Designing Improved Sediment Transport Visualizations

Monitoring, or more commonly, modeling of sediment transport in the coastal environment is a critical task with relevance to coastline stability, beach erosion, tracking environmental contaminants, and safety of navigation. Increased intensity and regularity of storms such as Superstorm Sandy heighten the importance of our understanding of sediment transport processes. A weakness of current modeling capabilities is the ability to easily visualize the result in an intuitive manner. Many of the available visualization software packages display only a single variable at once, usually as a two-dimensional, plan-view cross-section. With such limited display capabilities, sophisticated 3D models are undermined in both the interpretation of results and dissemination of information to the public. Here we explore a subset of existing modeling capabilities (specifically, modeling scour around man-made structures) and visualization solutions, examine their shortcomings and present a design for a 4D visualization for sediment transport studies that is based on perceptually-focused data visualization research and recent and ongoing developments in multivariate displays. Vector and scalar fields are co-displayed, yet kept independently identifiable utilizing human perception\u27s separation of color, texture, and motion. Bathymetry, sediment grain-size distribution, and forcing hydrodynamics are a subset of the variables investigated for simultaneous representation. Direct interaction with field data is tested to support rapid validation of sediment transport model results. Our goal is a tight integration of both simulated data and real world observations to support analysis and simulation of the impact of major sediment transport events such as hurricanes. We unite modeled results and field observations within a geodatabase designed as an application schema of the Arc Marine Data Model. Our real-world focus is on the Redbird Artificial Reef Site, roughly 18 nautical miles offshor- Delaware Bay, Delaware, where repeated surveys have identified active scour and bedform migration in 27 m water depth amongst the more than 900 deliberately sunken subway cars and vessels. Coincidently collected high-resolution multibeam bathymetry, backscatter, and side-scan sonar data from surface and autonomous underwater vehicle (AUV) systems along with complementary sub-bottom, grab sample, bottom imagery, and wave and current (via ADCP) datasets provide the basis for analysis. This site is particularly attractive due to overlap with the Delaware Bay Operational Forecast System (DBOFS), a model that provides historical and forecast oceanographic data that can be tested in hindcast against significant changes observed at the site during Superstorm Sandy and in predicting future changes through small-scale modeling around the individual reef objects

Beyond the plane-parallel and Newtonian approach: Wide-angle redshift distortions and convergence in general relativity

We extend previous analyses of wide-angle correlations in the galaxy power spectrum in redshift space to include all general relativistic effects. These general relativistic corrections to the standard approach become important on large scales and at high redshifts, and they lead to new terms in the wide-angle correlations. We show that in principle the new terms can produce corrections of nearly 10 % on Gpc scales over the usual Newtonian approximation. General relativistic corrections will be important for future large-volume surveys such as SKA and Euclid, although the problem of cosmic variance will present a challenge in observing this.Comment: 14 pages, 5 figures; Typo in equation 5 corrected; results unaffecte

BVRI Light Curves for 22 Type Ia Supernovae

We present 1210 Johnson/Cousins B,V,R, and I photometric observations of 22 recent type Ia supernovae (SNe Ia): SN 1993ac, SN 1993ae, SN 1994M, SN 1994S, SN 1994T, SN 1994Q, SN 1994ae, SN 1995D, SN 1995E, SN 1995al, SN 1995ac, SN 1995ak, SN 1995bd, SN 1996C, SN 1996X, SN 1996Z, SN 1996ab, SN 1996ai, SN 1996bk, SN 1996bl, SN 1996bo, and SN 1996bv. Most of the photometry was obtained at the Fred Lawrence Whipple Observatory (FLWO) of the Harvard-Smithsonian Center for Astrophysics in a cooperative observing plan aimed at improving the data base for SN Ia. The redshifts of the sample range from $cz$=1200 to 37000 km s$^{-1}$ with a mean of $cz$=7000 km s$^{-1}$.Comment: Accepted to the Astronomical Journal, 41 pages, 8 figure

Safety Aspects, Tolerability and Modeling of Retinofugal Alternating Current Stimulation

Background While alternating current stimulation (ACS) is gaining relevance as a tool in research and approaching clinical applications, its mechanisms of action remain unclear. A review by Schutter and colleagues argues for a retinal origin of transcranial ACS’ neuromodulatory effects. Interestingly, there is an alternative application form of ACS specifically targeting α-oscillations in the visual cortex via periorbital electrodes (retinofugal alternating current stimulation, rACS). To further compare these two methods and investigate retinal effects of ACS, we first aim to establish the safety and tolerability of rACS. ObjectiveThe goal of our research was to evaluate the safety of rACS via finite-element modeling, theoretical safety limits and subjective report. Methods20 healthy subjects were stimulated with rACS as well as photic stimulation and reported adverse events following stimulation. We analyzed stimulation parameters at electrode level as well as distributed metric estimates from an ultra-high spatial resolution magnetic resonance imaging (MRI)-derived finite element human head model and compared them to existing safety limits. ResultsTopographical modeling revealed the highest current densities in the anterior visual pathway, particularly retina and optic nerve. Stimulation parameters and finite element modeling estimates of rACS were found to be well below existing safety limits. No serious adverse events occurred. ConclusionOur findings are in line with existing safety guidelines for retinal and neural damage and establish the tolerability and feasibility of rACS. In comparison to tACS, retinofugal stimulation of the visual cortex provides an anatomically circumscribed model to systematically study the mechanisms of action of ACS

Pancreatic Cysts Identification Using Unstructured Information Management Architecture

poster abstractPancreatic cancer is one of the deadliest cancers, mostly diagnosed at late stages. Patients with pancreatic cysts are at higher risk of developing cancer and surveillance of these patients can help with early diagnosis. Much information about pancreatic cysts can be found in free text format in various medical narratives. In this retrospective study, a corpus of 1064 records from 44 patients at Indiana University Hospital from 1990 to 2012 was collected. A natural language processing system was developed and used to identify patients with pancreatic cysts. The input goes through series of tasks within the Unstructured Information Management Architecture (UIMA) framework consisting of report separation, metadata detection, sentence detection, concept annotation and writing into the database. Metadata such as medical record number (MRN), report id, report name, report date, report body were extracted from each report. Sentences were detected and concepts within each sentence were extracted using regular expression. Regular expression is a pattern of characters matching specific string of text. Our medical team assembled concepts that are used to identify pancreatic cysts in medical reports and additional keywords were added by searching through literature and Unified Medical Language System (UMLS) knowledge base. The Negex Algorithm was used to find out negation status of concepts. The 1064 reports were divided into sets of train and test sets. Two pancreatic-cyst surgeons created the gold standard data (Inter annotator agreement K=88%). The training set was analyzed to modify the regular expression. The concept identification using the NegEx algorithm resulted in precision and recall of 98.9% and 89% respectively. In order to improve the performance of negation detection, Stanford Dependency parser (SDP) was used. SDP finds out how words are related to each other in a sentence. SDP based negation algorithm improved the recall to 95.7%

Supernova Limits on the Cosmic Equation of State

We use Type Ia supernovae studied by the High-Z Supernova Search Team to constrain the properties of an energy component which may have contributed to accelerating the cosmic expansion. We find that for a flat geometry the equation of state parameter for the unknown component, alpha_x=P_x/rho_x, must be less than -0.55 (95% confidence) for any value of Omega_m and is further limited to alpha_x<-0.60 (95%) if Omega_m is assumed to be greater than 0.1 . These values are inconsistent with the unknown component being topological defects such as domain walls, strings, or textures. The supernova data are consistent with a cosmological constant (alpha_x=-1) or a scalar field which has had, on average, an equation of state parameter similar to the cosmological constant value of -1 over the redshift range of z=1 to the present. Supernova and cosmic microwave background observations give complementary constraints on the densities of matter and the unknown component. If only matter and vacuum energy are considered, then the current combined data sets provide direct evidence for a spatially flat Universe with Omega_tot=Omega_m+Omega_Lambda = 0.94 +/- 0.26 (1-sigma).Comment: Accepted for publication in ApJ, 3 figure

Pancreatic Cancer Risk Stratification based on Patient Family History

poster abstractBackground: Pancreatic cancer is the fourth leading cause of cancer-related deaths in the US with an annual death rate approximating the incidence (38,460 and 45,220 respectively according to 2013 American Cancer Society). Due to delayed diagnosis, only 8% of patients are amenable to surgical resection, resulting in a 5-year survival rate of less than 6%. Screening the general population for pancreatic cancer is not feasible because of its low incidence (12.1 per 100,000 per year) and the lack of accurate screening tools. However, patients with an inherited predisposition to pancreatic cancer would benefit from selective screening. Methods: Clinical notes of patients from Indiana University (IU) Hospitals were used in this study. A Natural Language Processing (NLP) system based on the Unstructured Information Management Architecture framework was developed to process the family history data and extract pancreatic cancer information. This was performed through a series of NLP processes including report separation, section separation, sentence detection and keyword extraction. The family members and their corresponding diseases were extracted using regular expressions. The Stanford dependency parser was used to accurately link the family member and their diseases. Negation analysis was done using the NegEx algorithm. PancPro risk-prediction software was used to assess the lifetime risk scores of pancreatic cancer for each patient according to his/her family history. A decision tree was constructed based on these scores. Results: A corpus of 2000 reports of patients at IU Hospitals from 1990 to 2012 was collected. The family history section was present in 249 of these reports containing 463 sentences. The system was able to identify 222 reports (accuracy 87.5%) and 458 sentences (accuracy 91.36%). Conclusion: The family history risk score will be used for patients’ pancreatic cancer risk stratification, thus contributing to selective screening

Ireland's Rejection of the Lisbon Treaty and the EU's Impasse.

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Prioritization of Features for Mobile Apps for Families in a Federal Nutrition Program for Low-Income Women, Infants, and Children: User-Centered Design Approach

Background: The Special Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC) is a federal nutrition program that provides nutritious food, education, and health care referrals to low-income women, infants, and children up to the age of 5 years. Although WIC is associated with positive health outcomes for each participant category, modernization and efficiency are needed at the clinic and shopping levels to increase program satisfaction and participation rates. New technologies, such as electronic benefits transfer (EBT), online nutrition education, and mobile apps, can provide opportunities to improve the WIC experience for participants. Objective: This formative study applies user-centered design principles to inform the layout and prioritization of features in mobile apps for low-income families participating in the WIC program. Methods: To identify and prioritize desirable app features, caregivers (N=22) of the children enrolled in WIC participated in individual semistructured interviews with a card sorting activity. Interviews were transcribed verbatim and analyzed using constant comparative analysis for themes. App features were ranked and placed into natural groupings by each participant. The sum and average of the rankings were calculated to understand which features were prioritized by the users. Natural groupings of features were labeled according to participant descriptions. Results: Natural groupings focused on the following categories: clinics/appointments, shopping/stores, education/assessments, location, and recipes/food. Themes from the interviews triangulated the results from the ranking activity. The priority app features were balance checking, an item scanner, and appointment scheduling. Other app features discussed and ranked included appointment reminders, nutrition training and quizzes, shopping lists, clinic and store locators, recipe gallery, produce calculator, and dietary preferences/allergies. Conclusions: This study demonstrates how a user-centered design process can aid the development of an app for low-income families participating in WIC to inform the effective design of the app features and user interface

Markov Chain Monte Carlo Estimation of Quantum States

We apply a Bayesian data analysis scheme known as the Markov Chain Monte Carlo (MCMC) to the tomographic reconstruction of quantum states. This method yields a vector, known as the Markov chain, which contains the full statistical information concerning all reconstruction parameters including their statistical correlations with no a priori assumptions as to the form of the distribution from which it has been obtained. From this vector can be derived, e. g. the marginal distributions and uncertainties of all model parameters and also of other quantities such as the purity of the reconstructed state. We demonstrate the utility of this scheme by reconstructing the Wigner function of phase-diffused squeezed states. These states posses non-Gaussian statistics and therefore represent a non-trivial case of tomographic reconstruction. We compare our results to those obtained through pure maximum-likelihood and Fisher information approaches.Comment: 8 pages, 8 figures. Accepted for publication in Phys. Rev.