Polymerization of ethylene oxide using yttrium isopropoxide

Well defined poly(ethylene oxide)s were prepared using yttrium isopropoxide as an initiator. End group analysis using 1H- and 13C NMR spectroscopy revealed that only polymers with isopropyl ether and hydroxyl end groups were produced. The molecular weight is controlled by the initial amount of initiator added and low polydispersity polymer (Mw/Mn ≈ 1.1) was isolated. Sequential polymerization indicated the suitability of this initiator for macromolecular engineering

New Classes of Off-Diagonal Cosmological Solutions in Einstein Gravity

In this work, we apply the anholonomic deformation method for constructing new classes of anisotropic cosmological solutions in Einstein gravity and/or generalizations with nonholonomic variables. There are analyzed four types of, in general, inhomogeneous metrics, defined with respect to anholonomic frames and their main geometric properties. Such spacetimes contain as particular cases certain conformal and/or frame transforms of the well known Friedman-Robertson-Walker, Bianchi, Kasner and Godel universes and define a great variety of cosmological models with generic off-diagonal metrics, local anisotropy and inhomogeneity. It is shown that certain nonholonomic gravitational configurations may mimic de Sitter like inflation scenaria and different anisotropic modifications without satisfying any classical false-vacuum equation of state. Finally, we speculate on perspectives when such off-diagonal solutions can be related to dark energy and dark matter problems in modern cosmology.Comment: latex2e, 11pt, 33 pages with table of content, a variant accepted to IJT

Electrical stimulation-induced cell clustering in cultured neural networks

Support: International Collaboration Program, NBS-ERC /KOSEF (Korea Science and Engineering Foundation); NIH NS-044287; Nanobiotechnology Centre (NBTC), an STC program of the National Science Foundation under Agreement Number ECS-9876771

Development and external validation of a deep learning algorithm for prognostication of cardiovascular outcomes

Background and Objectives: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression. Methods: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): A Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included. Results: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886-0.907) in men and 0.921 (0.908-0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860-0.876) in men and 0.889 (0.876-0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824-0.897) in men and 0.867 (0.830-0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women). Conclusions: A DL algorithm exhibited greater discriminative accuracy than Cox model approaches

Production and Decay of D_1(2420)^0 and D_2^*(2460)^0

We have investigated $D^{+}\pi^{-}$ and $D^{*+}\pi^{-}$ final states and observed the two established $L=1$ charmed mesons, the $D_1(2420)^0$ with mass $2421^{+1+2}_{-2-2}$ MeV/c$^{2}$ and width $20^{+6+3}_{-5-3}$ MeV/c$^{2}$ and the $D_2^*(2460)^0$ with mass $2465 \pm 3 \pm 3$ MeV/c$^{2}$ and width $28^{+8+6}_{-7-6}$ MeV/c$^{2}$. Properties of these final states, including their decay angular distributions and spin-parity assignments, have been studied. We identify these two mesons as the $j_{light}=3/2$ doublet predicted by HQET. We also obtain constraints on {\footnotesize $\Gamma_S/(\Gamma_S + \Gamma_D)$} as a function of the cosine of the relative phase of the two amplitudes in the $D_1(2420)^0$ decay.Comment: 15 pages in REVTEX format. hardcopies with figures can be obtained by sending mail to: [email protected]

Measurement of the branching fraction for Υ(1S)→τ+τ−\Upsilon (1S) \to \tau^+ \tau^-

We have studied the leptonic decay of the $\Upsilon (1S)$ resonance into tau pairs using the CLEO II detector. A clean sample of tau pair events is identified via events containing two charged particles where exactly one of the particles is an identified electron. We find $B(\Upsilon(1S) \to \tau^+ \tau^-) = (2.61~\pm~0.12~{+0.09\atop{-0.13}})$. The result is consistent with expectations from lepton universality.Comment: 9 pages, RevTeX, two Postscript figures available upon request, CLNS 94/1297, CLEO 94-20 (submitted to Physics Letters B

Measurement of the Decay Asymmetry Parameters in Λc+→Λπ+\Lambda_c^+ \to \Lambda\pi^+ and Λc+→Σ+π0\Lambda_c^+ \to \Sigma^+\pi^0

We have measured the weak decay asymmetry parameters (\aLC ) for two \LC\ decay modes. Our measurements are \aLC = -0.94^{+0.21+0.12}_{-0.06-0.06} for the decay mode $\Lambda_c^+ \to \Lambda\pi^+$ and \aLC = -0.45\pm 0.31 \pm 0.06 for the decay mode $\Lambda_c \to \Sigma^+\pi^0$. By combining these measurements with the previously measured decay rates, we have extracted the parity-violating and parity-conserving amplitudes. These amplitudes are used to test models of nonleptonic charmed baryon decay.Comment: 11 pages including the figures. Uses REVTEX and psfig macros. Figures as uuencoded postscript. Also available as http://w4.lns.cornell.edu/public/CLNS/1995/CLNS95-1319.p

Observation of the Ξc+\Xi_c^+ Charmed Baryon Decays to Σ+K−π+\Sigma^+ K^-\pi^+, Σ+Kˉ∗0\Sigma^+ \bar{K}^{*0}, and ΛK−π+π+\Lambda K^-\pi^+\pi^+

We have observed two new decay modes of the charmed baryon $\Xi_c^+$ into $\Sigma^+ K^-\pi^+$ and $\Sigma^+ \bar{K}^{*0}$ using data collected with the CLEO II detector. We also present the first measurement of the branching fraction for the previously observed decay mode $\Xi_c^+\to\Lambda K^-\pi^+\pi^+$. The branching fractions for these three modes relative to $\Xi_c^+\to\Xi^-\pi^+\pi^+$ are measured to be $1.18 \pm 0.26 \pm 0.17$, $0.92 \pm 0.27 \pm 0.14$, and $0.58 \pm 0.16 \pm 0.07$, respectively.Comment: 12 page uuencoded postscript file, postscript file also available through http://w4.lns.cornell.edu/public/CLN

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis : From the PARADIGM Registry

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume 651.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP