Determining the GRB (Redshift, Luminosity)-Distribution Using Burst Variability

We use the possible Cepheid-like luminosity estimator for the long-duration gamma-ray bursts (GRBs) developed by Reichart et al. (2000) to estimate the intrinsic luminosity, and thus the redshift, of 907 long-duration GRBs from the BATSE 4B catalog. We describe a method based on Bayesian inference which allows us to infer the intrinsic GRB burst rate as a function of redshift for bursts with estimated intrinsic luminosities and redshifts. We apply this method to the above sample of long-duration GRBs, and present some preliminary results

Quantitative modeling of the molecular steps underlying shut-off of rhodopsin activity in rod phototransduction

PURPOSE: To examine the predictions of alternative models for the stochastic shut-off of activated rhodopsin (R*) and their implications for the interpretation of experimentally recorded single-photon responses (SPRs) in mammalian rods. THEORY: We analyze the transitions that an activated R* molecule undergoes as a result of successive phosphorylation steps and arrestin binding. We consider certain simplifying cases for the relative magnitudes of the reaction rate constants and derive the probability distributions for the time to arrestin binding. In addition to the conventional model in which R* catalytic activity declines in a graded manner with successive phosphorylations, we analyze two cases in which the activity is assumed to occur not via multiple small steps upon each phosphorylation but via a single large step. We refer to these latter two cases as the binary R* shut-off and three-state R* shut-off models. METHODS: We simulate R*’s stochastic reactions numerically for the three models. In the simplifying cases for the ratio of rate constants in the binary and three-state models, we show that the probability distribution of the time to arrestin binding is accurately predicted. To simulate SPRs, we then integrate the differential equations for the downstream reactions using a standard model of the rod outer segment that includes longitudinal diffusion of cGMP and Ca²⁺. RESULTS: Our simulations of SPRs in the conventional model of graded shut-off of R* conform closely to the simulations in a recent study. However, the gain factor required to account for the observed mean SPR amplitude is higher than can be accounted for from biochemical experiments. In addition, a substantial minority of the simulated SPRs exhibit features that have not been reported in published experiments. Our simulations of SPRs using the model of binary R* shut-off appear to conform closely to experimental results for wild type (WT) mouse rods, and the required gain factor conforms to biochemical expectations. However, for the arrestin knockout (Arr−/−) phenotype, the predictions deviated from experimental findings and led us to invoke a low-activity state that R* enters before arrestin binding. Our simulations of this three-state R* shut-off model are very similar to those of the binary model in the WT case but are preferred because they appear to accurately predict the mean SPRs for four mutant phenotypes, Arr+/−, Arr−/−, GRK1+/−, and GRK1−/−, in addition to the WT phenotype. When we additionally treated the formation and shut-off of activated phosphodiesterase (E*) as stochastic, the simulated SPRs appeared even more similar to real SPRs, and there was very little change in the ensemble mean and standard deviation or in the amplitude distribution. CONCLUSIONS: We conclude that the conventional model of graded reduction in R* activity through successive phosphorylation steps appears to be inconsistent with experimental results. Instead, we find that two variants of a model in which R* activity initially remains high and then declines abruptly after several phosphorylation steps appears capable of providing a better description of experimentally measured SPRs.This work was supported by award number R01EY023603 from the US National Eye Institute

Solar Magnetic Tracking. IV. The Death of Magnetic Features

The removal of magnetic flux from the quiet-sun photosphere is important for maintaining the statistical steady-state of the magnetic field there, for determining the magnetic flux budget of the Sun, and for estimating the rate of energy injected into the upper solar atmosphere. Magnetic feature death is a measurable proxy for the removal of detectable flux. We used the SWAMIS feature tracking code to understand how nearly 20000 detected magnetic features die in an hour-long sequence of Hinode/SOT/NFI magnetograms of a region of quiet Sun. Of the feature deaths that remove visible magnetic flux from the photosphere, the vast majority do so by a process that merely disperses the previously-detected flux so that it is too small and too weak to be detected. The behavior of the ensemble average of these dispersals is not consistent with a model of simple planar diffusion, suggesting that the dispersal is constrained by the evolving photospheric velocity field. We introduce the concept of the partial lifetime of magnetic features, and show that the partial lifetime due to Cancellation of magnetic flux, 22 h, is 3 times slower than previous measurements of the flux turnover time. This indicates that prior feature-based estimates of the flux replacement time may be too short, in contrast with the tendency for this quantity to decrease as resolution and instrumentation have improved. This suggests that dispersal of flux to smaller scales is more important for the replacement of magnetic fields in the quiet Sun than observed bipolar cancellation. We conclude that processes on spatial scales smaller than those visible to Hinode dominate the processes of flux emergence and cancellation, and therefore also the quantity of magnetic flux that threads the photosphere.Comment: Accepted by Ap

