The extrasolar planet Gliese 581 d: a potentially habitable planet? (Corrigendum to arXiv:1009.5814)

We report here that the equation for H2O Rayleigh scattering was incorrectly stated in the original paper [arXiv:1009.5814]. Instead of a quadratic dependence on refractivity r, we accidentally quoted an r^4 dependence. Since the correct form of the equation was implemented into the model, scientific results are not affected.Comment: accepted to Astronomy&Astrophysic

Quantum approach to nucleation times of kinetic Ising ferromagnets

Low temperature dynamics of Ising ferromagnets under finite magnetic fields are studied in terms of quantum spin representations of stochastic evolution operators. These are constructed for the Glauber dynamic as well as for a modification of this latter, introduced by K. Park {\it et al.} in Phys. Rev. Lett. {\bf 92}, 015701 (2004). In both cases the relaxation time after a field quench is evaluated both numerically and analytically using the spectrum gap of the corresponding operators. The numerical work employs standard recursive techniques following a symmetrization of the evolution operator accomplished by a non-unitary spin rotation. The analytical approach uses low temperature limits to identify dominant terms in the eigenvalue problem. It is argued that the relaxation times already provide a measure of actual nucleation lifetimes under finite fields. The approach is applied to square, triangular and honeycomb lattices.Comment: 14 pages, 6 figure

Class Attendance and Students’ Evaluations of Teaching: Do No-Shows Bias Course Ratings and Rankings?

Background: Many university departments use students’ evaluations of teaching (SET) to compare and rank courses. However, absenteeism from class is often nonrandom and, therefore, SET for different courses might not be comparable. Objective: The present study aims to answer two questions. Are SET positively biased due to absenteeism? Do procedures, which adjust for absenteeism, change course rankings? Research Design: The author discusses the problem from a missing data perspective and present empirical results from regression models to determine which factors are simultaneously associated with students’ class attendance and course ratings. In order to determine the extent of these biases, the author then corrects average ratings for students’ absenteeism and inspect changes in course rankings resulting from this adjustment. Subjects: The author analyzes SET data on the individual level. One or more course ratings are available for each student. Measures: Individual course ratings and absenteeism served as the key outcomes. Results: Absenteeism decreases with rising teaching quality. Furthermore, both factors are systematically related to student and course attributes. Weighting students’ ratings by actual absenteeism leads to mostly small changes in ranks, which follow a power law. Only a few, average courses are disproportionally influenced by the adjustment. Weighting by predicted absenteeism leads to very small changes in ranks. Again, average courses are more strongly affected than courses of very high or low in quality. Conclusions: No-shows bias course ratings and rankings. SET are more appropriate to identify high- and low-quality courses than to determine the exact ranks of average courses

Low-temperature nucleation in a kinetic Ising model with soft stochastic dynamics

We study low-temperature nucleation in kinetic Ising models by analytical and simulational methods, confirming the general result for the average metastable lifetime, = A*exp(beta*Gamma) (beta = 1/kT) [E. Jordao Neves and R.H. Schonmann, Commun. Math. Phys. 137, 209 (1991)]. Contrary to common belief, we find that both A and Gamma depend significantly on the stochastic dynamic. In particular, for a ``soft'' dynamic, in which the effects of the interactions and the applied field factorize in the transition rates, Gamma does NOT simply equal the energy barrier against nucleation, as it does for the standard Glauber dynamic, which does not have this factorization property.Comment: 4 pages RevTex4, 2 figures. Phys. Rev. Lett., in pres

Incidence of End-Stage Renal Disease Among Newly Diagnosed Systemic Lupus Erythematosus Patients: The Georgia Lupus Registry.

OBJECTIVE: To estimate and identify factors associated with the incidence of all-cause end-stage renal disease (ESRD) among newly diagnosed systemic lupus erythematosus (SLE) patients. METHODS: Data from a national registry of treated ESRD were linked to data from a lupus registry of SLE patients who were newly diagnosed and living in Atlanta, Georgia, 2002-2004 (median followup 7.8 years). Cumulative incidence and incidence rates (ESRD treatment initiations per 1,000 patient-years) were calculated, and age- and race-adjusted Poisson models were used to calculate incidence rate ratios (IRRs). RESULTS: Among 344 newly diagnosed SLE patients, 29 initiated ESRD treatment over 2,603.8 years of followup. Incidence rates were 13.8 (95% confidence interval [95% CI] 9.4-20.3) among black patients and 3.3 (95% CI 0.8-13.0) among white patients, per 1,000 patient-years; corresponding 5-year cumulative incidence was 6.4% and 2.5% among black and white patients, respectively. Lupus nephritis documented prior to 2005, which occurred in 80% of those who progressed to ESRD, was the strongest risk factor for incident ESRD (IRR 6.7 [95% CI 2.7-16.8]; incidence rate 27.6 per 1,000 patient-years). Results suggested that patients who were black versus white (IRR 3.9 [95% CI 0.9-16.4]) or <18 years old (versus ≥30 years old) at diagnosis (IRR 2.1 [95% CI 0.9-5.3]) may be more likely to progress to ESRD, but incidence did not differ by sex or other characteristics. CONCLUSION: The incidence of all-cause ESRD among patients with a recent diagnosis of SLE is high in Georgia. Interventions to decrease ESRD incidence among newly diagnosed SLE patients should target young and black patients, as well as patients with lupus nephritis

A spectral survey of an ultra-hot Jupiter: Detection of metals in the transmission spectrum of KELT-9 b

Context: KELT-9 b exemplifies a newly emerging class of short-period gaseous exoplanets that tend to orbit hot, early type stars - termed ultra-hot Jupiters. The severe stellar irradiation heats their atmospheres to temperatures of $\sim 4,000$ K, similar to the photospheres of dwarf stars. Due to the absence of aerosols and complex molecular chemistry at such temperatures, these planets offer the potential of detailed chemical characterisation through transit and day-side spectroscopy. Studies of their chemical inventories may provide crucial constraints on their formation process and evolution history. Aims: To search the optical transmission spectrum of KELT-9 b for absorption lines by metals using the cross-correlation technique. Methods: We analyse 2 transits observed with the HARPS-N spectrograph. We use an isothermal equilibrium chemistry model to predict the transmission spectrum for each of the neutral and singly-ionized atoms with atomic numbers between 3 and 78. Of these, we identify the elements that are expected to have spectral lines in the visible wavelength range and use those as cross-correlation templates. Results: We detect absorption of Na I, Cr II, Sc II and Y II, and confirm previous detections of Mg I, Fe I, Fe II and Ti II. In addition, we find evidence of Ca I, Cr I, Co I, and Sr II that will require further observations to verify. The detected absorption lines are significantly deeper than model predictions, suggesting that material is transported to higher altitudes where the density is enhanced compared to a hydrostatic profile. There appears to be no significant blue-shift of the absorption spectrum due to a net day-to-night side wind. In particular, the strong Fe II feature is shifted by $0.18 \pm 0.27$ km~s$^{-1}$, consistent with zero. Using the orbital velocity of the planet we revise the steller and planetary masses and radii.Comment: Submitted to Astronomy and Astrophysics on January 18, 2019. Accepted on May 3, 2019. 26 pages, 11 figure

Clouds in the atmospheres of extrasolar planets. I. Climatic effects of multi-layered clouds for Earth-like planets and implications for habitable zones

The effects of multi-layered clouds in the atmospheres of Earth-like planets orbiting different types of stars are studied. The radiative effects of cloud particles are directly correlated with their wavelength-dependent optical properties. Therefore the incident stellar spectra may play an important role for the climatic effect of clouds. We discuss the influence of clouds with mean properties measured in the Earth's atmosphere on the surface temperatures and Bond albedos of Earth-like planets orbiting different types of main sequence dwarf stars.Comment: accepted for publication in A&

Machine learning based prediction of squamous cell carcinoma in ex vivo confocal laser scanning microscopy

Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin