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.

Automated Extraction of Flow Features

NASA Technical Reports Server (NTRS)

Dorney, Suzanne (Technical Monitor); Haimes, Robert

2004-01-01

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, recirculation 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; iso-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.

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.

Semi-automated 96-well liquid-liquid extraction for quantitation of drugs in biological fluids.

PubMed

Zhang, N; Hoffman, K L; Li, W; Rossi, D T

2000-02-01

A semi-automated liquid-liquid extraction (LLE) technique for biological fluid sample preparation was introduced for the quantitation of four drugs in rat plasma. All liquid transferring during the sample preparation was automated using a Tomtec Quadra 96 Model 320 liquid handling robot, which processed up to 96 samples in parallel. The samples were either in 96-deep-well plate or tube-rack format. One plate of samples can be prepared in approximately 1.5 h, and the 96-well plate is directly compatible with the autosampler of an LC/MS system. Selection of organic solvents and recoveries are discussed. Also, precision, relative error, linearity and quantitation of the semi automated LLE method are estimated for four example drugs using LC/MS/MS with a multiple reaction monitoring (MRM) approach. The applicability of this method and future directions are evaluated.

Automated solid-phase extraction workstations combined with quantitative bioanalytical LC/MS.

PubMed

Huang, N H; Kagel, J R; Rossi, D T

1999-03-01

An automated solid-phase extraction workstation was used to develop, characterize and validate an LC/MS/MS method for quantifying a novel lipid-regulating drug in dog plasma. Method development was facilitated by workstation functions that allowed wash solvents of varying organic composition to be mixed and tested automatically. Precision estimates for this approach were within 9.8% relative standard deviation (RSD) across the calibration range. Accuracy for replicate determinations of quality controls was between -7.2 and +6.2% relative error (RE) over 5-1,000 ng/ml(-1). Recoveries were evaluated for a wide variety of wash solvents, elution solvents and sorbents. Optimized recoveries were generally > 95%. A sample throughput benchmark for the method was approximately equal 8 min per sample. Because of parallel sample processing, 100 samples were extracted in less than 120 min. The approach has proven useful for use with LC/MS/MS, using a multiple reaction monitoring (MRM) approach.

A COMPARISON OF AUTOMATED AND TRADITIONAL METHODS FOR THE EXTRACTION OF ARSENICALS FROM FISH

EPA Science Inventory

An automated extractor employing accelerated solvent extraction (ASE) has been compared with a traditional sonication method of extraction for the extraction of arsenicals from fish tissue. Four different species of fish and a standard reference material, DORM-2, were subjected t...

Automated extraction of subdural electrode grid from post-implant MRI scans for epilepsy surgery

NASA Astrophysics Data System (ADS)

Pozdin, Maksym A.; Skrinjar, Oskar

2005-04-01

This paper presents an automated algorithm for extraction of Subdural Electrode Grid (SEG) from post-implant MRI scans for epilepsy surgery. Post-implant MRI scans are corrupted by the image artifacts caused by implanted electrodes. The artifacts appear as dark spherical voids and given that the cerebrospinal fluid is also dark in T1-weigthed MRI scans, it is a difficult and time-consuming task to manually locate SEG position relative to brain structures of interest. The proposed algorithm reliably and accurately extracts SEG from post-implant MRI scan, i.e. finds its shape and position relative to brain structures of interest. The algorithm was validated against manually determined electrode locations, and the average error was 1.6mm for the three tested subjects.

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.

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.

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.

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 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...

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.

Establishing a novel automated magnetic bead-based method for the extraction of DNA from a variety of forensic samples.

PubMed

Witt, Sebastian; Neumann, Jan; Zierdt, Holger; Gébel, Gabriella; Röscheisen, Christiane

2012-09-01

Automated systems have been increasingly utilized for DNA extraction by many forensic laboratories to handle growing numbers of forensic casework samples while minimizing the risk of human errors and assuring high reproducibility. The step towards automation however is not easy: The automated extraction method has to be very versatile to reliably prepare high yields of pure genomic DNA from a broad variety of sample types on different carrier materials. To prevent possible cross-contamination of samples or the loss of DNA, the components of the kit have to be designed in a way that allows for the automated handling of the samples with no manual intervention necessary. DNA extraction using paramagnetic particles coated with a DNA-binding surface is predestined for an automated approach. For this study, we tested different DNA extraction kits using DNA-binding paramagnetic particles with regard to DNA yield and handling by a Freedom EVO(®)150 extraction robot (Tecan) equipped with a Te-MagS magnetic separator. Among others, the extraction kits tested were the ChargeSwitch(®)Forensic DNA Purification Kit (Invitrogen), the PrepFiler™Automated Forensic DNA Extraction Kit (Applied Biosystems) and NucleoMag™96 Trace (Macherey-Nagel). After an extensive test phase, we established a novel magnetic bead extraction method based upon the NucleoMag™ extraction kit (Macherey-Nagel). The new method is readily automatable and produces high yields of DNA from different sample types (blood, saliva, sperm, contact stains) on various substrates (filter paper, swabs, cigarette butts) with no evidence of a loss of magnetic beads or sample cross-contamination. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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.

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.

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 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.

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.

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 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.

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.

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

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.

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.

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.

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.

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

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

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

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

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.

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.

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.

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 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.

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.

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.

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

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 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.

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%.

[Establishment of Automation System for Detection of Alcohol in Blood].

PubMed

Tian, L L; Shen, Lei; Xue, J F; Liu, M M; Liang, L J

2017-02-01

To establish an automation system for detection of alcohol content in blood. The determination was performed by automated workstation of extraction-headspace gas chromatography （HS-GC）. The blood collection with negative pressure, sealing time of headspace bottle and sample needle were checked and optimized in the abstraction of automation system. The automatic sampling was compared with the manual sampling. The quantitative data obtained by the automated workstation of extraction-HS-GC for alcohol was stable. The relative differences of two parallel samples were less than 5%. The automated extraction was superior to the manual extraction. A good linear relationship was obtained at the alcohol concentration range of 0.1-3.0 mg/mL （ r ≥0.999） with good repeatability. The method is simple and quick, with more standard experiment process and accurate experimental data. It eliminates the error from the experimenter and has good repeatability, which can be applied to the qualitative and quantitative detections of alcohol in blood. Copyright© by the Editorial Department of Journal of Forensic Medicine

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

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.

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

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.

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

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.

Automating Relational Database Design for Microcomputer Users.

ERIC Educational Resources Information Center

Pu, Hao-Che

1991-01-01

Discusses issues involved in automating the relational database design process for microcomputer users and presents a prototype of a microcomputer-based system (RA, Relation Assistant) that is based on expert systems technology and helps avoid database maintenance problems. Relational database design is explained and the importance of easy input…

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 for the analysis of 11-nor-delta9-tetrahydrocannabinol-9-carboxylic acid (THCCOOH) in urine using a six-head probe Hamilton Microlab 2200 system and gas chromatography-mass spectrometry.

PubMed

Whitter, P D; Cary, P L; Leaton, J I; Johnson, J E

1999-01-01

An automated extraction scheme for the analysis of 11 -nor-delta9-tetrahydrocannabinol-9-carboxylic acid using the Hamilton Microlab 2200, which was modified for gravity-flow solid-phase extraction, has been evaluated. The Hamilton was fitted with a six-head probe, a modular valve positioner, and a peristaltic pump. The automated method significantly increased sample throughput, improved assay consistency, and reduced the time spent performing the extraction. Extraction recovery for the automated method was > 90%. The limit of detection, limit of quantitation, and upper limit of linearity were equivalent to the manual method: 1.5, 3.0, and 300 ng/mL, respectively. Precision at the 15-ng/mL cut-off was as follows: mean = 14.4, standard deviation = 0.5, coefficient of variation = 3.5%. Comparison of 38 patient samples, extracted by the manual and automated extraction methods, demonstrated the following correlation statistics: r = .991, slope 1.029, and y-intercept -2.895. Carryover was < 0.3% at 1000 ng/mL. Aliquoting/extraction time for the automated method (48 urine samples) was 50 min, and the manual procedure required approximately 2.5 h. The automated aliquoting/extraction method on the Hamilton Microlab 2200 and its use in forensic applications are reviewed.

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.

Automated Fluid Feature Extraction from Transient Simulations

NASA Technical Reports Server (NTRS)

Haimes, Robert

1998-01-01

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: Shocks; Vortex ores; Regions of Recirculation; Boundary Layers; Wakes.

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.

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 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.

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.

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.

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

PubMed Central

2012-01-01

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

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...

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

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 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.

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

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.

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.

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.

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 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.

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.

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.

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.

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.

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

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 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

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.

Precision Relative Positioning for Automated Aerial Refueling from a Stereo Imaging System

DTIC Science & Technology

2015-03-01

PRECISION RELATIVE POSITIONING FOR AUTOMATED AERIAL REFUELING FROM A STEREO IMAGING SYSTEM THESIS Kyle P. Werner, 2Lt, USAF AFIT-ENG-MS-15-M-048...REFUELING FROM A STEREO IMAGING SYSTEM THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of...RELEASE; DISTRIBUTION UNLIMITED. AFIT-ENG-MS-15-M-048 PRECISION RELATIVE POSITIONING FOR AUTOMATED AERIAL REFUELING FROM A STEREO IMAGING SYSTEM THESIS

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.

Semi-automated and automated glioma grading using dynamic susceptibility-weighted contrast-enhanced perfusion MRI relative cerebral blood volume measurements.

PubMed

Friedman, S N; Bambrough, P J; Kotsarini, C; Khandanpour, N; Hoggard, N

2012-12-01

Despite the established role of MRI in the diagnosis of brain tumours, histopathological assessment remains the clinically used technique, especially for the glioma group. Relative cerebral blood volume (rCBV) is a dynamic susceptibility-weighted contrast-enhanced perfusion MRI parameter that has been shown to correlate to tumour grade, but assessment requires a specialist and is time consuming. We developed analysis software to determine glioma gradings from perfusion rCBV scans in a manner that is quick, easy and does not require a specialist operator. MRI perfusion data from 47 patients with different histopathological grades of glioma were analysed with custom-designed software. Semi-automated analysis was performed with a specialist and non-specialist operator separately determining the maximum rCBV value corresponding to the tumour. Automated histogram analysis was performed by calculating the mean, standard deviation, median, mode, skewness and kurtosis of rCBV values. All values were compared with the histopathologically assessed tumour grade. A strong correlation between specialist and non-specialist observer measurements was found. Significantly different values were obtained between tumour grades using both semi-automated and automated techniques, consistent with previous results. The raw (unnormalised) data single-pixel maximum rCBV semi-automated analysis value had the strongest correlation with glioma grade. Standard deviation of the raw data had the strongest correlation of the automated analysis. Semi-automated calculation of raw maximum rCBV value was the best indicator of tumour grade and does not require a specialist operator. Both semi-automated and automated MRI perfusion techniques provide viable non-invasive alternatives to biopsy for glioma tumour grading.

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.

Automation or De-automation

NASA Astrophysics Data System (ADS)

Gorlach, Igor; Wessel, Oliver

2008-09-01

In the global automotive industry, for decades, vehicle manufacturers have continually increased the level of automation of production systems in order to be competitive. However, there is a new trend to decrease the level of automation, especially in final car assembly, for reasons of economy and flexibility. In this research, the final car assembly lines at three production sites of Volkswagen are analysed in order to determine the best level of automation for each, in terms of manufacturing costs, productivity, quality and flexibility. The case study is based on the methodology proposed by the Fraunhofer Institute. The results of the analysis indicate that fully automated assembly systems are not necessarily the best option in terms of cost, productivity and quality combined, which is attributed to high complexity of final car assembly systems; some de-automation is therefore recommended. On the other hand, the analysis shows that low automation can result in poor product quality due to reasons related to plant location, such as inadequate workers' skills, motivation, etc. Hence, the automation strategy should be formulated on the basis of analysis of all relevant aspects of the manufacturing process, such as costs, quality, productivity and flexibility in relation to the local context. A more balanced combination of automated and manual assembly operations provides better utilisation of equipment, reduces production costs and improves throughput.

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

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. ...

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.

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 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.

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 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.

Automated in vivo platform for the discovery of functional food treatments of hypercholesterolemia.

PubMed

Littleton, Robert M; Haworth, Kevin J; Tang, Hong; Setchell, Kenneth D R; Nelson, Sandra; Hove, Jay R

2013-01-01

The zebrafish is becoming an increasingly popular model system for both automated drug discovery and investigating hypercholesterolemia. Here we combine these aspects and for the first time develop an automated high-content confocal assay for treatments of hypercholesterolemia. We also create two algorithms for automated analysis of cardiodynamic data acquired by high-speed confocal microscopy. The first algorithm computes cardiac parameters solely from the frequency-domain representation of cardiodynamic data while the second uses both frequency- and time-domain data. The combined approach resulted in smaller differences relative to manual measurements. The methods are implemented to test the ability of a methanolic extract of the hawthorn plant (Crataegus laevigata) to treat hypercholesterolemia and its peripheral cardiovascular effects. Results demonstrate the utility of these methods and suggest the extract has both antihypercholesterolemic and postitively inotropic properties.

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...

Orbital transfer vehicle launch operations study: Automated technology knowledge base, volume 4

NASA Technical Reports Server (NTRS)

1986-01-01

A simplified retrieval strategy for compiling automation-related bibliographies from NASA/RECON is presented. Two subsets of NASA Thesaurus subject terms were extracted: a primary list, which is used to obtain an initial set of citations; and a secondary list, which is used to limit or further specify a large initial set of citations. These subject term lists are presented in Appendix A as the Automated Technology Knowledge Base (ATKB) Thesaurus.

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.

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.

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.

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

UBO Detector - A cluster-based, fully automated pipeline for extracting white matter hyperintensities.

PubMed

Jiang, Jiyang; Liu, Tao; Zhu, Wanlin; Koncz, Rebecca; Liu, Hao; Lee, Teresa; Sachdev, Perminder S; Wen, Wei

2018-07-01

We present 'UBO Detector', a cluster-based, fully automated pipeline for extracting and calculating variables for regions of white matter hyperintensities (WMH) (available for download at https://cheba.unsw.edu.au/group/neuroimaging-pipeline). It takes T1-weighted and fluid attenuated inversion recovery (FLAIR) scans as input, and SPM12 and FSL functions are utilised for pre-processing. The candidate clusters are then generated by FMRIB's Automated Segmentation Tool (FAST). A supervised machine learning algorithm, k-nearest neighbor (k-NN), is applied to determine whether the candidate clusters are WMH or non-WMH. UBO Detector generates both image and text (volumes and the number of WMH clusters) outputs for whole brain, periventricular, deep, and lobar WMH, as well as WMH in arterial territories. The computation time for each brain is approximately 15 min. We validated the performance of UBO Detector by showing a) high segmentation (similarity index (SI) = 0.848) and volumetric (intraclass correlation coefficient (ICC) = 0.985) agreement between the UBO Detector-derived and manually traced WMH; b) highly correlated (r 2  > 0.9) and a steady increase of WMH volumes over time; and c) significant associations of periventricular (t = 22.591, p < 0.001) and deep (t = 14.523, p < 0.001) WMH volumes generated by UBO Detector with Fazekas rating scores. With parallel computing enabled in UBO Detector, the processing can take advantage of multi-core CPU's that are commonly available on workstations. In conclusion, UBO Detector is a reliable, efficient and fully automated WMH segmentation pipeline. Copyright © 2018 Elsevier Inc. All rights reserved.

Selecting a Relational Database Management System for Library Automation Systems.

ERIC Educational Resources Information Center

Shekhel, Alex; O'Brien, Mike

1989-01-01

Describes the evaluation of four relational database management systems (RDBMSs) (Informix Turbo, Oracle 6.0 TPS, Unify 2000 and Relational Technology's Ingres 5.0) to determine which is best suited for library automation. The evaluation criteria used to develop a benchmark specifically designed to test RDBMSs for libraries are discussed. (CLB)

Automating Nuclear-Safety-Related SQA Procedures with Custom Applications

DOE PAGES

Freels, James D.

2016-01-01

Nuclear safety-related procedures are rigorous for good reason. Small design mistakes can quickly turn into unwanted failures. Researchers at Oak Ridge National Laboratory worked with COMSOL to define a simulation app that automates the software quality assurance (SQA) verification process and provides results in less than 24 hours.

Automated In Vivo Platform for the Discovery of Functional Food Treatments of Hypercholesterolemia

PubMed Central

Littleton, Robert M.; Haworth, Kevin J.; Tang, Hong; Setchell, Kenneth D. R.; Nelson, Sandra; Hove, Jay R.

2013-01-01

The zebrafish is becoming an increasingly popular model system for both automated drug discovery and investigating hypercholesterolemia. Here we combine these aspects and for the first time develop an automated high-content confocal assay for treatments of hypercholesterolemia. We also create two algorithms for automated analysis of cardiodynamic data acquired by high-speed confocal microscopy. The first algorithm computes cardiac parameters solely from the frequency-domain representation of cardiodynamic data while the second uses both frequency- and time-domain data. The combined approach resulted in smaller differences relative to manual measurements. The methods are implemented to test the ability of a methanolic extract of the hawthorn plant (Crataegus laevigata) to treat hypercholesterolemia and its peripheral cardiovascular effects. Results demonstrate the utility of these methods and suggest the extract has both antihypercholesterolemic and postitively inotropic properties. PMID:23349685

Relation Extraction with Weak Supervision and Distributional Semantics

DTIC Science & Technology

2013-05-01

DATES COVERED 00-00-2013 to 00-00-2013 4 . TITLE AND SUBTITLE Relation Extraction with Weak Supervision and Distributional Semantics 5a...ix List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x 1 Introduction 1 2 Prior Work 4 ...2.1 Supervised relation extraction . . . . . . . . . . . . . . . . . . . . . 4 2.2 Distant supervision for relation extraction

Artificial intelligence issues related to automated computing operations

NASA Technical Reports Server (NTRS)

Hornfeck, William A.

1989-01-01

Large data processing installations represent target systems for effective applications of artificial intelligence (AI) constructs. The system organization of a large data processing facility at the NASA Marshall Space Flight Center is presented. The methodology and the issues which are related to AI application to automated operations within a large-scale computing facility are described. Problems to be addressed and initial goals are outlined.

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/.

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.

Managing expectations: assessment of chemistry databases generated by automated extraction of chemical structures from patents.

PubMed

Senger, Stefan; Bartek, Luca; Papadatos, George; Gaulton, Anna

2015-12-01

First public disclosure of new chemical entities often takes place in patents, which makes them an important source of information. However, with an ever increasing number of patent applications, manual processing and curation on such a large scale becomes even more challenging. An alternative approach better suited for this large corpus of documents is the automated extraction of chemical structures. A number of patent chemistry databases generated by using the latter approach are now available but little is known that can help to manage expectations when using them. This study aims to address this by comparing two such freely available sources, SureChEMBL and IBM SIIP (IBM Strategic Intellectual Property Insight Platform), with manually curated commercial databases. When looking at the percentage of chemical structures successfully extracted from a set of patents, using SciFinder as our reference, 59 and 51 % were also found in our comparison in SureChEMBL and IBM SIIP, respectively. When performing this comparison with compounds as starting point, i.e. establishing if for a list of compounds the databases provide the links between chemical structures and patents they appear in, we obtained similar results. SureChEMBL and IBM SIIP found 62 and 59 %, respectively, of the compound-patent pairs obtained from Reaxys. In our comparison of automatically generated vs. manually curated patent chemistry databases, the former successfully provided approximately 60 % of links between chemical structure and patents. It needs to be stressed that only a very limited number of patents and compound-patent pairs were used for our comparison. Nevertheless, our results will hopefully help to manage expectations of users of patent chemistry databases of this type and provide a useful framework for more studies like ours as well as guide future developments of the workflows used for the automated extraction of chemical structures from patents. The challenges we have encountered

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

Reduced Attention Allocation during Short Periods of Partially Automated Driving: An Event-Related Potentials Study

PubMed Central

Solís-Marcos, Ignacio; Galvao-Carmona, Alejandro; Kircher, Katja

2017-01-01

Research on partially automated driving has revealed relevant problems with driving performance, particularly when drivers’ intervention is required (e.g., take-over when automation fails). Mental fatigue has commonly been proposed to explain these effects after prolonged automated drives. However, performance problems have also been reported after just a few minutes of automated driving, indicating that other factors may also be involved. We hypothesize that, besides mental fatigue, an underload effect of partial automation may also affect driver attention. In this study, such potential effect was investigated during short periods of partially automated and manual driving and at different speeds. Subjective measures of mental demand and vigilance and performance to a secondary task (an auditory oddball task) were used to assess driver attention. Additionally, modulations of some specific attention-related event-related potentials (ERPs, N1 and P3 components) were investigated. The mental fatigue effects associated with the time on task were also evaluated by using the same measurements. Twenty participants drove in a fixed-base simulator while performing an auditory oddball task that elicited the ERPs. Six conditions were presented (5–6 min each) combining three speed levels (low, comfortable and high) and two automation levels (manual and partially automated). The results showed that, when driving partially automated, scores in subjective mental demand and P3 amplitudes were lower than in the manual conditions. Similarly, P3 amplitude and self-reported vigilance levels decreased with the time on task. Based on previous studies, these findings might reflect a reduction in drivers’ attention resource allocation, presumably due to the underload effects of partial automation and to the mental fatigue associated with the time on task. Particularly, such underload effects on attention could explain the performance decrements after short periods of automated

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

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.

