Manual Assignment Research Articles

A novel approach to optimize workflow in grid-based teleradiology applications

Background and objectiveThis study proposes an infrastructure with a reporting workflow optimization algorithm (RWOA) in order to interconnect facilities, reporting units and radiologists on a single access interface, to increase the efficiency of the reporting process by decreasing the medical report turnaround time and to increase the quality of medical reports by determining the optimum match between the inspection and radiologist in terms of subspecialty, workload and response time. MethodsWorkflow centric network architecture with an enhanced caching, querying and retrieving mechanism is implemented by seamlessly integrating Grid Agent and Grid Manager to conventional digital radiology systems. The inspection and radiologist attributes are modelled using a hierarchical ontology structure. Attribute preferences rated by radiologists and technical experts are formed into reciprocal matrixes and weights for entities are calculated utilizing Analytic Hierarchy Process (AHP). The assignment alternatives are processed by relation-based semantic matching (RBSM) and Integer Linear Programming (ILP). ResultsThe results are evaluated based on both real case applications and simulated process data in terms of subspecialty, response time and workload success rates. Results obtained using simulated data are compared with the outcomes obtained by applying Round Robin, Shortest Queue and Random distribution policies. The proposed algorithm is also applied to a real case teleradiology application process data where medical reporting workflow was performed based on manual assignments by the chief radiologist for 6225 inspections. ConclusionsRBSM gives the highest subspecialty success rate and integrating ILP with RBSM ratings as RWOA provides a better response time and workload distribution success rate. RWOA based image delivery also prevents bandwidth, storage or hardware related stuck and latencies. When compared with a real case teleradiology application where inspection assignments were performed manually, the proposed solution was found to increase the experience success rate by 13.25%, workload success rate by 63.76% and response time success rate by 120%. The total response time in the real case application data was improved by 22.39%.

Comparative studies on manual and automatic backbone chemical shift assignments of 2H/13C/15N-labeled Ube2g1

Ube2g1, one of human E2 enzymes, possesses an additional acidic loop in the vicinity of the active Cys90 residue. The presence of the backbone chemical shifts (bCSs) is essential for various NMR studies of Ube2g1. Triple resonance experiments for the bCSs assignment of 13C/15N-labeled Ube2g1 were not efficient due to the relatively high molecular size (more than 20 kDa). 2H/13C/15N-labeled Ube2g1 was prepared to increase the T2 relaxation time, and then the TROSY-based triple resonance spectra were recorded with 2H decoupling. After the bCSs of Ube2g1 were assigned manually, the resulting bCSs were compared with those from two different automatic assignment programs. The AutoAssign program that utilizes only the peak lists of the NMR spectra accomplished 69 % assignment of the bCSs. On the other hand, the RASPnmr program requires the additional reference CSs that can be predicted from a relevant PDB coordinate. The homologous models of Ube2g1 were generated by various modeling programs, and then their CSs were predicted by using the SHIFTX2 program. The implementations of the predicted bCSs into the RASPnmr analysis resulted in complete and accurate assignment of the bCSs of 2H/13C/15N-labeled Ube2g1. The reference bCSs calculated from various homologous models of Ube2g1 enabled the automatic bCSs assignment process by the RASPnmr program, and a similar application will be possible for the bCS assignments of other proteins.

CoreDevRec: Automatic Core Member Recommendation for Contribution Evaluation

The pull-based software development helps developers make contributions flexibly and efficiently. Core members evaluate code changes submitted by contributors, and decide whether to merge these code changes into repositories or not. Ideally, code changes are assigned to core members and evaluated within a short time after their submission. However, in reality, some popular projects receive many pull requests, and core members have difficulties in choosing pull requests which are to be evaluated. Therefore, there is a growing need for automatic core member recommendation, which improves the evaluation process. In this paper, we investigate pull requests with manual assignment. Results show that 3.2%�40.6% of pull requests are manually assigned to specific core members. To assist with the manual assignment, we propose CoreDevRec to recommend core members for contribution evaluation in GitHub. CoreDevRec uses support vector machines to analyze different kinds of features, including file paths of modified codes, relationships between contributors and core members, and activeness of core members. We evaluate CoreDevRec on 18651 pull requests of five popular projects in GitHub. Results show that CoreDevRec achieves accuracy from 72.9% to 93.5% for top 3 recommendation. In comparison with a baseline approach, CoreDevRec improves the accuracy from 18.7% to 81.3% for top 3 recommendation. Moreover, CoreDevRec even has higher accuracy than manual assignment in the project TrinityCore. We believe that CoreDevRec can improve the assignment of pull requests.

