros and cons of automated on-line extraction by turbulent flow chromatography versus dilute-and-shoot and comparison with established urine precipitation.

PubMed

Helfer, Andreas G; Michely, Julian A; Weber, Armin A; Meyer, Markus R; Maurer, Hans H

2017-02-01

Comprehensive urine screening for drugs and metabolites by LC-HR-MS/MS using Orbitrap technology has been described with precipitation as simple workup. In order to fasten, automate, and/or simplify the workup, on-line extraction by turbulent flow chromatography and a dilute-and-shoot approach were developed and compared. After chromatographic separation within 10min, the Q-Exactive mass spectrometer was run in full scan mode with positive/negative switching and subsequent data dependent acquisition mode. The workup approaches were validated concerning selectivity, recovery, matrix effects, process efficiency, and limits of identification and detection for typical drug representatives and metabolites. The total workup time for on-line extraction was 6min, for the dilution approach 3min. For comparison, the established urine precipitation and evaporation lasted 10min. The validation results were acceptable. The limits for on-line extraction were comparable with those described for precipitation, but lower than for dilution. Thanks to the high sensitivity of the LC-HR-MS/MS system, all three workup approaches were sufficient for comprehensive urine screening and allowed fast, reliable, and reproducible detection of cardiovascular drugs, drugs of abuse, and other CNS acting drugs after common doses. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated segmentation of middle hepatic vein in non-contrast x-ray CT images based on an atlas-driven approach

NASA Astrophysics Data System (ADS)

Kitagawa, Teruhiko; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Yokoyama, Ryujiro; Kondo, Hiroshi; Kanematsu, Masayuki; Hoshi, Hiroaki

2008-03-01

In order to support the diagnosis of hepatic diseases, understanding the anatomical structures of hepatic lobes and hepatic vessels is necessary. Although viewing and understanding the hepatic vessels in contrast media-enhanced CT images is easy, the observation of the hepatic vessels in non-contrast X-ray CT images that are widely used for the screening purpose is difficult. We are developing a computer-aided diagnosis (CAD) system to support the liver diagnosis based on non-contrast X-ray CT images. This paper proposes a new approach to segment the middle hepatic vein (MHV), a key structure (landmark) for separating the liver region into left and right lobes. Extraction and classification of hepatic vessels are difficult in non-contrast X-ray CT images because the contrast between hepatic vessels and other liver tissues is low. Our approach uses an atlas-driven method by the following three stages. (1) Construction of liver atlases of left and right hepatic lobes using a learning datasets. (2) Fully-automated enhancement and extraction of hepatic vessels in liver regions. (3) Extraction of MHV based on the results of (1) and (2). The proposed approach was applied to 22 normal liver cases of non-contrast X-ray CT images. The preliminary results show that the proposed approach achieves the success in 14 cases for MHV extraction.

Relationship between automation trust and operator performance for the novice and expert in spacecraft rendezvous and docking (RVD).

PubMed

Niu, Jianwei; Geng, He; Zhang, Yijing; Du, Xiaoping

2018-09-01

Operator trust in automation is a crucial factor influencing its use and operational performance. However, the relationship between automation trust and performance remains poorly understood and requires further investigation. The objective of this paper is to explore the difference in trust and performance on automation-aided spacecraft rendezvous and docking (RVD) between the novice and the expert and to investigate the relationship between automation trust and performance as well. We employed a two-factor mixed design, with training skill (novice and expert) and automation mode (manual RVD and automation aided RVD) serving as the two factors. Twenty participants, 10 novices and 10 experts, were recruited to conduct six RVD tasks for two automation levels. After the tasks, operator performance was recorded by the desktop hand-held docking training equipment. Operator trust was also measured by a 12-items questionnaire at the beginning and end of each trial. As a result, automation narrowed the performance gap significantly between the novice and the expert, and the automation trust showed a marginally significant difference between the novice and the expert. Furthermore, the result demonstrated that the attitude angle control error of the expert was related to the total trust score, whereas other automation performance indicators were not related to the total score of trust. However, automation performance was related to the dimensions of trust, such as entrust, harmful, and dependable. Copyright © 2018 Elsevier Ltd. All rights reserved.

