Library of medium-resolution Fiber Optic Echelle spectra of F, G, K, and M field dwarfs to giant stars

We present a library of Penn State Fiber Optic Echelle (FOE) observations of a sample of field stars with spectral types F to M and luminosity classes V to I. The spectral coverage is from 3800 to 10000 Å with a nominal resolving power of 12,000. These spectra include many of the spectral lines most widely used as optical and near-infrared indicators of chromospheric activity such as the Balmer lines (Hα to H epsilon), Ca II H & K, the Mg I b triplet, Na I D_1, D_2, He I D_3, and Ca II IRT lines. There are also a large number of photospheric lines, which can also be affected by chromospheric activity, and temperature-sensitive photospheric features such as TiO bands. The spectra have been compiled with the goal of providing a set of standards observed at medium resolution. We have extensively used such data for the study of active chromosphere stars by applying a spectral subtraction technique. However, the data set presented here can also be utilized in a wide variety of ways ranging from radial velocity templates to study of variable stars and stellar population synthesis. This library can also be used for spectral classification purposes and determination of atmospheric parameters (T_eff, log g, [Fe/H]). A digital version of all the fully reduced spectra is available via ftp and the World Wide Web (WWW) in FITS format.

On the size of the fibers of spectral maps induced by semialgebraic embeddings

Let S(M) be the ring of (continuous) semialgebraic functions on a semialgebraic set M and S*(M) its subring of bounded semialgebraic functions. In this work we compute the size of the fibers of the spectral maps Spec(j)1:Spec(S(N))→Spec(S(M)) and Spec(j)2:Spec(S*(N))→Spec(S*(M)) induced by the inclusion j:N M of a semialgebraic subset N of M. The ring S(M) can be understood as the localization of S*(M) at the multiplicative subset WM of those bounded semialgebraic functions on M with empty zero set. This provides a natural inclusion iM:Spec(S(M)) Spec(S*(M)) that reduces both problems above to an analysis of the fibers of the spectral map Spec(j)2:Spec(S*(N))→Spec(S*(M)). If we denote Z:=ClSpec(S*(M))(M N), it holds that the restriction map Spec(j)2|:Spec(S*(N)) Spec(j)2-1(Z)→Spec(S*(M)) Z is a homeomorphism. Our problem concentrates on the computation of the size of the fibers of Spec(j)2 at the points of Z. The size of the fibers of prime ideals "close" to the complement Y:=M N provides valuable information concerning how N is immersed inside M. If N is dense in M, the map Spec(j)2 is surjective and the generic fiber of a prime ideal p∈Z contains infinitely many elements. However, finite fibers may also appear and we provide a criterium to decide when the fiber Spec(j)2-1(p) is a finite set for p∈Z. If such is the case, our procedure allows us to compute the size s of Spec(j)2-1(p). If in addition N is locally compact and M is pure dimensional, s coincides with the number of minimal prime ideals contained in p. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Tear Secretion Induced by Selective Stimulation of Corneal and Conjunctival Sensory Nerve Fibers

Purpose. To measure the increase in tear secretion evoked by selective stimulation of the different populations of sensory receptors of the cornea and conjunctiva by using moderate and intense mechanical, chemical, and cold stimuli. Methods. Six healthy subjects participated in the study. Tear secretion was measured in both eyes by the Schirmer’s test conducted under control conditions and after stimulation of the center of the cornea and the temporal conjunctiva with a gas esthesiometer. Mechanical stimulation consisted in three pulses of 3 seconds’ duration of warmed air (at 34°C on the eye surface) applied at moderate (170 mL/min) and high (260 mL/min) flow rates. Cold thermal stimulation was made with cooled air that produced a corneal temperature drop of −1°C or −4.5°C. Chemical (acidic) stimulation was performed with a jet of gas containing a mixture of 80% CO2 in air. Results. The basal volume of tear secretion increased significantly (P < 0.05, paired t-test) after stimulation of the cornea with high-flow mechanical stimuli (260 mL/min), intense cooling pulses (−4.5°C), and chemical stimulation (80% CO2). The same stimuli were ineffective when applied to the conjunctiva. Moderate mechanical (170 mL/min) and cold (−1°C) stimulation of the cornea or the conjunctiva did not change significantly the volume of tear secretion. Conclusions. Reflex tear secretion caused by corneal stimulation seems to be chiefly due to activation of corneal polymodal nociceptors, whereas selective excitation of corneal mechanonociceptors or cold receptors appears to be less effective in evoking an augmented lacrimal secretion. Conjunctival receptors stimulated at equivalent levels do not evoke an increased tear secretion.

A Diagnostic Calculator for Detecting Glaucoma on the Basis of Retinal Nerve Fiber Layer, Optic Disc, and Retinal Ganglion Cell Analysis by Optical Coherence Tomography

Purpose: The purpose of this study was to develop and validate a multivariate predictive model to detect glaucoma by using a combination of retinal nerve fiber layer (RNFL), retinal ganglion cell-inner plexiform (GCIPL), and optic disc parameters measured using spectral-domain optical coherence tomography (OCT). Methods: Five hundred eyes from 500 participants and 187 eyes of another 187 participants were included in the study and validation groups, respectively. Patients with glaucoma were classified in five groups based on visual field damage. Sensitivity and specificity of all glaucoma OCT parameters were analyzed. Receiver operating characteristic curves (ROC) and areas under the ROC (AUC) were compared. Three predictive multivariate models (quantitative, qualitative, and combined) that used a combination of the best OCT parameters were constructed. A diagnostic calculator was created using the combined multivariate model. Results: The best AUC parameters were: inferior RNFL, average RNFL, vertical cup/disc ratio, minimal GCIPL, and inferior-temporal GCIPL. Comparisons among the parameters did not show that the GCIPL parameters were better than those of the RNFL in early and advanced glaucoma. The highest AUC was in the combined predictive model (0.937; 95% confidence interval, 0.911–0.957) and was significantly (P = 0.0001) higher than the other isolated parameters considered in early and advanced glaucoma. The validation group displayed similar results to those of the study group. Conclusions: Best GCIPL, RNFL, and optic disc parameters showed a similar ability to detect glaucoma. The combined predictive formula improved the glaucoma detection compared to the best isolated parameters evaluated. The diagnostic calculator obtained good classification from participants in both the study and validation groups.