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.

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.

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.

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.

Imaging mass spectrometry data reduction: automated feature identification and extraction.

PubMed

McDonnell, Liam A; van Remoortere, Alexandra; de Velde, Nico; van Zeijl, René J M; Deelder, André M

2010-12-01

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues. Copyright © 2010 American Society for Mass Spectrometry. Published by Elsevier Inc. All rights reserved.

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 feature extraction for retinal vascular biometry in zebrafish using OCT angiography

NASA Astrophysics Data System (ADS)

Bozic, Ivan; Rao, Gopikrishna M.; Desai, Vineet; Tao, Yuankai K.

2017-02-01

Zebrafish have been identified as an ideal model for angiogenesis because of anatomical and functional similarities with other vertebrates. The scale and complexity of zebrafish assays are limited by the need to manually treat and serially screen animals, and recent technological advances have focused on automation and improving throughput. Here, we use optical coherence tomography (OCT) and OCT angiography (OCT-A) to perform noninvasive, in vivo imaging of retinal vasculature in zebrafish. OCT-A summed voxel projections were low pass filtered and skeletonized to create an en face vascular map prior to connectivity analysis. Vascular segmentation was referenced to the optic nerve head (ONH), which was identified by automatically segmenting the retinal pigment epithelium boundary on the OCT structural volume. The first vessel branch generation was identified as skeleton segments with branch points closest to the ONH, and subsequent generations were found iteratively by expanding the search space outwards from the ONH. Biometric parameters, including length, curvature, and branch angle of each vessel segment were calculated and grouped by branch generation. Despite manual handling and alignment of each animal over multiple time points, we observe distinct qualitative patterns that enable unique identification of each eye from individual animals. We believe this OCT-based retinal biometry method can be applied for automated animal identification and handling in high-throughput organism-level pharmacological assays and genetic screens. In addition, these extracted features may enable high-resolution quantification of longitudinal vascular changes as a method for studying zebrafish models of retinal neovascularization and vascular remodeling.

Deep Learning for Automated Extraction of Primary Sites from Cancer Pathology Reports

DOE PAGES

Qiu, John; Yoon, Hong-Jun; Fearn, Paul A.; ...

2017-05-03

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. Here in this study we investigated deep learning and a convolutional neural network (CNN), for extracting ICDO- 3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations asmore » the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro and macro-F score increases of up to 0.132 and 0.226 respectively when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on CNN method and cancer site. Finally, these encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.« less

Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports.

PubMed

Qiu, John X; Yoon, Hong-Jun; Fearn, Paul A; Tourassi, Georgia D

2018-01-01

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro- and macro-F score increases of up to 0.132 and 0.226, respectively, when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on the CNN method and cancer site. These encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.

Deep Learning for Automated Extraction of Primary Sites from Cancer Pathology Reports

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

Qiu, John; Yoon, Hong-Jun; Fearn, Paul A.

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. Here in this study we investigated deep learning and a convolutional neural network (CNN), for extracting ICDO- 3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations asmore » the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro and macro-F score increases of up to 0.132 and 0.226 respectively when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on CNN method and cancer site. Finally, these encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.« less

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;

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%.

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.

Semi-automated extraction of longitudinal subglacial bedforms from digital terrain models - Two new methods

NASA Astrophysics Data System (ADS)

Jorge, Marco G.; Brennand, Tracy A.

2017-07-01

Relict drumlin and mega-scale glacial lineation (positive relief, longitudinal subglacial bedforms - LSBs) morphometry has been used as a proxy for paleo ice-sheet dynamics. LSB morphometric inventories have relied on manual mapping, which is slow and subjective and thus potentially difficult to reproduce. Automated methods are faster and reproducible, but previous methods for LSB semi-automated mapping have not been highly successful. Here, two new object-based methods for the semi-automated extraction of LSBs (footprints) from digital terrain models are compared in a test area in the Puget Lowland, Washington, USA. As segmentation procedures to create LSB-candidate objects, the normalized closed contour method relies on the contouring of a normalized local relief model addressing LSBs on slopes, and the landform elements mask method relies on the classification of landform elements derived from the digital terrain model. For identifying which LSB-candidate objects correspond to LSBs, both methods use the same LSB operational definition: a ruleset encapsulating expert knowledge, published morphometric data, and the morphometric range of LSBs in the study area. The normalized closed contour method was separately applied to four different local relief models, two computed in moving windows and two hydrology-based. Overall, the normalized closed contour method outperformed the landform elements mask method. The normalized closed contour method performed on a hydrological relief model from a multiple direction flow routing algorithm performed best. For an assessment of its transferability, the normalized closed contour method was evaluated on a second area, the Chautauqua drumlin field, Pennsylvania and New York, USA where it performed better than in the Puget Lowland. A broad comparison to previous methods suggests that the normalized relief closed contour method may be the most capable method to date, but more development is required.

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.

The current role of on-line extraction approaches in clinical and forensic toxicology.

PubMed

Mueller, Daniel M

2014-08-01

In today's clinical and forensic toxicological laboratories, automation is of interest because of its ability to optimize processes, to reduce manual workload and handling errors and to minimize exposition to potentially infectious samples. Extraction is usually the most time-consuming step; therefore, automation of this step is reasonable. Currently, from the field of clinical and forensic toxicology, methods using the following on-line extraction techniques have been published: on-line solid-phase extraction, turbulent flow chromatography, solid-phase microextraction, microextraction by packed sorbent, single-drop microextraction and on-line desorption of dried blood spots. Most of these published methods are either single-analyte or multicomponent procedures; methods intended for systematic toxicological analysis are relatively scarce. However, the use of on-line extraction will certainly increase in the near future.

Automatic information extraction from unstructured mammography reports using distributed semantics.

PubMed

Gupta, Anupama; Banerjee, Imon; Rubin, Daniel L

2018-02-01

To date, the methods developed for automated extraction of information from radiology reports are mainly rule-based or dictionary-based, and, therefore, require substantial manual effort to build these systems. Recent efforts to develop automated systems for entity detection have been undertaken, but little work has been done to automatically extract relations and their associated named entities in narrative radiology reports that have comparable accuracy to rule-based methods. Our goal is to extract relations in a unsupervised way from radiology reports without specifying prior domain knowledge. We propose a hybrid approach for information extraction that combines dependency-based parse tree with distributed semantics for generating structured information frames about particular findings/abnormalities from the free-text mammography reports. The proposed IE system obtains a F 1 -score of 0.94 in terms of completeness of the content in the information frames, which outperforms a state-of-the-art rule-based system in this domain by a significant margin. The proposed system can be leveraged in a variety of applications, such as decision support and information retrieval, and may also easily scale to other radiology domains, since there is no need to tune the system with hand-crafted information extraction rules. Copyright © 2018 Elsevier Inc. All rights reserved.

Automated anatomical labeling method for abdominal arteries extracted from 3D abdominal CT images

NASA Astrophysics Data System (ADS)

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

2012-02-01

This paper presents an automated anatomical labeling method of abdominal arteries. In abdominal surgery, understanding of blood vessel structure concerning with a target organ is very important. Branching pattern of blood vessels differs among individuals. It is required to develop a system that can assist understanding of a blood vessel structure and anatomical names of blood vessels of a patient. Previous anatomical labbeling methods for abdominal arteries deal with either of the upper or lower abdominal arteries. In this paper, we present an automated anatomical labeling method of both of the upper and lower abdominal arteries extracted from CT images. We obtain a tree structure of artery regions and calculate feature values for each branch. These feature values include the diameter, curvature, direction, and running vectors of a branch. Target arteries of this method are grouped based on branching conditions. The following processes are separately applied for each group. We compute candidate artery names by using classifiers that are trained to output artery names. A correction process of the candidate anatomical names based on the rule of majority is applied to determine final names. We applied the proposed method to 23 cases of 3D abdominal CT images. Experimental results showed that the proposed method is able to perform nomenclature of entire major abdominal arteries. The recall and the precision rates of labeling are 79.01% and 80.41%, respectively.

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.

An automated database case definition for serious bleeding related to oral anticoagulant use.

PubMed

Cunningham, Andrew; Stein, C Michael; Chung, Cecilia P; Daugherty, James R; Smalley, Walter E; Ray, Wayne A

2011-06-01

Bleeding complications are a serious adverse effect of medications that prevent abnormal blood clotting. To facilitate epidemiologic investigations of bleeding complications, we developed and validated an automated database case definition for bleeding-related hospitalizations. The case definition utilized information from an in-progress retrospective cohort study of warfarin-related bleeding in Tennessee Medicaid enrollees 30 years of age or older. It identified inpatient stays during the study period of January 1990 to December 2005 with diagnoses and/or procedures that indicated a current episode of bleeding. The definition was validated by medical record review for a sample of 236 hospitalizations. We reviewed 186 hospitalizations that had medical records with sufficient information for adjudication. Of these, 165 (89%, 95%CI: 83-92%) were clinically confirmed bleeding-related hospitalizations. An additional 19 hospitalizations (10%, 7-15%) were adjudicated as possibly bleeding-related. Of the 165 clinically confirmed bleeding-related hospitalizations, the automated database and clinical definitions had concordant anatomical sites (gastrointestinal, cerebral, genitourinary, other) for 163 (99%, 96-100%). For those hospitalizations with sufficient information to distinguish between upper/lower gastrointestinal bleeding, the concordance was 89% (76-96%) for upper gastrointestinal sites and 91% (77-97%) for lower gastrointestinal sites. A case definition for bleeding-related hospitalizations suitable for automated databases had a positive predictive value of between 89% and 99% and could distinguish specific bleeding sites. Copyright © 2011 John Wiley & Sons, Ltd.

Visual Routines for Extracting Magnitude Relations

ERIC Educational Resources Information Center

Michal, Audrey L.; Uttal, David; Shah, Priti; Franconeri, Steven L.

2016-01-01

Linking relations described in text with relations in visualizations is often difficult. We used eye tracking to measure the optimal way to extract such relations in graphs, college students, and young children (6- and 8-year-olds). Participants compared relational statements ("Are there more blueberries than oranges?") with simple…

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.

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

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

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.

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.

Exploiting graph kernels for high performance biomedical relation extraction.

PubMed

Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri

2018-01-30

Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM

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.

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.

Californian demonstration and validation of automated agricultural field extraction from multi-temporal Landsat data

NASA Astrophysics Data System (ADS)

Yan, L.; Roy, D. P.

2013-12-01

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. To date, field objects have not been extracted from satellite data over large areas because of computational constraints and because consistently processed appropriate resolution data have not been available or affordable. We present a fully automated computational methodology to extract agricultural fields from 30m Web Enabled Landsat data (WELD) time series and results for approximately 250,000 square kilometers (eleven 150 x 150 km WELD tiles) encompassing all the major agricultural areas of California. The extracted fields, including rectangular, circular, and irregularly shaped fields, are evaluated by comparison with manually interpreted Landsat field objects. Validation results are presented in terms of standard confusion matrix accuracy measures and also the degree of field object over-segmentation, under-segmentation, fragmentation and shape distortion. The apparent success of the presented field extraction methodology is due to several factors. First, the use of multi-temporal Landsat data, as opposed to single Landsat acquisitions, that enables crop rotations and inter-annual variability in the state of the vegetation to be accommodated for and provides more opportunities for cloud-free, non-missing and atmospherically uncontaminated surface observations. Second, the adoption of an object based approach, namely the variational region-based geometric active contour method that enables robust segmentation with only a small number of parameters and that requires no training data collection. Third, the use of a watershed algorithm to decompose connected segments belonging to multiple fields into coherent isolated field segments and a geometry based algorithm to detect and associate parts of

Enhancing biomedical text summarization using semantic relation extraction.

PubMed

Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao

2011-01-01

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.

Enhancing Biomedical Text Summarization Using Semantic Relation Extraction

PubMed Central

Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao

2011-01-01

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization. PMID:21887336

A hybrid model based on neural networks for biomedical relation extraction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Zhang, Shaowu; Sun, Yuanyuan; Yang, Liang

2018-05-01

Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network-based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein-protein interaction) PPI corpora and a (drug-drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance. Copyright © 2018 Elsevier Inc. All rights reserved.

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis

PubMed Central

Kleifges, Kelly; Bigdely-Shamlo, Nima; Kerick, Scott E.; Robbins, Kay A.

2017-01-01

Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/. PMID:28217081

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

PubMed

Kleifges, Kelly; Bigdely-Shamlo, Nima; Kerick, Scott E; Robbins, Kay A

2017-01-01

Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.

Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image

NASA Astrophysics Data System (ADS)

Al Fugura, A.'kif; Billa, Lawal; Pradhan, Biswajeet

2011-12-01

Coastline identification is important for surveying and mapping reasons. Coastline serves as the basic point of reference and is used on nautical charts for navigation purposes. Its delineation has become crucial and more important in the wake of the many recent earthquakes and tsunamis resulting in complete change and redraw of some shorelines. In a tropical country like Malaysia, presence of cloud cover hinders the application of optical remote sensing data. In this study a semi-automated technique and procedures are presented for shoreline delineation from RADARSAT-1 image. A scene of RADARSAT-1 satellite image was processed using enhanced filtering technique to identify and extract the shoreline coast of Kuala Terengganu, Malaysia. RADSARSAT image has many advantages over the optical data because of its ability to penetrate cloud cover and its night sensing capabilities. At first, speckles were removed from the image by using Lee sigma filter which was used to reduce random noise and to enhance the image and discriminate the boundary between land and water. The results showed an accurate and improved extraction and delineation of the entire coastline of Kuala Terrenganu. The study demonstrated the reliability of the image averaging filter in reducing random noise over the sea surface especially near the shoreline. It enhanced land-water boundary differentiation, enabling better delineation of the shoreline. Overall, the developed techniques showed the potential of radar imagery for accurate shoreline mapping and will be useful for monitoring shoreline changes during high and low tides as well as shoreline erosion in a tropical country like Malaysia.

Chemical-induced disease relation extraction with various linguistic features.

PubMed

Gu, Jinghang; Qian, Longhua; Zhou, Guodong

2016-01-01

Understanding the relations between chemicals and diseases is crucial in various biomedical tasks such as new drug discoveries and new therapy developments. While manually mining these relations from the biomedical literature is costly and time-consuming, such a procedure is often difficult to keep up-to-date. To address these issues, the BioCreative-V community proposed a challenging task of automatic extraction of chemical-induced disease (CID) relations in order to benefit biocuration. This article describes our work on the CID relation extraction task on the BioCreative-V tasks. We built a machine learning based system that utilized simple yet effective linguistic features to extract relations with maximum entropy models. In addition to leveraging various features, the hypernym relations between entity concepts derived from the Medical Subject Headings (MeSH)-controlled vocabulary were also employed during both training and testing stages to obtain more accurate classification models and better extraction performance, respectively. We demoted relation extraction between entities in documents to relation extraction between entity mentions. In our system, pairs of chemical and disease mentions at both intra- and inter-sentence levels were first constructed as relation instances for training and testing, then two classification models at both levels were trained from the training examples and applied to the testing examples. Finally, we merged the classification results from mention level to document level to acquire final relations between chemicals and diseases. Our system achieved promisingF-scores of 60.4% on the development dataset and 58.3% on the test dataset using gold-standard entity annotations, respectively. Database URL:https://github.com/JHnlp/BC5CIDTask. © The Author(s) 2016. Published by Oxford University Press.

Chemical-induced disease relation extraction via convolutional neural network.

PubMed

Gu, Jinghang; Sun, Fuqing; Qian, Longhua; Zhou, Guodong

2017-01-01

This article describes our work on the BioCreative-V chemical-disease relation (CDR) extraction task, which employed a maximum entropy (ME) model and a convolutional neural network model for relation extraction at inter- and intra-sentence level, respectively. In our work, relation extraction between entity concepts in documents was simplified to relation extraction between entity mentions. We first constructed pairs of chemical and disease mentions as relation instances for training and testing stages, then we trained and applied the ME model and the convolutional neural network model for inter- and intra-sentence level, respectively. Finally, we merged the classification results from mention level to document level to acquire the final relations between chemical and disease concepts. The evaluation on the BioCreative-V CDR corpus shows the effectiveness of our proposed approach. http://www.biocreative.org/resources/corpora/biocreative-v-cdr-corpus/. © The Author(s) 2017. Published by Oxford University Press.

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

Assessment of commercial NLP engines for medication information extraction from dictated clinical notes.

PubMed

Jagannathan, V; Mullett, Charles J; Arbogast, James G; Halbritter, Kevin A; Yellapragada, Deepthi; Regulapati, Sushmitha; Bandaru, Pavani

2009-04-01

We assessed the current state of commercial natural language processing (NLP) engines for their ability to extract medication information from textual clinical documents. Two thousand de-identified discharge summaries and family practice notes were submitted to four commercial NLP engines with the request to extract all medication information. The four sets of returned results were combined to create a comparison standard which was validated against a manual, physician-derived gold standard created from a subset of 100 reports. Once validated, the individual vendor results for medication names, strengths, route, and frequency were compared against this automated standard with precision, recall, and F measures calculated. Compared with the manual, physician-derived gold standard, the automated standard was successful at accurately capturing medication names (F measure=93.2%), but performed less well with strength (85.3%) and route (80.3%), and relatively poorly with dosing frequency (48.3%). Moderate variability was seen in the strengths of the four vendors. The vendors performed better with the structured discharge summaries than with the clinic notes in an analysis comparing the two document types. Although automated extraction may serve as the foundation for a manual review process, it is not ready to automate medication lists without human intervention.

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.

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

PubMed Central

2015-01-01

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

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

PubMed

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

2015-01-01

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

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.

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.

Automated Solid Phase Extraction (SPE) LC/NMR Applied to the Structural Analysis of Extractable Compounds from a Pharmaceutical Packaging Material of Construction.

PubMed

Norwood, Daniel L; Mullis, James O; Davis, Mark; Pennino, Scott; Egert, Thomas; Gonnella, Nina C

2013-01-01

The structural analysis (i.e., identification) of organic chemical entities leached into drug product formulations has traditionally been accomplished with techniques involving the combination of chromatography with mass spectrometry. These include gas chromatography/mass spectrometry (GC/MS) for volatile and semi-volatile compounds, and various forms of liquid chromatography/mass spectrometry (LC/MS or HPLC/MS) for semi-volatile and relatively non-volatile compounds. GC/MS and LC/MS techniques are complementary for structural analysis of leachables and potentially leachable organic compounds produced via laboratory extraction of pharmaceutical container closure/delivery system components and corresponding materials of construction. Both hyphenated analytical techniques possess the separating capability, compound specific detection attributes, and sensitivity required to effectively analyze complex mixtures of trace level organic compounds. However, hyphenated techniques based on mass spectrometry are limited by the inability to determine complete bond connectivity, the inability to distinguish between many types of structural isomers, and the inability to unambiguously determine aromatic substitution patterns. Nuclear magnetic resonance spectroscopy (NMR) does not have these limitations; hence it can serve as a complement to mass spectrometry. However, NMR technology is inherently insensitive and its ability to interface with chromatography has been historically challenging. This article describes the application of NMR coupled with liquid chromatography and automated solid phase extraction (SPE-LC/NMR) to the structural analysis of extractable organic compounds from a pharmaceutical packaging material of construction. The SPE-LC/NMR technology combined with micro-cryoprobe technology afforded the sensitivity and sample mass required for full structure elucidation. Optimization of the SPE-LC/NMR analytical method was achieved using a series of model compounds

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.

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

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.

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

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.

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.

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.

Analyzing Automated Instructional Systems: Metaphors from Related Design Professions.

ERIC Educational Resources Information Center

Jonassen, David H.; Wilson, Brent G.

Noting that automation has had an impact on virtually every manufacturing and information operation in the world, including instructional design (ID), this paper suggests three basic metaphors for automating instructional design activities: (1) computer-aided design and manufacturing (CAD/CAM) systems; (2) expert system advisor systems; and (3)…

Automated Semantic Indices Related to Cognitive Function and Rate of Cognitive Decline

ERIC Educational Resources Information Center

Pakhomov, Serguei V. S.; Hemmy, Laura S.; Lim, Kelvin O.

2012-01-01

The objective of our study is to introduce a fully automated, computational linguistic technique to quantify semantic relations between words generated on a standard semantic verbal fluency test and to determine its cognitive and clinical correlates. Cognitive differences between patients with Alzheimer's disease and mild cognitive impairment are…

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

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

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.

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.

Automatic sentence extraction for the detection of scientific paper relations

NASA Astrophysics Data System (ADS)

Sibaroni, Y.; Prasetiyowati, S. S.; Miftachudin, M.

2018-03-01

The relations between scientific papers are very useful for researchers to see the interconnection between scientific papers quickly. By observing the inter-article relationships, researchers can identify, among others, the weaknesses of existing research, performance improvements achieved to date, and tools or data typically used in research in specific fields. So far, methods that have been developed to detect paper relations include machine learning and rule-based methods. However, a problem still arises in the process of sentence extraction from scientific paper documents, which is still done manually. This manual process causes the detection of scientific paper relations longer and inefficient. To overcome this problem, this study performs an automatic sentences extraction while the paper relations are identified based on the citation sentence. The performance of the built system is then compared with that of the manual extraction system. The analysis results suggested that the automatic sentence extraction indicates a very high level of performance in the detection of paper relations, which is close to that of manual sentence extraction.

Table Extraction from Web Pages Using Conditional Random Fields to Extract Toponym Related Data

NASA Astrophysics Data System (ADS)

Luthfi Hanifah, Hayyu'; Akbar, Saiful

2017-01-01

Table is one of the ways to visualize information on web pages. The abundant number of web pages that compose the World Wide Web has been the motivation of information extraction and information retrieval research, including the research for table extraction. Besides, there is a need for a system which is designed to specifically handle location-related information. Based on this background, this research is conducted to provide a way to extract location-related data from web tables so that it can be used in the development of Geographic Information Retrieval (GIR) system. The location-related data will be identified by the toponym (location name). In this research, a rule-based approach with gazetteer is used to recognize toponym from web table. Meanwhile, to extract data from a table, a combination of rule-based approach and statistical-based approach is used. On the statistical-based approach, Conditional Random Fields (CRF) model is used to understand the schema of the table. The result of table extraction is presented on JSON format. If a web table contains toponym, a field will be added on the JSON document to store the toponym values. This field can be used to index the table data in accordance to the toponym, which then can be used in the development of GIR system.

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.

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

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.

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.

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.

Automation of ⁹⁹Tc extraction by LOV prior ICP-MS detection: application to environmental samples.

PubMed

Rodríguez, Rogelio; Leal, Luz; Miranda, Silvia; Ferrer, Laura; Avivar, Jessica; García, Ariel; Cerdà, Víctor

2015-02-01

A new, fast, automated and inexpensive sample pre-treatment method for (99)Tc determination by inductively coupled plasma-mass spectrometry (ICP-MS) detection is presented. The miniaturized approach is based on a lab-on-valve (LOV) system, allowing automatic separation and preconcentration of (99)Tc. Selectivity is provided by the solid phase extraction system used (TEVA resin) which retains selectively pertechnetate ion in diluted nitric acid solution. The proposed system has some advantages such as minimization of sample handling, reduction of reagents volume, improvement of intermediate precision and sample throughput, offering a significant decrease of both time and cost per analysis in comparison to other flow techniques and batch methods. The proposed LOV system has been successfully applied to different samples of environmental interest (water and soil) with satisfactory recoveries, between 94% and 98%. The detection limit (LOD) of the developed method is 0.005 ng. The high durability of the resin and its low amount (32 mg), its good intermediate precision (RSD 3.8%) and repeatability (RSD 2%) and its high extraction frequency (up to 5 h(-1)) makes this method an inexpensive, high precision and fast tool for monitoring (99)Tc in environmental samples. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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

Modeling Increased Complexity and the Reliance on Automation: FLightdeck Automation Problems (FLAP) Model

NASA Technical Reports Server (NTRS)

Ancel, Ersin; Shih, Ann T.

2014-01-01

This paper highlights the development of a model that is focused on the safety issue of increasing complexity and reliance on automation systems in transport category aircraft. Recent statistics show an increase in mishaps related to manual handling and automation errors due to pilot complacency and over-reliance on automation, loss of situational awareness, automation system failures and/or pilot deficiencies. Consequently, the aircraft can enter a state outside the flight envelope and/or air traffic safety margins which potentially can lead to loss-of-control (LOC), controlled-flight-into-terrain (CFIT), or runway excursion/confusion accidents, etc. The goal of this modeling effort is to provide NASA's Aviation Safety Program (AvSP) with a platform capable of assessing the impacts of AvSP technologies and products towards reducing the relative risk of automation related accidents and incidents. In order to do so, a generic framework, capable of mapping both latent and active causal factors leading to automation errors, is developed. Next, the framework is converted into a Bayesian Belief Network model and populated with data gathered from Subject Matter Experts (SMEs). With the insertion of technologies and products, the model provides individual and collective risk reduction acquired by technologies and methodologies developed within AvSP.

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.

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...

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

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.

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

Munitions related feature extraction from LIDAR data.

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

Roberts, Barry L.

2010-06-01

The characterization of former military munitions ranges is critical in the identification of areas likely to contain residual unexploded ordnance (UXO). Although these ranges are large, often covering tens-of-thousands of acres, the actual target areas represent only a small fraction of the sites. The challenge is that many of these sites do not have records indicating locations of former target areas. The identification of target areas is critical in the characterization and remediation of these sites. The Strategic Environmental Research and Development Program (SERDP) and Environmental Security Technology Certification Program (ESTCP) of the DoD have been developing and implementing techniquesmore » for the efficient characterization of large munitions ranges. As part of this process, high-resolution LIDAR terrain data sets have been collected over several former ranges. These data sets have been shown to contain information relating to former munitions usage at these ranges, specifically terrain cratering due to high-explosives detonations. The location and relative intensity of crater features can provide information critical in reconstructing the usage history of a range, and indicate areas most likely to contain UXO. We have developed an automated procedure using an adaptation of the Circular Hough Transform for the identification of crater features in LIDAR terrain data. The Circular Hough Transform is highly adept at finding circular features (craters) in noisy terrain data sets. This technique has the ability to find features of a specific radius providing a means of filtering features based on expected scale and providing additional spatial characterization of the identified feature. This method of automated crater identification has been applied to several former munitions ranges with positive results.« less

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.

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.

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...

Toward automated classification of consumers' cancer-related questions with a new taxonomy of expected answer types.

PubMed

McRoy, Susan; Jones, Sean; Kurmally, Adam

2016-09-01

This article examines methods for automated question classification applied to cancer-related questions that people have asked on the web. This work is part of a broader effort to provide automated question answering for health education. We created a new corpus of consumer-health questions related to cancer and a new taxonomy for those questions. We then compared the effectiveness of different statistical methods for developing classifiers, including weighted classification and resampling. Basic methods for building classifiers were limited by the high variability in the natural distribution of questions and typical refinement approaches of feature selection and merging categories achieved only small improvements to classifier accuracy. Best performance was achieved using weighted classification and resampling methods, the latter yielding an accuracy of F1 = 0.963. Thus, it would appear that statistical classifiers can be trained on natural data, but only if natural distributions of classes are smoothed. Such classifiers would be useful for automated question answering, for enriching web-based content, or assisting clinical professionals to answer questions. © The Author(s) 2015.

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.

Deriving pathway maps from automated text analysis using a grammar-based approach.

PubMed

Olsson, Björn; Gawronska, Barbara; Erlendsson, Björn

2006-04-01

We demonstrate how automated text analysis can be used to support the large-scale analysis of metabolic and regulatory pathways by deriving pathway maps from textual descriptions found in the scientific literature. The main assumption is that correct syntactic analysis combined with domain-specific heuristics provides a good basis for relation extraction. Our method uses an algorithm that searches through the syntactic trees produced by a parser based on a Referent Grammar formalism, identifies relations mentioned in the sentence, and classifies them with respect to their semantic class and epistemic status (facts, counterfactuals, hypotheses). The semantic categories used in the classification are based on the relation set used in KEGG (Kyoto Encyclopedia of Genes and Genomes), so that pathway maps using KEGG notation can be automatically generated. We present the current version of the relation extraction algorithm and an evaluation based on a corpus of abstracts obtained from PubMed. The results indicate that the method is able to combine a reasonable coverage with high accuracy. We found that 61% of all sentences were parsed, and 97% of the parse trees were judged to be correct. The extraction algorithm was tested on a sample of 300 parse trees and was found to produce correct extractions in 90.5% of the cases.

Extraction of gravitational waves in numerical relativity.

PubMed

Bishop, Nigel T; Rezzolla, Luciano

2016-01-01

A numerical-relativity calculation yields in general a solution of the Einstein equations including also a radiative part, which is in practice computed in a region of finite extent. Since gravitational radiation is properly defined only at null infinity and in an appropriate coordinate system, the accurate estimation of the emitted gravitational waves represents an old and non-trivial problem in numerical relativity. A number of methods have been developed over the years to "extract" the radiative part of the solution from a numerical simulation and these include: quadrupole formulas, gauge-invariant metric perturbations, Weyl scalars, and characteristic extraction. We review and discuss each method, in terms of both its theoretical background as well as its implementation. Finally, we provide a brief comparison of the various methods in terms of their inherent advantages and disadvantages.

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.

Asleep at the automated wheel-Sleepiness and fatigue during highly automated driving.

PubMed

Vogelpohl, Tobias; Kühn, Matthias; Hummel, Thomas; Vollrath, Mark

2018-03-20

Due to the lack of active involvement in the driving situation and due to monotonous driving environments drivers with automation may be prone to become fatigued faster than manual drivers (e.g. Schömig et al., 2015). However, little is known about the progression of fatigue during automated driving and its effects on the ability to take back manual control after a take-over request. In this driving simulator study with Nö=ö60 drivers we used a three factorial 2ö×ö2ö×ö12 mixed design to analyze the progression (12ö×ö5ömin; within subjects) of driver fatigue in drivers with automation compared to manual drivers (between subjects). Driver fatigue was induced as either mainly sleep related or mainly task related fatigue (between subjects). Additionally, we investigated the drivers' reactions to a take-over request in a critical driving scenario to gain insights into the ability of fatigued drivers to regain manual control and situation awareness after automated driving. Drivers in the automated driving condition exhibited facial indicators of fatigue after 15 to 35ömin of driving. Manual drivers only showed similar indicators of fatigue if they suffered from a lack of sleep and then only after a longer period of driving (approx. 40ömin). Several drivers in the automated condition closed their eyes for extended periods of time. In the driving with automation condition mean automation deactivation times after a take-over request were slower for a certain percentage (about 30%) of the drivers with a lack of sleep (Mö=ö3.2; SDö=ö2.1ös) compared to the reaction times after a long drive (Mö=ö2.4; SDö=ö0.9ös). Drivers with automation also took longer than manual drivers to first glance at the speed display after a take-over request and were more likely to stay behind a braking lead vehicle instead of overtaking it. Drivers are unable to stay alert during extended periods of automated driving without non-driving related tasks. Fatigued drivers could

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.

Automated Agricultural Field Extraction from Multi-temporal Web Enabled Landsat Data

NASA Astrophysics Data System (ADS)

Yan, L.; Roy, D. P.

2012-12-01

Agriculture has caused significant anthropogenic surface change. In many regions agricultural field sizes may be increasing to maximize yields and reduce costs resulting in decreased landscape spatial complexity and increased homogenization of land uses with potential for significant biogeochemical and ecological effects. To date, studies of the incidence, drivers and impacts of changing field sizes have not been undertaken over large areas because of computational constraints and because consistently processed appropriate resolution data have not been available or affordable. The Landsat series of satellites provides near-global coverage, long term, and appropriate spatial resolution (30m) satellite data to document changing field sizes. The recent free availability of all the Landsat data in the U.S. Landsat archive now provides the opportunity to study field size changes in a global and consistent way. Commercial software can be used to extract fields from Landsat data but are inappropriate for large area application because they require considerable human interaction. This paper presents research to develop and validate an automated computational Geographic Object Based Image Analysis methodology to extract agricultural fields and derive field sizes from Web Enabled Landsat Data (WELD) (http://weld.cr.usgs.gov/). WELD weekly products (30m reflectance and brightness temperature) are classified into Satellite Image Automatic Mapper™ (SIAM™) spectral categories and an edge intensity map and a map of the probability of each pixel being agricultural are derived from five years of 52 weeks of WELD and corresponding SIAM™ data. These data are fused to derive candidate agriculture field segments using a variational region-based geometric active contour model. Geometry-based algorithms are used to decompose connected segments belonging to multiple fields into coherent isolated field objects with a divide and conquer strategy to detect and merge partial circle

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

Discovery of Predicate-Oriented Relations among Named Entities Extracted from Thai Texts

NASA Astrophysics Data System (ADS)

Tongtep, Nattapong; Theeramunkong, Thanaruk

Extracting named entities (NEs) and their relations is more difficult in Thai than in other languages due to several Thai specific characteristics, including no explicit boundaries for words, phrases and sentences; few case markers and modifier clues; high ambiguity in compound words and serial verbs; and flexible word orders. Unlike most previous works which focused on NE relations of specific actions, such as work_for, live_in, located_in, and kill, this paper proposes more general types of NE relations, called predicate-oriented relation (PoR), where an extracted action part (verb) is used as a core component to associate related named entities extracted from Thai Texts. Lacking a practical parser for the Thai language, we present three types of surface features, i.e. punctuation marks (such as token spaces), entity types and the number of entities and then apply five alternative commonly used learning schemes to investigate their performance on predicate-oriented relation extraction. The experimental results show that our approach achieves the F-measure of 97.76%, 99.19%, 95.00% and 93.50% on four different types of predicate-oriented relation (action-location, location-action, action-person and person-action) in crime-related news documents using a data set of 1,736 entity pairs. The effects of NE extraction techniques, feature sets and class unbalance on the performance of relation extraction are explored.

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.

Quantitative analysis of ex vivo colorectal epithelium using an automated feature extraction algorithm for microendoscopy image data

PubMed Central

Prieto, Sandra P.; Lai, Keith K.; Laryea, Jonathan A.; Mizell, Jason S.; Muldoon, Timothy J.

2016-01-01

Abstract. Qualitative screening for colorectal polyps via fiber bundle microendoscopy imaging has shown promising results, with studies reporting high rates of sensitivity and specificity, as well as low interobserver variability with trained clinicians. A quantitative image quality control and image feature extraction algorithm (QFEA) was designed to lessen the burden of training and provide objective data for improved clinical efficacy of this method. After a quantitative image quality control step, QFEA extracts field-of-view area, crypt area, crypt circularity, and crypt number per image. To develop and validate this QFEA, a training set of microendoscopy images was collected from freshly resected porcine colon epithelium. The algorithm was then further validated on ex vivo image data collected from eight human subjects, selected from clinically normal appearing regions distant from grossly visible tumor in surgically resected colorectal tissue. QFEA has proven flexible in application to both mosaics and individual images, and its automated crypt detection sensitivity ranges from 71 to 94% despite intensity and contrast variation within the field of view. It also demonstrates the ability to detect and quantify differences in grossly normal regions among different subjects, suggesting the potential efficacy of this approach in detecting occult regions of dysplasia. PMID:27335893

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

Automated sea floor extraction from underwater video

NASA Astrophysics Data System (ADS)

Kelly, Lauren; Rahmes, Mark; Stiver, James; McCluskey, Mike

2016-05-01

Ocean floor mapping using video is a method to simply and cost-effectively record large areas of the seafloor. Obtaining visual and elevation models has noteworthy applications in search and recovery missions. Hazards to navigation are abundant and pose a significant threat to the safety, effectiveness, and speed of naval operations and commercial vessels. This project's objective was to develop a workflow to automatically extract metadata from marine video and create image optical and elevation surface mosaics. Three developments made this possible. First, optical character recognition (OCR) by means of two-dimensional correlation, using a known character set, allowed for the capture of metadata from image files. Second, exploiting the image metadata (i.e., latitude, longitude, heading, camera angle, and depth readings) allowed for the determination of location and orientation of the image frame in mosaic. Image registration improved the accuracy of mosaicking. Finally, overlapping data allowed us to determine height information. A disparity map was created using the parallax from overlapping viewpoints of a given area and the relative height data was utilized to create a three-dimensional, textured elevation map.

A neural joint model for entity and relation extraction from biomedical text.

PubMed

Li, Fei; Zhang, Meishan; Fu, Guohong; Ji, Donghong

2017-03-31

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

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.

Automated and visual scoring methods of cerebral white matter hyperintensities: relation with age and cognitive function.

PubMed

Tiehuis, A M; Vincken, K L; Mali, W P T M; Kappelle, L J; Anbeek, P; Algra, A; Biessels, G J

2008-01-01

A reliable scoring method for ischemic cerebral white matter hyperintensities (WMH) will help to clarify the causes and consequences of these brain lesions. We compared an automated and two visual WMH scoring methods in their relations with age and cognitive function. MRI of the brain was performed on 154 participants of the Utrecht Diabetic Encephalopathy Study. WMH volumes were obtained with an automated segmentation method. Visual rating of deep and periventricular WMH (DWMH and PWMH) was performed with the Scheltens scale and the Rotterdam Scan Study (RSS) scale, respectively. Cognition was assessed with a battery of 11 tests. Within the whole study group, the association with age was most evident for the automated measured WMH volume (beta = 0.43, 95% CI = 0.29-0.57). With regard to cognition, automated measured WMH volume and Scheltens DWMH were significantly associated with information processing speed (beta = -0.22, 95% CI = -0.40 to -0.06; beta = -0.26, 95% CI = -0.42 to -0.10), whereas RSS PWMH were associated with attention and executive function (beta = -0.19, 95% CI = -0.36 to -0.02). Measurements of WMH with an automated quantitative segmentation method are comparable with visual rating scales and highly suitable for use in future studies to assess the relationship between WMH and subtle impairments in cognitive function. (c) 2007 S. Karger AG, Basel.

CD-REST: a system for extracting chemical-induced disease relation in literature.

PubMed

Xu, Jun; Wu, Yonghui; Zhang, Yaoyun; Wang, Jingqi; Lee, Hee-Jin; Xu, Hua

2016-01-01

Mining chemical-induced disease relations embedded in the vast biomedical literature could facilitate a wide range of computational biomedical applications, such as pharmacovigilance. The BioCreative V organized a Chemical Disease Relation (CDR) Track regarding chemical-induced disease relation extraction from biomedical literature in 2015. We participated in all subtasks of this challenge. In this article, we present our participation system Chemical Disease Relation Extraction SysTem (CD-REST), an end-to-end system for extracting chemical-induced disease relations in biomedical literature. CD-REST consists of two main components: (1) a chemical and disease named entity recognition and normalization module, which employs the Conditional Random Fields algorithm for entity recognition and a Vector Space Model-based approach for normalization; and (2) a relation extraction module that classifies both sentence-level and document-level candidate drug-disease pairs by support vector machines. Our system achieved the best performance on the chemical-induced disease relation extraction subtask in the BioCreative V CDR Track, demonstrating the effectiveness of our proposed machine learning-based approaches for automatic extraction of chemical-induced disease relations in biomedical literature. The CD-REST system provides web services using HTTP POST request. The web services can be accessed fromhttp://clinicalnlptool.com/cdr The online CD-REST demonstration system is available athttp://clinicalnlptool.com/cdr/cdr.html. Database URL:http://clinicalnlptool.com/cdr;http://clinicalnlptool.com/cdr/cdr.html. © The Author(s) 2016. Published by Oxford University Press.

