Dynamics of light propagation in spatiotemporal dielectric structures

Propagation, transmission and reflection properties of linearly polarized plane waves and arbitrarily short electromagnetic pulses in one-dimensional dispersionless dielectric media possessing an arbitrary space-time dependence of the refractive index are studied by using a two-component, highly symmetric version of Maxwell's equations. The use of any slow varying amplitude approximation is avoided. Transfer matrices of sharp nonstationary interfaces are calculated explicitly, together with the amplitudes of all secondary waves produced in the scattering. Time-varying multilayer structures and spatiotemporal lenses in various configurations are investigated analytically and numerically in a unified approach. Several new effects are reported, such as pulse compression, broadening and spectral manipulation of pulses by a spatiotemporal lens, and the closure of the forbidden frequency gaps with the subsequent opening of wavenumber bandgaps in a generalized Bragg reflector

Copeptin is associated with mortality and outcome in patients with acute intracerebral hemorrhage

Spontaneous intracerebral hemorrhage (ICH) accounts for a high mortality and morbidity. Early prediction of outcome is crucial for optimized care and treatment decision. Copeptin, the C-terminal part of provasopressin, has emerged as a new prognostic marker in a variety of diseases, but its prognostic value in ICH is unknown

Strong approximation of lacunary series with random gaps

We investigate the asymptotic behavior of sums (Formula presented.), where f is a mean zero, smooth periodic function on (Formula presented.) and (Formula presented.) is a random sequence such that the gaps (Formula presented.) are i.i.d. Our result shows that, in contrast to the classical Salem–Zygmund theory, the almost sure behavior of lacunary series with random gaps can be described very precisely without any assumption on the size of the gaps. © 2017 Springer-Verlag Wie

The role of copeptin as a diagnostic and prognostic biomarker for risk stratification in the emergency department

The hypothalamic-pituitary-adrenal axis is activated in response to stress. One of the activated hypothalamic hormones is arginine vasopressin, a hormone involved in hemodynamics and osmoregulation. Copeptin, the C-terminal part of the arginine vasopressin precursor peptide, is a sensitive and stable surrogate marker for arginine vasopressin release. Measurement of copeptin levels has been shown to be useful in a variety of clinical scenarios, particularly as a prognostic marker in patients with acute diseases such as lower respiratory tract infection, heart disease and stroke. The measurement of copeptin levels may provide crucial information for risk stratification in a variety of clinical situations. As such, the emergency department appears to be the ideal setting for its potential use. This review summarizes the recent progress towards determining the prognostic and diagnostic value of copeptin in the emergency department

Sex differences in the association between plasma copeptin and incident type 2 diabetes: the Prevention of Renal and Vascular Endstage Disease (PREVEND) study

AIMS/HYPOTHESIS: Vasopressin plays a role in osmoregulation, glucose homeostasis and inflammation. Therefore, plasma copeptin, the stable C-terminal portion of the precursor of vasopressin, has strong potential as a biomarker for the cardiometabolic syndrome and diabetes. Previous results were contradictory, which may be explained by differences between men and women in responsiveness of the vasopressin system. The aim of this study was to evaluate the usefulness of copeptin for prediction of future type 2 diabetes in men and women separately. METHODS: From the Prevention of Renal and Vascular Endstage Disease (PREVEND) study, 4,063 women and 3,909 men without diabetes at baseline were included. A total of 208 women and 288 men developed diabetes during a median follow-up of 7.7 years. RESULTS: In multivariable-adjusted models, we observed a stronger association of copeptin with risk of future diabetes in women (OR 1.49 [95% CI 1.24, 1.79]) than in men (OR 1.01 [95% CI 0.85, 1.19]) (p (interaction) < 0.01). The addition of copeptin to the Data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR) clinical model improved the discriminative value (C-statistic,+0.007, p = 0.02) and reclassification (integrated discrimination improvement [IDI] = 0.004, p < 0.01) in women. However, we observed no improvement in men. The additive value of copeptin in women was maintained when other independent predictors, such as glucose, high sensitivity C-reactive protein (hs-CRP) and 24 h urinary albumin excretion (UAE), were included in the model. CONCLUSIONS/INTERPRETATION: The association of plasma copeptin with the risk of developing diabetes was stronger in women than in men. Plasma copeptin alone, and along with existing biomarkers (glucose, hs-CRP and UAE), significantly improved the risk prediction for diabetes in women

