Automated Software Tool Research Articles

Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm.

The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was [Formula: see text], with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors.

Automated Quantitative Plaque Analysis for Discrimination of Coronary Chronic Total Occlusion and Subtotal Occlusion in Computed Tomography Angiography.

The aim of this study was to evaluate the utility of automated plaque analysis in differentiating chronic total occlusion (CTO) from subtotal occlusion (SO) in patients with ambiguous coronary lesions on coronary computed tomography angiography (CTA). A total of 63 patients with 63 ambiguous coronary lesions on CTA were included. The lesion length (LL), diameter stenosis, plaque volume and composition, remodeling index, and contrast density difference (CDD) (reflecting intraluminal contrast kinetics over the lesion) were assessed using an automatic software tool. All patients underwent invasive coronary angiography. Coronary angiography confirmed 28 CTOs and 35 SOs. CTOs showed significantly longer LL (6.4±12.3 vs. 1.0±2.2 mm, P=0.03) and higher CDD (74%±31% vs. 55%±32%, P=0.02) compared with SO. The optimal thresholds for prediction of CTO for CDD and LL were ≥43% and ≥1 mm, respectively (max. sensitivity: 82% for CDD, max. specificity: 77% for LL). The guidewire manipulation time correlated with LL (r=0.529, P=0.004) and CDD (r=0.435, P=0.021) in lesions attempted by percutaneous coronary intervention. Automated computed tomography plaque analysis may be applied as a noninvasive tool to differentiate CTO from SO.

SModelS: A Tool for Making Systematic Use of Simplified Models Results

We present an automated software tool ”SModelS” to systematically confront theories Beyond the Standard Model (BSM) with experimental data. The tool consists of a general procedure to decompose such BSM theories into their Simplified Models Spectra (SMS). In addition, SModelS features a database containing the majority of the published SMS results of CMS and ATLAS. These results consist of the 95% confidence level upper limits on signal production cross sections. The two components together allow us to quickly confront any BSM model with LHC results. As a show-case example we will briefly discuss an application of our procedure to a specific supersymmetric model.It is one of our ongoing efforts to extend the framework to include also efficiency maps produced either by the experimental collaborations, by efforts performed within the phenomenological groups, or possibly also by ourselves.While the current implementation can handle null results only, it is our ultimate goal to build the Next Standard Model in a bottom-up fashion from both negative and positive results of several experiments. The implementation is open source, written in python, and available from http://smodels.hephy.at.

SmiFISH and FISH-quant - a flexible single RNA detection approach with super-resolution capability.

Single molecule FISH (smFISH) allows studying transcription and RNA localization by imaging individual mRNAs in single cells. We present smiFISH (single molecule inexpensive FISH), an easy to use and flexible RNA visualization and quantification approach that uses unlabelled primary probes and a fluorescently labelled secondary detector oligonucleotide. The gene-specific probes are unlabelled and can therefore be synthesized at low cost, thus allowing to use more probes per mRNA resulting in a substantial increase in detection efficiency. smiFISH is also flexible since differently labelled secondary detector probes can be used with the same primary probes. We demonstrate that this flexibility allows multicolor labelling without the need to synthesize new probe sets. We further demonstrate that the use of a specific acrydite detector oligonucleotide allows smiFISH to be combined with expansion microscopy, enabling the resolution of transcripts in 3D below the diffraction limit on a standard microscope. Lastly, we provide improved, fully automated software tools from probe-design to quantitative analysis of smFISH images. In short, we provide a complete workflow to obtain automatically counts of individual RNA molecules in single cells.

EARLINET Single Calculus Chain – technical – Part 2: Calculation of optical products