Automated detection of arterial wall boundaries based on correlation between adjacent receive scan lines for elasticity imaging

In our series of studies on the ultrasonic assessment of the regional elasticity of the arterial wall, the change in the thickness of the arterial wall caused by the heartbeat was measured by the ultrasonic phased-tracking method. In the measurement of elasticity, the boundaries of the anterior and posterior walls must be assigned in advance. Currently the boundaries are manually determined by the operator, which is time-consuming and results in observer variability. In this paper, we propose an automated method for the detection of arterial wall boundaries using multiscale dynamic programming, in which the cost function includes the correlation term between ultrasonic echoes in adjacent receive scan lines. The correlation term enables boundary detection that is more robust than conventional methods against noises. The effectiveness of the proposed method was validated using a phantom and applied to the in vivo measurements of carotid arteries. The root mean square error between the results obtained by the proposed method and the manual assignment of the boundaries was significantly improved.

Fast and accurate resonance assignment of small-to-large proteins by combining automated and manual approaches.

The process of resonance assignment is fundamental to most NMR studies of protein structure and dynamics. Unfortunately, the manual assignment of residues is tedious and time-consuming, and can represent a significant bottleneck for further characterization. Furthermore, while automated approaches have been developed, they are often limited in their accuracy, particularly for larger proteins. Here, we address this by introducing the software COMPASS, which, by combining automated resonance assignment with manual intervention, is able to achieve accuracy approaching that from manual assignments at greatly accelerated speeds. Moreover, by including the option to compensate for isotope shift effects in deuterated proteins, COMPASS is far more accurate for larger proteins than existing automated methods. COMPASS is an open-source project licensed under GNU General Public License and is available for download from http://www.liu.se/forskning/foass/tidigare-foass/patrik-lundstrom/software?l=en. Source code and binaries for Linux, Mac OS X and Microsoft Windows are available.

Process Analytics Approach for R&D Project Selection

R&D project selection plays an important role in government funding agencies, as allocation of billions of dollars among the proposals deemed highly influential and contributive solely depend on it. Efficacious assignment of reviewers is one of the most critical processes that controls the quality of the entire project selection and also has a serious implication on business profit. Current methods that focus on workflow automation are more efficient than manual assignment; however, they are not effective, as they fail to consider the real insight of core tasks. Other decision models that analyze core tasks are effective but inefficient when handling large amounts of submissions, and they suffer from irrelevant assignment. Furthermore, they largely ignore real deep insight of back-end data such as quality of the reviewers (e.g., quality and citation impact of their produced research) and the effect of social relationships in project selection processes that are essential for identifying reviewers for interdisciplinary proposal evaluation. In light of these deficiencies, this research proposes a novel hybrid process analytics approach to decompose the complex reviewer assignment process into manageable subprocesses and applies data-driven decision models cum process analytics systematically from a triangular perspective via the research analytics framework to achieve high operational efficiencies and high-quality assignment. It also analyzes big data from scientific databases and generates visualized decision-ready information to support effective decision making. The proposed approach has been implemented to aid the project selection process of the largest funding agency in China and has been tested. The test results show that the proposed approach has the potential to add great benefits, including cost saving, improved effectiveness, and increased business value.

Optimizing Marine Security Guard Assignments

Abstract : The Marine Corps Embassy Security Group (MCESG) assigns 1,500 Marine Security Guards (MSGs) to 149 embassy detachments annually. While attempting to fulfill several billet requirements, MCESG strives to balance MSG experience levels at each embassy detachment and assign MSGs to their preferred posts. The current assignment process is accomplished manually by three Marines and takes more than 6,000 hours per year. This thesis presents the Marine Security Guard Assignment Tool (MSGAT). MSGAT is an Excel-based decision support tool that utilizes a system of workbooks to guide MCESG through a streamlined data collection and provide optimal assignments. MSGAT assignments result in a higher satisfaction when compared with manual assignments. MSGAT has had an immediate and quantifiable impact on the assignment process. It has reduced person-hours by 80%, increased overall assignment quality and efficiency, and improved the operational readiness of MCESG by optimizing MSG assignments.

