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Medical imaging, a key component in clinical diagnosis of and research on numerous medical conditions, is very costly and can generate massive datasets. For instance, a single scanned subject produces hundreds of thousands of images and millions of key-value metadata pairs that must be verified to ensure instrument and research protocol compliance. Many projects lack funds to reacquire images if data quality issues are detected later. Data quality assurance (QA) requires continuous involvement by all stakeholders and use of specific quality control (QC) methods to identify data issues likely to require post-processing correction or real-time re-acquisition. While many useful QC methods exist, they are often designed for specific use-cases with limited scope and documentation, making integration with other setups difficult. We present the Scalable Quality Assurance for Neuroimaging (SQAN), an open-source software suite developed by Indiana University for protocol quality control and instrumental validation on medical imaging data. SQAN includes a comprehensive QC Engine that ensures adherence to a research study’s protocol. A modern, intuitive web portal serves a wide range of users including researchers, scanner technologists and data scientists, each of whom approach QC with unique priorities, expertise, insights and expectations. Since Fall 2017, a fully operational SQAN instance has supported 50+ research projects, and has QC’d ∼3.5 million images and over 700 million metadata tags. SQAN is designed to scale to any imaging center’s QC needs, and to extend beyond protocol QC toward image-level QC and integration with pipeline and non-imaging database systems.

Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities., Design Type(s)data integration objective • database creation objectiveMeasurement Type(s)brainTechnology Type(s)magnetic resonance imagingFactor Type(s)Voxel SizeSample Characteristic(s)Homo sapiens • brain Machine-accessible metadata file describing the reported data (ISA-Tab format)

Diffusion-weighted magnetic resonance imaging (dMRI) allows for non-invasive, detailed examination of the white matter structures of the brain. White matter tract specific measures based on either the diffusion tensor model (e.g. FA, ADC, and MD) or tractography (e.g. volume, streamline count or density) are often compared between groups of subjects to localize differences within the white matter. Less commonly examined is the shape of the individual white matter tracts. In this paper, we propose to use the Laplace-Beltrami (LB) spectrum as a descriptor of the shape of white matter tracts. We provide an open, automated pipeline for the computation of the LB spectrum on segmented white matter tracts and demonstrate its efficacy through machine learning classification experiments. We show that the LB spectrum allows for distinguishing subjects diagnosed with bipolar disorder from age and sex matched healthy controls, with classification accuracy reaching 95%. We further demonstrate that the results cannot be explained by traditional measures, such as tract volume, streamline count, or mean and total length. The results indicate that there is valuable information in the anatomical shape of the human white matter tracts.

The Astronomy scientific community has embraced Big Data processing challenges, e.g. associated with time-domain astronomy, and come up with a variety of novel and efficient data processing solutions. However, data processing is only a small part of the Big Data challenge. Efficient knowledge discovery and scientific advancement in the Big Data era requires new and equally efficient tools: modern user interfaces for searching, identifying and viewing data online without direct access to the data; tracking of data provenance; searching, plotting and analyzing metadata; interactive visual analysis, especially of (time-dependent) image data; and the ability to execute pipelines on supercomputing and cloud resources with minimal user overhead or expertise even to novice computing users. The Trident project at Indiana University offers a comprehensive web and cloud-based microservice software suite that enables the straight forward deployment of highly customized Scalable Compute Archive (SCA) systems; including extensive visualization and analysis capabilities, with minimal amount of additional coding. Trident seamlessly scales up or down in terms of data volumes and computational needs, and allows feature sets within a web user interface to be quickly adapted to meet individual project requirements. Domain experts only have to provide code or business logic about handling/visualizing their domain's data products and about executing their pipelines and application work flows. Trident's microservices architecture is made up of light-weight services connected by a REST API and/or a message bus; a web interface elements are built using NodeJS, AngularJS, and HighCharts JavaScript libraries among others while backend services are written in NodeJS, PHP/Zend, and Python. The software suite currently consists of (1) a simple work flow execution framework to integrate, deploy, and execute pipelines and applications (2) a progress service to monitor work flows and sub-work flows (3) ImageX, an interactive image visualization service (3) an authentication and authorization service (4) a data service that handles archival, staging and serving of data products, and (5) a notification service that serves statistical collation and reporting needs of various projects. Several other additional components are under development. Trident is an umbrella project, that evolved from the One Degree Imager, Portal, Pipeline, and Archive (ODI-PPA) project which we had initially refactored toward (1) a powerful analysis/visualization portal for Globular Cluster System (GCS) survey data collected by IU researchers, 2) a data search and download portal for the IU Electron Microscopy Center's data (EMC-SCA), 3) a prototype archive for the Ludwig Maximilian University's Wide Field Imager. The new Trident software has been used to deploy (1) a metadata quality control and analytics portal (RADY-SCA) for DICOM formatted medical imaging data produced by the IU Radiology Center, 2) Several prototype work flows for different domains, 3) a snapshot tool within IU's Karst Desktop environment, 4) a limited component-set to serve GIS data within the IU GIS web portal. Trident SCA systems leverage supercomputing and storage resources at Indiana University but can be configured to make use of any cloud/grid resource, from local workstations/servers to (inter)national supercomputing facilities such as XSEDE.

