Masked Urethral Injury by Urinary Catheter in a Female Dog

This report describes the importance of careful diagnosis of intrapelvic urethra in cases of pelvic fractures. A 2-year-old intacted female Spitz with multiple pelvic fractures following traffic accident was treated with internal fixation. Urethral catheter was dislodged and patient discharged in good conditions. Next day, the patient was readmitted with vomiting and dysuria. Retrograde urethrography (RUG) revealed a urethral rupture by a broken bone fragment at bladder outlet which repaired by urethral anastomosis. A delayed diagnosis of urethral rupture was because of absence of the signs at the time of first visit. Moreover early urethral catheterization can mask the problems of urinary tract. As urethral rupture in female dog has been reported uncommon compared with male, examination including RUG should more careful

Measurement of Resonance Parameters of Orbitally Excited Narrow B^0 Mesons

We report a measurement of resonance parameters of the orbitally excited (L=1) narrow B^0 mesons in decays to B^{(*)+}\pi^- using 1.7/fb of data collected by the CDF II detector at the Fermilab Tevatron. The mass and width of the B^{*0}_2 state are measured to be m(B^{*0}_2) = 5740.2^{+1.7}_{-1.8}(stat.) ^{+0.9}_{-0.8}(syst.) MeV/c^2 and \Gamma(B^{*0}_2) = 22.7^{+3.8}_{-3.2}(stat.) ^{+3.2}_{-10.2}(syst.) MeV/c^2. The mass difference between the B^{*0}_2 and B^0_1 states is measured to be 14.9^{+2.2}_{-2.5}(stat.) ^{+1.2}_{-1.4}(syst.) MeV/c^2, resulting in a B^0_1 mass of 5725.3^{+1.6}_{-2.2}(stat.) ^{+1.4}_{-1.5}(syst.) MeV/c^2. This is currently the most precise measurement of the masses of these states and the first measurement of the B^{*0}_2 width.Comment: 7 pages, 1 figure, 1 table. Submitted to Phys.Rev.Let

Measurement of the fraction of t-tbar production via gluon-gluon fusion in p-pbar collisions at sqrt(s)=1.96 TeV

We present a measurement of the ratio of t-tbar production cross section via gluon-gluon fusion to the total t-tbar production cross section in p-pbar collisions at sqrt{s}=1.96 TeV at the Tevatron. Using a data sample with an integrated luminosity of 955/pb recorded by the CDF II detector at Fermilab, we select events based on the t-tbar decay to lepton+jets. Using an artificial neural network technique we discriminate between t-tbar events produced via q-qbar annihilation and gluon-gluon fusion, and find Cf=(gg->ttbar)/(pp->ttbar)<0.33 at the 68% confidence level. This result is combined with a previous measurement to obtain the most precise measurement of this quantity, Cf=0.07+0.15-0.07.Comment: submitted to Phys. Rev.

Search for lepton flavor violating decays of a heavy neutral particle in p-pbar collisions at root(s)=1.8 TeV

We report on a search for a high mass, narrow width particle that decays directly to e+mu, e+tau, or mu+tau. We use approximately 110 pb^-1 of data collected with the Collider Detector at Fermilab from 1992 to 1995. No evidence of lepton flavor violating decays is found. Limits are set on the production and decay of sneutrinos with R-parity violating interactions.Comment: Figure 2 fixed. Reference 4 fixed. Minor changes to tex

A web of stakeholders and strategies: A case of broadband diffusion in South Korea

When a new technology is launched, its diffusion becomes an issue of importance. There are various stakeholders that influence diffusion. The question that remains to be determined is their identification and roles. This paper outlines how the strategies pursued by a government acting as the key stakeholder affected the diffusion of a new technology. The analysis is based on a theoretical framework derived from innovation diffusion and stakeholder theories. The empirical evidence comes from a study of broadband development in South Korea. A web of stakeholders and strategies is drawn in order to identify the major stakeholders involved and highlight their relations. The case of South Korea offers implications for other countries that are pursuing broadband diffusion strategies

Gastric Osteoma in a Dog

An eight year old female dog was referred with anorexia, nervousness and emaciation. At the point of time, severe lifelessness was the only symptom. Then euthanasia was done according to the owner’s decision. As a result of postmortem examination, thin white matters were found on the gastric mucosa of the greater curvature and there were no other significant gross findings. Tissue specimens were collected from the gastric wall, esophagus, gall bladder, aorta, heart, kidneys, liver, mesenteric lymph node, lungs, urinary bladder and spleen and processed for histopathology. Microscopically, the masses of stomach were consisted of well-differentiated osteoid tissues, the compact bone-osteocytes and the matured lamellated bone with Haversian system. It was diagnosed as osteoma of the stomach. Other organs were free on such histological findings

Neural network modelling of RC deep beam shear strength

YesA 9 x 18 x 1 feed-forward neural network (NN) model trained using a resilient back-propagation algorithm and early stopping technique is constructed to predict the shear strength of deep reinforced concrete beams. The input layer covering geometrical and material properties of deep beams has nine neurons, and the corresponding output is the shear strength. Training, validation and testing of the developed neural network have been achieved using a comprehensive database compiled from 362 simple and 71 continuous deep beam specimens. The shear strength predictions of deep beams obtained from the developed NN are in better agreement with test results than those determined from strut-and-tie models. The mean and standard deviation of the ratio between predicted capacities using the NN and measured shear capacities are 1.028 and 0.154, respectively, for simple deep beams, and 1.0 and 0.122, respectively, for continuous deep beams. In addition, the trends ascertained from parametric study using the developed NN have a consistent agreement with those observed in other experimental and analytical investigations

Stuck in the slow lane: reconceptualising the links between gender, transport and social exclusion

This article draws upon primary research undertaken with over 3,000 women in the North East of England to explore the links between women, transport and the labour market. The research, funded by the ESF, advances the idea of spatiality as a social construction and builds on seminal studies relating to women and poverty to consider the way in which a gender division of transport constrains women's mobility and restricts their employment opportunities. It is likely to contribute to important debates, concerning strategies to tackle worklessness and the most effective spatial level at which to configure public transport networks

Evidence accumulation models with R: A practical guide to hierarchical Bayesian methods

Evidence accumulation models are a useful tool to allow researchers to investigate the latent cognitive variables that underlie response time and response accuracy. However, applying evidence accumulation models can be difficult because they lack easily computable forms. Numerical methods are required to determine the parameters of evidence accumulation that best correspond to the fitted data. When applied to complex cognitive models, such numerical methods can require substantial computational power which can lead to infeasibly long compute times. In this paper, we provide efficient, practical software and a step-by-step guide to fit evidence accumulation models with Bayesian methods. The software, written in C++, is provided in an R package: 'ggdmc'. The software incorporates three important ingredients of Bayesian computation, (1) the likelihood functions of two common response time models, (2) the Markov chain Monte Carlo (MCMC) algorithm (3) a population-based MCMC sampling method. The software has gone through stringent checks to be hosted on the Comprehensive R Archive Network (CRAN) and is free to download. We illustrate its basic use and an example of fitting complex hierarchical Wiener diffusion models to four shooting-decision data sets

Top Quark Mass Measurement in the Lepton plus Jets Channel Using a Modified Matrix Element Method

46 pages, 16 figures. Edited in response to referee comments and resubmitted to Phys. Rev. DWe report a measurement of the top quark mass, m_t, obtained from ppbar collisions at sqrt(s) = 1.96 TeV at the Fermilab Tevatron using the CDF II detector. We analyze a sample corresponding to an integrated luminosity of 1.9 fb^-1. We select events with an electron or muon, large missing transverse energy, and exactly four high-energy jets in the central region of the detector, at least one of which is tagged as coming from a b quark. We calculate a signal likelihood using a matrix element integration method, with effective propagators to take into account assumptions on event kinematics. Our event likelihood is a function of m_t and a parameter JES that determines /in situ/ the calibration of the jet energies. We use a neural network discriminant to distinguish signal from background events. We also apply a cut on the peak value of each event likelihood curve to reduce the contribution of background and badly reconstructed events. Using the 318 events that pass all selection criteria, we find m_t = 172.7 +/- 1.8 (stat. + JES) +/- 1.2 (syst.) GeV/c^2.We report a measurement of the top quark mass, mt, obtained from pp̅ collisions at √s=1.96  TeV at the Fermilab Tevatron using the CDF II detector. We analyze a sample corresponding to an integrated luminosity of 1.9  fb-1. We select events with an electron or muon, large missing transverse energy, and exactly four high-energy jets in the central region of the detector, at least one of which is tagged as coming from a b quark. We calculate a signal likelihood using a matrix element integration method, where the matrix element is modified by using effective propagators to take into account assumptions on event kinematics. Our event likelihood is a function of mt and a parameter JES (jet energy scale) that determines in situ the calibration of the jet energies. We use a neural network discriminant to distinguish signal from background events. We also apply a cut on the peak value of each event likelihood curve to reduce the contribution of background and badly reconstructed events. Using the 318 events that pass all selection criteria, we find mt=172.7±1.8(stat+JES)±1.2(syst)  GeV/c2.Peer reviewe