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.

PubMed

Burlina, Philippe M; Joshi, Neil; Pekala, Michael; Pacheco, Katia D; Freund, David E; Bressler, Neil M

2017-11-01

Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage. Identification, though, can be time-intensive and requires expertly trained individuals. To develop methods for automatically detecting AMD from fundus images using a novel application of deep learning methods to the automated assessment of these images and to leverage artificial intelligence advances. Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer learning and universal features and with a trained clinical grader. Age-related macular degeneration automated detection was applied to a 2-class classification problem in which the task was to distinguish the disease-free/early stages from the referable intermediate/advanced stages. Using several experiments that entailed different data partitioning, the performance of the machine algorithms and human graders in evaluating over 130 000 images that were deidentified with respect to age, sex, and race/ethnicity from 4613 patients against a gold standard included in the National Institutes of Health Age-related Eye Disease Study data set was evaluated. Accuracy, receiver operating characteristics and area under the curve, and kappa score. The deep convolutional neural network method yielded accuracy (SD) that ranged between 88.4% (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94 and 0.96, and kappa coefficient (SD

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

PubMed

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

2017-05-01

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

A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques.

PubMed

Tsipouras, Markos G; Giannakeas, Nikolaos; Tzallas, Alexandros T; Tsianou, Zoe E; Manousou, Pinelopi; Hall, Andrew; Tsoulos, Ioannis; Tsianos, Epameinondas

2017-03-01

Collagen proportional area (CPA) extraction in liver biopsy images provides the degree of fibrosis expansion in liver tissue, which is the most characteristic histological alteration in hepatitis C virus (HCV). Assessment of the fibrotic tissue is currently based on semiquantitative staging scores such as Ishak and Metavir. Since its introduction as a fibrotic tissue assessment technique, CPA calculation based on image analysis techniques has proven to be more accurate than semiquantitative scores. However, CPA has yet to reach everyday clinical practice, since the lack of standardized and robust methods for computerized image analysis for CPA assessment have proven to be a major limitation. The current work introduces a three-stage fully automated methodology for CPA extraction based on machine learning techniques. Specifically, clustering algorithms have been employed for background-tissue separation, as well as for fibrosis detection in liver tissue regions, in the first and the third stage of the methodology, respectively. Due to the existence of several types of tissue regions in the image (such as blood clots, muscle tissue, structural collagen, etc.), classification algorithms have been employed to identify liver tissue regions and exclude all other non-liver tissue regions from CPA computation. For the evaluation of the methodology, 79 liver biopsy images have been employed, obtaining 1.31% mean absolute CPA error, with 0.923 concordance correlation coefficient. The proposed methodology is designed to (i) avoid manual threshold-based and region selection processes, widely used in similar approaches presented in the literature, and (ii) minimize CPA calculation time. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

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.

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.

Flight-deck automation: Promises and problems

NASA Technical Reports Server (NTRS)

Wiener, E. L.; Curry, R. E.

1980-01-01

The state of the art in human factors in flight-deck automation is presented. A number of critical problem areas are identified and broad design guidelines are offered. Automation-related aircraft accidents and incidents are discussed as examples of human factors problems in automated flight.

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.

Feature extraction and selection strategies for automated target recognition

NASA Astrophysics Data System (ADS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-04-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving.

PubMed

Hergeth, Sebastian; Lorenz, Lutz; Vilimek, Roman; Krems, Josef F

2016-05-01

The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving simulator study. Earlier research from other domains indicates that drivers' automation trust might be inferred from gaze behavior, such as monitoring frequency. The gaze behavior and self-reported automation trust of 35 participants attending to a visually demanding non-driving-related task (NDRT) during highly automated driving was evaluated. The relationship between dispositional, situational, and learned automation trust with gaze behavior was compared. Overall, there was a consistent relationship between drivers' automation trust and gaze behavior. Participants reporting higher automation trust tended to monitor the automation less frequently. Further analyses revealed that higher automation trust was associated with lower monitoring frequency of the automation during NDRTs, and an increase in trust over the experimental session was connected with a decrease in monitoring frequency. We suggest that (a) the current results indicate a negative relationship between drivers' self-reported automation trust and monitoring frequency, (b) gaze behavior provides a more direct measure of automation trust than other behavioral measures, and (c) with further refinement, drivers' automation trust during highly automated driving might be inferred from gaze behavior. Potential applications of this research include the estimation of drivers' automation trust and reliance during highly automated driving. © 2016, Human Factors and Ergonomics Society.

Key Relation Extraction from Biomedical Publications.

PubMed

Huang, Lan; Wang, Ye; Gong, Leiguang; Kulikowski, Casimir; Bai, Tian

2017-01-01

Within the large body of biomedical knowledge, recent findings and discoveries are most often presented as research articles. Their number has been increasing sharply since the turn of the century, presenting ever-growing challenges for search and discovery of knowledge and information related to specific topics of interest, even with the help of advanced online search tools. This is especially true when the goal of a search is to find or discover key relations between important concepts or topic words. We have developed an innovative method for extracting key relations between concepts from abstracts of articles. The method focuses on relations between keywords or topic words in the articles. Early experiments with the method on PubMed publications have shown promising results in searching and discovering keywords and their relationships that are strongly related to the main topic of an article.

An automated method to analyze language use in patients with schizophrenia and their first-degree relatives

PubMed Central

Elvevåg, Brita; Foltz, Peter W.; Rosenstein, Mark; DeLisi, Lynn E.

2009-01-01

Communication disturbances are prevalent in schizophrenia, and since it is a heritable illness these are likely present - albeit in a muted form - in the relatives of patients. Given the time-consuming, and often subjective nature of discourse analysis, these deviances are frequently not assayed in large scale studies. Recent work in computational linguistics and statistical-based semantic analysis has shown the potential and power of automated analysis of communication. We present an automated and objective approach to modeling discourse that detects very subtle deviations between probands, their first-degree relatives and unrelated healthy controls. Although these findings should be regarded as preliminary due to the limitations of the data at our disposal, we present a brief analysis of the models that best differentiate these groups in order to illustrate the utility of the method for future explorations of how language components are differentially affected by familial and illness related issues. PMID:20383310

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning.

PubMed

Feng, Yuntian; Zhang, Hongjun; Hao, Wenning; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q -Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

PubMed Central

Zhang, Hongjun; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score. PMID:28894463

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.

Social network extraction based on Web: 1. Related superficial methods

NASA Astrophysics Data System (ADS)

Khairuddin Matyuso Nasution, Mahyuddin

2018-01-01

Often the nature of something affects methods to resolve the related issues about it. Likewise, methods to extract social networks from the Web, but involve the structured data types differently. This paper reveals several methods of social network extraction from the same sources that is Web: the basic superficial method, the underlying superficial method, the description superficial method, and the related superficial methods. In complexity we derive the inequalities between methods and so are their computations. In this case, we find that different results from the same tools make the difference from the more complex to the simpler: Extraction of social network by involving co-occurrence is more complex than using occurrences.

Feature Extraction and Selection Strategies for Automated Target Recognition

NASA Technical Reports Server (NTRS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-01-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Background Knowledge in Learning-Based Relation Extraction

ERIC Educational Resources Information Center

Do, Quang Xuan

2012-01-01

In this thesis, we study the importance of background knowledge in relation extraction systems. We not only demonstrate the benefits of leveraging background knowledge to improve the systems' performance but also propose a principled framework that allows one to effectively incorporate knowledge into statistical machine learning models for…

Automatic extraction of relations between medical concepts in clinical texts

PubMed Central

Harabagiu, Sanda; Roberts, Kirk

2011-01-01

Objective A supervised machine learning approach to discover relations between medical problems, treatments, and tests mentioned in electronic medical records. Materials and methods A single support vector machine classifier was used to identify relations between concepts and to assign their semantic type. Several resources such as Wikipedia, WordNet, General Inquirer, and a relation similarity metric inform the classifier. Results The techniques reported in this paper were evaluated in the 2010 i2b2 Challenge and obtained the highest F1 score for the relation extraction task. When gold standard data for concepts and assertions were available, F1 was 73.7, precision was 72.0, and recall was 75.3. F1 is defined as 2*Precision*Recall/(Precision+Recall). Alternatively, when concepts and assertions were discovered automatically, F1 was 48.4, precision was 57.6, and recall was 41.7. Discussion Although a rich set of features was developed for the classifiers presented in this paper, little knowledge mining was performed from medical ontologies such as those found in UMLS. Future studies should incorporate features extracted from such knowledge sources, which we expect to further improve the results. Moreover, each relation discovery was treated independently. Joint classification of relations may further improve the quality of results. Also, joint learning of the discovery of concepts, assertions, and relations may also improve the results of automatic relation extraction. Conclusion Lexical and contextual features proved to be very important in relation extraction from medical texts. When they are not available to the classifier, the F1 score decreases by 3.7%. In addition, features based on similarity contribute to a decrease of 1.1% when they are not available. PMID:21846787

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.

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.

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.

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.

Three Experiments Examining the Use of Electroencephalogram,Event-Related Potentials, and Heart-Rate Variability for Real-Time Human-Centered Adaptive Automation Design

NASA Technical Reports Server (NTRS)

Prinzel, Lawrence J., III; Parasuraman, Raja; Freeman, Frederick G.; Scerbo, Mark W.; Mikulka, Peter J.; Pope, Alan T.

2003-01-01

Adaptive automation represents an advanced form of human-centered automation design. The approach to automation provides for real-time and model-based assessments of human-automation interaction, determines whether the human has entered into a hazardous state of awareness and then modulates the task environment to keep the operator in-the-loop , while maintaining an optimal state of task engagement and mental alertness. Because adaptive automation has not matured, numerous challenges remain, including what the criteria are, for determining when adaptive aiding and adaptive function allocation should take place. Human factors experts in the area have suggested a number of measures including the use of psychophysiology. This NASA Technical Paper reports on three experiments that examined the psychophysiological measures of event-related potentials, electroencephalogram, and heart-rate variability for real-time adaptive automation. The results of the experiments confirm the efficacy of these measures for use in both a developmental and operational role for adaptive automation design. The implications of these results and future directions for psychophysiology and human-centered automation design are discussed.

Effects of Non-Driving Related Task Modalities on Takeover Performance in Highly Automated Driving.

PubMed

Wandtner, Bernhard; Schömig, Nadja; Schmidt, Gerald

2018-04-01

Aim of the study was to evaluate the impact of different non-driving related tasks (NDR tasks) on takeover performance in highly automated driving. During highly automated driving, it is allowed to engage in NDR tasks temporarily. However, drivers must be able to take over control when reaching a system limit. There is evidence that the type of NDR task has an impact on takeover performance, but little is known about the specific task characteristics that account for performance decrements. Thirty participants drove in a simulator using a highly automated driving system. Each participant faced five critical takeover situations. Based on assumptions of Wickens's multiple resource theory, stimulus and response modalities of a prototypical NDR task were systematically manipulated. Additionally, in one experimental group, the task was locked out simultaneously with the takeover request. Task modalities had significant effects on several measures of takeover performance. A visual-manual texting task degraded performance the most, particularly when performed handheld. In contrast, takeover performance with an auditory-vocal task was comparable to a baseline without any task. Task lockout was associated with faster hands-on-wheel times but not altered brake response times. Results showed that NDR task modalities are relevant factors for takeover performance. An NDR task lockout was highly accepted by the drivers and showed moderate benefits for the first takeover reaction. Knowledge about the impact of NDR task characteristics is an enabler for adaptive takeover concepts. In addition, it might help regulators to make decisions on allowed NDR tasks during automated driving.

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.

Automation in organizations: Eternal conflict

NASA Technical Reports Server (NTRS)

Dieterly, D. L.

1981-01-01

Some ideas on and insights into the problems associated with automation in organizations are presented with emphasis on the concept of automation, its relationship to the individual, and its impact on system performance. An analogy is drawn, based on an American folk hero, to emphasize the extent of the problems encountered when dealing with automation within an organization. A model is proposed to focus attention on a set of appropriate dimensions. The function allocation process becomes a prominent aspect of the model. The current state of automation research is mentioned in relation to the ideas introduced. Proposed directions for an improved understanding of automation's effect on the individual's efficiency are discussed. The importance of understanding the individual's perception of the system in terms of the degree of automation is highlighted.

A semi-automated methodology for finding lipid-related GO terms.

PubMed

Fan, Mengyuan; Low, Hong Sang; Wenk, Markus R; Wong, Limsoon

2014-01-01

Although semantic similarity in Gene Ontology (GO) and other approaches may be used to find similar GO terms, there is yet a method to systematically find a class of GO terms sharing a common property with high accuracy (e.g., involving human curation). We have developed a methodology to address this issue and applied it to identify lipid-related GO terms, owing to the important and varied roles of lipids in many biological processes. Our methodology finds lipid-related GO terms in a semi-automated manner, requiring only moderate manual curation. We first obtain a list of lipid-related gold-standard GO terms by keyword search and manual curation. Then, based on the hypothesis that co-annotated GO terms share similar properties, we develop a machine learning method that expands the list of lipid-related terms from the gold standard. Those terms predicted most likely to be lipid related are examined by a human curator following specific curation rules to confirm the class labels. The structure of GO is also exploited to help reduce the curation effort. The prediction and curation cycle is repeated until no further lipid-related term is found. Our approach has covered a high proportion, if not all, of lipid-related terms with relatively high efficiency. http://compbio.ddns.comp.nus.edu.sg/∼lipidgo. © The Author(s) 2014. Published by Oxford University Press.

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.

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.

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.

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.

Cockpit Adaptive Automation and Pilot Performance

NASA Technical Reports Server (NTRS)

Parasuraman, Raja

2001-01-01

The introduction of high-level automated systems in the aircraft cockpit has provided several benefits, e.g., new capabilities, enhanced operational efficiency, and reduced crew workload. At the same time, conventional 'static' automation has sometimes degraded human operator monitoring performance, increased workload, and reduced situation awareness. Adaptive automation represents an alternative to static automation. In this approach, task allocation between human operators and computer systems is flexible and context-dependent rather than static. Adaptive automation, or adaptive task allocation, is thought to provide for regulation of operator workload and performance, while preserving the benefits of static automation. In previous research we have reported beneficial effects of adaptive automation on the performance of both pilots and non-pilots of flight-related tasks. For adaptive systems to be viable, however, such benefits need to be examined jointly in the context of a single set of tasks. The studies carried out under this project evaluated a systematic method for combining different forms of adaptive automation. A model for effective combination of different forms of adaptive automation, based on matching adaptation to operator workload was proposed and tested. The model was evaluated in studies using IFR-rated pilots flying a general-aviation simulator. Performance, subjective, and physiological (heart rate variability, eye scan-paths) measures of workload were recorded. The studies compared workload-based adaptation to to non-adaptive control conditions and found evidence for systematic benefits of adaptive automation. The research provides an empirical basis for evaluating the effectiveness of adaptive automation in the cockpit. The results contribute to the development of design principles and guidelines for the implementation of adaptive automation in the cockpit, particularly in general aviation, and in other human-machine systems. Project goals

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.

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.

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.

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.

Extraction of a group-pair relation: problem-solving relation from web-board documents.

PubMed

Pechsiri, Chaveevan; Piriyakul, Rapepun

2016-01-01

This paper aims to extract a group-pair relation as a Problem-Solving relation, for example a DiseaseSymptom-Treatment relation and a CarProblem-Repair relation, between two event-explanation groups, a problem-concept group as a symptom/CarProblem-concept group and a solving-concept group as a treatment-concept/repair concept group from hospital-web-board and car-repair-guru-web-board documents. The Problem-Solving relation (particularly Symptom-Treatment relation) including the graphical representation benefits non-professional persons by supporting knowledge of primarily solving problems. The research contains three problems: how to identify an EDU (an Elementary Discourse Unit, which is a simple sentence) with the event concept of either a problem or a solution; how to determine a problem-concept EDU boundary and a solving-concept EDU boundary as two event-explanation groups, and how to determine the Problem-Solving relation between these two event-explanation groups. Therefore, we apply word co-occurrence to identify a problem-concept EDU and a solving-concept EDU, and machine-learning techniques to solve a problem-concept EDU boundary and a solving-concept EDU boundary. We propose using k-mean and Naïve Bayes to determine the Problem-Solving relation between the two event-explanation groups involved with clustering features. In contrast to previous works, the proposed approach enables group-pair relation extraction with high accuracy.

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 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.

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.

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).

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.

Improving treatment plan evaluation with automation

PubMed Central

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.

2016-01-01

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 physics 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 33 checklist 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 successfully 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. PACS number(s): 87.55.‐x, 87.55.N‐, 87.55.Qr, 87.55.tm, 89.20.Bb PMID:27929478

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.

Sortal anaphora resolution to enhance relation extraction from biomedical literature.

PubMed

Kilicoglu, Halil; Rosemblat, Graciela; Fiszman, Marcelo; Rindflesch, Thomas C

2016-04-14

Entity coreference is common in biomedical literature and it can affect text understanding systems that rely on accurate identification of named entities, such as relation extraction and automatic summarization. Coreference resolution is a foundational yet challenging natural language processing task which, if performed successfully, is likely to enhance such systems significantly. In this paper, we propose a semantically oriented, rule-based method to resolve sortal anaphora, a specific type of coreference that forms the majority of coreference instances in biomedical literature. The method addresses all entity types and relies on linguistic components of SemRep, a broad-coverage biomedical relation extraction system. It has been incorporated into SemRep, extending its core semantic interpretation capability from sentence level to discourse level. We evaluated our sortal anaphora resolution method in several ways. The first evaluation specifically focused on sortal anaphora relations. Our methodology achieved a F1 score of 59.6 on the test portion of a manually annotated corpus of 320 Medline abstracts, a 4-fold improvement over the baseline method. Investigating the impact of sortal anaphora resolution on relation extraction, we found that the overall effect was positive, with 50 % of the changes involving uninformative relations being replaced by more specific and informative ones, while 35 % of the changes had no effect, and only 15 % were negative. We estimate that anaphora resolution results in changes in about 1.5 % of approximately 82 million semantic relations extracted from the entire PubMed. Our results demonstrate that a heavily semantic approach to sortal anaphora resolution is largely effective for biomedical literature. Our evaluation and error analysis highlight some areas for further improvements, such as coordination processing and intra-sentential antecedent selection.

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

Automated Desalting Apparatus

NASA Technical Reports Server (NTRS)

Spencer, Maegan K.; Liu, De-Ling; Kanik, Isik; Beegle, Luther

2010-01-01

Because salt and metals can mask the signature of a variety of organic molecules (like amino acids) in any given sample, an automated system to purify complex field samples has been created for the analytical techniques of electrospray ionization/ mass spectroscopy (ESI/MS), capillary electrophoresis (CE), and biological assays where unique identification requires at least some processing of complex samples. This development allows for automated sample preparation in the laboratory and analysis of complex samples in the field with multiple types of analytical instruments. Rather than using tedious, exacting protocols for desalting samples by hand, this innovation, called the Automated Sample Processing System (ASPS), takes analytes that have been extracted through high-temperature solvent extraction and introduces them into the desalting column. After 20 minutes, the eluent is produced. This clear liquid can then be directly analyzed by the techniques listed above. The current apparatus including the computer and power supplies is sturdy, has an approximate mass of 10 kg, and a volume of about 20 20 20 cm, and is undergoing further miniaturization. This system currently targets amino acids. For these molecules, a slurry of 1 g cation exchange resin in deionized water is packed into a column of the apparatus. Initial generation of the resin is done by flowing sequentially 2.3 bed volumes of 2N NaOH and 2N HCl (1 mL each) to rinse the resin, followed by .5 mL of deionized water. This makes the pH of the resin near neutral, and eliminates cross sample contamination. Afterward, 2.3 mL of extracted sample is then loaded into the column onto the top of the resin bed. Because the column is packed tightly, the sample can be applied without disturbing the resin bed. This is a vital step needed to ensure that the analytes adhere to the resin. After the sample is drained, oxalic acid (1 mL, pH 1.6-1.8, adjusted with NH4OH) is pumped into the column. Oxalic acid works as a

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...

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.

Bioprocessing automation in cell therapy manufacturing: Outcomes of special interest group automation workshop.

PubMed

Ball, Oliver; Robinson, Sarah; Bure, Kim; Brindley, David A; Mccall, David

2018-04-01

Phacilitate held a Special Interest Group workshop event in Edinburgh, UK, in May 2017. The event brought together leading stakeholders in the cell therapy bioprocessing field to identify present and future challenges and propose potential solutions to automation in cell therapy bioprocessing. Here, we review and summarize discussions from the event. Deep biological understanding of a product, its mechanism of action and indication pathogenesis underpin many factors relating to bioprocessing and automation. To fully exploit the opportunities of bioprocess automation, therapeutics developers must closely consider whether an automation strategy is applicable, how to design an 'automatable' bioprocess and how to implement process modifications with minimal disruption. Major decisions around bioprocess automation strategy should involve all relevant stakeholders; communication between technical and business strategy decision-makers is of particular importance. Developers should leverage automation to implement in-process testing, in turn applicable to process optimization, quality assurance (QA)/ quality control (QC), batch failure control, adaptive manufacturing and regulatory demands, but a lack of precedent and technical opportunities can complicate such efforts. Sparse standardization across product characterization, hardware components and software platforms is perceived to complicate efforts to implement automation. The use of advanced algorithmic approaches such as machine learning may have application to bioprocess and supply chain optimization. Automation can substantially de-risk the wider supply chain, including tracking and traceability, cryopreservation and thawing and logistics. The regulatory implications of automation are currently unclear because few hardware options exist and novel solutions require case-by-case validation, but automation can present attractive regulatory incentives. Copyright © 2018 International Society for Cellular Therapy

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

A Customized Attention-Based Long Short-Term Memory Network for Distant Supervised Relation Extraction.

PubMed

He, Dengchao; Zhang, Hongjun; Hao, Wenning; Zhang, Rui; Cheng, Kai

2017-07-01

Distant supervision, a widely applied approach in the field of relation extraction can automatically generate large amounts of labeled training corpus with minimal manual effort. However, the labeled training corpus may have many false-positive data, which would hurt the performance of relation extraction. Moreover, in traditional feature-based distant supervised approaches, extraction models adopt human design features with natural language processing. It may also cause poor performance. To address these two shortcomings, we propose a customized attention-based long short-term memory network. Our approach adopts word-level attention to achieve better data representation for relation extraction without manually designed features to perform distant supervision instead of fully supervised relation extraction, and it utilizes instance-level attention to tackle the problem of false-positive data. Experimental results demonstrate that our proposed approach is effective and achieves better performance than traditional methods.

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

Concept recognition for extracting protein interaction relations from biomedical text

PubMed Central

Baumgartner, William A; Lu, Zhiyong; Johnson, Helen L; Caporaso, J Gregory; Paquette, Jesse; Lindemann, Anna; White, Elizabeth K; Medvedeva, Olga; Cohen, K Bretonnel; Hunter, Lawrence

2008-01-01

Background: Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing. Results: Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist. Conclusion: Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet . PMID:18834500

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.

Dried Blood Spot Proteomics: Surface Extraction of Endogenous Proteins Coupled with Automated Sample Preparation and Mass Spectrometry Analysis

NASA Astrophysics Data System (ADS)

Martin, Nicholas J.; Bunch, Josephine; Cooper, Helen J.

2013-08-01

Dried blood spots offer many advantages as a sample format including ease and safety of transport and handling. To date, the majority of mass spectrometry analyses of dried blood spots have focused on small molecules or hemoglobin. However, dried blood spots are a potentially rich source of protein biomarkers, an area that has been overlooked. To address this issue, we have applied an untargeted bottom-up proteomics approach to the analysis of dried blood spots. We present an automated and integrated method for extraction of endogenous proteins from the surface of dried blood spots and sample preparation via trypsin digestion by use of the Advion Biosciences Triversa Nanomate robotic platform. Liquid chromatography tandem mass spectrometry of the resulting digests enabled identification of 120 proteins from a single dried blood spot. The proteins identified cross a concentration range of four orders of magnitude. The method is evaluated and the results discussed in terms of the proteins identified and their potential use as biomarkers in screening programs.

A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems.

PubMed

Peng, Yifan; Torii, Manabu; Wu, Cathy H; Vijay-Shanker, K

2014-08-23

Text mining is increasingly used in the biomedical domain because of its ability to automatically gather information from large amount of scientific articles. One important task in biomedical text mining is relation extraction, which aims to identify designated relations among biological entities reported in literature. A relation extraction system achieving high performance is expensive to develop because of the substantial time and effort required for its design and implementation. Here, we report a novel framework to facilitate the development of a pattern-based biomedical relation extraction system. It has several unique design features: (1) leveraging syntactic variations possible in a language and automatically generating extraction patterns in a systematic manner, (2) applying sentence simplification to improve the coverage of extraction patterns, and (3) identifying referential relations between a syntactic argument of a predicate and the actual target expected in the relation extraction task. A relation extraction system derived using the proposed framework achieved overall F-scores of 72.66% for the Simple events and 55.57% for the Binding events on the BioNLP-ST 2011 GE test set, comparing favorably with the top performing systems that participated in the BioNLP-ST 2011 GE task. We obtained similar results on the BioNLP-ST 2013 GE test set (80.07% and 60.58%, respectively). We conducted additional experiments on the training and development sets to provide a more detailed analysis of the system and its individual modules. This analysis indicates that without increasing the number of patterns, simplification and referential relation linking play a key role in the effective extraction of biomedical relations. In this paper, we present a novel framework for fast development of relation extraction systems. The framework requires only a list of triggers as input, and does not need information from an annotated corpus. Thus, we reduce the involvement of domain

Extracting microRNA-gene relations from biomedical literature using distant supervision

PubMed Central

Clarke, Luka A.; Couto, Francisco M.

2017-01-01

Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel. PMID:28263989

Extracting microRNA-gene relations from biomedical literature using distant supervision.

PubMed

Lamurias, Andre; Clarke, Luka A; Couto, Francisco M

2017-01-01

Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel.

Effects of a psychophysiological system for adaptive automation on performance, workload, and the event-related potential P300 component

NASA Technical Reports Server (NTRS)

Prinzel, Lawrence J 3rd; Freeman, Frederick G.; Scerbo, Mark W.; Mikulka, Peter J.; Pope, Alan T.

2003-01-01

The present study examined the effects of an electroencephalographic- (EEG-) based system for adaptive automation on tracking performance and workload. In addition, event-related potentials (ERPs) to a secondary task were derived to determine whether they would provide an additional degree of workload specificity. Participants were run in an adaptive automation condition, in which the system switched between manual and automatic task modes based on the value of each individual's own EEG engagement index; a yoked control condition; or another control group, in which task mode switches followed a random pattern. Adaptive automation improved performance and resulted in lower levels of workload. Further, the P300 component of the ERP paralleled the sensitivity to task demands of the performance and subjective measures across conditions. These results indicate that it is possible to improve performance with a psychophysiological adaptive automation system and that ERPs may provide an alternative means for distinguishing among levels of cognitive task demand in such systems. Actual or potential applications of this research include improved methods for assessing operator workload and performance.

An automated flow injection system for metal determination by flame atomic absorption spectrometry involving on-line fabric disk sorptive extraction technique.

PubMed

Anthemidis, A; Kazantzi, V; Samanidou, V; Kabir, A; Furton, K G

2016-08-15

A novel flow injection-fabric disk sorptive extraction (FI-FDSE) system was developed for automated determination of trace metals. The platform was based on a minicolumn packed with sol-gel coated fabric media in the form of disks, incorporated into an on-line solid-phase extraction system, coupled with flame atomic absorption spectrometry (FAAS). This configuration provides minor backpressure, resulting in high loading flow rates and shorter analytical cycles. The potentials of this technique were demonstrated for trace lead and cadmium determination in environmental water samples. The applicability of different sol-gel coated FPSE media was investigated. The on-line formed complex of metal with ammonium pyrrolidine dithiocarbamate (APDC) was retained onto the fabric surface and methyl isobutyl ketone (MIBK) was used to elute the analytes prior to atomization. For 90s preconcentration time, enrichment factors of 140 and 38 and detection limits (3σ) of 1.8 and 0.4μgL(-1) were achieved for lead and cadmium determination, respectively, with a sampling frequency of 30h(-1). The accuracy of the proposed method was estimated by analyzing standard reference materials and spiked water samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals.

PubMed

Sudarshan, Vidya K; Acharya, U Rajendra; Oh, Shu Lih; Adam, Muhammad; Tan, Jen Hong; Chua, Chua Kuang; Chua, Kok Poo; Tan, Ru San

2017-04-01

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Automation U.S.A.: Overcoming Barriers to Automation.

ERIC Educational Resources Information Center

Brody, Herb

1985-01-01

Although labor unions and inadequate technology play minor roles, the principal barrier to factory automation is "fear of change." Related problems include long-term benefits, nontechnical executives, and uncertainty of factory cost accounting. Industry support for university programs is helping to educate engineers to design, implement, and…

Personnel Administration in an Automated Environment.

ERIC Educational Resources Information Center

Leinbach, Philip E.; And Others

1990-01-01

Fourteen articles address issues related to library personnel administration in an automated environment, such as education for automation, salaries, impact of technology, expert systems, core competencies, administrative issues, technology services, job satisfaction, and performance appraisal. A selected annotated bibliography is included. (MES)

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.

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...

Altering user' acceptance of automation through prior automation exposure.

PubMed

Bekier, Marek; Molesworth, Brett R C

2017-06-01

Air navigation service providers worldwide see increased use of automation as one solution to overcome the capacity constraints imbedded in the present air traffic management (ATM) system. However, increased use of automation within any system is dependent on user acceptance. The present research sought to determine if the point at which an individual is no longer willing to accept or cooperate with automation can be manipulated. Forty participants underwent training on a computer-based air traffic control programme, followed by two ATM exercises (order counterbalanced), one with and one without the aid of automation. Results revealed after exposure to a task with automation assistance, user acceptance of high(er) levels of automation ('tipping point') decreased; suggesting it is indeed possible to alter automation acceptance. Practitioner Summary: This paper investigates whether the point at which a user of automation rejects automation (i.e. 'tipping point') is constant or can be manipulated. The results revealed after exposure to a task with automation assistance, user acceptance of high(er) levels of automation decreased; suggesting it is possible to alter automation acceptance.

Extracting Related Words from Anchor Text Clusters by Focusing on the Page Designer's Intention

NASA Astrophysics Data System (ADS)

Liu, Jianquan; Chen, Hanxiong; Furuse, Kazutaka; Ohbo, Nobuo

Approaches for extracting related words (terms) by co-occurrence work poorly sometimes. Two words frequently co-occurring in the same documents are considered related. However, they may not relate at all because they would have no common meanings nor similar semantics. We address this problem by considering the page designer’s intention and propose a new model to extract related words. Our approach is based on the idea that the web page designers usually make the correlative hyperlinks appear in close zone on the browser. We developed a browser-based crawler to collect “geographically” near hyperlinks, then by clustering these hyperlinks based on their pixel coordinates, we extract related words which can well reflect the designer’s intention. Experimental results show that our method can represent the intention of the web page designer in extremely high precision. Moreover, the experiments indicate that our extracting method can obtain related words in a high average precision.

Pilot workload, performance and aircraft control automation

NASA Technical Reports Server (NTRS)

Hart, S. G.; Sheridan, T. B.

1984-01-01

Conceptual and practical issues associated with the design, operation, and performance of advanced systems and the impact of such systems on the human operators are reviewed. The development of highly automated systems is driven by the availability of new technology and the requirement that operators safely and economically perform more and more activities in increasingly difficult and hostile environments. It is noted that the operators workload may become a major area of concern in future design considerations. Little research was done to determine how automation and workload relate to each other, although it is assumed that the abstract, supervisory, or management roles that are performed by operators of highly automated systems will impose increased mental workload. The relationship between performance and workload is discussed in relation to highly complex and automated environments.

Complacency and Automation Bias in the Use of Imperfect Automation.

PubMed

Wickens, Christopher D; Clegg, Benjamin A; Vieane, Alex Z; Sebok, Angelia L

2015-08-01

We examine the effects of two different kinds of decision-aiding automation errors on human-automation interaction (HAI), occurring at the first failure following repeated exposure to correctly functioning automation. The two errors are incorrect advice, triggering the automation bias, and missing advice, reflecting complacency. Contrasts between analogous automation errors in alerting systems, rather than decision aiding, have revealed that alerting false alarms are more problematic to HAI than alerting misses are. Prior research in decision aiding, although contrasting the two aiding errors (incorrect vs. missing), has confounded error expectancy. Participants performed an environmental process control simulation with and without decision aiding. For those with the aid, automation dependence was created through several trials of perfect aiding performance, and an unexpected automation error was then imposed in which automation was either gone (one group) or wrong (a second group). A control group received no automation support. The correct aid supported faster and more accurate diagnosis and lower workload. The aid failure degraded all three variables, but "automation wrong" had a much greater effect on accuracy, reflecting the automation bias, than did "automation gone," reflecting the impact of complacency. Some complacency was manifested for automation gone, by a longer latency and more modest reduction in accuracy. Automation wrong, creating the automation bias, appears to be a more problematic form of automation error than automation gone, reflecting complacency. Decision-aiding automation should indicate its lower degree of confidence in uncertain environments to avoid the automation bias. © 2015, Human Factors and Ergonomics Society.

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.

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

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.

Human-Centered Aviation Automation: Principles and Guidelines

NASA Technical Reports Server (NTRS)

Billings, Charles E.

1996-01-01

This document presents principles and guidelines for human-centered automation in aircraft and in the aviation system. Drawing upon operational experience with highly automated aircraft, it describes classes of problems that have occurred in these vehicles, the effects of advanced automation on the human operators of the aviation system, and ways in which these problems may be avoided in the design of future aircraft and air traffic management automation. Many incidents and a few serious accidents suggest that these problems are related to automation complexity, autonomy, coupling, and opacity, or inadequate feedback to operators. An automation philosophy that emphasizes improved communication, coordination and cooperation between the human and machine elements of this complex, distributed system is required to improve the safety and efficiency of aviation operations in the future.

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 intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes

PubMed Central

de Sisternes, Luis; Jonna, Gowtham; Moss, Jason; Marmor, Michael F.; Leng, Theodore; Rubin, Daniel L.

2017-01-01

This work introduces and evaluates an automated intra-retinal segmentation method for spectral-domain optical coherence (SD-OCT) retinal images. While quantitative assessment of retinal features in SD-OCT data is important, manual segmentation is extremely time-consuming and subjective. We address challenges that have hindered prior automated methods, including poor performance with diseased retinas relative to healthy retinas, and data smoothing that obscures image features such as small retinal drusen. Our novel segmentation approach is based on the iterative adaptation of a weighted median process, wherein a three-dimensional weighting function is defined according to image intensity and gradient properties, and a set of smoothness constraints and pre-defined rules are considered. We compared the segmentation results for 9 segmented outlines associated with intra-retinal boundaries to those drawn by hand by two retinal specialists and to those produced by an independent state-of-the-art automated software tool in a set of 42 clinical images (from 14 patients). These images were obtained with a Zeiss Cirrus SD-OCT system, including healthy, early or intermediate AMD, and advanced AMD eyes. As a qualitative evaluation of accuracy, a highly experienced third independent reader blindly rated the quality of the outlines produced by each method. The accuracy and image detail of our method was superior in healthy and early or intermediate AMD eyes (98.15% and 97.78% of results not needing substantial editing) to the automated method we compared against. While the performance was not as good in advanced AMD (68.89%), it was still better than the manual outlines or the comparison method (which failed in such cases). We also tested our method’s performance on images acquired with a different SD-OCT manufacturer, collected from a large publicly available data set (114 healthy and 255 AMD eyes), and compared the data quantitatively to reference standard markings of the

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.

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.

Selecting automation for the clinical chemistry laboratory.

PubMed

Melanson, Stacy E F; Lindeman, Neal I; Jarolim, Petr

2007-07-01

Laboratory automation proposes to improve the quality and efficiency of laboratory operations, and may provide a solution to the quality demands and staff shortages faced by today's clinical laboratories. Several vendors offer automation systems in the United States, with both subtle and obvious differences. Arriving at a decision to automate, and the ensuing evaluation of available products, can be time-consuming and challenging. Although considerable discussion concerning the decision to automate has been published, relatively little attention has been paid to the process of evaluating and selecting automation systems. To outline a process for evaluating and selecting automation systems as a reference for laboratories contemplating laboratory automation. Our Clinical Chemistry Laboratory staff recently evaluated all major laboratory automation systems in the United States, with their respective chemistry and immunochemistry analyzers. Our experience is described and organized according to the selection process, the important considerations in clinical chemistry automation, decisions and implementation, and we give conclusions pertaining to this experience. Including the formation of a committee, workflow analysis, submitting a request for proposal, site visits, and making a final decision, the process of selecting chemistry automation took approximately 14 months. We outline important considerations in automation design, preanalytical processing, analyzer selection, postanalytical storage, and data management. Selecting clinical chemistry laboratory automation is a complex, time-consuming process. Laboratories considering laboratory automation may benefit from the concise overview and narrative and tabular suggestions provided.

Intelligent Automation Approach for Improving Pilot Situational Awareness

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

2004-01-01

Automation in the aviation domain has been increasing for the past two decades. Pilot reaction to automation varies from highly favorable to highly critical depending on both the pilot's background and how effectively the automation is implemented. We describe a user-centered approach for automation that considers the pilot's tasks and his needs related to accomplishing those tasks. Further, we augment rather than replace how the pilot currently fulfills his goals, relying on redundant displays that offer the pilot an opportunity to build trust in the automation. Our prototype system automates the interpretation of hydraulic system faults of the UH-60 helicopter. We describe the problem with the current system and our methodology for resolving it.

Automated Vehicle Regulation: An Energy and Emissions Perspective

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

Levine, Aaron

This presentation provides a summary of the current automated vehicles polices in the United States and how they related to reducing greenhouse gas (GHG) emissions. The presentation then looks at future automated vehicle trends that will increase and reduce GHG emissions and what current policies utilized in other areas of law could be adapted for automated vehicle GHG emissions.

Improving the driver-automation interaction: an approach using automation uncertainty.

PubMed

Beller, Johannes; Heesen, Matthias; Vollrath, Mark

2013-12-01

The aim of this study was to evaluate whether communicating automation uncertainty improves the driver-automation interaction. A false system understanding of infallibility may provoke automation misuse and can lead to severe consequences in case of automation failure. The presentation of automation uncertainty may prevent this false system understanding and, as was shown by previous studies, may have numerous benefits. Few studies, however, have clearly shown the potential of communicating uncertainty information in driving. The current study fills this gap. We conducted a driving simulator experiment, varying the presented uncertainty information between participants (no uncertainty information vs. uncertainty information) and the automation reliability (high vs.low) within participants. Participants interacted with a highly automated driving system while engaging in secondary tasks and were required to cooperate with the automation to drive safely. Quantile regressions and multilevel modeling showed that the presentation of uncertainty information increases the time to collision in the case of automation failure. Furthermore, the data indicated improved situation awareness and better knowledge of fallibility for the experimental group. Consequently, the automation with the uncertainty symbol received higher trust ratings and increased acceptance. The presentation of automation uncertaintythrough a symbol improves overall driver-automation cooperation. Most automated systems in driving could benefit from displaying reliability information. This display might improve the acceptance of fallible systems and further enhances driver-automation cooperation.

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.

Workshop on Office Automation and Telecommunication: Applying the Technology.