The use of dramatization to teach social studies to hearing-impaired children

This paper reviews a study to determine the effectiveness of dramatization in teaching social studies to hearing impaired children

Small unmanned aerial system (SUAS) flight and mission control support system (FMCSS) design

Unmanned Aerial Systems (UAS) are playing a significant role in the Global War on Terrorism (GWOT). Until recently, small UAS (SUAS) were an insignificant part of these efforts. Now their numbers exceed those of their larger counterparts by an order of magnitude. Future projections anticipate a growing demand for SUAS making now the best time to examine the functions they perform in order to make better decisions concerning their future design and development. This thesis provides a brief history of UAS and discusses the current capabilities and mission areas in which they perform. Their relevance to modern warfare and assumptions concerning their future roles on the battlefield is presented. Predominant UAS missions are identified, as well as the technical requirements deemed necessary for their success. A generic UAS functional model is developed to illustrate where the challenges and technology gaps manifest in SUAS design. Possible technology solutions that could fill these gaps are presented and a field experiment is conducted to demonstrate the feasibility of several possible solutions. The goal of this thesis is to identify existing technology gaps and offer technology solutions that lead to better design of future SUAS flight and mission control support systems (FMCSS).http://archive.org/details/smallunmannederi109452574Approved for public release; distribution is unlimited

Existentially Understanding Tourism in Locale : A Dwelling Perspective

Peer reviewedPostprin

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

sscMap: An extensible Java application for connecting small-molecule drugs using gene-expression signatures

Background: Connectivity mapping is a process to recognize novel pharmacological and toxicological properties in small molecules by comparing their gene expression signatures with others in a database. A simple and robust method for connectivity mapping with increased specificity and sensitivity was recently developed, and its utility demonstrated using experimentally derived gene signatures. Results: This paper introduces sscMap (statistically significant connections' map), a Java application designed to undertake connectivity mapping tasks using the recently published method. The software is bundled with a default collection of reference gene-expression profiles based on the publicly available dataset from the Broad Institute Connectivity Map 02, which includes data from over 7000 Affymetrix microarrays, for over 1000 small-molecule compounds, and 6100 treatment instances in 5 human cell lines. In addition, the application allows users to add their custom collections of reference profiles and is applicable to a wide range of other 'omics technologies. Conclusions: The utility of sscMap is two fold. First, it serves to make statistically significant connections between a user-supplied gene signature and the 6100 core reference profiles based on the Broad Institute expanded dataset. Second, it allows users to apply the same improved method to custom-built reference profiles which can be added to the database for future referencing. The software can be freely downloaded from http://purl.oclc.org/NET/sscMapComment: 3 pages, 1 table, 1 eps figur

In-depth critical analysis of complications following robot-assisted radical cystectomy with intracorporeal urinary diversion

Background: Robot-assisted radical cystectomy with intracorporeal urinary diversion (iRARC) is an attractive option to open cystectomy, but the benefit in terms of improved outcomes is not established. Objective: To evaluate the early postoperative morbidity and mortality of patients undergoing iRARC and conduct a critical analysis of complications using standardised reporting criteria as stratified according to urinary diversion. Design, setting, and participants: A total of 134 patients underwent iRARC for bladder cancer at a single centre between June 2011 and July 2015. Intervention: Radical cystectomy with iRARC. Outcome measurements and statistical analysis: Patient demographics, pathologic data, and 90-d perioperative mortality and complications were recorded. Complications were reported according to the Clavien-Dindo (CD) classification and stratified according to urinary diversion type and either surgical or medical complications. The chi-square test and t test were used for categorical and continuous variables respectively. Multivariable logistic regression was performed on variables with significance in univariate analysis. Results and limitations: The 90-d all complication rate following ileal conduit and continent diversion was 68% and 82.4%, and major complications were 21.0% and 20.6% respectively. The 90-d mortality was 3% and 2.9% for ileal conduit and continent diversion patients, respectively. On multivariate analysis, the blood transfusion requirement was independently associated with major complications (p = 0.002) and all 30-d (p = 0.002) and 90-d (p = 0.012) major complications. Male patients were associated with 90-d major complications (p = 0.015). Critical analysis identified that surgical complications were responsible for 39.4% of all 90-d major complications. The incidence of surgical complications did not decline with increasing number of iRARC cases performed (p = 0.742, r = 0.31). Limitations of this study include its retrospective nature, limited sample size, and limited multivariate analysis due to the low number of major complications events. Conclusions: Although complications following iRARC are common, most are low grade. A critical analysis identified surgical complications as a cause of major complications. Addressing this issue could have a significant impact on lowering the morbidity associated with iRARC. Patient summary: We looked at the surgical outcomes in bladder cancer patients treated with minimally invasive robotic surgery. We found that surgical complications account for most major complications and previous surgical experience may be a confounding factor when interpreting results from a different centre even in a randomised trial setting