An integrated enhancement and reconstruction strategy for the quantitative extraction of actin stress fibers from fluorescence micrographs.

PubMed

Zhang, Zhen; Xia, Shumin; Kanchanawong, Pakorn

2017-05-22

The stress fibers are prominent organization of actin filaments that perform important functions in cellular processes such as migration, polarization, and traction force generation, and whose collective organization reflects the physiological and mechanical activities of the cells. Easily visualized by fluorescence microscopy, the stress fibers are widely used as qualitative descriptors of cell phenotypes. However, due to the complexity of the stress fibers and the presence of other actin-containing cellular features, images of stress fibers are relatively challenging to quantitatively analyze using previously developed approaches, requiring significant user intervention. This poses a challenge for the automation of their detection, segmentation, and quantitative analysis. Here we describe an open-source software package, SFEX (Stress Fiber Extractor), which is geared for efficient enhancement, segmentation, and analysis of actin stress fibers in adherent tissue culture cells. Our method made use of a carefully chosen image filtering technique to enhance filamentous structures, effectively facilitating the detection and segmentation of stress fibers by binary thresholding. We subdivided the skeletons of stress fiber traces into piecewise-linear fragments, and used a set of geometric criteria to reconstruct the stress fiber networks by pairing appropriate fiber fragments. Our strategy enables the trajectory of a majority of stress fibers within the cells to be comprehensively extracted. We also present a method for quantifying the dimensions of the stress fibers using an image gradient-based approach. We determine the optimal parameter space using sensitivity analysis, and demonstrate the utility of our approach by analyzing actin stress fibers in cells cultured on various micropattern substrates. We present an open-source graphically-interfaced computational tool for the extraction and quantification of stress fibers in adherent cells with minimal user input. This facilitates the automated extraction of actin stress fibers from fluorescence images. We highlight their potential uses by analyzing images of cells with shapes constrained by fibronectin micropatterns. The method we reported here could serve as the first step in the detection and characterization of the spatial properties of actin stress fibers to enable further detailed morphological analysis.

Automated clean-up, separation and detection of polycyclic aromatic hydrocarbons in particulate matter extracts from urban dust and diesel standard reference materials using a 2D-LC/2D-GC system.

PubMed

Ahmed, Trifa M; Lim, Hwanmi; Bergvall, Christoffer; Westerholm, Roger

2013-10-01

A multidimensional, on-line coupled liquid chromatographic/gas chromatographic system was developed for the quantification of polycyclic aromatic hydrocarbons (PAHs). A two-dimensional liquid chromatographic system (2D-liquid chromatography (LC)), with three columns having different selectivities, was connected on-line to a two-dimensional gas chromatographic system (2D-gas chromatography (GC)). Samples were cleaned up by combining normal elution and column back-flush of the LC columns to selectively remove matrix constituents and isolate well-defined, PAH enriched fractions. Using this system, the sequential removal of polar, mono/diaromatic, olefinic and alkane compounds from crude extracts was achieved. The LC/GC coupling was performed using a fused silica transfer line into a programmable temperature vaporizer (PTV) GC injector. Using the PTV in the solvent vent mode, excess solvent was removed and the enriched PAH sample extract was injected into the GC. The 2D-GC setup consisted of two capillary columns with different stationary phase selectivities. Heart-cutting of selected PAH compounds in the first GC column (first dimension) and transfer of these to the second GC column (second dimension) increased the baseline resolutions of closely eluting PAHs. The on-line system was validated using the standard reference materials SRM 1649a (urban dust) and SRM 1975 (diesel particulate extract). The PAH concentrations measured were comparable to the certified values and the fully automated LC/GC system performed the clean-up, separation and detection of PAHs in 16 extracts in less than 24 h. The multidimensional, on-line 2D-LC/2D-GC system eliminated manual handling of the sample extracts and minimised the risk of sample loss and contamination, while increasing accuracy and precision.

Lab-In-Syringe automation of stirring-assisted room-temperature headspace extraction coupled online to gas chromatography with flame ionization detection for determination of benzene, toluene, ethylbenzene, and xylenes in surface waters.

PubMed

Horstkotte, Burkhard; Lopez de Los Mozos Atochero, Natalia; Solich, Petr

2018-06-22

Online coupling of Lab-In-Syringe automated headspace extraction to gas chromatography has been studied. The developed methodology was successfully applied to surface water analysis using benzene, toluene, ethylbenzene, and xylenes as model analytes. The extraction system consisted of an automatic syringe pump with a 5 mL syringe into which all solutions and air for headspace formation were aspirated. The syringe piston featured a longitudinal channel, which allowed connecting the syringe void directly to a gas chromatograph with flame ionization detector via a transfer capillary. Gas injection was achieved via opening a computer-controlled pinch valve and compressing the headspace, upon which separation was initialized. Extractions were performed at room temperature; yet sensitivity comparable to previous work was obtained by high headspace to sample ratio V HS /V Sample of 1.6:1 and injection of about 77% of the headspace. Assistance by in-syringe magnetic stirring yielded an about threefold increase in extraction efficiency. Interferences were compensated by using chlorobenzene as an internal standard. Syringe cleaning and extraction lasting over 10 min was carried out in parallel to the chromatographic run enabling a time of analysis of <19 min. Excellent peak area repeatabilities with RSD of <4% when omitting and <2% RSD when using internal standard corrections on 100 μg L -1 level were achieved. An average recovery of 97.7% and limit of detection of 1-2 μg L -1 were obtained in analyses of surface water. Copyright © 2018 Elsevier B.V. All rights reserved.

Extracting leaf area index using viewing geometry effects-A new perspective on high-resolution unmanned aerial system photography

NASA Astrophysics Data System (ADS)

Roth, Lukas; Aasen, Helge; Walter, Achim; Liebisch, Frank

2018-07-01

Extraction of leaf area index (LAI) is an important prerequisite in numerous studies related to plant ecology, physiology and breeding. LAI is indicative for the performance of a plant canopy and of its potential for growth and yield. In this study, a novel method to estimate LAI based on RGB images taken by an unmanned aerial system (UAS) is introduced. Soybean was taken as the model crop of investigation. The method integrates viewing geometry information in an approach related to gap fraction theory. A 3-D simulation of virtual canopies helped developing and verifying the underlying model. In addition, the method includes techniques to extract plot based data from individual oblique images using image projection, as well as image segmentation applying an active learning approach. Data from a soybean field experiment were used to validate the method. The thereby measured LAI prediction accuracy was comparable with the one of a gap fraction-based handheld device (R2 of 0.92 , RMSE of 0.42 m 2m-2) and correlated well with destructive LAI measurements (R2 of 0.89 , RMSE of 0.41 m2 m-2). These results indicate that, if respecting the range (LAI ≤ 3) the method was tested for, extracting LAI from UAS derived RGB images using viewing geometry information represents a valid alternative to destructive and optical handheld device LAI measurements in soybean. Thereby, we open the door for automated, high-throughput assessment of LAI in plant and crop science.

Automated image analysis for quantitative fluorescence in situ hybridization with environmental samples.