ERIC Educational Resources Information Center

Mitchell, Bill

This document contains 12 outlines that forecast the office of the future. The outlines cover the following topics: (1) office automation definition and objectives; (2) functional categories of office automation software packages for mini and mainframe computers; (3) office automation-related software for microcomputers; (4) office automation…

Examination of Automation-Induced Complacency and Individual Difference Variates

NASA Technical Reports Server (NTRS)

Prinzel, Lawrence J., III; DeVries, Holly; Freeman, Fred G.; Mikulka, Peter

2001-01-01

Automation-induced complacency has been documented as a cause or contributing factor in many airplane accidents throughout the last two decades. It is surmised that the condition results when a crew is working in highly reliable automated environments in which they serve as supervisory controllers monitoring system states for occasional automation failures. Although many reports have discussed the dangers of complacency, little empirical research has been produced to substantiate its harmful effects on performance as well as what factors produce complacency. There have been some suggestions, however, that individual characteristics could serve as possible predictors of performance in automated systems. The present study examined relationship between the individual differences of complacency potential, boredom proneness, and cognitive failure, automation-induced complacency. Workload and boredom scores were also collected and analyzed in relation to the three individual differences. The results of the study demonstrated that there are personality individual differences that are related to whether an individual will succumb to automation-induced complacency. Theoretical and practical implications are discussed.

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

Distant supervision for neural relation extraction integrated with word attention and property features.

PubMed

Qu, Jianfeng; Ouyang, Dantong; Hua, Wen; Ye, Yuxin; Li, Ximing

2018-04-01

Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts. However, the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances. To address the above issues, we propose a novel neural relation extraction model. First, we develop a word-level attention mechanism to distinguish the importance of each individual word in a sentence, increasing the attention weights for those critical words. Second, we investigate the semantic information from word embeddings of target entities, which can be developed as a supplementary feature for the extractor. Experimental results show that our model outperforms previous state-of-the-art baselines. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automation in haemostasis.

PubMed

Huber, A R; Méndez, A; Brunner-Agten, S

2013-01-01

Automatia, an ancient Greece goddess of luck who makes things happen by themselves and on her own will without human engagement, is present in our daily life in the medical laboratory. Automation has been introduced and perfected by clinical chemistry and since then expanded into other fields such as haematology, immunology, molecular biology and also coagulation testing. The initial small and relatively simple standalone instruments have been replaced by more complex systems that allow for multitasking. Integration of automated coagulation testing into total laboratory automation has become possible in the most recent years. Automation has many strengths and opportunities if weaknesses and threats are respected. On the positive side, standardization, reduction of errors, reduction of cost and increase of throughput are clearly beneficial. Dependence on manufacturers, high initiation cost and somewhat expensive maintenance are less favourable factors. The modern lab and especially the todays lab technicians and academic personnel in the laboratory do not add value for the doctor and his patients by spending lots of time behind the machines. In the future the lab needs to contribute at the bedside suggesting laboratory testing and providing support and interpretation of the obtained results. The human factor will continue to play an important role in testing in haemostasis yet under different circumstances.

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.

Automated Power Assessment for Helicopter Turboshaft Engines

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Litt, Jonathan S.

2008-01-01

An accurate indication of available power is required for helicopter mission planning purposes. Available power is currently estimated on U.S. Army Blackhawk helicopters by performing a Maximum Power Check (MPC), a manual procedure performed by maintenance pilots on a periodic basis. The MPC establishes Engine Torque Factor (ETF), an indication of available power. It is desirable to replace the current manual MPC procedure with an automated approach that will enable continuous real-time assessment of available power utilizing normal mission data. This report presents an automated power assessment approach which processes data currently collected within helicopter Health and Usage Monitoring System (HUMS) units. The overall approach consists of: 1) a steady-state data filter which identifies and extracts steady-state operating points within HUMS data sets; 2) engine performance curve trend monitoring and updating; and 3) automated ETF calculation. The algorithm is coded in MATLAB (The MathWorks, Inc.) and currently runs on a PC. Results from the application of this technique to HUMS mission data collected from UH-60L aircraft equipped with T700-GE-701C engines are presented and compared to manually calculated ETF values. Potential future enhancements are discussed.

Architecture Views Illustrating the Service Automation Aspect of SOA

NASA Astrophysics Data System (ADS)

Gu, Qing; Cuadrado, Félix; Lago, Patricia; Duenãs, Juan C.

Earlier in this book, Chapter 8 provided a detailed analysis of service engineering, including a review of service engineering techniques and methodologies. This chapter is closely related to Chapter 8 as shows how such approaches can be used to develop a service, with particular emphasis on the identification of three views (the automation decision view, degree of service automation view and service automation related data view) that structure and ease elicitation and documentation of stakeholders' concerns. This is carried out through two large case studies to learn the industrial needs in illustrating services deployment and configuration automation. This set of views adds to the more traditional notations like UML, the visual power of attracting the attention of their users to the addressed concerns, and assist them in their work. This is especially crucial in service oriented architecting where service automation is highly demanded.

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.

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.

Managing Automation: A Process, Not a Project.

ERIC Educational Resources Information Center

Hoffmann, Ellen

1988-01-01

Discussion of issues in management of library automation includes: (1) hardware, including systems growth and contracts; (2) software changes, vendor relations, local systems, and microcomputer software; (3) item and authority databases; (4) automation and library staff, organizational structure, and managing change; and (5) environmental issues,…

Cockpit automation

NASA Technical Reports Server (NTRS)

Wiener, Earl L.

1988-01-01

The aims and methods of aircraft cockpit automation are reviewed from a human-factors perspective. Consideration is given to the mixed pilot reception of increased automation, government concern with the safety and reliability of highly automated aircraft, the formal definition of automation, and the ground-proximity warning system and accidents involving controlled flight into terrain. The factors motivating automation include technology availability; safety; economy, reliability, and maintenance; workload reduction and two-pilot certification; more accurate maneuvering and navigation; display flexibility; economy of cockpit space; and military requirements.

Multispectral Image Road Extraction Based Upon Automated Map Conflation

NASA Astrophysics Data System (ADS)

Chen, Bin

Road network extraction from remotely sensed imagery enables many important and diverse applications such as vehicle tracking, drone navigation, and intelligent transportation studies. There are, however, a number of challenges to road detection from an image. Road pavement material, width, direction, and topology vary across a scene. Complete or partial occlusions caused by nearby buildings, trees, and the shadows cast by them, make maintaining road connectivity difficult. The problems posed by occlusions are exacerbated with the increasing use of oblique imagery from aerial and satellite platforms. Further, common objects such as rooftops and parking lots are made of materials similar or identical to road pavements. This problem of common materials is a classic case of a single land cover material existing for different land use scenarios. This work addresses these problems in road extraction from geo-referenced imagery by leveraging the OpenStreetMap digital road map to guide image-based road extraction. The crowd-sourced cartography has the advantages of worldwide coverage that is constantly updated. The derived road vectors follow only roads and so can serve to guide image-based road extraction with minimal confusion from occlusions and changes in road material. On the other hand, the vector road map has no information on road widths and misalignments between the vector map and the geo-referenced image are small but nonsystematic. Properly correcting misalignment between two geospatial datasets, also known as map conflation, is an essential step. A generic framework requiring minimal human intervention is described for multispectral image road extraction and automatic road map conflation. The approach relies on the road feature generation of a binary mask and a corresponding curvilinear image. A method for generating the binary road mask from the image by applying a spectral measure is presented. The spectral measure, called anisotropy-tunable distance (ATD

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.

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.

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.

Integrating Multiple On-line Knowledge Bases for Disease-Lab Test Relation Extraction.

PubMed

Zhang, Yaoyun; Soysal, Ergin; Moon, Sungrim; Wang, Jingqi; Tao, Cui; Xu, Hua

2015-01-01

A computable knowledge base containing relations between diseases and lab tests would be a great resource for many biomedical informatics applications. This paper describes our initial step towards establishing a comprehensive knowledge base of disease and lab tests relations utilizing three public on-line resources. LabTestsOnline, MedlinePlus and Wikipedia are integrated to create a freely available, computable disease-lab test knowledgebase. Disease and lab test concepts are identified using MetaMap and relations between diseases and lab tests are determined based on source-specific rules. Experimental results demonstrate a high precision for relation extraction, with Wikipedia achieving the highest precision of 87%. Combining the three sources reached a recall of 51.40%, when compared with a subset of disease-lab test relations extracted from a reference book. Moreover, we found additional disease-lab test relations from on-line resources, indicating they are complementary to existing reference books for building a comprehensive disease and lab test relation knowledge base.

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

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.

I trust it, but I don't know why: effects of implicit attitudes toward automation on trust in an automated system.

PubMed

Merritt, Stephanie M; Heimbaugh, Heather; LaChapell, Jennifer; Lee, Deborah

2013-06-01

This study is the first to examine the influence of implicit attitudes toward automation on users' trust in automation. Past empirical work has examined explicit (conscious) influences on user level of trust in automation but has not yet measured implicit influences. We examine concurrent effects of explicit propensity to trust machines and implicit attitudes toward automation on trust in an automated system. We examine differential impacts of each under varying automation performance conditions (clearly good, ambiguous, clearly poor). Participants completed both a self-report measure of propensity to trust and an Implicit Association Test measuring implicit attitude toward automation, then performed an X-ray screening task. Automation performance was manipulated within-subjects by varying the number and obviousness of errors. Explicit propensity to trust and implicit attitude toward automation did not significantly correlate. When the automation's performance was ambiguous, implicit attitude significantly affected automation trust, and its relationship with propensity to trust was additive: Increments in either were related to increases in trust. When errors were obvious, a significant interaction between the implicit and explicit measures was found, with those high in both having higher trust. Implicit attitudes have important implications for automation trust. Users may not be able to accurately report why they experience a given level of trust. To understand why users trust or fail to trust automation, measurements of implicit and explicit predictors may be necessary. Furthermore, implicit attitude toward automation might be used as a lever to effectively calibrate trust.

Total lipid extraction of homogenized and intact lean fish muscles using pressurized fluid extraction and batch extraction techniques.

PubMed

Isaac, Giorgis; Waldebäck, Monica; Eriksson, Ulla; Odham, Göran; Markides, Karin E

2005-07-13

The reliability and efficiency of pressurized fluid extraction (PFE) technique for the extraction of total lipid content from cod and the effect of sample treatment on the extraction efficiency have been evaluated. The results were compared with two liquid-liquid extraction methods, traditional and modified methods according to Jensen. Optimum conditions were found to be with 2-propanol/n-hexane (65:35, v/v) as a first and n-hexane/diethyl ether (90:10, v/v) as a second solvent, 115 degrees C, and 10 min of static time. PFE extracts were cleaned up using the same procedure as in the methods according to Jensen. When total lipid yields obtained from homogenized cod muscle using PFE were compared yields obtained with original and modified Jensen methods, PFE gave significantly higher yields, approximately 10% higher (t test, P < 0.05). Infrared and NMR spectroscopy suggested that the additional material that inflates the gravimetric results is rather homogeneous and is primarily consists of phospholipid with headgroups of inositidic and/or glycosidic nature. The comparative study demonstrated that PFE is an alternative suitable technique to extract total lipid content from homogenized cod (lean fish) and herring (fat fish) muscle showing a precision comparable to that obtained with the traditional and modified Jensen methods. Despite the necessary cleanup step, PFE showed important advantages in the solvent consumption was cut by approximately 50% and automated extraction was possible.

Kernel-Based Learning for Domain-Specific Relation Extraction

NASA Astrophysics Data System (ADS)

Basili, Roberto; Giannone, Cristina; Del Vescovo, Chiara; Moschitti, Alessandro; Naggar, Paolo

In a specific process of business intelligence, i.e. investigation on organized crime, empirical language processing technologies can play a crucial role. The analysis of transcriptions on investigative activities, such as police interrogatories, for the recognition and storage of complex relations among people and locations is a very difficult and time consuming task, ultimately based on pools of experts. We discuss here an inductive relation extraction platform that opens the way to much cheaper and consistent workflows. The presented empirical investigation shows that accurate results, comparable to the expert teams, can be achieved, and parametrization allows to fine tune the system behavior for fitting domain-specific requirements.

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%).

Automated reliability assessment for spectroscopic redshift measurements

NASA Astrophysics Data System (ADS)

Jamal, S.; Le Brun, V.; Le Fèvre, O.; Vibert, D.; Schmitt, A.; Surace, C.; Copin, Y.; Garilli, B.; Moresco, M.; Pozzetti, L.

2018-03-01

Context. Future large-scale surveys, such as the ESA Euclid mission, will produce a large set of galaxy redshifts (≥106) that will require fully automated data-processing pipelines to analyze the data, extract crucial information and ensure that all requirements are met. A fundamental element in these pipelines is to associate to each galaxy redshift measurement a quality, or reliability, estimate. Aim. In this work, we introduce a new approach to automate the spectroscopic redshift reliability assessment based on machine learning (ML) and characteristics of the redshift probability density function. Methods: We propose to rephrase the spectroscopic redshift estimation into a Bayesian framework, in order to incorporate all sources of information and uncertainties related to the redshift estimation process and produce a redshift posterior probability density function (PDF). To automate the assessment of a reliability flag, we exploit key features in the redshift posterior PDF and machine learning algorithms. Results: As a working example, public data from the VIMOS VLT Deep Survey is exploited to present and test this new methodology. We first tried to reproduce the existing reliability flags using supervised classification in order to describe different types of redshift PDFs, but due to the subjective definition of these flags (classification accuracy 58%), we soon opted for a new homogeneous partitioning of the data into distinct clusters via unsupervised classification. After assessing the accuracy of the new clusters via resubstitution and test predictions (classification accuracy 98%), we projected unlabeled data from preliminary mock simulations for the Euclid space mission into this mapping to predict their redshift reliability labels. Conclusions: Through the development of a methodology in which a system can build its own experience to assess the quality of a parameter, we are able to set a preliminary basis of an automated reliability assessment for

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.

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.

The BAARA (Biological AutomAted RAdiotracking) System: A New Approach in Ecological Field Studies

PubMed Central

Řeřucha, Šimon; Bartonička, Tomáš; Jedlička, Petr; Čížek, Martin; Hlouša, Ondřej; Lučan, Radek; Horáček, Ivan

2015-01-01

Radiotracking is an important and often the only possible method to explore specific habits and the behaviour of animals, but it has proven to be very demanding and time-consuming, especially when frequent positioning of a large group is required. Our aim was to address this issue by making the process partially automated, to mitigate the demands and related costs. This paper presents a novel automated tracking system that consists of a network of automated tracking stations deployed within the target area. Each station reads the signals from telemetry transmitters, estimates the bearing and distance of the tagged animals and records their position. The station is capable of tracking a theoretically unlimited number of transmitters on different frequency channels with the period of 5–15 seconds per single channel. An ordinary transmitter that fits within the supported frequency band might be used with BAARA (Biological AutomAted RAdiotracking); an extra option is the use of a custom-programmable transmitter with configurable operational parameters, such as the precise frequency channel or the transmission parameters. This new approach to a tracking system was tested for its applicability in a series of field and laboratory tests. BAARA has been tested within fieldwork explorations of Rousettus aegyptiacus during field trips to Dakhla oasis in Egypt. The results illustrate the novel perspective which automated radiotracking opens for the study of spatial behaviour, particularly in addressing topics in the domain of population ecology. PMID:25714910

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.

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.

Safety in the Automated Office.

ERIC Educational Resources Information Center

Graves, Pat R.; Greathouse, Lillian R.

1990-01-01

Office automation has introduced new hazards to the workplace: electrical hazards related to computer wiring, musculoskeletal problems resulting from use of computer terminals and design of work stations, and environmental concerns related to ventilation, noise levels, and office machine chemicals. (SK)

CRIE: An automated analyzer for Chinese texts.

PubMed

Sung, Yao-Ting; Chang, Tao-Hsing; Lin, Wei-Chun; Hsieh, Kuan-Sheng; Chang, Kuo-En

2016-12-01

Textual analysis has been applied to various fields, such as discourse analysis, corpus studies, text leveling, and automated essay evaluation. Several tools have been developed for analyzing texts written in alphabetic languages such as English and Spanish. However, currently there is no tool available for analyzing Chinese-language texts. This article introduces a tool for the automated analysis of simplified and traditional Chinese texts, called the Chinese Readability Index Explorer (CRIE). Composed of four subsystems and incorporating 82 multilevel linguistic features, CRIE is able to conduct the major tasks of segmentation, syntactic parsing, and feature extraction. Furthermore, the integration of linguistic features with machine learning models enables CRIE to provide leveling and diagnostic information for texts in language arts, texts for learning Chinese as a foreign language, and texts with domain knowledge. The usage and validation of the functions provided by CRIE are also introduced.

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

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.

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...

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

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

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).

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.

Autonomy and Automation

NASA Technical Reports Server (NTRS)

Shively, Jay

2017-01-01

A significant level of debate and confusion has surrounded the meaning of the terms autonomy and automation. Automation is a multi-dimensional concept, and we propose that Remotely Piloted Aircraft Systems (RPAS) automation should be described with reference to the specific system and task that has been automated, the context in which the automation functions, and other relevant dimensions. In this paper, we present definitions of automation, pilot in the loop, pilot on the loop and pilot out of the loop. We further propose that in future, the International Civil Aviation Organization (ICAO) RPAS Panel avoids the use of the terms autonomy and autonomous when referring to automated systems on board RPA. Work Group 7 proposes to develop, in consultation with other workgroups, a taxonomy of Levels of Automation for RPAS.

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

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 synthetic scene generation

NASA Astrophysics Data System (ADS)

Givens, Ryan N.

Physics-based simulations generate synthetic imagery to help organizations anticipate system performance of proposed remote sensing systems. However, manually constructing synthetic scenes which are sophisticated enough to capture the complexity of real-world sites can take days to months depending on the size of the site and desired fidelity of the scene. This research, sponsored by the Air Force Research Laboratory's Sensors Directorate, successfully developed an automated approach to fuse high-resolution RGB imagery, lidar data, and hyperspectral imagery and then extract the necessary scene components. The method greatly reduces the time and money required to generate realistic synthetic scenes and developed new approaches to improve material identification using information from all three of the input datasets.

Automated carotid artery intima layer regional segmentation.

PubMed

Meiburger, Kristen M; Molinari, Filippo; Acharya, U Rajendra; Saba, Luca; Rodrigues, Paulo; Liboni, William; Nicolaides, Andrew; Suri, Jasjit S

2011-07-07

Evaluation of the carotid artery wall is essential for the assessment of a patient's cardiovascular risk or for the diagnosis of cardiovascular pathologies. This paper presents a new, completely user-independent algorithm called carotid artery intima layer regional segmentation (CAILRS, a class of AtheroEdge™ systems), which automatically segments the intima layer of the far wall of the carotid ultrasound artery based on mean shift classification applied to the far wall. Further, the system extracts the lumen-intima and media-adventitia borders in the far wall of the carotid artery. Our new system is characterized and validated by comparing CAILRS borders with the manual tracings carried out by experts. The new technique is also benchmarked with a semi-automatic technique based on a first-order absolute moment edge operator (FOAM) and compared to our previous edge-based automated methods such as CALEX (Molinari et al 2010 J. Ultrasound Med. 29 399-418, 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CULEX (Delsanto et al 2007 IEEE Trans. Instrum. Meas. 56 1265-74, Molinari et al 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CALSFOAM (Molinari et al Int. Angiol. (at press)), and CAUDLES-EF (Molinari et al J. Digit. Imaging (at press)). Our multi-institutional database consisted of 300 longitudinal B-mode carotid images. In comparison to semi-automated FOAM, CAILRS showed the IMT bias of -0.035 ± 0.186 mm while FOAM showed -0.016 ± 0.258 mm. Our IMT was slightly underestimated with respect to the ground truth IMT, but showed uniform behavior over the entire database. CAILRS outperformed all the four previous automated methods. The system's figure of merit was 95.6%, which was lower than that of the semi-automated method (98%), but higher than that of the other automated techniques.

Automated carotid artery intima layer regional segmentation

NASA Astrophysics Data System (ADS)

Meiburger, Kristen M.; Molinari, Filippo; Rajendra Acharya, U.; Saba, Luca; Rodrigues, Paulo; Liboni, William; Nicolaides, Andrew; Suri, Jasjit S.