<i>Gaia</i> Data Release 1. Summary of the astrometric, photometric, and survey properties

Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. Aims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Methods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue. Results. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the HIPPARCOS and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ∼3000 Cepheid and RR-Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr−1 for the proper motions. A systematic component of ∼0.3 mas should be added to the parallax uncertainties. For the subset of ∼94 000 HIPPARCOS stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr−1. For the secondary astrometric data set, the typical uncertainty of the positions is ∼10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ∼0.03 mag over the magnitude range 5 to 20.7. Conclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data

Adaptive Strategy for the Statistical Analysis of Connectomes

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores

Biomarkers Enhance Discrimination and Prognosis of Type 2 Myocardial Infarction

Background: The observed incidence of type 2 myocardial infarction (T2MI) is expected to increase with the implementation of increasingly sensitive cardiac troponin (cTn) assays. However, it remains to be determined how to diagnose, risk stratify and treat patients with T2MI. We aimed to discriminate and risk-stratify T2MI using biomarkers. Methods: Patients presenting to the Emergency Department with chest pain, enrolled in the CHOPIN study, were retrospectively analyzed. Two cardiologists adjudicated type 1 MI (T1MI) and T2MI. The prognostic ability of several biomarkers alone or in combination to discriminate T2MI from T1MI was investigated using receiver operating characteristic (ROC) curve analysis. The biomarkers analyzed were cTnI, copeptin, mid-regional pro-atrial natriuretic peptide (MRproANP), C-terminal pro-endothelin-1 (CT-proET1), mid-regional pro-adrenomedullin (MRproADM) and procalcitonin. Prognostic utility of these biomarkers for all-cause mortality and major adverse cardiovascular event (MACE: a composite of acute MI, unstable angina pectoris, reinfarction, heart failure, and stroke) at 180-day follow-up was also investigated. Results: Among the 2071 patients, T1MI and T2MI were adjudicated in 94 and 176 patients, respectively. Patients with T1MI had higher levels of baseline cTnI, while those with T2MI had higher baseline levels of MR-proANP, CT-proET1, MR-proADM, and procalcitonin. The area under the ROC curve (AUC) for the diagnosis of T2MI was higher for CT-proET1, MRproADM and MR-proANP (0.765, 0.750, and 0.733, respectively) than for cTnI (0.631). Combining all biomarkers resulted in a similar accuracy to a model using clinical variables and cTnI (0.854 versus 0.884, p = 0.294). Addition of biomarkers to the clinical model yielded the highest AUC (0.917). Other biomarkers, but not cTnI, were associated with mortality and MACE at 180-day among all patients, with no interaction between the diagnosis of T1MI or T2MI. Conclusions: Assessment of biomarkers reflecting pathophysiologic processes occurring with T2MI might help differentiate it from T1MI. Additionally, all biomarkers measured, except cTnI, were significant predictors of prognosis, regardless of type of MI

Optics and Quantum Electronics

Contains table of contents for Section 3 and reports on twenty-one research projects.Joint Services Electronics Program Contract DAAL03-89-C-0001Joint Services Electronics Program Contract DAAL03-92-C-0001U.S. Air Force - Office of Scientific Research Contract F49620-91-C-0091Charles S. Draper Laboratories Contract DL-H-441629MIT Lincoln LaboratoryCharles S. Draper Laboratories, Inc. Contract DL-H-418478Fujitsu LaboratoriesNational Science Foundation Grant ECS 90-12787National Center for Integrated PhotonicsNational Science Foundation Grant EET 88-15834National Science Foundation Grant ECS 85-52701U.S. Air Force - Office of Scientific Research Contract F49620-88-C-0089U.S. Navy - Office of Naval Research Contract N00014-91-C-0084U.S. Navy - Office of Naval Research Grant N00014-91-J-1956Johnson and Johnson Research GrantNational Institutes of Health Contract 2-R01-GM35459U.S. Department of Energy Grant DE-FG02-89 ER14012-A00