Processing astronomical data to science readiness was and remains a challenge, in particular in the case of multi detector instruments such as wide-field imagers. One such instrument, the WIYN One Degree Imager, is available to the astronomical community at large, and, in order to be scientifically useful to its varied user community on a short timescale, provides its users fully calibrated data in addition to the underlying raw data. However, time-efficient re-processing of the often large datasets with improved calibration data and/or software requires more than just a large number of CPU-cores and disk space. This is particularly relevant if all computing resources are general purpose and shared with a large number of users in a typical university setup. Our approach to address this challenge is a flexible framework, combining the best of both high performance (large number of nodes, internal communication) and high throughput (flexible/variable number of nodes, no dedicated hardware) computing. Based on the Advanced Message Queuing Protocol, we a developed a Server-Manager- Worker framework. In addition to the server directing the work flow and the worker executing the actual work, the manager maintains a list of available worker, adds and/or removes individual workers from the worker pool, and re-assigns worker to different tasks. This provides the flexibility of optimizing the worker pool to the current task and workload, improves load balancing, and makes the most efficient use of the available resources. We present performance benchmarks and scaling tests, showing that, today and using existing, commodity shared- use hardware we can process data with data throughputs (including data reduction and calibration) approaching that expected in the early 2020s for future observatories such as the Large Synoptic Survey Telescope.

The One Degree Imager - Portal, Pipeline, and Archive (ODI-PPA) has included the Image Explorer interactive image visualization tool since it went operational. Portal users were able to quickly open up several ODI images within any HTML5 capable web browser, adjust the scaling, apply color maps, and perform other basic image visualization steps typically done on a desktop client like DS9. However, the original design of the Image Explorer required lossless PNG tiles to be generated and stored for all raw and reduced ODI images thereby taking up tens of TB of spinning disk space even though a small fraction of those images were being accessed by portal users at any given time. It also caused significant overhead on the portal web application and the Apache webserver used by ODI-PPA. We found it hard to merge in improvements made to a similar deployment in another project's portal. To address these concerns, we re-architected Image Explorer from scratch and came up with ImageX, a set of microservices that are part of the IU Trident project software suite, with rapid interactive visualization capabilities useful for ODI data and beyond. We generate a full resolution JPEG image for each raw and reduced ODI FITS image before producing a JPG tileset, one that can be rendered using the ImageX frontend code at various locations as appropriate within a web portal (for example: on tabular image listings, views allowing quick perusal of a set of thumbnails or other image sifting activities). The new design has decreased spinning disk requirements, uses AngularJS for the client side Model/View code (instead of depending on backend PHP Model/View/Controller code previously used), OpenSeaDragon to render the tile images, and uses nginx and a lightweight NodeJS application to serve tile images thereby significantly decreasing the Time To First Byte latency by a few orders of magnitude. We plan to extend ImageX for non-FITS images including electron microscopy and radiology scan images, and its featureset to include basic functions like image overlay and colormaps. Users needing more advanced visualization and analysis capabilities could use a desktop tool like DS9+IRAF on another IU Trident project called StarDock, without having to download Gigabytes of FITS image data.

Surging data volumes make it increasingly unfeasible to transfer astronomical datasets to the local systems of individual scientists. Centralized pipelines offer some relief, but lack flexibility to fulfill the needs of all users. We have developed a system that leverages the Docker container application virtualization software. Along with a suite of commonly used astronomy applications, users can configure a container with their own custom software and analysis tools. Our StarDock system will move the users container to the data, and expose the requested dataset, allowing our users to safely and securely process their data without needlessly transferring hundreds of gigabytes.