PubMed

Zhou, Zhi; Pons, Marie Noëlle; Raskin, Lutgarde; Zilles, Julie L

2007-05-01

When fluorescence in situ hybridization (FISH) analyses are performed with complex environmental samples, difficulties related to the presence of microbial cell aggregates and nonuniform background fluorescence are often encountered. The objective of this study was to develop a robust and automated quantitative FISH method for complex environmental samples, such as manure and soil. The method and duration of sample dispersion were optimized to reduce the interference of cell aggregates. An automated image analysis program that detects cells from 4',6'-diamidino-2-phenylindole (DAPI) micrographs and extracts the maximum and mean fluorescence intensities for each cell from corresponding FISH images was developed with the software Visilog. Intensity thresholds were not consistent even for duplicate analyses, so alternative ways of classifying signals were investigated. In the resulting method, the intensity data were divided into clusters using fuzzy c-means clustering, and the resulting clusters were classified as target (positive) or nontarget (negative). A manual quality control confirmed this classification. With this method, 50.4, 72.1, and 64.9% of the cells in two swine manure samples and one soil sample, respectively, were positive as determined with a 16S rRNA-targeted bacterial probe (S-D-Bact-0338-a-A-18). Manual counting resulted in corresponding values of 52.3, 70.6, and 61.5%, respectively. In two swine manure samples and one soil sample 21.6, 12.3, and 2.5% of the cells were positive with an archaeal probe (S-D-Arch-0915-a-A-20), respectively. Manual counting resulted in corresponding values of 22.4, 14.0, and 2.9%, respectively. This automated method should facilitate quantitative analysis of FISH images for a variety of complex environmental samples.

Automated detection of submerged navigational obstructions in freshwater impoundments with hull mounted sidescan sonar

NASA Astrophysics Data System (ADS)

Morris, Phillip A.

The prevalence of low-cost side scanning sonar systems mounted on small recreational vessels has created improved opportunities to identify and map submerged navigational hazards in freshwater impoundments. However, these economical sensors also present unique challenges for automated techniques. This research explores related literature in automated sonar imagery processing and mapping technology, proposes and implements a framework derived from these sources, and evaluates the approach with video collected from a recreational grade sonar system. Image analysis techniques including optical character recognition and an unsupervised computer automated detection (CAD) algorithm are employed to extract the transducer GPS coordinates and slant range distance of objects protruding from the lake bottom. The retrieved information is formatted for inclusion into a spatial mapping model. Specific attributes of the sonar sensors are modeled such that probability profiles may be projected onto a three dimensional gridded map. These profiles are computed from multiple points of view as sonar traces crisscross or come near each other. As lake levels fluctuate over time so do the elevation points of view. With each sonar record, the probability of a hazard existing at certain elevations at the respective grid points is updated with Bayesian mechanics. As reinforcing data is collected, the confidence of the map improves. Given a lake's current elevation and a vessel draft, a final generated map can identify areas of the lake that have a high probability of containing hazards that threaten navigation. The approach is implemented in C/C++ utilizing OpenCV, Tesseract OCR, and QGIS open source software and evaluated in a designated test area at Lake Lavon, Collin County, Texas.

Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone

PubMed Central

Ascenzi, Maria-Grazia; Du, Xia; Harding, James I; Beylerian, Emily N; de Silva, Brian M; Gross, Ben J; Kastein, Hannah K; Wang, Weiguang; Lyons, Karen M; Schaeffer, Hayden

2014-01-01

Microscopy imaging of mouse growth plates is extensively used in biology to understand the effect of specific molecules on various stages of normal bone development and on bone disease. Until now, such image analysis has been conducted by manual detection. In fact, when existing automated detection techniques were applied, morphological variations across the growth plate and heterogeneity of image background color, including the faint presence of cells (chondrocytes) located deeper in tissue away from the image’s plane of focus, and lack of cell-specific features, interfered with identification of cell. We propose the first method of automated detection and morphometry applicable to images of cells in the growth plate of long bone. Through ad hoc sequential application of the Retinex method, anisotropic diffusion and thresholding, our new cell detection algorithm (CDA) addresses these challenges on bright-field microscopy images of mouse growth plates. Five parameters, chosen by the user in respect of image characteristics, regulate our CDA. Our results demonstrate effectiveness of the proposed numerical method relative to manual methods. Our CDA confirms previously established results regarding chondrocytes’ number, area, orientation, height and shape of normal growth plates. Our CDA also confirms differences previously found between the genetic mutated mouse Smad1/5CKO and its control mouse on fluorescence images. The CDA aims to aid biomedical research by increasing efficiency and consistency of data collection regarding arrangement and characteristics of chondrocytes. Our results suggest that automated extraction of data from microscopy imaging of growth plates can assist in unlocking information on normal and pathological development, key to the underlying biological mechanisms of bone growth. PMID:25525552

SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials

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

Wahi-Anwar, M; Lo, P; Kim, H

Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifiesmore » the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel component to automatically verify image acquisition parameters and automated adherence to specifications. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant support from: U01 CA181156.« less

Heart rate calculation from ensemble brain wave using wavelet and Teager-Kaiser energy operator.

PubMed

Srinivasan, Jayaraman; Adithya, V

2015-01-01

Electroencephalogram (EEG) signal artifacts are caused by various factors, such as, Electro-oculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG), movement artifact and line interference. The relatively high electrical energy cardiac activity causes EEG artifacts. In EEG signal processing the general approach is to remove the ECG signal. In this paper, we introduce an automated method to extract the ECG signal from EEG using wavelet and Teager-Kaiser energy operator for R-peak enhancement and detection. From the detected R-peaks the heart rate (HR) is calculated for clinical diagnosis. To check the efficiency of our method, we compare the HR calculated from ECG signal recorded in synchronous with EEG. The proposed method yields a mean error of 1.4% for the heart rate and 1.7% for mean R-R interval. The result illustrates that, proposed method can be used for ECG extraction from single channel EEG and used in clinical diagnosis like estimation for stress analysis, fatigue, and sleep stages classification studies as a multi-model system. In addition, this method eliminates the dependence of additional synchronous ECG in extraction of ECG from EEG signal process.

Latent semantic analysis.

PubMed

Evangelopoulos, Nicholas E

2013-11-01

This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents. LSA as a theory of meaning defines a latent semantic space where documents and individual words are represented as vectors. LSA as a computational technique uses linear algebra to extract dimensions that represent that space. This representation enables the computation of similarity among terms and documents, categorization of terms and documents, and summarization of large collections of documents using automated procedures that mimic the way humans perform similar cognitive tasks. We present some technical details, various illustrative examples, and discuss a number of applications from linguistics, psychology, cognitive science, education, information science, and analysis of textual data in general. WIREs Cogn Sci 2013, 4:683-692. doi: 10.1002/wcs.1254 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. © 2013 John Wiley & Sons, Ltd.

An experiment in big data: storage, querying and visualisation of data taken from the Liverpool Telescope's wide field cameras

NASA Astrophysics Data System (ADS)

Barnsley, R. M.; Steele, Iain A.; Smith, R. J.; Mawson, Neil R.

2014-07-01

The Small Telescopes Installed at the Liverpool Telescope (STILT) project has been in operation since March 2009, collecting data with three wide field unfiltered cameras: SkycamA, SkycamT and SkycamZ. To process the data, a pipeline was developed to automate source extraction, catalogue cross-matching, photometric calibration and database storage. In this paper, modifications and further developments to this pipeline will be discussed, including a complete refactor of the pipeline's codebase into Python, migration of the back-end database technology from MySQL to PostgreSQL, and changing the catalogue used for source cross-matching from USNO-B1 to APASS. In addition to this, details will be given relating to the development of a preliminary front-end to the source extracted database which will allow a user to perform common queries such as cone searches and light curve comparisons of catalogue and non-catalogue matched objects. Some next steps and future ideas for the project will also be presented.

Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches.

PubMed

Yousef Kalafi, Elham; Tan, Wooi Boon; Town, Christopher; Dhillon, Sarinder Kaur

2016-12-22

Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods. Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%. The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented.