Abstract. In this paper we present the automated software tool ELDA (EARLINET Lidar Data Analyzer) for the retrieval of profiles of optical particle properties from lidar signals. This tool is one of the calculus modules of the EARLINET Single Calculus Chain (SCC) which allows for the analysis of the data of many different lidar systems of EARLINET in an automated, unsupervised way. ELDA delivers profiles of particle extinction coefficients from Raman signals as well as profiles of particle backscatter coefficients from combinations of Raman and elastic signals or from elastic signals only. Those analyses start from pre-processed signals which have already been corrected for background, range dependency and hardware specific effects. An expert group reviewed all algorithms and solutions for critical calculus subsystems which are used within EARLINET with respect to their applicability for automated retrievals. Those methods have been implemented in ELDA. Since the software was designed in a modular way, it is possible to add new or alternative methods in future. Most of the implemented algorithms are well known and well documented, but some methods have especially been developed for ELDA, e.g., automated vertical smoothing and temporal averaging or the handling of effective vertical resolution in the case of lidar ratio retrievals, or the merging of near-range and far-range products. The accuracy of the retrieved profiles was tested following the procedure of the EARLINET-ASOS algorithm inter-comparison exercise which is based on the analysis of synthetic signals. Mean deviations, mean relative deviations, and normalized root-mean-square deviations were calculated for all possible products and three height layers. In all cases, the deviations were clearly below the maximum allowed values according to the EARLINET quality requirements.

Combined untargeted and targeted fingerprinting with comprehensive two-dimensional chromatography for volatiles and ripening indicators in olive oil

Comprehensive two-dimensional gas chromatography (GC × GC) is the most effective multidimensional separation technique for in-depth investigations of complex samples of volatiles (VOC) in food. However, each analytical run produces dense, multi-dimensional data, so elaboration and interpretation of chemical information is challenging.This study exploits recent advances of GC × GC-MS chromatographic fingerprinting to study VOCs distributions from Extra Virgin Olive Oil (EVOO) samples of a single botanical origin (Picual), cultivated in well-defined plots in Granada (Spain), and harvested at different maturation stages. A new integrated work-flow, fully supported by dedicated and automated software tools, combines untargeted and targeted (UT) approaches based on peak-region features to achieve the most inclusive fingerprinting.Combined results from untargeted and targeted methods are consistent, reliable, and informative on discriminant features (analytes) correlated with optimal ripening of olive fruits and sensory quality of EVOOs. The great flexibility of the UT fingerprinting here adopted enables retrospective analysis with great confidence and provides data to validate the transferability of ripening indicators ((Z)-3-hexenal, (Z)-2-hexenal, (E)-2-pentenal, nonanal, 6-methyl-5-hepten-2-one, octane) to external samples sets. Direct image comparison, based on visual features, also is investigated for quick and effective pair-wise investigations. Its implementation with reliable metadata generated by UT fingerprinting confirms the maturity of 2D data elaboration tools and makes advanced image processing a real perspective.

The validity and reliability of an automated method of scoring dental arch relationships in unilateral cleft lip and palate using the modified Huddart-Bodenham scoring system.

To evaluate an automated software tool for the assessment of dental arch relationships using the modified Huddart and Bodenham (MHB) index. Cohort of 43 models of subjects aged 9-21 with UCLP and the ten GOSLON reference models sets. The 53 sets of plaster models were scored using the MHB index and scanned with a benchtop scanner. The digital models were MHB scored visually using a commercial software program and landmarked for automatic scoring using a software plug-in. Scoring/landmarking was undertaken by three observers and repeated after 1 month. Intra- and inter-observer reproducibility were tested using Cronbach's alpha and intraclass correlation coefficients (ICC) (threshold > 0.9). Bland-Altman plots demonstrated inter-observer agreement for each model format. Random and systematic error with digital landmark identification error were determined using the x, y, and z co-ordinates for 28 models digitized twice 1 month apart using Cronbach's alpha and a t-test, respectively. Intra-operator landmark identification was excellent (Cronbach's alpha = 0.933) with no differences between sessions (P > 0.05). Intra-observer reproducibility was excellent for all examiners (Cronbach's alpha and ICC 0.986-0.988). Inter-observer reproducibility was highest for the software plug-in (0.991), followed by plaster (0.989) and OrthoAnalyzer (0.979) and Bland-Altman plots confirmed no systematic bias and greater consistency of scores with the automated software. The automated MHB software tool is valid, reproducible, and the most objective method of assessing maxillary arch constriction for patients with UCLP. The authors declare no conflict of interest or financial relationship with any organization or software used within the study.

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-Imaging.

With its single cell sensitivity over volumes as large as or larger than a mouse, cryo-imaging enables imaging of stem cell biodistribution, homing, engraftment, and molecular mechanisms. We developed and evaluated a highly automated software tool to detect fluorescently labeled stem cells within very large (∼200 GB) cryo-imaging datasets. Cell detection steps are: preprocess, remove immaterial regions, spatially filter to create features, identify candidate pixels, classify pixels using bagging decision trees, segment cell patches, and perform 3D labeling. There are options for analysis and visualization. To train the classifier, we created synthetic images by placing realistic digital cell models onto cryo-images of control mice devoid of cells. Very good cell detection results were (precision=98.49%, recall=99.97%) for synthetic cryo-images, (precision=97.81%, recall=97.71%) for manually evaluated, actual cryo-images, and false positives in control mice. An α-multiplier applied to features allows one to correct for experimental variations in cell brightness due to labeling. On dim cells (37% of standard brightness), with correction, we improved recall (49.26%→ 99.36%) without a significant drop in precision (99.99%→ 99.75%) . With tail vein injection, multipotent adult progenitor cells in a graft-versus-host-disease model in the first days post injection were predominantly found in lung, liver, spleen, and bone marrow. Distribution was not simply related to blood flow. The lung contained clusters of cells while other tissues contained single cells. Our methods provided stem cell distribution anywhere in mouse with single cell sensitivity. Methods should provide a rational means of evaluating dosing, delivery methods, cell enhancements, and mechanisms for therapeutic cells.

FMRat: an extension of SPM for a fully automatic analysis of rodent brain functional magnetic resonance series.

The purpose of this study was to develop a multi-platform automatic software tool for full processing of fMRI rodent studies. Existing tools require the usage of several different plug-ins, a significant user interaction and/or programming skills. Based on a user-friendly interface, the tool provides statistical parametric brain maps (t and Z) and percentage of signal change for user-provided regions of interest. The tool is coded in MATLAB (MathWorks(®)) and implemented as a plug-in for SPM (Statistical Parametric Mapping, the Wellcome Trust Centre for Neuroimaging). The automatic pipeline loads default parameters that are appropriate for preclinical studies and processes multiple subjects in batch mode (from images in either Nifti or raw Bruker format). In advanced mode, all processing steps can be selected or deselected and executed independently. Processing parameters and workflow were optimized for rat studies and assessed using 460 male-rat fMRI series on which we tested five smoothing kernel sizes and three different hemodynamic models. A smoothing kernel of FWHM = 1.2 mm (four times the voxel size) yielded the highest t values at the somatosensorial primary cortex, and a boxcar response function provided the lowest residual variance after fitting. fMRat offers the features of a thorough SPM-based analysis combined with the functionality of several SPM extensions in a single automatic pipeline with a user-friendly interface. The code and sample images can be downloaded from https://github.com/HGGM-LIM/fmrat .

Semi-automated hippocampal segmentation in people with cognitive impairment using an age appropriate template for registration.

To evaluate a new semi-automated segmentation method for calculating hippocampal volumes and to compare results with standard software tools in a cohort of people with subjective memory complaints (SMC) and mild cognitive impairment (MCI). Data from 58 participants, 39 with SMC (17 male, 22 female, mean age 72.6) and 19 with MCI (6 male, 13 female, mean age 74.3), were analyzed. For each participant, T1-weighted images were acquired using an MPRAGE sequence on a 3 Tesla MRI system. Hippocampal volumes (left, right, and total) were calculated with a new, age appropriate registration template, based on older people and using the advanced software tool ANTs (Advanced Normalization Tools). The results were compared with manual tracing (seen as the reference standard) and two widely accepted automated software tools (FSL, FreeSurfer). The hippocampal volumes, calculated by using the age appropriate registration template were significantly (P < 0.05) more accurate (mean volume accuracy more than 90%) than those obtained with FreeSurfer and FSL (both less than 70%). Dice coefficients for the hippocampal segmentations with the new template method (75.3%) were slightly, but significantly (P < 0.05) higher than those from FreeSurfer (72.4%). These results suggest that an age appropriate registration template might be a more accurate alternative to calculate hippocampal volumes when manual segmentation is not feasible.

Automated Software for Streamlining Optimisation of Resource Planning for an Additive Manufacturing System

The use of additive manufacturing (AM) systems in scale production has rapidly increased in recent years. The growing tendency to adopt AM technologies into established manufacturing systems has led to research that considers the optimisation of both process and resource planning. In order to maximise the outputs of such a production process, planning must be conducted rigorously. This paper proposes an automated software tool, called EasyPlan, which streamlines the optimisation of resource planning. The algorithm is developed using LabVIEW and is demonstrated for an AM component from the medical industry. For the evaluation process, parameters such as stock levels, delivery terms and technical charts of the products are provided. A user friendly interface is developed, making EasyPlan versatile to all types of environments.

English

Middleware is currently used in all the customized software systems in order to felicitate the applications to majorly work on their functionality not on to much of process, memory management, load Analysis, counter, Timers, Database [DB], fault detection & correction and other such constraints which are very important in building the network machine software’s like what we use in 3G and 4G networks. (i.e. in IMS, IP Multimedia Subsystems). The Middleware of IMS will provide API’s [Application Program Interfaces] which will be used by the applications to perform their set of functionality. The middleware also provides platform for running 3rd party application software that support the functionality of the other applications on the upper layer of the stack. So this middleware APIs have to be checked for their functionality in the maintenance mode after every new feature addition, deletion or modification of the existing ones. So Regression Testing has to be done to validate the working of the APIs. Since these APIs are very important for application to work fine without any malfunctioning. This paper presents the variant of the testing technique which was followed previously in manual terms by replacing it with automated software tool set which reduces the time, error factor prior to every software release, making the network software application error free and work in a much effective manner.

An empirical strategy to detect bacterial transcript structure from directional RNA-seq transcriptome data.

BackgroundAs sequencing costs are being lowered continuously, RNA-seq has gradually been adopted as the first choice for comparative transcriptome studies with bacteria. Unlike microarrays, RNA-seq can directly detect cDNA derived from mRNA transcripts at a single nucleotide resolution. Not only does this allow researchers to determine the absolute expression level of genes, but it also conveys information about transcript structure. Few automatic software tools have yet been established to investigate large-scale RNA-seq data for bacterial transcript structure analysis.ResultsIn this study, 54 directional RNA-seq libraries from Salmonella serovar Typhimurium (S. Typhimurium) 14028s were examined for potential relationships between read mapping patterns and transcript structure. We developed an empirical method, combined with statistical tests, to automatically detect key transcript features, including transcriptional start sites (TSSs), transcriptional termination sites (TTSs) and operon organization. Using our method, we obtained 2,764 TSSs and 1,467 TTSs for 1331 and 844 different genes, respectively. Identification of TSSs facilitated further discrimination of 215 putative sigma 38 regulons and 863 potential sigma 70 regulons. Combining the TSSs and TTSs with intergenic distance and co-expression information, we comprehensively annotated the operon organization in S. Typhimurium 14028s.ConclusionsOur results show that directional RNA-seq can be used to detect transcriptional borders at an acceptable resolution of ±10-20 nucleotides. Technical limitations of the RNA-seq procedure may prevent single nucleotide resolution. The automatic transcript border detection methods, statistical models and operon organization pipeline that we have described could be widely applied to RNA-seq studies in other bacteria. Furthermore, the TSSs, TTSs, operons, promoters and unstranslated regions that we have defined for S. Typhimurium 14028s may constitute valuable resources that can be used for comparative analyses with other Salmonella serotypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1555-8) contains supplementary material, which is available to authorized users.

Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language.

In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).

Software Tools for Automation of Parallel Programming on the Basis of Algebra of Algorithms

The development of the algebra-algorithmic methodology and tools for automated design and generation of programs for graphics processing units is proposed. A particular feature of the proposed approach is the use of high-level specifications that are close to natural-language specifications and also the application of a method that ensures the syntactical correctness of algorithms and programs being designed. The approach was implemented in a toolkit destined for interactively designing algorithm schemes and generating programs. The use of this toolkit is illustrated by the development of a parallel program in the field of meteorology.

Bioanalytical laboratory automation development: why should we and how could we collaborate?

Bioanalytical laboratory automation development: why should we and how could we collaborate?

Application of Computer Vision Algorithms for Evaluating the Performance of Cotton Pickers

Collecting data on cotton field's agricultural background and quality of the cotton pickers' work is one of the most important works in preparation for the cotton picking by machine. The use of advanced information technologies for the collection and processing of these data allows to create automated software tools that significantly reduce the time and cost. Creating the algorithms that can be applied in the development of such programs is one of the urgent problems of computer vision and agricultural sciences. Algorithms for determining the number of green bolls based on the analysis of images of a section of cotton field from different angles are proposed and experimentally tested. Keywords: image analysis, Canny's edge detection method, Hough transform, decision trees, algorithm C4.5.