The Open Science Grid (OSG) relies upon the network as a critical part of the distributed infrastructures it enables. In 2012, OSG added a new focus area in networking with a goal of becoming the primary source of network information for its members and collaborators. This includes gathering, organizing, and providing network metrics to guarantee effective network usage and prompt detection and resolution of any network issues, including connection failures, congestion, and traffic routing. In September of 2015, this service was deployed into the OSG production environment. We will report on the creation, implementation, testing, and deployment of the OSG Networking Service. Starting from organizing the deployment of perfSONAR toolkits within OSG and its partners, to the challenges of orchestrating regular testing between sites, to reliably gathering the resulting network metrics and making them available for users, virtual organizations, and higher level services, all aspects of implementation will be reviewed. In particular, several higher-level services were developed to bring the OSG network service to its full potential. These include a web-based mesh configuration system, which allows central scheduling and management of all the network tests performed by the instances; a set of probes to continually gather metrics from the remote instances and publish it to different sources; a central network datastore (esmond), which provides interfaces to access the network monitoring information in close to real time and historically (up to a year) giving the state of the tests; and a perfSONAR infrastructure monitor system, ensuring the current perfSONAR instances are correctly configured and operating as intended. We will also describe the challenges we encountered in ongoing operations of the network service and how we have evolved our procedures to address those challenges. Finally we will describe our plans for future extensions and improvements to the service.

The One Degree Imager-Portal, Pipeline, and Archive (ODI-PPA) is a web science gateway that provides astronomers a modern web interface that acts as a single point of access to their data, and rich computational and visualization capabilities. Its goal is to support scientists in handling complex data sets, and to enhance WIYN Observatory's scientific productivity beyond data acquisition on its 3.5m telescope. ODI-PPA is designed, with periodic user feedback, to be a compute archive that has built-in frameworks including: (1) Collections that allow an astronomer to create logical collations of data products intended for publication, further research, instructional purposes, or to execute data processing tasks (2) Image Explorer and Source Explorer, which together enable real-time interactive visual analysis of massive astronomical data products within an HTML5 capable web browser, and overlaid standard catalog and Source Extractor-generated source markers (3) Workflow framework which enables rapid integration of data processing pipelines on an associated compute cluster and users to request such pipelines to be executed on their data via custom user interfaces. ODI-PPA is made up of several light-weight services connected by a message bus; the web portal built using Twitter/Bootstrap, AngularJS and jQuery JavaScript libraries, and backend services written in PHP (using the Zend framework) and Python; it leverages supercomputing and storage resources at Indiana University. ODI-PPA is designed to be reconfigurable for use in other science domains with large and complex datasets, including an ongoing offshoot project for electron microscopy data.

The journey of a monitoring probe from its development phase to the moment its execution result is presented in an availability report is a complex process. It goes through multiple phases such as development, testing, integration, release, deployment, execution, data aggregation, computation, and reporting. Further, it involves people with different roles (developers, site managers, VO[1] managers, service managers, management), from different middleware providers (ARC[2], dCache[3], gLite[4], UNICORE[5] and VDT[6]), consortiums (WLCG[7], EMI[11], EGI[15], OSG[13]), and operational teams (GOC[16], OMB[8], OTAG[9], CSIRT[10]). The seamless harmonization of these distributed actors is in daily use for monitoring of the WLCG infrastructure. In this paper we describe the monitoring of the WLCG infrastructure from the operational perspective. We explain the complexity of the journey of a monitoring probe from its execution on a grid node to the visualization on the MyWLCG[27] portal where it is exposed to other clients. This monitoring workflow profits from the interoperability established between the SAM[19] and RSV[20] frameworks. We show how these two distributed structures are capable of uniting technologies and hiding the complexity around them, making them easy to be used by the community. Finally, the different supported deployment strategies, tailored not only for monitoring the entire infrastructure but also for monitoring sites and virtual organizations, are presented and the associated operational benefits highlighted.

Gas gangrene, which is a severe and life-threatening disease, has been very rare in the head and neck region. A case of gas gangrene spreading within the bilateral cheek, oral floor, neck and upper mediastinum is reported. A 51-year-old man having a history of liver cirrhosis was admitted to Nagoya City University Hospital, due to persistent swelling and pain in his neck following gingibitis of his lower jaw, in spite of medical treatments involving antibiotics. He had high fever and jaundice. Severe swelling and redness were observed in his face, neck and upper chest wall. Macroscopic hematuria, leukocytosis, thrombocytopenia were also observed. He was recovered after transient shock with three times complementary surgical drainages and debridements under general anesthesia, supported by effective antibiotics. The foci of abscess were successfully oriented by computed tomography at every operation. Thus the computed tomography was very useful to determine the indication and suitable method of drainage and debridement of gas gangrene.