A Novel Machine Learning Approach Toward Quality Assessment of Sensor Data

A novel machine learning approach to assess the quality of sensor data using an ensemble classification framework is presented in this paper. The quality of sensor data is indicated by discrete quality flags that indicate the level of uncertainty associated with a sensor reading. Depending on the domain and the problem under consideration, the level of uncertainty is different and thus unsupervised methods like outlier detection fails to match the expectation. The quality flags are normally assigned by domain experts. Considering the volume of sensor data, manual assignment is a laborious task and subject to human error. Given a representative set of labelled data, a supervised classification approach is thus a feasible alternative. The nature of sensor data, however, poses some challenges to the classification task. Data of dubious quality exists in such data sets with very small frequency leading to the class imbalance problem. We thus adopt a cluster oriented sampling approach to address the imbalance issue. In addition, it is beneficial to train multiple classifiers to improve the overall classification accuracy. We thus produce multiple under-sampled training sets using cluster oriented sampling and train base classifiers on each of them. Decisions produced by the base classifiers are fused into a single decision using majority voting. We have evaluated the proposed ensemble classification framework by assessing the quality of marine sensor data obtained from sensors situated at Sullivans Cove, Hobart, Australia. Experimental results reveal that the proposed framework agrees with expert judgement with high accuracy and achieves superior classification performance than other state-of-the-art approaches.

Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins

Protein subcellular localization is a major determinant of protein function. However, this important protein feature is often described in terms of discrete and qualitative categories of subcellular compartments, and therefore it has limited applications in quantitative protein function analyses. Here, we present Protein Localization Analysis and Search Tools (PLAST), an automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization patterns based on microscopy images. PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments, without requiring manual assignment or supervised learning of these compartments. Using the budding yeast Saccharomyces cerevisiae as a model system, we show that PLAST is more accurate than existing, qualitative protein localization annotations in identifying known co-localized proteins. Furthermore, we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations. First, we identified proteins that have similar localization patterns and participate in closely-related biological processes, but do not necessarily form stable complexes with each other or localize at the same organelles. Second, we found an association between spatial and functional divergences of proteins during evolution. Surprisingly, as proteins with common ancestors evolve, they tend to develop more diverged subcellular localization patterns, but still occupy similar numbers of compartments. This suggests that divergence of protein localization might be more frequently due to the development of more specific localization patterns over ancestral compartments than the occupation of new compartments. PLAST enables systematic and quantitative analyses of protein localization-function relationships, and will be useful to elucidate protein functions and how these functions were acquired in cells from different organisms or species. A public web interface of PLAST is available at http://plast.bii.a-star.edu.sg.

Influence of automated disc margin determination on Stratus OCT optic nerve head measurements

PurposeTo analyze the influence of manual correction of the automatically determined edge of the optic nerve head (ONH) in optic disc measurements in cases in which the optical coherence tomography did not identify the disc limits correctly.MethodsThe study included 127 eyes from 127 consecutive patients with glaucoma, suspects, and healthy individuals. In a retrospective analysis, eyes that underwent testing with the Stratus OCT (software version 4.0, Carl Zeiss Meditec, Dublin, CA, USA) Fast Optic Disc protocol were evaluated. Forty-seven eyes in which either the manual assignment was not necessary or the signal strength was below six were excluded. After image acquisition and processing, one expert examiner manually corrected the determination of the edge of the ONH, identified as the end of the retinal pigment epithelium/choriocapillaris complex. Disc area, cup area, rim area, and cup/disc area ratio results were compared before and after the optic disc margin manually corrected determination. Paired t-test was performed to evaluate the differences, and Bland–Altman plots were used to display the relationships between measurements.ResultsEighty eyes from 80 individuals were included in the analyses. No statistically signifiant difference (P=.538) was found when analyzing results obtained with automated and manual determination of rim area (mean ± standard deviation; 1.30±0.45 mm2 and 1.29±0.39 mm2, respectively). Cup area (1.39±0.58 mm2 and 1.31±0.55 mm2, respectively), cup/disc area ratio (0.50±0.16 mm2 and 0.49±0.15 mm2, respectively), and disc area results (2.69±0.55 mm2 and 2.60±0.51 mm2, respectively) were significantly different.ConclusionThe Stratus OCT ONH Report results were little influenced when optic disc limits were manually determined. Therefore the standard automated Stratus OCT disc margin assignment is adequate, and manual correction is not necessary.

Automation of block assignment planning using a diagram-based scenario modeling method

Most shipbuilding scheduling research so far has focused on the load level on the dock plan. This is because the dock is the least extendable resource in shipyards, and its overloading is difficult to resolve. However, once dock scheduling is completed, making a plan that makes the best use of the rest of the resources in the shipyard to minimize any additional cost is also important. Block assignment planning is one of the midterm planning tasks; it assigns a block to the facility (factory/shop or surface plate) that will actually manufacture the block according to the block characteristics and current situation of the facility. It is one of the most heavily loaded midterm planning tasks and is carried out manually by experienced workers. In this study, a method of representing the block assignment rules using a diagram was suggested through analysis of the existing manual process. A block allocation program was developed which automated the block assignment process according to the rules represented by the diagram. The planning scenario was validated through a case study that compared the manual assignment and two automated block assignment results.

RASP: rapid and robust backbone chemical shift assignments from protein structure

Chemical shift prediction has an unappreciated power to guide backbone resonance assignment in cases where protein structure is known. Here we describe Resonance Assignment by chemical Shift Prediction (RASP), a method that exploits this power to derive protein backbone resonance assignments from chemical shift predictions. Robust assignments can be obtained from a minimal set of only the most sensitive triple-resonance experiments, even for spectroscopically challenging proteins. Over a test set of 154 proteins RASP assigns 88% of residues with an accuracy of 99.7%, using only information available from HNCO and HNCA spectra. Applied to experimental data from a challenging 34kDa protein, RASP assigns 90% of manually assigned residues using only 40% of the experimental data required for the manual assignment. RASP has the potential to significantly accelerate the backbone assignment process for a wide range of proteins for which structural information is available, including those for which conventional assignment strategies are not feasible.

Paper-to-reviewer assignment, based on expertise degree of reviewers and relevance degree between reviewers and papers

Automating the process of paper-to-reviewer assignment is a difficult task to be adequately resolved. Many papers concerning the related topics have been published, but there is still scare of systematic research applications. In most real world conference management, the assignment task is carried out manually by the programme committee, lacking of intelligent assigning rules and efficient matching method. The manual assignment is not only of low efficiency but also does not guarantee to result in the best solution. Given such situation, our paper sets out to analyse the problem of reviewer selection and propose a method for automatically matching papers with reviewers. Our objective is to reduce the loads of both programme committee and reviewers and make the conference-paper assignment task effectual. In this paper, we address this issue of paper-to-reviewer assignment and propose a method to model reviewers, based on the matching degree between reviewers and papers by combining preference-based approach and topic-based approach. We explain the assignment algorithm and show the evaluation results in comparison with Hungarian algorithm.

Stereoscopizing cel animations

While hand-drawn cel animation is a world-wide popular form of art and entertainment, introducing stereoscopic effect into it remains difficult and costly, due to the lack of physical clues. In this paper, we propose a method to synthesize convincing stereoscopic cel animations from ordinary 2D inputs, without labor-intensive manual depth assignment nor 3D geometry reconstruction. It is mainly automatic due to the need of producing lengthy animation sequences, but with the option of allowing users to adjust or constrain all intermediate results. The system fits nicely into the existing production flow of cel animation. By utilizing the T-junction cue available in cartoons, we first infer the initial, but not reliable, ordering of regions. One of our major contributions is to resolve the temporal inconsistency of ordering by formulating it as a graph-cut problem. However, the resultant ordering remains insufficient for generating convincing stereoscopic effect, as ordering cannot be directly used for depth assignment due to its discontinuous nature. We further propose to synthesize the depth through an optimization process with the ordering formulated as constraints. This is our second major contribution. The optimized result is the spatiotemporally smooth depth for synthesizing stereoscopic effect. Our method has been evaluated on a wide range of cel animations and convincing stereoscopic effect is obtained in all cases.

Referee assignment in the Chilean football league using integer programming and patterns

AbstractThis article uses integer linear programming to address the referee assignment problem in the First Division of the Chilean professional football league. The proposed approach considers balance in the number of matches each referee must officiate, the frequency of each referee being assigned to a given team, the distance each referee must travel over the course of a season, and the appropriate pairings of referee experience or skill category with the importance of the matches. Two methodologies are studied, one traditional and the other a pattern‐based formulation inspired by the home‐away patterns for scheduling season match calendars. Both methodologies are tested in real‐world and experimental instances, reporting results that improve significantly on the manual assignments. The pattern‐based formulation attains major reductions in execution times, solving real instances to optimality in just a few seconds, while the traditional one takes anywhere from several minutes to more than an hour.

Reliability of exclusively NOESY-based automated resonance assignment and structure determination of proteins

Protein structure determination by NMR can in principle be speeded up both by reducing the measurement time on the NMR spectrometer and by a more efficient analysis of the spectra. Here we study the reliability of protein structure determination based on a single type of spectra, namely nuclear Overhauser effect spectroscopy (NOESY), using a fully automated procedure for the sequence-specific resonance assignment with the recently introduced FLYA algorithm, followed by combined automated NOE distance restraint assignment and structure calculation with CYANA. This NOESY-FLYA method was applied to eight proteins with 63-160 residues for which resonance assignments and solution structures had previously been determined by the Northeast Structural Genomics Consortium (NESG), and unrefined and refined NOESY data sets have been made available for the Critical Assessment of Automated Structure Determination of Proteins by NMR project. Using only peak lists from three-dimensional (13)C- or (15)N-resolved NOESY spectra as input, the FLYA algorithm yielded for the eight proteins 91-98 % correct backbone and side-chain assignments if manually refined peak lists are used, and 64-96 % correct assignments based on raw peak lists. Subsequent structure calculations with CYANA then produced structures with root-mean-square deviation (RMSD) values to the manually determined reference structures of 0.8-2.0 Å if refined peak lists are used. With raw peak lists, calculations for 4 proteins converged resulting in RMSDs to the reference structure of 0.8-2.8 Å, whereas no convergence was obtained for the four other proteins (two of which did already not converge with the correct manual resonance assignments given as input). These results show that, given high-quality experimental NOESY peak lists, the chemical shift assignments can be uncovered, without any recourse to traditional through-bond type assignment experiments, to an extent that is sufficient for calculating accurate three-dimensional structures.

Analyzing conformational dynamics of single P-glycoprotein transporters by Förster resonance energy transfer using hidden Markov models

Single-molecule Förster resonance energy (smFRET) transfer has become a powerful tool for observing conformational dynamics of biological macromolecules. Analyzing smFRET time trajectories allows to identify the state transitions occuring on reaction pathways of molecular machines. Previously, we have developed a smFRET approach to monitor movements of the two nucleotide binding domains (NBDs) of P-glycoprotein (Pgp) during ATP hydrolysis driven drug transport in solution. One limitation of this initial work was that single-molecule photon bursts were analyzed by visual inspection with manual assignment of individual FRET levels. Here a fully automated analysis of Pgp smFRET data using hidden Markov models (HMM) for transitions up to 9 conformational states is applied. We propose new estimators for HMMs to integrate the information of fluctuating intensities in confocal smFRET measurements of freely diffusing lipid bilayer bound membrane proteins in solution. HMM analysis strongly supports that under conditions of steady state turnover, conformational states with short NBD distances and short dwell times are more populated compared to conditions without nucleotide or transport substrate present.

Weighting tags and paths in XML documents according to their topic generalization

Text-centric (or document-centric) XML document retrieval aims to rank search results according to their relevance to a given query. To do this, most existing methods mainly rely on content terms and often ignore an important factor – the XML tags and paths, which are useful in determining the important contents of a document. In some previous studies, each unique tag/path is assigned a weight based on domain (expert) knowledge. However, such a manual assignment is both inefficient and subjective. In this paper, we propose an automatic method to infer the weights of tags/paths according to the topical relationship between the corresponding elements and the whole documents. The more the corresponding element can generalize the document’s topic, the more the tag/path is considered to be important. We define a model based on Average Topic Generalization (ATG), which integrates several features used in previous studies. We evaluate the performance of the ATG-based model on two real data sets, the IEEECS collection and the Wikipedia collection, from two different perspectives: the correlation between the weights generated by ATG and those set by experts, and the performance of XML retrieval based on ATG. Experimental results show that the tag/path weights generated by ATG are highly correlated with the manually assigned weights, and the ATG model significantly improves XML retrieval effectiveness.

Comparison of different approaches for automatic bark detection on log images

The sawmilling industry stores and measures logs with bark in order to maximize efficiency, quality conservation and preservation. However since billing is based on the diameter under bark, it is necessary to differentiate between bark areas and wood areas automatically or via manual assignment. This paper compares different methodologies to automatically differentiate between these areas based on colour images of the log surface of two species, spruce and pine. Additionally, the performance of the different methodologies is evaluated using the proportion of correctly detected bark areas, correctly detected wood areas and the total amount of detected bark and wood areas. In the end, an algorithm taking into account colour and texture information was found to perform well on both species. Based on a larger dataset, this methodology has the potential to detect the diameter under bark based on measurements of logs with bark.

Interlimb coordination during a cooperative bimanual object manipulation task

This experiment examined asymmetries in the execution of an object manipulation task that requires the coordinated use of both hands. To this end, twenty right-hand-dominant participants performed a bimanual object manipulation task, which required that they reach for and grasp two objects located on a tabletop, fit the two objects through a hole in a horizontally or vertically oriented fitting board, and then rotate the objects 180° to produce a “beep” tone. Overall, the two hands were highly synchronized at the start, but not at end, of each movement phase. The decrease in interlimb coupling at later stages of the movement phase was primarily driven by the shorter movement time values for the dominant right hand. In addition, degree of left object rotation was greater than the right object, irrespective of board orientation. In sum, the results suggest that manual asymmetries and role assignment are not hardwired constraints, but depend on the overall task constraints and the manner in which the task is conceptualized.