2011-07-01

Evaluation of the carotid artery wall is essential for the assessment of a patient's cardiovascular risk or for the diagnosis of cardiovascular pathologies. This paper presents a new, completely user-independent algorithm called carotid artery intima layer regional segmentation (CAILRS, a class of AtheroEdge™ systems), which automatically segments the intima layer of the far wall of the carotid ultrasound artery based on mean shift classification applied to the far wall. Further, the system extracts the lumen-intima and media-adventitia borders in the far wall of the carotid artery. Our new system is characterized and validated by comparing CAILRS borders with the manual tracings carried out by experts. The new technique is also benchmarked with a semi-automatic technique based on a first-order absolute moment edge operator (FOAM) and compared to our previous edge-based automated methods such as CALEX (Molinari et al 2010 J. Ultrasound Med. 29 399-418, 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CULEX (Delsanto et al 2007 IEEE Trans. Instrum. Meas. 56 1265-74, Molinari et al 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CALSFOAM (Molinari et al Int. Angiol. (at press)), and CAUDLES-EF (Molinari et al J. Digit. Imaging (at press)). Our multi-institutional database consisted of 300 longitudinal B-mode carotid images. In comparison to semi-automated FOAM, CAILRS showed the IMT bias of -0.035 ± 0.186 mm while FOAM showed -0.016 ± 0.258 mm. Our IMT was slightly underestimated with respect to the ground truth IMT, but showed uniform behavior over the entire database. CAILRS outperformed all the four previous automated methods. The system's figure of merit was 95.6%, which was lower than that of the semi-automated method (98%), but higher than that of the other automated techniques.

76 FR 45792 - Proposed Reissuance of a General NPDES Permit for Facilities Related to Oil and Gas Extraction

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-01

... General NPDES Permit for Facilities Related to Oil and Gas Extraction AGENCY: Environmental Protection... (GP) regulating activities related to the extraction of oil and gas on the North Slope of the Brooks... intended to regulate activities related to the extraction of oil and gas on the North Slope of the Brooks...

Human factors evaluation of level 2 and level 3 automated driving concepts : past research, state of automation technology, and emerging system concepts.

DOT National Transportation Integrated Search

2014-07-01

Within the context of automation Levels 2 and 3, this report documents the proceedings from a literature review of key : human factors studies that was performed related to automated vehicle operations. This document expands and updates : the results...

Pathology report data extraction from relational database using R, with extraction from reports on melanoma of skin as an example.

PubMed

Ye, Jay J

2016-01-01

Different methods have been described for data extraction from pathology reports with varying degrees of success. Here a technique for directly extracting data from relational database is described. Our department uses synoptic reports modified from College of American Pathologists (CAP) Cancer Protocol Templates to report most of our cancer diagnoses. Choosing the melanoma of skin synoptic report as an example, R scripting language extended with RODBC package was used to query the pathology information system database. Reports containing melanoma of skin synoptic report in the past 4 and a half years were retrieved and individual data elements were extracted. Using the retrieved list of the cases, the database was queried a second time to retrieve/extract the lymph node staging information in the subsequent reports from the same patients. 426 synoptic reports corresponding to unique lesions of melanoma of skin were retrieved, and data elements of interest were extracted into an R data frame. The distribution of Breslow depth of melanomas grouped by year is used as an example of intra-report data extraction and analysis. When the new pN staging information was present in the subsequent reports, 82% (77/94) was precisely retrieved (pN0, pN1, pN2 and pN3). Additional 15% (14/94) was retrieved with certain ambiguity (positive or knowing there was an update). The specificity was 100% for both. The relationship between Breslow depth and lymph node status was graphed as an example of lesion-specific multi-report data extraction and analysis. R extended with RODBC package is a simple and versatile approach well-suited for the above tasks. The success or failure of the retrieval and extraction depended largely on whether the reports were formatted and whether the contents of the elements were consistently phrased. This approach can be easily modified and adopted for other pathology information systems that use relational database for data management.

Semi-automated extraction of landslides in Taiwan based on SPOT imagery and DEMs

NASA Astrophysics Data System (ADS)

Eisank, Clemens; Hölbling, Daniel; Friedl, Barbara; Chen, Yi-Chin; Chang, Kang-Tsung

2014-05-01

The vast availability and improved quality of optical satellite data and digital elevation models (DEMs), as well as the need for complete and up-to-date landslide inventories at various spatial scales have fostered the development of semi-automated landslide recognition systems. Among the tested approaches for designing such systems, object-based image analysis (OBIA) stepped out to be a highly promising methodology. OBIA offers a flexible, spatially enabled framework for effective landslide mapping. Most object-based landslide mapping systems, however, have been tailored to specific, mainly small-scale study areas or even to single landslides only. Even though reported mapping accuracies tend to be higher than for pixel-based approaches, accuracy values are still relatively low and depend on the particular study. There is still room to improve the applicability and objectivity of object-based landslide mapping systems. The presented study aims at developing a knowledge-based landslide mapping system implemented in an OBIA environment, i.e. Trimble eCognition. In comparison to previous knowledge-based approaches, the classification of segmentation-derived multi-scale image objects relies on digital landslide signatures. These signatures hold the common operational knowledge on digital landslide mapping, as reported by 25 Taiwanese landslide experts during personal semi-structured interviews. Specifically, the signatures include information on commonly used data layers, spectral and spatial features, and feature thresholds. The signatures guide the selection and implementation of mapping rules that were finally encoded in Cognition Network Language (CNL). Multi-scale image segmentation is optimized by using the improved Estimation of Scale Parameter (ESP) tool. The approach described above is developed and tested for mapping landslides in a sub-region of the Baichi catchment in Northern Taiwan based on SPOT imagery and a high-resolution DEM. An object

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.

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

NASA Technical Reports Server (NTRS)

Griffin, Sandy (Editor)

1987-01-01

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered.

A knowledge-based approach to automated planning for hepatocellular carcinoma.

PubMed

Zhang, Yujie; Li, Tingting; Xiao, Han; Ji, Weixing; Guo, Ming; Zeng, Zhaochong; Zhang, Jianying

2018-01-01

To build a knowledge-based model of liver cancer for Auto-Planning, a function in Pinnacle, which is used as an automated inverse intensity modulated radiation therapy (IMRT) planning system. Fifty Tomotherapy patients were enrolled to extract the dose-volume histograms (DVHs) information and construct the protocol for Auto-Planning model. Twenty more patients were chosen additionally to test the model. Manual planning and automatic planning were performed blindly for all twenty test patients with the same machine and treatment planning system. The dose distributions of target and organs at risks (OARs), along with the working time for planning, were evaluated. Statistically significant results showed that automated plans performed better in target conformity index (CI) while mean target dose was 0.5 Gy higher than manual plans. The differences between target homogeneity indexes (HI) of the two methods were not statistically significant. Additionally, the doses of normal liver, left kidney, and small bowel were significantly reduced with automated plan. Particularly, mean dose and V15 of normal liver were 1.4 Gy and 40.5 cc lower with automated plans respectively. Mean doses of left kidney and small bowel were reduced with automated plans by 1.2 Gy and 2.1 Gy respectively. In contrast, working time was also significantly reduced with automated planning. Auto-Planning shows availability and effectiveness in our knowledge-based model for liver cancer. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

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…

Introduction matters: Manipulating trust in automation and reliance in automated driving.

PubMed

Körber, Moritz; Baseler, Eva; Bengler, Klaus

2018-01-01

Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and take-over performance. Forty participants encountered three situations in a 17-min highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants' reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group's mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be

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.

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.

Bayesian Safety Risk Modeling of Human-Flightdeck Automation Interaction

NASA Technical Reports Server (NTRS)

Ancel, Ersin; Shih, Ann T.

2015-01-01

Usage of automatic systems in airliners has increased fuel efficiency, added extra capabilities, enhanced safety and reliability, as well as provide improved passenger comfort since its introduction in the late 80's. However, original automation benefits, including reduced flight crew workload, human errors or training requirements, were not achieved as originally expected. Instead, automation introduced new failure modes, redistributed, and sometimes increased workload, brought in new cognitive and attention demands, and increased training requirements. Modern airliners have numerous flight modes, providing more flexibility (and inherently more complexity) to the flight crew. However, the price to pay for the increased flexibility is the need for increased mode awareness, as well as the need to supervise, understand, and predict automated system behavior. Also, over-reliance on automation is linked to manual flight skill degradation and complacency in commercial pilots. As a result, recent accidents involving human errors are often caused by the interactions between humans and the automated systems (e.g., the breakdown in man-machine coordination), deteriorated manual flying skills, and/or loss of situational awareness due to heavy dependence on automated systems. This paper describes the development of the increased complexity and reliance on automation baseline model, named FLAP for FLightdeck Automation Problems. The model development process starts with a comprehensive literature review followed by the construction of a framework comprised of high-level causal factors leading to an automation-related flight anomaly. The framework was then converted into a Bayesian Belief Network (BBN) using the Hugin Software v7.8. The effects of automation on flight crew are incorporated into the model, including flight skill degradation, increased cognitive demand and training requirements along with their interactions. Besides flight crew deficiencies, automation system

Determination of 74 new psychoactive substances in serum using automated in-line solid-phase extraction-liquid chromatography-tandem mass spectrometry.

PubMed

Lehmann, Sabrina; Kieliba, Tobias; Beike, Justus; Thevis, Mario; Mercer-Chalmers-Bender, Katja

2017-10-01

A detailed description is given of the development and validation of a fully automated in-line solid-phase extraction-liquid chromatography-tandem mass spectrometry (SPE-LC-MS/MS) method capable of detecting 90 central-stimulating new psychoactive substances (NPS) and 5 conventional amphetamine-type stimulants (amphetamine, 3,4-methylenedioxy-methamphetamine (MDMA), 3,4-methylenedioxy-amphetamine (MDA), 3,4-methylenedioxy-N-ethyl-amphetamine (MDEA), methamphetamine) in serum. The aim was to apply the validated method to forensic samples. The preparation of 150μL of serum was performed by an Instrument Top Sample Preparation (ITSP)-SPE with mixed mode cation exchanger cartridges. The extracts were directly injected into an LC-MS/MS system, using a biphenyl column and gradient elution with 2mM ammonium formate/0.1% formic acid and acetonitrile/0.1% formic acid as mobile phases. The chromatographic run time amounts to 9.3min (including re-equilibration). The total cycle time is 11min, due to the interlacing between sample preparation and analysis. The method was fully validated using 69 NPS and five conventional amphetamine-type stimulants, according to the guidelines of the Society of Toxicological and Forensic Chemistry (GTFCh). The guidelines were fully achieved for 62 analytes (with a limit of detection (LOD) between 0.2 and 4μg/L), whilst full validation was not feasible for the remaining 12 analytes. For the fully validated analytes, the method achieved linearity in the 5μg/L (lower limit of quantification, LLOQ) to 250μg/L range (coefficients of determination>0.99). Recoveries for 69 of these compounds were greater than 50%, with relative standard deviations≤15%. The validated method was then tested for its capability in detecting a further 21 NPS, thus totalling 95 tested substances. An LOD between 0.4 and 1.6μg/L was obtained for these 21 additional qualitatively-measured substances. The method was subsequently successfully applied to 28 specimens from

Towards a Relation Extraction Framework for Cyber-Security Concepts

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

Jones, Corinne L; Bridges, Robert A; Huffer, Kelly M

In order to assist security analysts in obtaining information pertaining to their network, such as novel vulnerabilities, exploits, or patches, information retrieval methods tailored to the security domain are needed. As labeled text data is scarce and expensive, we follow developments in semi-supervised NLP and implement a bootstrapping algorithm for extracting security entities and their relationships from text. The algorithm requires little input data, specifically, a few relations or patterns (heuristics for identifying relations), and incorporates an active learning component which queries the user on the most important decisions to prevent drifting the desired relations. Preliminary testing on a smallmore » corpus shows promising results, obtaining precision of .82.« less

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

PPInterFinder—a mining tool for extracting causal relations on human proteins from literature

PubMed Central

Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar

2013-01-01

One of the most common and challenging problem in biomedical text mining is to mine protein–protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder—a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems. Database URL: http://www.biomining-bu.in/ppinterfinder/ PMID:23325628

PPInterFinder--a mining tool for extracting causal relations on human proteins from literature.

PubMed

Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar

2013-01-01

One of the most common and challenging problem in biomedical text mining is to mine protein-protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder--a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems. DATABASE URL: http://www.biomining-bu.in/ppinterfinder/

Benchmarking Glucose Results through Automation: The 2009 Remote Automated Laboratory System Report

PubMed Central

Anderson, Marcy; Zito, Denise; Kongable, Gail

2010-01-01

Background Hyperglycemia in the adult inpatient population remains a topic of intense study in U.S. hospitals. Most hospitals have established glycemic control programs but are unable to determine their impact. The 2009 Remote Automated Laboratory System (RALS) Report provides trends in glycemic control over 4 years to 576 U.S. hospitals to support their effort to manage inpatient hyperglycemia. Methods A proprietary software application feeds de-identified patient point-of-care blood glucose (POC-BG) data from the Medical Automation Systems RALS-Plus data management system to a central server. Analyses include the number of tests and the mean and median BG results for intensive care unit (ICU), non-ICU, and each hospital compared to the aggregate of the other hospitals. Results More than 175 million BG results were extracted from 2006–2009; 25% were from the ICU. Mean range of BG results for all inpatients in 2006, 2007, 2008, and 2009 was 142.2–201.9, 145.6–201.2, 140.6–205.7, and 140.7–202.4 mg/dl, respectively. The range for ICU patients was 128–226.5, 119.5–219.8, 121.6–226.0, and 121.1–217 mg/dl, respectively. The range for non-ICU patients was 143.4–195.5, 148.6–199.8, 145.2–201.9, and 140.7–203.6 mg/dl, respectively. Hyperglycemia rates of >180 mg/dl in 2008 and 2009 were examined, and hypoglycemia rates of <40 mg/dl (severe) and <70 mg/dl (moderate) in both 2008 and 2009 were calculated. Conclusions From these data, hospitals can determine the current state of glycemic control in their hospital and in comparison to other hospitals. For many, glycemic control has improved. Automated POC-BG data management software can assist in this effort. PMID:21129348

ACCELERATED SOLVENT EXTRACTION COMBINED WITH ...

EPA Pesticide Factsheets

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 the EPA Region 1 Laboratory, under the Regional Methods Program administered through the ORD Office of Science Policy. The aim of the project was to develop an approach for the rapid removal of water in high moisture content solids (e.g., wetland sediments) in preparation for analysis via Method 8270. Alternative methods for water removal have been investigated to enhance compound solid concentrations and improve extraction efficiency, with the use of pressure filtration providing a high-throughput alternative for removal of the majority of free water in sediments and sludges. In order to eliminate problems with phase separation during extraction of solids using Accelerated Solvent Extraction, a variation of a water-isopropanol extraction method developed at the USGS National Water Quality Laboratory in Denver, CO is being employed. The concentrations of target compounds in water-isopropanol extraction fluids are subsequently analyzed using an automated Solid Phase Extraction (SPE)-GC/MS method developed in our laboratory. The coupled approaches for dewatering, extraction, and target compound identification-quantitation provide a useful alternative to enhance sample throughput for Me

A Fully Automated Approach to Spike Sorting.

PubMed

Chung, Jason E; Magland, Jeremy F; Barnett, Alex H; Tolosa, Vanessa M; Tooker, Angela C; Lee, Kye Y; Shah, Kedar G; Felix, Sarah H; Frank, Loren M; Greengard, Leslie F

2017-09-13

Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible. Copyright © 2017 Elsevier Inc. All rights reserved.

Automated Glycan Assembly of Oligosaccharides Related to Arabinogalactan Proteins.

PubMed

Bartetzko, Max P; Schuhmacher, Frank; Hahm, Heung Sik; Seeberger, Peter H; Pfrengle, Fabian

2015-09-04

Arabinogalactan proteins are heavily glycosylated proteoglycans in plants. Their glycan portion consists of type-II arabinogalactan polysaccharides whose heterogeneity hampers the assignment of the arabinogalactan protein function. Synthetic chemistry is key to the procurement of molecular probes for plant biologists. Described is the automated glycan assembly of 14 oligosaccharides from four monosaccharide building blocks. These linear and branched glycans represent key structural features of natural type-II arabinogalactans and will serve as tools for arabinogalactan biology.

Ginkgo biloba extract for age-related macular degeneration.

PubMed

Evans, Jennifer R

2013-01-31

Ginkgo is used in the treatment of peripheral vascular disease and 'cerebral insufficiency'. It is thought to have several potential mechanisms of action including increased blood flow, platelet activating factor antagonism, and prevention of membrane damage caused by free radicals. Vascular factors and oxidative damage are thought to be two potential mechanisms in the pathology of age-related macular degeneration (AMD). The objective of this review was to determine the effect of Ginkgo biloba extract on the progression of AMD. We searched CENTRAL (which contains the Cochrane Eyes and Vision Group Trials Register) (The Cochrane Library 2012, Issue 10), Ovid MEDLINE, Ovid MEDLINE In-Process and Other Non-Indexed Citations, Ovid MEDLINE Daily, Ovid OLDMEDLINE (January 1946 to October 2012), EMBASE (January 1980 to October 2012), Allied and Complementary Medicine Database (AMED) (January 1985 to October 2012), OpenGrey (System for Information on Grey Literature in Europe) (www.opengrey.eu/), the metaRegister of Controlled Trials (mRCT) (www.controlled-trials.com), ClinicalTrials.gov (www.clinicaltrials.gov) and the WHO International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp/search/en). We did not use any date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 5 October 2012. We searched the reference lists of identified reports and the Science Citation Index. We also contacted investigators of included studies for additional information. All randomised trials in people with AMD where Ginkgo biloba extract had been compared to control were included. The review author extracted data using a standardised form. The data were verified with the trial investigators. Trial quality was assessed. Two published trials were identified that randomised a total of 119 people. In one study conducted in France, 20 people were randomly allocated to Gingko biloba extract EGb 761 80 mg twice daily or placebo. In

Human communication needs and organizational productivity: the potential impact of office automation.

PubMed

Culnan, M J; Bair, J H

1983-05-01

Much of what white collar workers do in offices is communication-related. White collar workers make up the majority of the labor force in the United States today and the majority of current labor costs. Because office automation represents more productive structured techniques for handling both written and oral communication, office automation therefore offers the potential to make organizations more productive by improving organizational communication. This article: (1) defines communication, (2) identifies the potential benefits to be realized from implementing office automation, and (3) offers caveats related to the implementation of office automation systems. Realization of the benefits of office automation depends upon the degree to which new modes of communication may be successfully substituted for traditional modes.

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.

PPI-IRO: a two-stage method for protein-protein interaction extraction based on interaction relation ontology.

PubMed

Li, Chuan-Xi; Chen, Peng; Wang, Ru-Jing; Wang, Xiu-Jie; Su, Ya-Ru; Li, Jinyan

2014-01-01

Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identification of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifies and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At first, IRO is applied in a binary classifier to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the significant performance of IRO on relation sentences classification and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and BioInfer, respectively, which are superior to most existing extraction methods.

Modulation of GABAA receptors by valerian extracts is related to the content of valerenic acid.

PubMed

Trauner, Gabriele; Khom, Sophia; Baburin, Igor; Benedek, Birgit; Hering, Steffen; Kopp, Brigitte

2008-01-01

Valeriana Officinalis L . is a traditionally used sleep remedy, however, the mechanism of action and the substances responsible for its sedative and sleep-enhancing properties are not fully understood. As we previously identified valerenic acid as a subunit-specific allosteric modulator of GABAA receptors, we now investigated the relation between modulation of GABAA receptors by Valerian extracts of different polarity and the content of sesquiterpenic acids (valerenic acid, acetoxyvalerenic acid). All extracts were analysed by HPLC concerning the content of sesquiterpenic acids. GABAA receptors composed of alpha 1, beta 2 and gamma 2S subunits were expressed in Xenopus laevis oocytes and the modulation of chloride currents through GABAA receptors (IGABA) by Valerian extracts was investigated using the two-microelectrode voltage clamp technique. Apolar extracts induced a significant enhancement of IGABA, whereas polar extracts showed no effect. These results were confirmed by fractionating a highly active ethyl acetate extract: again fractions with high contents of valerenic acid exhibited strong receptor activation. In addition, removal of sesquiterpenic acids from the ethyl acetate extract led to a loss of I (GABA) enhancement. In conclusion, our data show that the extent of GABAA receptor modulation by Valerian extracts is related to the content of valerenic acid.

