Archive Server Research Articles

The New Swift/UVOT+MaNGA (SwiM) Value-added Catalog

We present the the new Swift/UVOT+MaNGA (SwiM) catalog (SwiM_v4.1). SwiM_v4.1 is designed to study star formation and dust attenuation within nearby galaxies given the unique overlap of Swift/UVOT near-ultraviolet (NUV) imaging and MaNGA integral field optical spectroscopy. SwiM_v4.1 comprises 559 objects, ∼4 times more than the original SwiM catalog (SwiM_v3.1), spans the redshift range z ≈ 0.0002–0.1482, and provides a more diverse and rich sample. Approximately 5% of the final MaNGA sample is included in SwiM_v4.1, and 42% of the SwiM_v4.1 galaxies are cross-listed with other well-known catalogs. We present the same data as SwiM_v3.1, including UVOT images, Sloan Digital Sky Survey (SDSS) images, and MaNGA emission-line and spectral index maps with the same pixel size and angular resolution for each galaxy, and a file containing galaxy and observational properties. We designed SwiM_v4.1 to be unbiased, which resulted in some objects having low signal-to-noise ratios in their MaNGA or Swift data. We addressed this by providing a new file containing the fraction of science-ready pixels in each MaNGA emission-line map, and the integrated flux and inverse variance for all three NUV filters. The uniform angular resolution and sampling in SwiM_v4.1 will help answer a number of scientific questions, including constraining quenching and attenuation in the local Universe and studying the effects of black hole feedback. The galaxy maps, catalog files, and their associated data models are publicly released on the SDSS website (a description of the SwiM VAC is provided at https://www.sdss4.org/dr17/data_access/value-added-catalogs/?vac_id=swift-manga-value-added-catalog, and the data are stored on the SDSS Science Archive Server at https://data.sdss.org/sas/dr17/manga/swim/v4.1/).

Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging

To establish a deep learning (DL) model in differentiating borderline ovarian tumor (BOT) from epithelial ovarian cancer (EOC) on conventional MR imaging. We retrospectively enrolled 201 patients of 102 pathologically proven BOTs and 99 EOCs at OB/GYN hospital Fudan University, between January 2015 and December 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MR images were used for lesion area determination. We trained a U-net++ model with deep supervision to segment the lesion area on MR images. Then, the segmented regions were fed into a classification model based on DL network to categorize ovarian masses automatically. For ovarian lesion segmentation, the mean dice similarity coefficient (DSC) of the trained U-net++ model in the testing dataset achieved 0.73 pm 0.25, 0.76 pm 0.18, and 0.60 pm 0.24 in the sagittal T2WI, coronal T2WI, and axial T1WI images, respectively. The DL model by combined T2WI computerized network could differentiate BOT from EOC with a significantly higher AUC of 0.87, an accuracy of 83.7%, a sensitivity of 75.0% and a specificity of 87.5%. In comparison, the AUC yielded by radiologist was only 0.75, with an accuracy of 75.5%, a sensitivity of 96.0% and specificity of 54.2% (P < 0.001).The trained DL network model derived from routine MR imaging could help to distinguish BOT from EOC with a high accuracy, which was superior to radiologists’ assessment.

Display Performance of X-ray Image Output from DICOM-embedded Printer to Paper-based Hard Copy Print: Comparison with X-ray Image on LCD

We have been using a paper-based hard copy print (paper print) system of X-ray images, in which digital imaging and communications in medicine (DICOM) data can be directly output on papers from medical imaging systems or from a picture archiving and communication system (PACS) server, and they are utilized as patient referral materials or for preoperative planning. The purpose of this study was to compare the display performance of X-ray images on the printed paper and that on the liquid crystal display (LCD). We measured contrast response to verify consistency of image appearance on both display systems. The contrast resolution was assessed by a CDRAD phantom. The spatial resolution was assessed by an X-ray test chart. The contrast response of the paper printer was not concordant with the grayscale standard display function (GSDF). The difference between the measured contrast response and the ideal GSDF on the paper was large in the high-density area. The low-contrast resolution on the paper was inferior to that on the LCD. The spatial resolving power on the paper was superior to that on the LCD. The display performance of the paper printer for X-ray images was clarified. X-ray images printed on the paper should be used carefully taking account of their characteristics of display performance.

A computerized diagnostic model for automatically evaluating placenta accrete spectrum disorders based on the combined MR radiomics-clinical signatures

We aimed to establish a computerized diagnostic model to predict placenta accrete spectrum (PAS) disorders based on T2-weighted MR imaging. We recruited pregnant women with clinically suspected PAS disorders between January 2015 and December 2018 in our institution. All preoperative T2-weighted imaging (T2WI) MR images were manually outlined on the picture archive communication system terminal server. A nnU-Net network for automatic segmentation and the corresponding radiomics features extracted from the segmented region were applied to build a radiomics-clinical model for PAS disorders identification. Taking the surgical or pathological findings as the reference standard, we compared this computerized model’s diagnostic performance in detecting PAS disorders. In the training cohort, our model combining both radiomics and clinical characteristics yielded an accuracy of 0.771, a sensitivity of 0.854, and a specificity of 0.750 in identifying PAS disorders. In the testing cohort, this model achieved a segmentation mean Dice coefficient of 0.890 and yielded an accuracy of 0.825, a sensitivity of 0.830 and a specificity of 0.822. In the external validation cohort, this computer-aided diagnostic model yielded an accuracy of 0.690, a sensitivity of 0.929 and a specificity of 0.467 in identifying placenta increta. In the present study, a machine learning model based on preoperative T2WI-based imaging had high accuracy in identifying PAS disorders in respect of surgical and histological findings.

Dicoogle Open Source: The Establishment of a New Paradigm in Medical Imaging.

The rapid and continuous growth of data volume and its heterogeneity has become one of the most noticeable trends in healthcare, namely in medical imaging. This evolution led to the deployment of specialized information systems supported by the DICOM standard that enables the interoperability of distinct components, including imaging modalities, repositories, and visualization workstations. However, the complexity of these ecosystems leads to challenging learning curves and makes it time-consuming to mock and apply new ideas. Dicoogle is an extensible medical imaging archive server that emerges as a tool to overcome those challenges. Its extensible architecture allows the fast development of new advanced features or extends existent ones. It is currently a fundamental enabling technology in collaborative and telehealthcare environments, including research projects, screening programs, and teleradiology services. The framework is supported by a Learning Pack that includes a description of the web programmatic interface, a software development kit, documentation, and implementation samples. This article gives an in-depth view of the Dicoogle ecosystem, state-of-the-art contributions, and community impact. It starts by presenting an overview of its architectural concept, highlights some of the most representative research backed up by Dicoogle, some remarks obtained from its use in teaching, and worldwide usage statistics of the software. Finally, the positioning of Dicoogle in the medical imaging software field is discussed through comparison with other well-known solutions.

Protected Network Architecture for Ensuring Consistency of Medical Data through Validation of User Behavior and DICOM Archive Integrity

The problem of consistency of medical data in Hospital Data Management Systems is considered in the context of correctness of medical images stored in a PACS (Picture Archiving and Communication System) and legality of actions authorized users perform when accessing MIS (Medical Information System) facilities via web interfaces. The purpose of the study is to develop a SIEM-like (Security Information and Event Management) architecture for offline analysis of DICOM (Digital Imaging and Communications in Medicine) archive integrity and users’ activity. To achieve amenable accuracy when validating DICOM archive integrity, two aspects are taken into account: correctness of periodicity of the incoming data stream and correctness of the image data (time series) itself for the considered modality. Validation of users’ activity assumes application of model-driven approaches using state-of-the-art machine learning methods. This paper proposes a network architecture with guard clusters to protect sensitive components like the DICOM archive and application server of the MIS. New server roles were designed to perform traffic interception, data analysis and alert management without reconfiguration of production software components. The cluster architecture allows the analysis of incoming big data streams with high availability, providing horizontal scalability and fault tolerance. To minimize possible harm from spurious DICOM files the approach should be considered as an addition to other securing techniques like watermarking, encrypting and testing data conformance with a standard.

Genotype by Environment interactions for egg number and egg weight of five dual-purpose chicken breeds in different zones of Oromia region in Ethiopia

The African Chicken Genetic Gain (ACGG) project (https://africacgg.net/) aims at backyard poultry optimization by commercial dual-purpose breeds introduction into Africa. To measure benefits, genotype by environment interaction (GxE) analysis provides guidance while predicting environmental effects on production traits of breeds. A survey among Ethiopian poultry smallholders showed egg sale being the most important purpose of keeping village chickens in Oromia. Data was available about laying of 894 ACGG chickens in Oromia. Hence current research questions were: 1) Does GxE take place? 2) Which breed performs best regarding laying and in which environment within Oromia? Traits investigated were egg number and egg weight of five breeds (S-RIR, Sasso, Horro, Kuroiler and Koekoek) located in three zones (East Hararge, East and West Shoa) and 5 districts (Adami Tulu, Bako Tibe, Dano, Dugda and Haromaya) in Oromia. Observations were taken as group measure performing weighted analyses. GxE was only present for egg number with magnitude strongest for zone. S-RIR performed best for both traits in both environments, except Kuroiler performing better in East Shoa for egg number and Koekoek for egg weight. This indicates success of crossbreed S-RIR. Sasso and Horro performed worst supported by previous research for Horro but not Sasso. Low precipitation in East Shoa caused bigger distance in egg number predictions, being higher for S-RIR and lower for Horro and Sasso compared to West Shoa. Apart from these final conclusions, social context of breeding and data collection difficulties should not be forgotten. Just like relevance of other performance trait analyses.

The Sloan Digital Sky Survey Quasar Catalog: Sixteenth Data Release

We present the final Sloan Digital Sky Survey IV (SDSS-IV) quasar catalog from Data Release 16 of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). This catalog comprises the largest selection of spectroscopically confirmed quasars to date. The full catalog includes two sub-catalogs: a "superset" of all SDSS-IV/eBOSS objects targeted as quasars containing 1,440,615 observations and a quasar-only catalog containing 750,414 quasars, including 225,082 new quasars appearing in an SDSS data release for the first time, as well as known quasars from SDSS-I/II/III. We present automated identification and redshift information for these quasars alongside data from visual inspections for 320,161 spectra. The quasar-only catalog is estimated to be 99.8% complete with 0.3% to 1.3% contamination. Automated and visual inspection redshifts are supplemented by redshifts derived via principal component analysis and emission lines. We include emission line redshifts for H$\alpha$, H$\beta$, Mg II, C III], C IV, and Ly$\alpha$. Identification and key characteristics generated by automated algorithms are presented for 99,856 Broad Absorption Line quasars and 35,686 Damped Lyman Alpha quasars. In addition to SDSS photometric data, we also present multi-wavelength data for quasars from GALEX, UKIDSS, WISE, FIRST, ROSAT/2RXS, XMM-Newton, and Gaia. Calibrated digital optical spectra for these quasars can be obtained from the SDSS Science Archive Server.

Trials and tribulations of coronavirus disease-2019 research: with a few bright lights in the fog.

Trials and tribulations of coronavirus disease-2019 research: with a few bright lights in the fog.

A Cloud-Ready Architecture for Shared Medical Imaging Repository.

Nowadays usage paradigms of medical imaging resources are requesting vendor-neutral archives, accessible through standard interfaces, with multi-repository support. Regional repositories shared by distinct institutions, tele-radiology as a service at cloud, teaching, and research archives are illustrative examples of this new reality. However, traditional production environments have a server archive instance per functional domain where every registered client application has access to all studies. This paper proposes an innovator ownership concept and access control mechanisms that provide a multi-repository environment and integrates well with standard protocols. A secure accounting mechanism for medical imaging repositories was designed and instantiated as an extension of a well-known open-source archive. A new web service layer was implemented to provide a vendor-neutral solution compliant with modern DICOM Web protocols for storage, search, and retrieval of medical imaging data. The concept validation was done through the integration of proposed architecture in an open-source solution. A quantitative assessment was performed for evaluating the impact of the mechanism in the usual DICOM Web operations. This article proposes a secure accounting architecture able to easily convert a standard medical imaging archive server in a multi-repository solution. The proposal validation was done through a set of tests that demonstrated its robustness and usage feasibility in a production environment. The proposed system offers new services, fundamental in a new era of cloud-based operations, with acceptable temporal costs.

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution.

To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)-based endometrial cancer (EC) MR imaging (ECM). We retrospectively enrolled 530 patients with pathologically proven EC at our institution between January 1, 2013, and December 31, 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both sagittal and coronal T2WI-based MR images were used for lesion area determination. All MR images were divided into two groups: deep (more than 50%) and shallow (less than 50%) MI based on their pathological diagnosis. We trained a detection model based on YOLOv3 algorithm to locate the lesion area on ECM. Then, the detected regions were fed into a classification model based on DL network to identify MI depth automatically. In the testing dataset, the trained model detected lesion regions with an average precision rate of 77.14% and 86.67% in both sagittal and coronal images, respectively. The classification model yielded an accuracy of 84.78%, a sensitivity of 66.67%, a specificity of 87.50%, a positive predictive value of 44.44%, and a negative predictive value of 94.59% in determining deep MI. The radiologists and trained network model together yielded an accuracy of 86.2%, a sensitivity of 77.8%, a specificity of 87.5%, a positive predictive value of 48.3%, and a negative predictive value of 96.3%. In this study, the DL network model derived from MR imaging provided a competitive, time-efficient diagnostic performance in MI depth identification. • The models established with the deep learning method could help improve the diagnostic confidence and performance of MI identification based on endometrial cancer MR imaging. • The models enabled the classification of endometrial cancer MR images to the two categories with a sensitivity of 0.67, a specificity of 0.88, and an accuracy of 0.85. • Using the detected lesion region to evaluate myometrial invasion depth could remove redundant information in the image and provide more effective features.

Clinical Chemistry Views and Policy on Preprints

Recently, funders including the National Institutes of Health, Howard Hughes Medical Institute, Wellcome Trust, Cancer Research UK, French National Alliance for Life Sciences and Health, and the Chan Zuckerberg Initiative have encouraged the practice of using preprints to speed up the dissemination of scientific findings, capitalize on the use of crowd sourcing to evaluate the merit of the presented work, and broaden the availability of reported findings to other than journal subscribers. This approach, however, has not been universally accepted by scientists, particularly in cases of potential commercial applications from the work or in very competitive disciplines. For example, the repository ChemRxiv for organic chemistry is poorly used because of the highly competitive nature of that field (1). Furthermore, only 11% of posted papers receive comments from the scientific community (1); thus, feedback appears to be minimal and certainly not equivalent or comparable to the peer-review process. The policies of journals for preprints fall into 4 categories4: 1. Outright rejection: If a paper is posted on either a commercial or noncommercial archival server, it will be considered as a duplicate publication and therefore rejected without evaluation. Currently, a majority of biology journals refuse to consider manuscripts posted as preprints (1) (e.g., New England Journal of Medicine ). 2. Rejection unless the …

Usefulness of Biological Fingerprints and Template Matching Techniques in Bedside Chest Radiography for Patient Identification and Preventing Filing Mistakes

The purpose of this study was to investigate whether patients can be identified by using biological fingerprints extracted from bedside chest radiographs and template matching techniques for preventing filing mistakes in a picture archiving and communication system (PACS) server. A total of 400 bedside chest radiographs from 100 male and 100 female patients with current and previous images were used for evaluating patient identification performance. Five biological fingerprints were extracted from 200 previous images using the averaged bedside chest radiographs, produced for each sex and detector size. The correlation values of 200 same patients and 39,800 different patients were calculated as a similarity index, and used for the receiver operating characteristic (ROC) analysis. The patient identification performance was examined by using the correlation index calculated by the summation of correlation values obtained from five biological fingerprints. The sensitivity at 90.0% specificity was calculated using the correlation index. The correlation index for same patients was higher than that for different patients. The area under the ROC curve was 0.974. The patient identification performance was 76.0% (152/200), and the sensitivity at 90.0% specificity was 93.4% (37168/39800). Our results suggest that the proposed method may potentially be useful for preventing filing mistakes in bedside chest radiographs on a PACS server.