Large distributed computing collaborations, such as the Worldwide LHC Computing Grid (WLCG), face many issues when it comes to providing a working grid environment for their users. One of these is exchanging tickets between various ticketing systems in use by grid collaborations. Ticket systems such as Footprints, RT, Remedy, and ServiceNow all have different schema that must be addressed in order to provide a reliable exchange of information between support entities and users in different grid environments. To combat this problem, OSG Operations has created a ticket synchronization interface called GOC-TX that relies on web services instead of error-prone email parsing methods of the past. Synchronizing tickets between different ticketing systems allows any user or support entity to work on a ticket in their home environment, thus providing a familiar and comfortable place to provide updates without having to learn another ticketing system. The interface is built in a way that it is generic enough that it can be customized for nearly any ticketing system with a web-service interface with only minor changes. This allows us to be flexible and rapidly bring new ticket synchronization online. Synchronization can be triggered by different methods including mail, web services interface, and active messaging. GOC-TX currently interfaces with Global Grid User Support (GGUS) for WLCG, Remedy at Brookhaven National Lab (BNL), and Request Tracker (RT) at the Virtual Data Toolkit (VDT). Work is progressing on the Fermi National Accelerator Laboratory (FNAL) ServiceNow synchronization. This paper will explain the problems faced by OSG and how they led OSG to create and implement this ticket synchronization system along with the technical details that allow synchronization to be preformed at a production level.

One of the major operational issues faced by large multi-institutional collaborations is permitting its users and support staff to use their native ticket tracking environment while also exchanging these tickets with collaborators. After several failed attempts at email-parser based ticket exchanges, the OSG Operations Group has designed a comprehensive ticket synchronizing application. The GOC-TX application uses web-service interfaces offered by various commercial, open source and other homegrown ticketing systems, to synchronize tickets between two or more of these systems. GOC-TX operates independently from any ticketing system. It can be triggered by one ticketing system via email, active messaging, or a web-services call to check for current sync-status, pull applicable recent updates since prior synchronizations to the source ticket, and apply the updates to a destination ticket. The currently deployed production version of GOC-TX is able to synchronize tickets between the Numara Footprints ticketing system used by the OSG and the following systems: European Grid Initiative's system Global Grid User Support (GGUS) and the Request Tracker (RT) system used by Brookhaven. Additional interfaces to the BMC Remedy system used by Fermilab, and to other instances of RT used by other OSG partners, are expected to be completed in summer 2010. A fully configurable open source version is expected to be made available by early autumn 2010. This paper will cover the structure of the GOC-TX application, its evolution, and the problems encountered by OSG Operations group with ticket exchange within the OSG Collaboration.

腹壁瘢痕ヘルニア57症例の検討を行った. 男女比は1: 3.75と女性に多く, 年齢は50, 60歳台で頻度が高く, 既往手術では虫垂炎14例, 産婦人科領域14例, 胃7例, 胆道系7例, イレウス5例, 大腸4例, 人工肛門3例, その他3例であった. 既往手術からヘルニア発生までの期間は1年未満から最長50年まで見られ, 胃, 胆道系, イレウス手術では発生が早く, 虫垂炎, 産婦人科領域では術後長期間経過して発生するものが多数見られた. ヘルニア発生の因子として虫垂炎では創感染, イレウスでは同一創での再開腹が挙げられる. 当教室で一時期行った上腹部正中切開に対する腹膜筋層一層縫合は, 肥満者においてはヘルニア発生の誘因となった.

Since November 1980, the EEA stapling device has been used to create an end-to-side esophagojejunostomy in 238 patients who underwent total gastrectomy. Failures in the stapling technique were divided into two groups: immediate and delayed. Immediate failures were observed and corrected at the time of surgery. In ten cases, the muscular layer of the esophagus was disrupted, and in three the mucosal layer of the jejunum was disrupted. There were 18 delayed complications: eight cases of anastomotic leakage (3.4%), and ten of stenosis (4.2%). The leakage in one case was classified as major, and those in the other seven as minor. Of the ten stenosis cases, there were five of membranous stricture, four of granular stricture, and one of narrowing after major leakage healing. Only this last case required further surgical treatment. The other nine were successfully dilated either endoscopically or by bougienage.

Primary adenocarcinoma of the appendix, a rare disease, commonly presents with symptom such as apendicitis or abdominal mass. Recently we encountered a patient with adenocarcinoma of the appendix presenting with right strangulated inguinal hernia. A 81-year-old female, with the chief complaint of painfull (bulging) in the right inguinal area, was operatied on for suspected strangurated hernia. In the hernia sac, a 2×2×2cm tumor of appendix, its' peripheral area was infected and necrosed, was found. Neoplasm of the appendix presenting with hernia is so rare that only 4 cases has been reported in the foreign literature and this case is the first report in Japan