Automated tumor analysis for molecular profiling in lung cancer

PubMed Central

Boyd, Clinton; James, Jacqueline A.; Loughrey, Maurice B.; Hougton, Joseph P.; Boyle, David P.; Kelly, Paul; Maxwell, Perry; McCleary, David; Diamond, James; McArt, Darragh G.; Tunstall, Jonathon; Bankhead, Peter; Salto-Tellez, Manuel

2015-01-01

The discovery and clinical application of molecular biomarkers in solid tumors, increasingly relies on nucleic acid extraction from FFPE tissue sections and subsequent molecular profiling. This in turn requires the pathological review of haematoxylin & eosin (H&E) stained slides, to ensure sample quality, tumor DNA sufficiency by visually estimating the percentage tumor nuclei and tumor annotation for manual macrodissection. In this study on NSCLC, we demonstrate considerable variation in tumor nuclei percentage between pathologists, potentially undermining the precision of NSCLC molecular evaluation and emphasising the need for quantitative tumor evaluation. We subsequently describe the development and validation of a system called TissueMark for automated tumor annotation and percentage tumor nuclei measurement in NSCLC using computerized image analysis. Evaluation of 245 NSCLC slides showed precise automated tumor annotation of cases using Tissuemark, strong concordance with manually drawn boundaries and identical EGFR mutational status, following manual macrodissection from the image analysis generated tumor boundaries. Automated analysis of cell counts for % tumor measurements by Tissuemark showed reduced variability and significant correlation (p < 0.001) with benchmark tumor cell counts. This study demonstrates a robust image analysis technology that can facilitate the automated quantitative analysis of tissue samples for molecular profiling in discovery and diagnostics. PMID:26317646

Automated Passive Capillary Lysimeters for Estimating Water Drainage in the Vadose Zone

NASA Astrophysics Data System (ADS)

Jabro, J.; Evans, R.

2009-04-01

In this study, we demonstrated and evaluated the performance and accuracy of an automated PCAP lysimeters that we designed for in-situ continuous measuring and estimating of drainage water below the rootzone of a sugarbeet-potato-barley rotation under two irrigation frequencies. Twelve automated PCAPs with sampling surface dimensions of 31 cm width * 91 cm long and 87 cm in height were placed 90 cm below the soil surface in a Lihen sandy loam. Our state-of-the-art design incorporated Bluetooth wireless technology to enable an automated datalogger to transmit drainage water data simultaneously every 15 minutes to a remote host and had a greater efficiency than other types of lysimeters. It also offered a significantly larger coverage area (2700 cm2) than similarly designed vadose zone lysimeters. The cumulative manually extracted drainage water was compared with the cumulative volume of drainage water recorded by the datalogger from the tipping bucket using several statistical methods. Our results indicated that our automated PCAPs are accurate and provided convenient means for estimating water drainage in the vadose zone without the need for costly and manually time-consuming supportive systems.

Automated smoother for the numerical decoupling of dynamics models.

PubMed

Vilela, Marco; Borges, Carlos C H; Vinga, Susana; Vasconcelos, Ana Tereza R; Santos, Helena; Voit, Eberhard O; Almeida, Jonas S

2007-08-21

Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure. In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from in-vivo NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations. The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental time series.

An Interactive Tool For Semi-automated Statistical Prediction Using Earth Observations and Models

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Berhane, F.; Tadesse, T.

2015-12-01

We developed a semi-automated statistical prediction tool applicable to concurrent analysis or seasonal prediction of any time series variable in any geographic location. The tool was developed using Shiny, JavaScript, HTML and CSS. A user can extract a predictand by drawing a polygon over a region of interest on the provided user interface (global map). The user can select the Climatic Research Unit (CRU) precipitation or Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as predictand. They can also upload their own predictand time series. Predictors can be extracted from sea surface temperature, sea level pressure, winds at different pressure levels, air temperature at various pressure levels, and geopotential height at different pressure levels. By default, reanalysis fields are applied as predictors, but the user can also upload their own predictors, including a wide range of compatible satellite-derived datasets. The package generates correlations of the variables selected with the predictand. The user also has the option to generate composites of the variables based on the predictand. Next, the user can extract predictors by drawing polygons over the regions that show strong correlations (composites). Then, the user can select some or all of the statistical prediction models provided. Provided models include Linear Regression models (GLM, SGLM), Tree-based models (bagging, random forest, boosting), Artificial Neural Network, and other non-linear models such as Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS). Finally, the user can download the analysis steps they used, such as the region they selected, the time period they specified, the predictand and predictors they chose and preprocessing options they used, and the model results in PDF or HTML format. Key words: Semi-automated prediction, Shiny, R, GLM, ANN, RF, GAM, MARS