Arctic berry extracts target the gut–liver axis to alleviate metabolic endotoxaemia, insulin resistance and hepatic steatosis in diet-induced obese mice

There is growing evidence that fruit polyphenols exert beneficial effects on the metabolic syndrome, but the underlying mechanisms remain poorly understood. In the present study, we aimed to analyse the effects of polyphenolic extracts from five types of Arctic berries in a model of diet-induced obesity. Male C57BL/6J mice were fed a high-fat/high-sucrose (HFHS) diet and orally treated with extracts of bog blueberry (BBE), cloudberry (CLE), crowberry (CRE), alpine bearberry (ABE), lingonberry (LGE) or vehicle (HFHS) for 8weeks. An additional group of standard-chow-fed, vehicle-treated mice was included as a reference control for diet-induced obesity. OGTTs and insulin tolerance tests were conducted, and both plasma insulin and C-peptide were assessed throughout the OGTT. Quantitative PCR, western blot analysis and ELISAs were used to assess enterohepatic immunometabolic features. Faecal DNA was extracted and 16S rRNA gene-based analysis was used to profile the gut microbiota. Treatment with CLE, ABE and LGE, but not with BBE or CRE, prevented both fasting hyperinsulinaemia (mean ± SEM [pmol/l]: chow 67.2 ± 12.3, HFHS 153.9 ± 19.3, BBE 114.4 ± 14.3, CLE 82.5 ± 13.0, CRE 152.3 ± 24.4, ABE 90.6 ± 18.0, LGE 95.4 ± 10.5) and postprandial hyperinsulinaemia (mean ± SEM AUC [pmol/l × min]: chow 14.3 ± 1.4, HFHS 31.4 ± 3.1, BBE 27.2 ± 4.0, CLE 17.7 ± 2.2, CRE 32.6 ± 6.3, ABE 22.7 ± 18.0, LGE 23.9 ± 2.5). None of the berry extracts affected C-peptide levels or body weight gain. Levels of hepatic serine phosphorylated Akt were 1.6-, 1.5- and 1.2-fold higher with CLE, ABE and LGE treatment, respectively, and hepatic carcinoembryonic antigen-related cell adhesion molecule (CEACAM)-1 tyrosine phosphorylation was 0.6-, 0.7- and 0.9-fold increased in these mice vs vehicle-treated, HFHS-fed mice. These changes were associated with reduced liver triacylglycerol deposition, lower circulating endotoxins, alleviated hepatic and intestinal inflammation, and major gut microbial alterations (e.g. bloom of Akkermansia muciniphila, Turicibacter and Oscillibacter) in CLE-, ABE- and LGE-treated mice. Our findings reveal novel mechanisms by which polyphenolic extracts from ABE, LGE and especially CLE target the gut-liver axis to protect diet-induced obese mice against metabolic endotoxaemia, insulin resistance and hepatic steatosis, which importantly improves hepatic insulin clearance. These results support the potential benefits of these Arctic berries and their integration into health programmes to help attenuate obesity-related chronic inflammation and metabolic disorders. All raw sequences have been deposited in the public European Nucleotide Archive server under accession number PRJEB19783 ( https://www.ebi.ac.uk/ena/data/view/PRJEB19783 ).

The central server of the Border Guard's distributed multimedia system for monitoring and visualisation of ongoing and archival events

ABSTRACTThe paper presents the architecture and functionalities of the central server (CENTER) of the distributed system for the Polish Border Guard (BG) for monitoring maritime areas. The overall system has been extended to incorporate, apart from map data, also different multimedia elements such as video from cameras or audio from telephone connections operated by BG units. This requires new system elements: Archive Servers for storing new types of data and Events Visualization Post for the presentation of new media along with the presentation of ongoing or archival tactical situations on maps. The paper focuses on new functionalities of the CENTER and integration of new elements with the rest of the system.

