HPMVS: A High Performance Visualization Tool Suite that Assists in Kidney Assessment

This paper introduces an interactive volume visualization tool suite, the High Performance Medical Visualization tool Suite (HPMVS). The suite of tools is designed to aid medical staff in the assessment of renal disorders such as those caused by the von Hippel Lindau (VHL) Syndrome. The tools are useful for image-based evaluation of the severity and progression of disease and for planning and monitoring treatment. The configuration of HPMVS can provide near real-time visualization by allowing highly intensive computations to be computed on a supercomputer and less intensive computations and final display to be realized on a desktop workstation. Focus in this paper is on the exploration and extraction tools and the tool set configuration

Mining and Analyzing the Italian Parliament: Party Structure and Evolution

The roll calls of the Italian Parliament in the XVI legislature are studied by employing multidimensional scaling, hierarchical clustering, and network analysis. In order to detect changes in voting behavior, the roll calls have been divided in seven periods of six months each. All the methods employed pointed out an increasing fragmentation of the political parties endorsing the previous government that culminated in its downfall. By using the concept of modularity at different resolution levels, we identify the community structure of Parliament and its evolution in each of the considered time periods. The analysis performed revealed as a valuable tool in detecting trends and drifts of Parliamentarians. It showed its effectiveness at identifying political parties and at providing insights on the temporal evolution of groups and their cohesiveness, without having at disposal any knowledge about political membership of Representatives.Comment: 27 pages, 14 figure

An Ensemble Approach for Annotating Source Code Identifiers with Part-of-speech Tags

This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble\u27s annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to promote the future amelioration of these problems through further research. Our results show that the ensemble achieves 75\% accuracy at the identifier level and 84-86\% accuracy at the word level. This is an increase of +17\% points at the identifier level from the closest independent part-of-speech tagger

Tissue is the issue-sarcoidosis following ABVD chemotherapy for Hodgkin's lymphoma: a case report

Thirty two year old Caucasian female presented 2 months post partum with fevers, cough and shortness of breath. CT scan of the chest to rule out pulmonary embolism revealed mediastinal lymphadenopathy. Biopsy of the nodes revealed classic Hodgkin's lymphoma and she received ABVD chemotherapy. She was in remission as confirmed by a PET/CT scan. Five months later she had another PET/CT scan which showed areas of hypermetabolism indicating a possible relapse. Biopsy revealed sarcoidosis. She received steroids and 18 months later remained in clinical remission. This rare case of sarcoid following classic Hodgkin's lymphoma illustrates that clinical presentation, physical exam, lab investigations and even PET/CT scans may not be able to discriminate between Hodgkin's lymphoma and sarcoidosis. Tissue biopsy and pathological diagnosis remain the gold standard

Defining the Epidemiology of Safety Risks in Neonatal Intensive Care Unit Patients Requiring Surgery

OBJECTIVE: The aim of the study was to determine the incidence, type, severity, preventability, and contributing factors of nonroutine events (NREs)-events perceived by care providers or skilled observers as a deviations from optimal care based on the clinical situation-in the perioperative (i.e., preoperative, operative, and postoperative) care of surgical neonates in the neonatal intensive care unit and operating room. METHODS: A prospective observational study of noncardiac surgical neonates, who received preoperative and postoperative neonatal intensive care unit care, was conducted at an urban academic children\u27s hospital between November 1, 2016, and March 31, 2018. One hundred twenty-nine surgical cases in 109 neonates were observed. The incidence and description of NREs were collected via structured researcher-administered survey tool of involved clinicians. Primary measurements included clinicians\u27 ratings of NRE severity and contributory factors and trained research assistants\u27 ratings of preventability. RESULTS: One or more NREs were reported in 101 (78%) of 129 observed cases for 247 total NREs. Clinicians reported 2 (2) (median, interquartile range) NREs per NRE case with a maximum severity of 3 (1) (possible range = 1-5). Trained research assistants rated 47% of NREs as preventable and 11% as severe and preventable. The relative risks for National Surgical Quality Improvement Program - pediatric major morbidity and 30-day mortality were 1.17 (95% confidence interval = 0.92-1.48) and 1.04 (95% confidence interval = 1.00-1.08) in NRE cases versus non-NRE cases. CONCLUSIONS: The incidence of NREs in neonatal perioperative care at an academic children\u27s hospital was high and of variable severity with a myriad of contributory factors

A Lyman-alpha-only AGN from the Sloan Digital Sky Survey

The Sloan Digital Sky Survey has discovered a z=2.4917 radio-loud active galactic nucleus (AGN) with a luminous, variable, low-polarization UV continuum, H I two-photon emission, and a moderately broad Lyman-alpha line (FWHM = 1430 km/s) but without obvious metal-line emission. SDSS J113658.36+024220.1 does have associated metal-line absorption in three distinct, narrow systems spanning a velocity range of 2710 km/s. Despite certain spectral similarities, SDSS J1136+0242 is not a Lyman-break galaxy. Instead, the Ly-alpha and two-photon emission can be attributed to an extended, low-metallicity narrow-line region. The unpolarized continuum argues that we see SDSS J1136+0242 very close to the axis of any ionization cone present. We can conceive of two plausible explanations for why we see a strong UV continuum but no broad-line emission in this `face-on radio galaxy' model for SDSS J1136+0242: the continuum could be relativistically beamed synchrotron emission which swamps the broad-line emission; or, more likely, SDSS J1136+0242 could be similar to PG 1407+265, a quasar in which for some unknown reason the high-ionization emission lines are very broad, very weak, and highly blueshifted.Comment: AJ, in press, 10 pages emulateapj forma

High-Redshift Quasars Found in Sloan Digital Sky Survey Commissioning Data IV: Luminosity Function from the Fall Equatorial Stripe Sampl

This is the fourth paper in a series aimed at finding high-redshift quasars from five-color imaging data taken along the Celestial Equator by the SDSS. during its commissioning phase. In this paper, we use the color-selected sample of 39 luminous high-redshift quasars presented in Paper III to derive the evolution of the quasar luminosity function over the range of 3.6<z<5.0, and -27.5<M_1450<-25.5 (Omega=1, H_0=50 km s^-1 Mpc^-1). We use the selection function derived in Paper III to correct for sample incompleteness. The luminosity function is estimated using three different methods: (1) the 1/V_a estimator; (2) a maximum likelihood solution, assuming that the density of quasars depends exponentially on redshift and as a power law in luminosity and (3) Lynden-Bell's non-parametric C^- estimator. All three methods give consistent results. The luminous quasar density decreases by a factor of ~ 6 from z=3.5 to z=5.0, consistent with the decline seen from several previous optical surveys at z<4.5. The luminosity function follows psi(L) ~ L^{-2.5} for z~4 at the bright end, significantly flatter than the bright end luminosity function psi(L) \propto L^{-3.5} found in previous studies for z<3, suggesting that the shape of the quasar luminosity function evolves with redshift as well, and that the quasar evolution from z=2 to 5 cannot be described as pure luminosity evolution. Possible selection biases and the effect of dust extinction on the redshift evolution of the quasar density are also discussed.Comment: AJ accepted, with minor change

SDSSJ103913.70+533029.7: A Super Star Cluster in the Outskirts of a Galaxy Merger

We describe the serendipitous discovery in the spectroscopic data of the Sloan Digital Sky Survey of a star-like object, SDSSJ103913.70+533029.7, at a heliocentric radial velocity of +1012 km/s. Its proximity in position and velocity to the spiral galaxy NGC 3310 suggests an association with the galaxy. At this distance, SDSSJ103913.70+533029.7 has the luminosity of a super star cluster and a projected distance of 17 kpc from NGC 3310. Its spectroscopic and photometric properties imply a mass of > 10^6 solar masses and an age close to that of the tidal shells seen around NGC 3310, suggesting that it formed in the event which formed the shells.Comment: Accepted by AJ: 4 figures (1 color

Associations of Air Pollution and Pediatric Asthma in Cleveland, Ohio

Air pollution has been associated with poor health outcomes and continues to be a risk factor for respiratory health in children. While higher particulate matter (PM) levels are associated with increased frequency of symptoms, lower lung function, and increase airway inflammation from asthma, the precise composition of the particles that are more highly associated with poor health outcomes or healthcare utilization are not fully elucidated. PM is measured quantifiably by current air pollution monitoring systems. To better determine sources of PM and speciation of such sources, a particulate matter (PM) source apportionment study, the Cleveland Multiple Air Pollutant Study (CMAPS), was conducted in Cleveland, Ohio, in 2009–2010, which allowed more refined assessment of associations with health outcomes. This article presents an evaluation of short-term (daily) and long-term associations between motor vehicle and industrial air pollution components and pediatric asthma emergency department (ED) visits by evaluating two sets of air quality data with healthcare utilization for pediatric asthma. Exposure estimates were developed using land use regression models for long-term exposures for nitrogen dioxide (NO2) and coarse (i.e., with aerodynamic diameters between 2.5 and 10 μm) particulate matter (PM) and the US EPA Positive Matrix Factorization receptor model for short-term exposures to fine (μm) and coarse PM components. Exposure metrics from these two approaches were used in asthma ED visit prevalence and time series analyses to investigate seasonal-averaged short- and long-term impacts of both motor vehicles and industry emissions. Increased pediatric asthma ED visits were found for LUR coarse PM and NO2 estimates, which were primarily contributed by motor vehicles. Consistent, statistically significant associations with pediatric asthma visits were observed, with short-term exposures to components of fine and coarse iron PM associated with steel production. Our study is the first to combine spatial and time series analysis of ED visits for asthma using the same periods and shows that PM related to motor vehicle emissions and iron/steel production are associated with increased pediatric asthma visits

Redshift measurement and spectral classification for eBOSS galaxies with the redmonster software

We describe the redmonster automated redshift measurement and spectral classification software designed for the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey IV (SDSS-IV). We describe the algorithms, the template standard and requirements, and the newly developed galaxy templates to be used on eBOSS spectra. We present results from testing on early data from eBOSS, where we have found a 90.5% automated redshift and spectral classification success rate for the luminous red galaxy sample (redshifts 0.6 ≲ z ≲ 1.0). The redmonster performance meets the eBOSS cosmology requirements for redshift classification and catastrophic failures and represents a significant improvement over the previous pipeline. We describe the empirical processes used to determine the optimum number of additive polynomial terms in our models and an acceptable ΔXr2 threshold for declaring statistical confidence. Statistical errors on redshift measurement due to photon shot noise are assessed, and we find typical values of a few tens of km s-1. An investigation of redshift differences in repeat observations scaled by error estimates yields a distribution with a Gaussian mean and standard deviation of μ ∼ 0.01 and σ ∼ 0.65, respectively, suggesting the reported statistical redshift uncertainties are over-estimated by ∼54%. We assess the effects of object magnitude, signal-to-noise ratio, fiber number, and fiber head location on the pipeline's redshift success rate. Finally, we describe directions of ongoing development.Publisher PDFPeer reviewe