Centre d’ Ecologie Fonctionnelle et Evolutive, UMR 5175, CNRS, Universit e Paul-Val ery Montpellier, Ecole Pratique des Hautes Etudes, France and Evolution, Behaviour and Environment Group, School of Life Sciences, University of Sussex, Brighton, UK *Corresponding author. Evolution, Behaviour and Environment Group, School of Life Sciences, University of Sussex, Brighton, UK; Tel: 44 (0)1273 872862. E-mail:ted.morrow@sussex.ac.uk
Abstract The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction with complex discrete and continuous trait evolution, divergence-time dating, and coalescent demographic models in an efficient statistical inference engine using Markov chain Monte Carlo integration. A convenient, cross-platform, graphical user interface allows the flexible construction of complex evolutionary analyses.
The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All of the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 deg2 of sky, more than triples the number of H-band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 stars from the Multi-object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). The APOGEE outputs now include the measured abundances of 15 different elements for each star. In total, SDSS-III added 5200 deg2 of ugriz imaging; 155,520 spectra of 138,099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2) survey; 2,497,484 BOSS spectra of 1,372,737 galaxies, 294,512 quasars, and 247,216 stars over 9376 deg2; 618,080 APOGEE spectra of 156,593 stars; and 197,040 MARVELS spectra of 5513 stars. Since its first light in 1998, SDSS has imaged over 1/3 of the Celestial sphere in five bands and obtained over five million astronomical spectra.
So far in this course we have dealt entirely either with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus or with the evolution of molecular sequences. Even last week when we were dealing with population genomic data, data from hundreds or thousands of loci, we were treating the variation at each locus separately and combining results across loci. I have some old notes on gametic disequilibrium and how allele frequencies change at two loci simultaneously, but they’re in the “Old notes, no longer updated” section of the book version of these notes (https://figshare.com/articles/journal_contribution/Lecture_notes_ in_population_genetics/100687), and we didn’t discuss them. In every example we’ve considered so far we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to
Large-scale subsurface hydrogen storage is critical for transitioning towards renewable, economically viable, and emission-free energy technologies. Although preliminary studies on geochemical interactions between different minerals, aqueous ions, and other dissolved gasses with H<sub>2</sub> have helped partially quantify the degree of hydrogen loss in the subsurface, the long-term changes in abiotic hydrogen–brine–rock interactions are still not well understood due to variable rates of mineral dissolution/precipitation and redox transformations under different conditions of reservoirs. One of the potentially understudied aspects of these complex geochemical interactions is the role of iron on the redox interactions and subsequent impact on long-term (100 years) hydrogen cycling. The theoretical modeling conducted in this study indicates that the evolution of secondary iron-bearing minerals, such as siderite and magnetite, produced after H<sub>2</sub>-induced reductive dissolution of primary Fe<sup>3+</sup>-bearing phases can result in different degrees of hydrogen loss. Low dissolved Fe<sup>2+</sup> activity (<10<sup>−4</sup>) in the formation water can govern the transformation of secondary siderite to magnetite within 100 years, eventually accelerating the H<sub>2</sub> consumption through reductive dissolution. Quantitative modeling demonstrates that such secondary iron mineral transformations need to be studied to understand the long-term behavior of hydrogen in storage sites.
Any disease or infection that can spread spontaneously from animals to humans or humans to animals is called
zoonosis. The origin of more than 60% of human infections is zoonotic diseases. It covers many pathogens,
including bacteria, viruses, fungi, protozoa, and parasites. The emergence, distribution, and patterns of
zoonoses are significantly influenced by several factors, including climate change, animal movement, agentrelated factors, natural factors, and human impacts. Q fever has been neglected as a zoonotic disease in many
developing countries. The causative agent of this disease is the bacterium Coxiella burnetii (C. burnetii), which
is resistant to environmental factors such as heat and many disinfectant compounds, resulting in long-term risk
of disease for humans and animals. Since the infection is usually asymptomatic, it is mainly undiagnosed in
animals until adverse pregnancy outcomes occur in a herd. In humans, infection leads to severe endocarditis
and vascular infection in chronic cases. Despite the importance of this disease, limited information is available
about the molecular epidemiology and evolution of this pathogen. Genomic studies can also help to investigate
the prevalence of this disease. Likewise, the pathogenesis of C. burnetii should be examined by molecular
studies. Programs of awareness and ensuring the pasteurization of dairy products before human consumption
will help prevent many zoonotic diseases, including Q fever.