Automated solid-phase extraction-liquid chromatography-tandem mass spectrometry analysis of 11-nor-Delta9-tetrahydrocannabinol-9-carboxylic acid in human urine specimens: application to a high-throughput urine analysis laboratory.

PubMed

Robandt, P V; Klette, K L; Sibum, M

2009-10-01

An automated solid-phase extraction coupled with liquid chromatography and tandem mass spectrometry (SPE-LC-MS-MS) method for the analysis of 11-nor-Delta(9)-tetrahydrocannabinol-9-carboxylic acid (THC-COOH) in human urine specimens was developed. The method was linear (R(2) = 0.9986) to 1000 ng/mL with no carryover evidenced at 2000 ng/mL. Limits of quantification and detection were found to be 2 ng/mL. Interrun precision was evaluated at the 15 ng/mL level over nine batches spanning 15 days (n = 45). The coefficient of variation (%CV) was found to be 5.5% over the course of the validation. Intrarun precision of a 15 ng/mL control (n = 5) ranged from 0.58% CV to 7.4% CV for the same set of analytical batches. Interference was tested using (+/-)-11-hydroxy-Delta(9)-tetrahydrocannabinol, cannabidiol, (-)-Delta(8)-tetrahydrocannabinol, and cannabinol. One hundred and nineteen specimens previously found to contain THC-COOH by a previously validated gas chromatographic mass spectrometry (GC-MS) procedure were compared to the SPE-LC-MS-MS method. Excellent agreement was found (R(2) = 0.9925) for the parallel comparison study. The automated SPE procedure eliminates the human factors of specimen handling, extraction, and derivatization, thereby reducing labor costs and rework resulting from human error or technique issues. Additionally, method runtime is greatly reduced (e.g., during parallel studies the SPE-LC-MS-MS instrument was often finished with analysis by the time the technician finished the offline SPE and derivatization procedure prior to the GC-MS analysis).

Automated Extraction of Flow Features

NASA Technical Reports Server (NTRS)

Dorney, Suzanne (Technical Monitor); Haimes, Robert

2005-01-01

Computational Fluid Dynamics (CFD) simulations are routinely performed as part of the design process of most fluid handling devices. In order to efficiently and effectively use the results of a CFD simulation, visualization tools are often used. These tools are used in all stages of the CFD simulation including pre-processing, interim-processing, and post-processing, to interpret the results. Each of these stages requires visualization tools that allow one to examine the geometry of the device, as well as the partial or final results of the simulation. An engineer will typically generate a series of contour and vector plots to better understand the physics of how the fluid is interacting with the physical device. Of particular interest are detecting features such as shocks, re-circulation zones, and vortices (which will highlight areas of stress and loss). As the demand for CFD analyses continues to increase the need for automated feature extraction capabilities has become vital. In the past, feature extraction and identification were interesting concepts, but not required in understanding the physics of a steady flow field. This is because the results of the more traditional tools like; isc-surface, cuts and streamlines, were more interactive and easily abstracted so they could be represented to the investigator. These tools worked and properly conveyed the collected information at the expense of a great deal of interaction. For unsteady flow-fields, the investigator does not have the luxury of spending time scanning only one "snapshot" of the simulation. Automated assistance is required in pointing out areas of potential interest contained within the flow. This must not require a heavy compute burden (the visualization should not significantly slow down the solution procedure for co-processing environments). Methods must be developed to abstract the feature of interest and display it in a manner that physically makes sense.