Kinematic Clues to Bar Evolution for Galaxies in the Local Universe: Why the Fastest Rotating Bars are Rotating Most Slowly

We have used Spitzer images of a sample of 68 barred spiral galaxies in the local universe to make systematic measurements of bar length and bar strength. We combine these with precise determinations of the corotation radii associated with the bars, taken from our previous study, which used the phase change from radial inflow to radial outflow of gas at corotation, based on high-resolution two-dimensional velocity fields in Hα taken with a Fabry-Pérot spectrometer. After presenting the histograms of the derived bar parameters, we study their dependence on the galaxy morphological type and on the total stellar mass of the host galaxy, and then produce a set of parametric plots. These include the bar pattern speed versus bar length, the pattern speed normalized with the characteristic pattern speed of the outer disk versus the bar strength, and the normalized pattern speed versus R, the ratio of corotation radius to bar length. To provide guidelines for our interpretation, we used recently published simulations, including disk and dark matter halo components. Our most striking conclusion is that bars with values of R < 1.4, previously considered dynamically fast rotators, can be among the slowest rotators both in absolute terms and when their pattern speeds are normalized. The simulations confirm that this is because as the bars are braked, they can grow longer more quickly than the outward drift of the corotation radius. We conclude that dark matter halos have indeed slowed down the rotation of bars on Gyr timescales. © 2017. The American Astronomical Society. All rights reserved.

A mixture of experts model for predicting persistent weather patterns

Weather and atmospheric patterns are often persistent. The simplest weather forecasting method is the so-called persistence model, which assumes that the future state of a system will be similar (or equal) to the present state. Machine learning (ML) models are widely used in different weather forecasting applications, but they need to be compared to the persistence model to analyse whether they provide a competitive solution to the problem at hand. In this paper, we devise a new model for predicting low-visibility in airports using the concepts of mixture of experts. Visibility level is coded as two different ordered categorical variables: Cloud height and runway visual height. The underlying system in this application is stagnant approximately in 90% of the cases, and standard ML models fail to improve on the performance of the persistence model. Because of this, instead of trying to simply beat the persistence model using ML, we use this persistence as a baseline and learn an ordinal neural network model that refines its results by focusing on learning weather fluctuations. The results show that the proposal outperforms persistence and other ordinal autoregressive models, especially for longer time horizon predictions and for the runway visual height variable

Subtidal macrozoobenthos communities from northern Chile during and post El Niño 1997–1998

Despite a large amount of climatic and oceanographic information dealing with the recurring climate phenomenon El Niño (EN) and its well known impact on diversity of marine benthic communities, most published data are rather descriptive and consequently our understanding of the underlying mechanisms and processes that drive community structure during EN are still very scarce. In this study, we address two questions on the effects of EN on macrozoobenthic communities: (1) how does EN affect species diversity of the communities in northern Chile? and (2) is EN a phenomenon that restarts community assembling processes by affecting species interactions in northern Chile? To answer these questions, we compared species diversity and co-occurrence patterns of soft-bottoms macrozoobenthos communities from the continental shelf off northern Chile during (March 1998) and after (September 1998) the strong EN event 1997–1998. The methods used varied from species diversity and species co-occurrence analyses to multivariate ordination methods. Our results indicate that EN positively affects diversity of macrozoobenthos communities in the study area, increasing the species richness and diversity and decreasing the species dominance. EN represents a strong disturbance that affects species interactions that rule the species assembling processes in shallow-water, sea-bottom environments

Envenomations by Bothrops and Crotalus Snakes Induce the Release of Mitochondrial Alarmins

Skeletal muscle necrosis is a common manifestation of viperid snakebite envenomations. Venoms from snakes of the genus Bothrops, such as that of B. asper, induce muscle tissue damage at the site of venom injection, provoking severe local pathology which often results in permanent sequelae. In contrast, the venom of the South American rattlesnake Crotalus durissus terrificus, induces a clinical picture of systemic myotoxicity, i.e., rhabdomyolysis, together with neurotoxicity. It is known that molecules released from damaged muscle might act as ‘danger’ signals. These are known as ‘alarmins’, and contribute to the inflammatory reaction by activating the innate immune system. Here we show that the venoms of B. asper and C. d. terrificus release the mitochondrial markers mtDNA (from the matrix) and cytochrome c (Cyt c) from the intermembrane space, from ex vivo mouse tibialis anterior muscles. Cyt c was released to a similar extent by the two venoms whereas B. asper venom induced the release of higher amounts of mtDNA, thus reflecting hitherto some differences in their pathological action on muscle mitochondria. At variance, injection of these venoms in mice resulted in a different time-course of mtDNA release, with B. asper venom inducing an early onset increment in plasma levels and C. d. terrificus venom provoking a delayed release. We suggest that the release of mitochondrial ‘alarmins’ might contribute to the local and systemic inflammatory events characteristic of snakebite envenomations

The rise and fall of an extraordinary Ca-rich transient: The discovery of ATLAS19dqr/SN 2019bkc

This work presents the observations and analysis of ATLAS19dqr/SN 2019bkc, an extraordinary rapidly evolving transient event located in an isolated environment, tens of kiloparsecs from any likely host. Its light curves rise to maximum light in 5-6 d and then display a decline of Δm15 ∼ 5 mag. With such a pronounced decay, it has one of the most rapidly evolving light curves known for a stellar explosion. The early spectra show similarities to normal and "ultra-stripped" type Ic SNe, but the early nebular phase spectra, which were reached just over two weeks after explosion, display prominent calcium lines, marking SN 2019bkc as a Ca-rich transient. The Ca emission lines at this phase show an unprecedented and unexplained blueshift of 10 000-12 000 km s-1. Modelling of the light curve and the early spectra suggests that the transient had a low ejecta mass of 0.2-0.4 M⊙ and a low kinetic energy of (2-4) × 1050 erg, giving a specific kinetic energy Ek/Mej ∼ 1 [1051 erg]/M⊙. The origin of this event cannot be unambiguously defined. While the abundance distribution used to model the spectra marginally favours a progenitor of white dwarf origin through the tentative identification of Ar II, the specific kinetic energy, which is defined by the explosion mechanism, is found to be more similar to an ultra-stripped core-collapse events. SN 2019bkc adds to the diverse range of physical properties shown by Ca-rich events. © ESO 2020