The Sloan Digital Sky Survey Quasar Catalog: Twelfth data release

We present the Data Release 12 Quasar catalog (DR12Q) from the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey III. This catalog includes all SDSS-III/BOSS objects that were spectroscopically targeted as quasar candidates during the full survey and that are confirmed as quasars via visual inspection of the spectra, have luminosities Mi [z = 2] &lt; −20.5 (in a ΛCDM cosmology with H0 = 70 km s-1 Mpc-1, ΩM = 0.3, and ΩΛ = 0.7), and either display at least one emission line with a full width at half maximum (FWHM) larger than 500 km s-1 or, if not, have interesting/complex absorption features. The catalog also includes previously known quasars (mostly from SDSS-I and II) that were reobserved by BOSS. The catalog contains 297 301 quasars (272 026 are new discoveries since the beginning of SDSS-III) detected over 9376 deg2 with robust identification and redshift measured by a combination of principal component eigenspectra. The number of quasars with z &gt; 2.15 (184 101, of which 167 742 are new discoveries) is about an order of magnitude greater than the number of z &gt; 2.15 quasars known prior to BOSS. Redshifts and FWHMs are provided for the strongest emission lines (C iv, C iii], Mg ii). The catalog identifies 29 580 broad absorption line quasars and lists their characteristics. For each object, the catalog presents five-band (u, g, r, i, z) CCD-based photometry with typical accuracy of 0.03 mag together with some information on the optical morphology and the selection criteria. When available, the catalog also provides information on the optical variability of quasars using SDSS and Palomar Transient Factory multi-epoch photometry. The catalog also contains X-ray, ultraviolet, near-infrared, and radio emission properties of the quasars, when available, from other large-area surveys. The calibrated digital spectra, covering the wavelength region 3600–10 500 Å at a spectral resolution in the range 1300 &lt; R &lt; 2500, can be retrieved from the SDSS Catalog Archive Server. We also provide a supplemental list of an additional 4841 quasars that have been identified serendipitously outside of the superset defined to derive the main quasar catalog.

SU-E-I-75: Development of New Biological Fingerprints for Patient Recognition to Identify Misfiled Images in a PACS Server

Purpose: We are trying to develop an image-searching technique to identify misfiled images in a picture archiving and communication system (PACS) server by using five biological fingerprints: the whole lung field, cardiac shadow, superior mediastinum, lung apex, and right lower lung. Each biological fingerprint in a chest radiograph includes distinctive anatomical structures to identify misfiled images. The whole lung field was less effective for evaluating the similarity between two images than the other biological fingerprints. This was mainly due to the variation in the positioning for chest radiographs. The purpose of this study is to develop new biological fingerprints that could reduce influence of differences in the positioning for chest radiography. Methods: Two hundred patients were selected randomly from our database (36,212 patients). These patients had two images each (current and previous images). Current images were used as the misfiled images in this study. A circumscribed rectangular area of the lung and the upper half of the rectangle were selected automatically as new biological fingerprints. These biological fingerprints were matched to all previous images in the database. The degrees of similarity between the two images were calculated for the same and different patients. The usefulness of new the biological fingerprintsmore » for automated patient recognition was examined in terms of receiver operating characteristic (ROC) analysis. Results: Area under the ROC curves (AUCs) for the circumscribed rectangle of the lung, upper half of the rectangle, and whole lung field were 0.980, 0.994, and 0.950, respectively. The new biological fingerprints showed better performance in identifying the patients correctly than the whole lung field. Conclusion: We have developed new biological fingerprints: circumscribed rectangle of the lung and upper half of the rectangle. These new biological fingerprints would be useful for automated patient identification system because they are less affected by positioning differences during imaging.« less