Age differences in the takeover of vehicle control and engagement in non-driving-related activities in simulated driving with conditional automation.

PubMed

Clark, Hallie; Feng, Jing

2017-09-01

High-level vehicle automation has been proposed as a valuable means to enhance the mobility of older drivers, as older drivers experience age-related declines in many cognitive functions that are vital for safe driving. Recent research attempted to examine age differences in how engagement in non-driving-related activities impact driving performance, by instructing drivers to engage in mandatory pre-designed activities. While the mandatory engagement method allows a precise control of the timing and mental workload of the non-driving-related activities, it is different from how a driver would naturally engage in these activities. This study allowed younger (age 18-35, mean age=19.9years) and older drivers (age 62-81, mean age=70.4years) to freely decide when and how to engage in voluntarily chosen non-driving-related activities during simulated driving with conditional automation. We coded video recordings of participants' engagement in non-driving-related activities. We examined the effect of age, level of activity-engagement and takeover notification interval on vehicle control performance during the takeover, by comparing between the high and low engagement groups in younger and older drivers, across two takeover notification interval conditions. We found that both younger and older drivers engaged in various non-driving-related activities during the automated driving portion, with distinct preferences on the type of activity for each age group (i.e., while younger drivers mostly used an electronic device, older drivers tended to converse). There were also significant differences between the two age groups and between the two notification intervals on various driving performance measures. Older drivers benefited more than younger drivers from the longer interval in terms of response time to notifications. Voluntary engagement in non-driving-related activities did not impair takeover performance in general, although there was a trend of older drivers who were

Comparison of Two Commercial Automated Nucleic Acid Extraction and Integrated Quantitation Real-Time PCR Platforms for the Detection of Cytomegalovirus in Plasma

PubMed Central

Tsai, Huey-Pin; Tsai, You-Yuan; Lin, I-Ting; Kuo, Pin-Hwa; Chen, Tsai-Yun; Chang, Kung-Chao; Wang, Jen-Ren

2016-01-01

Quantitation of cytomegalovirus (CMV) viral load in the transplant patients has become a standard practice for monitoring the response to antiviral therapy. The cut-off values of CMV viral load assays for preemptive therapy are different due to the various assay designs employed. To establish a sensitive and reliable diagnostic assay for preemptive therapy of CMV infection, two commercial automated platforms including m2000sp extraction system integrated the Abbott RealTime (m2000rt) and the Roche COBAS AmpliPrep for extraction integrated COBAS Taqman (CAP/CTM) were evaluated using WHO international CMV standards and 110 plasma specimens from transplant patients. The performance characteristics, correlation, and workflow of the two platforms were investigated. The Abbott RealTime assay correlated well with the Roche CAP/CTM assay (R2 = 0.9379, P<0.01). The Abbott RealTime assay exhibited higher sensitivity for the detection of CMV viral load, and viral load values measured with Abbott RealTime assay were on average 0.76 log10 IU/mL higher than those measured with the Roche CAP/CTM assay (P<0.0001). Workflow analysis on a small batch size at one time, using the Roche CAP/CTM platform had a shorter hands-on time than the Abbott RealTime platform. In conclusion, these two assays can provide reliable data for different purpose in a clinical virology laboratory setting. PMID:27494707

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.

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%.

Clinical Laboratory Automation: A Case Study

PubMed Central

Archetti, Claudia; Montanelli, Alessandro; Finazzi, Dario; Caimi, Luigi; Garrafa, Emirena

2017-01-01

Background This paper presents a case study of an automated clinical laboratory in a large urban academic teaching hospital in the North of Italy, the Spedali Civili in Brescia, where four laboratories were merged in a unique laboratory through the introduction of laboratory automation. Materials and Methods The analysis compares the preautomation situation and the new setting from a cost perspective, by considering direct and indirect costs. It also presents an analysis of the turnaround time (TAT). The study considers equipment, staff and indirect costs. Results The introduction of automation led to a slight increase in equipment costs which is highly compensated by a remarkable decrease in staff costs. Consequently, total costs decreased by 12.55%. The analysis of the TAT shows an improvement of nonemergency exams while emergency exams are still validated within the maximum time imposed by the hospital. Conclusions The strategy adopted by the management, which was based on re-using the available equipment and staff when merging the pre-existing laboratories, has reached its goal: introducing automation while minimizing the costs. Significance for public health Automation is an emerging trend in modern clinical laboratories with a positive impact on service level to patients and on staff safety as shown by different studies. In fact, it allows process standardization which, in turn, decreases the frequency of outliers and errors. In addition, it induces faster processing times, thus improving the service level. On the other side, automation decreases the staff exposition to accidents strongly improving staff safety. In this study, we analyse a further potential benefit of automation, that is economic convenience. We study the case of the automated laboratory of one of the biggest hospital in Italy and compare the cost related to the pre and post automation situation. Introducing automation lead to a cost decrease without affecting the service level to patients

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.

Effects of automation of information-processing functions on teamwork.

PubMed

Wright, Melanie C; Kaber, David B

2005-01-01

We investigated the effects of automation as applied to different stages of information processing on team performance in a complex decision-making task. Forty teams of 2 individuals performed a simulated Theater Defense Task. Four automation conditions were simulated with computer assistance applied to realistic combinations of information acquisition, information analysis, and decision selection functions across two levels of task difficulty. Multiple measures of team effectiveness and team coordination were used. Results indicated different forms of automation have different effects on teamwork. Compared with a baseline condition, an increase in automation of information acquisition led to an increase in the ratio of information transferred to information requested; an increase in automation of information analysis resulted in higher team coordination ratings; and automation of decision selection led to better team effectiveness under low levels of task difficulty but at the cost of higher workload. The results support the use of early and intermediate forms of automation related to acquisition and analysis of information in the design of team tasks. Decision-making automation may provide benefits in more limited contexts. Applications of this research include the design and evaluation of automation in team environments.

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.

Approaches to automated protein crystal harvesting

PubMed Central

Deller, Marc C.; Rupp, Bernhard

2014-01-01

The harvesting of protein crystals is almost always a necessary step in the determination of a protein structure using X-ray crystallographic techniques. However, protein crystals are usually fragile and susceptible to damage during the harvesting process. For this reason, protein crystal harvesting is the single step that remains entirely dependent on skilled human intervention. Automation has been implemented in the majority of other stages of the structure-determination pipeline, including cloning, expression, purification, crystallization and data collection. The gap in automation between crystallization and data collection results in a bottleneck in throughput and presents unfortunate opportunities for crystal damage. Several automated protein crystal harvesting systems have been developed, including systems utilizing microcapillaries, microtools, microgrippers, acoustic droplet ejection and optical traps. However, these systems have yet to be commonly deployed in the majority of crystallography laboratories owing to a variety of technical and cost-related issues. Automation of protein crystal harvesting remains essential for harnessing the full benefits of fourth-generation synchrotrons, free-electron lasers and microfocus beamlines. Furthermore, automation of protein crystal harvesting offers several benefits when compared with traditional manual approaches, including the ability to harvest microcrystals, improved flash-cooling procedures and increased throughput. PMID:24637746

Extracted facial feature of racial closely related faces

NASA Astrophysics Data System (ADS)

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

2010-02-01

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

Automated segmentation of foveal avascular zone in fundus fluorescein angiography.

PubMed

Zheng, Yalin; Gandhi, Jagdeep Singh; Stangos, Alexandros N; Campa, Claudio; Broadbent, Deborah M; Harding, Simon P

2010-07-01

PURPOSE. To describe and evaluate the performance of a computerized automated segmentation technique for use in quantification of the foveal avascular zone (FAZ). METHODS. A computerized technique for automated segmentation of the FAZ using images from fundus fluorescein angiography (FFA) was applied to 26 transit-phase images obtained from patients with various grades of diabetic retinopathy. The area containing the FAZ zone was first extracted from the original image and smoothed by a Gaussian kernel (sigma = 1.5). An initializing contour was manually placed inside the FAZ of the smoothed image and iteratively moved by the segmentation program toward the FAZ boundary. Five tests with different initializing curves were run on each of 26 images to assess reproducibility. The accuracy of the program was also validated by comparing results obtained by the program with the FAZ boundaries manually delineated by medical retina specialists. Interobserver performance was then evaluated by comparing delineations from two of the experts. RESULTS. One-way analysis of variance indicated that the disparities between different tests were not statistically significant, signifying excellent reproducibility for the computer program. There was a statistically significant linear correlation between the results obtained by automation and manual delineations by experts. CONCLUSIONS. This automated segmentation program can produce highly reproducible results that are comparable to those made by clinical experts. It has the potential to assist in the detection and management of foveal ischemia and to be integrated into automated grading systems.

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.

Automation, parallelism, and robotics for proteomics.

PubMed

Alterovitz, Gil; Liu, Jonathan; Chow, Jijun; Ramoni, Marco F

2006-07-01

The speed of the human genome project (Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C. et al., Nature 2001, 409, 860-921) was made possible, in part, by developments in automation of sequencing technologies. Before these technologies, sequencing was a laborious, expensive, and personnel-intensive task. Similarly, automation and robotics are changing the field of proteomics today. Proteomics is defined as the effort to understand and characterize proteins in the categories of structure, function and interaction (Englbrecht, C. C., Facius, A., Comb. Chem. High Throughput Screen. 2005, 8, 705-715). As such, this field nicely lends itself to automation technologies since these methods often require large economies of scale in order to achieve cost and time-saving benefits. This article describes some of the technologies and methods being applied in proteomics in order to facilitate automation within the field as well as in linking proteomics-based information with other related research areas.

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

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

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.

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.

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...

Automating Frame Analysis

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

Sanfilippo, Antonio P.; Franklin, Lyndsey; Tratz, Stephen C.

2008-04-01

Frame Analysis has come to play an increasingly stronger role in the study of social movements in Sociology and Political Science. While significant steps have been made in providing a theory of frames and framing, a systematic characterization of the frame concept is still largely lacking and there are no rec-ognized criteria and methods that can be used to identify and marshal frame evi-dence reliably and in a time and cost effective manner. Consequently, current Frame Analysis work is still too reliant on manual annotation and subjective inter-pretation. The goal of this paper is to present an approach to themore » representation, acquisition and analysis of frame evidence which leverages Content Analysis, In-formation Extraction and Semantic Search methods to provide a systematic treat-ment of a Frame Analysis and automate frame annotation.« less

Automation Bias: Decision Making and Performance in High-Tech Cockpits

NASA Technical Reports Server (NTRS)

Mosier, Kathleen L.; Skitka, Linda J.; Heers, Susan; Burdick, Mark; Rosekind, Mark R. (Technical Monitor)

1997-01-01

Automated aids and decision support tools are rapidly becoming indispensible tools in high-technology cockpits, and are assuming increasing control of "cognitive" flight tasks, such as calculating fuel-efficient routes, navigating, or detecting and diagnosing system malfunctions and abnormalities. This study was designed to investigate "automation bias," a recently documented factor in the use of automated aids and decision support systems. The term refers to omission and commission errors resulting from the use of automated cues as a heuristic replacement for vigilant information seeking and processing. Glass-cockpit pilots flew flight scenarios involving automation "events," or opportunities for automation-related omission and commission errors. Pilots who perceived themselves as "accountable" for their performance and strategies of interaction with the automation were more likely to double-check automated functioning against other cues, and less likely to commit errors. Pilots were also likely to erroneously "remember" the presence of expected cues when describing their decision-making processes.

Automating Collection of Pain-Related Patient-Reported Outcomes to Enhance Clinical Care and Research.

PubMed

Owen-Smith, Ashli; Mayhew, Meghan; Leo, Michael C; Varga, Alexandra; Benes, Lindsay; Bonifay, Allison; DeBar, Lynn

2018-05-01

Chronic pain is highly prevalent, and the ability to routinely measure patients' pain and treatment response using validated patient-reported outcome (PRO) assessments is important to clinical care. Despite this recognition, systematic use in everyday clinical care is rare. The aims of this study were to (1) describe infrastructure designed to automate PRO data collection, (2) compare study-enhanced PRO completion rates to those in clinical care, and (3) evaluate patient response rates by method of PRO administration and sociodemographic and/or clinical characteristics. The Pain Program for Active Coping and Training (PPACT) is a pragmatic clinical trial conducted within three regions of the Kaiser Permanente health care system. PPACT evaluates the effect of integrative primary care-based pain management services on outcomes for chronic pain patients on long-term opioid treatment. We implemented a tiered process for quarterly assessment of PROs to supplement clinical collection and ensure adequate trial data using three methods: web-based personal health records (PHR), automated interactive voice response (IVR) calls, and live outreach. Among a subset of PPACT participants examined (n = 632), the tiered study-enhanced PRO completion rates were higher than in clinical care: 96% completed ≥ 1 study-administered PRO with mean of 3.46 (SD = 0.85) vs. 74% completed in clinical care with a mean of 2.43 (SD = 2.08). Among all PPACT participants at 3 months (n = 831), PRO completion was 86% and analyses of response by key characteristics found only that participant age predicted an increased likelihood of responding to PHR and IVR outreach. Adherence to pain-related PRO data collection using our enhanced tiered approach was high. No demographic or clinical identifiers other than age were associated with differential response by modality. Successful ancillary support should employ multimodal electronic health record functionalities for PRO administration

Automated Processing Workflow for Ambient Seismic Recordings

NASA Astrophysics Data System (ADS)

Girard, A. J.; Shragge, J.

2017-12-01

Structural imaging using body-wave energy present in ambient seismic data remains a challenging task, largely because these wave modes are commonly much weaker than surface wave energy. In a number of situations body-wave energy has been extracted successfully; however, (nearly) all successful body-wave extraction and imaging approaches have focused on cross-correlation processing. While this is useful for interferometric purposes, it can also lead to the inclusion of unwanted noise events that dominate the resulting stack, leaving body-wave energy overpowered by the coherent noise. Conversely, wave-equation imaging can be applied directly on non-correlated ambient data that has been preprocessed to mitigate unwanted energy (i.e., surface waves, burst-like and electromechanical noise) to enhance body-wave arrivals. Following this approach, though, requires a significant preprocessing effort on often Terabytes of ambient seismic data, which is expensive and requires automation to be a feasible approach. In this work we outline an automated processing workflow designed to optimize body wave energy from an ambient seismic data set acquired on a large-N array at a mine site near Lalor Lake, Manitoba, Canada. We show that processing ambient seismic data in the recording domain, rather than the cross-correlation domain, allows us to mitigate energy that is inappropriate for body-wave imaging. We first develop a method for window selection that automatically identifies and removes data contaminated by coherent high-energy bursts. We then apply time- and frequency-domain debursting techniques to mitigate the effects of remaining strong amplitude and/or monochromatic energy without severely degrading the overall waveforms. After each processing step we implement a QC check to investigate improvements in the convergence rates - and the emergence of reflection events - in the cross-correlation plus stack waveforms over hour-long windows. Overall, the QC analyses suggest that

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.

Automated solid-phase extraction-liquid chromatography-tandem mass spectrometry analysis of 6-acetylmorphine in human urine specimens: application for a high-throughput urine analysis laboratory.

PubMed

Robandt, P V; Bui, H M; Scancella, J M; Klette, K L

2010-10-01

An automated solid-phase extraction-liquid chromatography- tandem mass spectrometry (SPE-LC-MS-MS) method using the Spark Holland Symbiosis Pharma SPE-LC coupled to a Waters Quattro Micro MS-MS was developed for the analysis of 6-acetylmorphine (6-AM) in human urine specimens. The method was linear (R² = 0.9983) to 100 ng/mL, with no carryover at 200 ng/mL. Limits of quantification and detection were found to be 2 ng/mL. Interrun precision calculated as percent coefficient of variation (%CV) and evaluated by analyzing five specimens at 10 ng/mL over nine batches (n = 45) was 3.6%. Intrarun precision evaluated from 0 to 100 ng/mL ranged from 1.0 to 4.4%CV. Other opioids (codeine, morphine, oxycodone, oxymorphone, hydromorphone, hydrocodone, and norcodeine) did not interfere in the detection, quantification, or chromatography of 6-AM or the deuterated internal standard. The quantified values for 41 authentic human urine specimens previously found to contain 6-AM by a validated gas chromatography (GC)-MS method were compared to those obtained by the SPE-LC-MS-MS method. The SPE-LC-MS-MS procedure eliminates the human factors of specimen handling, extraction, and derivatization, thereby reducing labor costs and rework resulting from human error or technique issues. The time required for extraction and analysis was reduced by approximately 50% when compared to a validated 6-AM procedure using manual SPE and GC-MS analysis.

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.

Automation, consolidation, and integration in autoimmune diagnostics.

PubMed

Tozzoli, Renato; D'Aurizio, Federica; Villalta, Danilo; Bizzaro, Nicola

2015-08-01

Over the past two decades, we have witnessed an extraordinary change in autoimmune diagnostics, characterized by the progressive evolution of analytical technologies, the availability of new tests, and the explosive growth of molecular biology and proteomics. Aside from these huge improvements, organizational changes have also occurred which brought about a more modern vision of the autoimmune laboratory. The introduction of automation (for harmonization of testing, reduction of human error, reduction of handling steps, increase of productivity, decrease of turnaround time, improvement of safety), consolidation (combining different analytical technologies or strategies on one instrument or on one group of connected instruments) and integration (linking analytical instruments or group of instruments with pre- and post-analytical devices) opened a new era in immunodiagnostics. In this article, we review the most important changes that have occurred in autoimmune diagnostics and present some models related to the introduction of automation in the autoimmunology laboratory, such as automated indirect immunofluorescence and changes in the two-step strategy for detection of autoantibodies; automated monoplex immunoassays and reduction of turnaround time; and automated multiplex immunoassays for autoantibody profiling.

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.

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.

Data Mining: The Art of Automated Knowledge Extraction

NASA Astrophysics Data System (ADS)

Karimabadi, H.; Sipes, T.

2012-12-01

Data mining algorithms are used routinely in a wide variety of fields and they are gaining adoption in sciences. The realities of real world data analysis are that (a) data has flaws, and (b) the models and assumptions that we bring to the data are inevitably flawed, and/or biased and misspecified in some way. Data mining can improve data analysis by detecting anomalies in the data, check for consistency of the user model assumptions, and decipher complex patterns and relationships that would not be possible otherwise. The common form of data collected from in situ spacecraft measurements is multi-variate time series which represents one of the most challenging problems in data mining. We have successfully developed algorithms to deal with such data and have extended the algorithms to handle streaming data. In this talk, we illustrate the utility of our algorithms through several examples including automated detection of reconnection exhausts in the solar wind and flux ropes in the magnetotail. We also show examples from successful applications of our technique to analysis of 3D kinetic simulations. With an eye to the future, we provide an overview of our upcoming plans that include collaborative data mining, expert outsourcing data mining, computer vision for image analysis, among others. Finally, we discuss the integration of data mining algorithms with web-based services such as VxOs and other Heliophysics data centers and the resulting capabilities that it would enable.

Automation reliability in unmanned aerial vehicle control: a reliance-compliance model of automation dependence in high workload.

PubMed

Dixon, Stephen R; Wickens, Christopher D

2006-01-01

Two experiments were conducted in which participants navigated a simulated unmanned aerial vehicle (UAV) through a series of mission legs while searching for targets and monitoring system parameters. The goal of the study was to highlight the qualitatively different effects of automation false alarms and misses as they relate to operator compliance and reliance, respectively. Background data suggest that automation false alarms cause reduced compliance, whereas misses cause reduced reliance. In two studies, 32 and 24 participants, including some licensed pilots, performed in-lab UAV simulations that presented the visual world and collected dependent measures. Results indicated that with the low-reliability aids, false alarms correlated with poorer performance in the system failure task, whereas misses correlated with poorer performance in the concurrent tasks. Compliance and reliance do appear to be affected by false alarms and misses, respectively, and are relatively independent of each other. Practical implications are that automated aids must be fairly reliable to provide global benefits and that false alarms and misses have qualitatively different effects on performance.

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.

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