Toward microfluidic design automation: a new system simulation toolkit for the in silico evaluation of droplet-based lab-on-a-chip systems

Miniaturization of biological and chemical assays in lab-on-a-chip systems is a highly topical field of research. The pressure-driven droplet-based microfluidic platform is a promising way to realize these miniaturized systems by expanding the capability of assays with special features that are unreached by traditional workflows. Full custom centric design of droplet-based microfluidic lab-on-a-chip systems leads to a high system integration level and design complexity. In our work, we report on a software toolkit based on the Kirchhoff laws for modeling droplet traffic and processing for even complex microfluidic networks. Experimental validation of the simulation results was performed utilizing directional droplet transport switching in a circular channel element. This structure can be employed as a benchmark system for the experimental validation of the obtained simulation results. As a result of these experiments, our design and simulation toolkit meet the requirements for a versatile and low-risk development of custom lab-on-a-chip devices. Together with our conceptual model of microfluidic networks, most of the development problems arising with complex lab-on-a-chip applications can be solved. Due to the high computational speed, the algorithm allows an interactive in silico evaluation of even complex sample-processing workflows in droplet-based microfluidic devices prior any preparation of prototypes. Summarizing the developed toolkit may become the foundation for the future development of software tools for a microfluidic design automation. As a result of this new way of simulation-based application-driven development, the advantages of lab-on-a-chip will be accessible for more people through the easy, versatile and efficient transformation from complex laboratory workflows to compact and easy to use lab-on-a-chip applications.

경영교육인증에 따른 수산경영학 교과과정 및 학업이수의 변화 분석과 과제

Recently the rapid changes of the social environment make both university and every each department should have to renovate and adjust all of the educational components. The most dominant and issuable change of business administration education is the Business Education Accreditation which was certified by the KABEA(Korean Association of Business Education Accreditation). And the only two department of fisheries business administration which in Gyeongsang National University and Pukyong National University has been accredited recently. Though The department of fisheries business administration in Korea has been influenced by that Accreditation, there is few research and analysis on educational change. So it is very important that research on the curricular constitution and change of the curriculum for considering the its own effect and influence. This paper aims to explore the curricular constitution, change and course fulfillment of fisheries business administration after the business education accreditation based on the case of the department of fisheries business administration in Gyeongsang National University. The analysis of fisheries business administration change will help develop sustainable curriculum and give more guideline for identifying the core of fisheries business administration. The method of analysing that in this research has adopt the social network analysis(SNA) and automated software tool which is the most dominant technique in contents analysis including the Krkwics and Pajek. The SNA is evaluated that most popular, rigorous and firm methodology for analyzing, examining and revising some concepts or objects in the context of semantics. The result of this study shows that the ratio of fisheries business administration class has been decreased but still the most fulfilled course. And English mediated class has been avoided though that sort of the class has been increased. In the view point of course fulfillment, it has been potential and inefficient problems that some classes that including financial related and accounting related class would not been accomplished appropriately. The findings of a graduate depth interview which has been done about 4 years for understanding that student show that there`s some recognition gap of fisheries business administration in staffs of human resource management. And the level of job satisfaction in fisheries business still somewhat low and even worse the chance of recruitment also unsteady.

Proposed data model for the next version of the synthetic biology open language.

While the first version of the Synthetic Biology Open Language (SBOL) has been adopted by several academic and commercial genetic design automation (GDA) software tools, it only covers a limited number of the requirements for a standardized exchange format for synthetic biology. In particular, SBOL Version 1.1 is capable of representing DNA components and their hierarchical composition via sequence annotations. This proposal revises SBOL Version 1.1, enabling the representation of a wider range of components with and without sequences, including RNA components, protein components, small molecules, and molecular complexes. It also introduces modules to instantiate groups of components on the basis of their shared function and assert molecular interactions between components. By increasing the range of structural and functional descriptions in SBOL and allowing for their composition, the proposed improvements enable SBOL to represent and facilitate the exchange of a broader class of genetic designs.