The Sloan Digital Sky Survey quasar catalog: tenth data release

We present the Data Release 10 Quasar (DR10Q) catalog from the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey III. The catalog includes all BOSS objects that were targeted as quasar candidates during the first 2.5 years of the survey and that are confirmed as quasars via visual inspection of the spectra. The catalog also includes known quasars (mostly from SDSS-I and II) that were reobserved by BOSS. The catalog contains 166,583 quasars (74,454 are new discoveries since SDSS-DR9) detected over 6,373 deg$^{2}$ with robust identification and redshift measured by a combination of principal component eigenspectra. The number of quasars with $z>2.15$ (117,668) is $\sim$5 times greater than the number of $z>2.15$ quasars known prior to BOSS. Redshifts and FWHMs are provided for the strongest emission lines (CIV, CIII, MgII). The catalog identifies 16,461 broad absorption line quasars and gives their characteristics. For each object, the catalog presents five-band (u, g, r, i, z) CCD-based photometry with typical accuracy of 0.03 mag and information on the optical morphology and selection method. The catalog also contains X-ray, ultraviolet, near-infrared, and radio emission properties of the quasars, when available, from other large-area surveys. The calibrated digital spectra cover the wavelength region 3,600-10,500\AA\ at a spectral resolution in the range 1,300$<$R$<$2,500; the spectra can be retrieved from the SDSS Catalog Archive Server. We also provide a supplemental list of an additional 2,376 quasars that have been identified among the galaxy targets of the SDSS-III/BOSS.

Computerized image-searching method for finding correct patients for misfiled chest radiographs in a PACS server by use of biological fingerprints

We have developed an automated image-searching method based on biological fingerprints for identifying correct patients in misfiled chest radiographs in a picture archiving and communication system (PACS) server. We used five biological fingerprints including distinctive anatomic structures in a misfiled chest radiograph of an unknown patient to find another image of the same patient stored with correct patient information in a PACS server. The correlation values were determined for the corresponding biological fingerprints in all images in the image server. The correlation indices as a measure of the overall similarity of the two images were determined from the summation of five correlation values and the combination of correlation values with the weighting factors. Finally, the correct patient was identified automatically by the image with the highest correlation index. By use of the summation of five correlation values as the correlation index, 78.0% (156/200) of the 200 patients for misfiled images were correctly identified in the database. When we applied the weighting factors for each biological fingerprint to determine the correlation index, the performance in identifying the correct patient was improved to 87.5% (175/200). An additional 5.0% (10/200) of images were included in the Top 10 ranking of the correlation index in the database. These cases could be identified manually by radiology personnel. We conclude that the automated image-searching method based on biological fingerprints with weighting factors would be useful for identification of the correct patient in the case of misfiled chest radiographs in a PACS server.

AKARI-CAS—Online Service for AKARI All-Sky Catalogues

ABSTRACTThe AKARI All-Sky Catalogues are an important infrared astronomical database for next-generation astronomy that take over the IRAS catalog. We have developed an online service, AKARI Catalogue Archive Server (AKARI-CAS), for astronomers. The service includes useful and attractive search tools and visual tools. One of the new features of AKARI-CAS is cached SIMBAD/NED entries, which can match AKARI catalogs with other catalogs stored in SIMBAD or NED. To allow advanced queries to the databases, direct input of SQL is also supported. In those queries, fast dynamic cross-identification between registered catalogs is a remarkable feature. In addition, multiwavelength quick-look images are displayed in the visualization tools, which will increase the value of the service. In the construction of our service, we considered a wide variety of astronomers’ requirements. As a result of our discussion, we concluded that supporting users’ SQL submissions is the best solution for the requirements. Therefore, we implemented an RDBMS layer so that it covered important facilities, including the whole processing of tables. We found that PostgreSQL is the best open-source RDBMS products for such purpose, and we wrote codes for both simple and advanced searches into the SQL stored functions. To implement such stored functions for fast radial search and cross-identification with minimum cost, we applied a simple technique that is not based on HTM or HEALPix. In contrast, the online application layer became compact and was written in simple procedural PHP codes. In total, our system realizes cost-effective maintenance and enhancements.