The Stellar Populations of Low-redshift Clusters

We present some preliminary results from an on-going study of the evolution of stellar populations in rich clusters of galaxies. This sample contains core line-strength measurements from 183 galaxies with b_J <= 19.5 from four clusters with ~0.04. Using predictions from stellar population models to compare with our measured line strengths we can derive relative luminosity-weighted mean ages and metallicities for the stellar populations in each of our clusters. We also investigate the Mgb'-sigma and Hbeta_G'-sigma scaling relations. We find that, consistent with previous results, Mgb' is correlated with sigma, the likely explanation being that larger galaxies are better at retaining their heavier elements due to their larger potentials. Hbeta', on the other hand, we find to be anti-correlated with sigma. This result implies that the stellar populations in larger galaxies are older than in smaller galaxies.Comment: 3 pages, 2 figures, to appear in the Proceedings of IAU Colloquium 195: "Outskirts of Galaxy Clusters: intense life in the suburbs", Torino Italy, March 12-16 200

Which Classes of Structures Are Both Pseudo-elementary and Definable by an Infinitary Sentence?

When classes of structures are not first-order definable, we might still try to find a nice description. There are two common ways for doing this. One is to expand the language, leading to notions of pseudo-elementary classes, and the other is to allow infinite conjuncts and disjuncts. In this paper we examine the intersection. Namely, we address the question: Which classes of structures are both pseudo-elementary and $\mathcal{L}_{\omega_1 \omega}$-elementary? We find that these are exactly the classes that can be defined by an infinitary formula that has no infinitary disjunctions

Mood instability : significance, definition and measurement

Mood instability is common, and an important feature of several psychiatric disorders. We discuss the definition and measurement of mood instability, and review its prevalence, characteristics, neurobiological correlates and clinical implications. We suggest that mood instability has underappreciated transdiagnostic potential as an investigational and therapeutic target

SIGMA: spectral interpretation using gaussian mixtures and autoencoder

Identification of unknown micro- and nano-sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging data sets, particularly scanning electron microscope—energy dispersive X-ray spectroscopy (SEM-EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non-ideal sample preparation, and the presence of mixed chemical signals generated within the electron-beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine-learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel-wise elemental data points modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non-negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM-EDS data sets: a synthetic mixture sample and a real-world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background-subtracted single-phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe-bearing particles and their background-subtracted chemical signals. We demonstrate a flexible and adaptable approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user-friendly Python code with a graphical user interface for ease of use by general users

Integration of genetic and physical maps of the Primula vulgaris S locus and localization by chromosome in situ hybridization

•Heteromorphic flower development in Primula is controlled by the S locus. The S locus genes, which control anther position, pistil length and pollen size in pin and thrum flowers, have not yet been characterized. We have integrated S-linked genes, marker sequences and mutant phenotypes to create a map of the P. vulgaris S locus region that will facilitate the identification of key S locus genes. We have generated, sequenced and annotated BAC sequences spanning the S locus, and identified its chromosomal location. •We have employed a combination of classical genetics and three-point crosses with molecular genetic analysis of recombinants to generate the map. We have characterized this region by Illumina sequencing and bioinformatic analysis, together with chromosome in situ hybridization. •We present an integrated genetic and physical map across the P. vulgaris S locus flanked by phenotypic and DNA sequence markers. BAC contigs encompass a 1.5-Mb genomic region with 1 Mb of sequence containing 82 S-linked genes anchored to overlapping BACs. The S locus is located close to the centromere of the largest metacentric chromosome pair. •These data will facilitate the identification of the genes that orchestrate heterostyly in Primula and enable evolutionary analyses of the S locus

Canalization in the Critical States of Highly Connected Networks of Competing Boolean Nodes

Canalization is a classic concept in Developmental Biology that is thought to be an important feature of evolving systems. In a Boolean network it is a form of network robustness in which a subset of the input signals control the behavior of a node regardless of the remaining input. It has been shown that Boolean networks can become canalized if they evolve through a frustrated competition between nodes. This was demonstrated for large networks in which each node had K=3 inputs. Those networks evolve to a critical steady-state at the boarder of two phases of dynamical behavior. Moreover, the evolution of these networks was shown to be associated with the symmetry of the evolutionary dynamics. We extend these results to the more highly connected K>3 cases and show that similar canalized critical steady states emerge with the same associated dynamical symmetry, but only if the evolutionary dynamics is biased toward homogeneous Boolean functions.Comment: 8 pages, 5 figure

'Candidatus Phytoplasma malaysianum', a novel taxon associated with virescence and phyllody of Madagascar periwinkle (Catharanthus roseus).

This study addressed the taxonomic position and group classification of a phytoplasma responsible for virescence and phyllody symptoms in naturally diseased Madagascar periwinkle plants in western Malaysia. Unique regions in the 16S rRNA gene from the Malaysian periwinkle virescence (MaPV) phytoplasma distinguished the phytoplasma from all previously described 'Candidatus Phytoplasma' species. Pairwise sequence similarity scores, calculated through alignment of full-length 16S rRNA gene sequences, revealed that the MaPV phytoplasma 16S rRNA gene shared 96.5 % or less sequence similarity with that of previously described 'Ca. Phytoplasma' species, justifying the recognition of the MaPV phytoplasma as a reference strain of a novel taxon, 'Candidatus Phytoplasma malaysianum'. The 16S rRNA gene F2nR2 fragment from the MaPV phytoplasma exhibited a distinct restriction fragment length polymorphism (RFLP) profile and the pattern similarity coefficient values were lower than 0.85 with representative phytoplasmas classified in any of the 31 previously delineated 16Sr groups; therefore, the MaPV phytoplasma was designated a member of a new 16Sr group, 16SrXXXII. Phytoplasmas affiliated with this novel taxon and the new group included diverse strains infecting periwinkle, coconut palm and oil palm in Malaysia. Three phytoplasmas were characterized as representatives of three distinct subgroups, 16SrXXXII-A, 16SrXXXII-B and 16SrXXXII-C, respectively

Promiscuous actions of small molecule inhibitors of the protein kinase D-class IIa HDAC axis in striated muscle

AbstractPKD-mediated phosphorylation of class IIa HDACs frees the MEF2 transcription factor to activate genes that govern muscle differentiation and growth. Studies of the regulation and function of this signaling axis have involved MC1568 and Gö-6976, which are small molecule inhibitors of class IIa HDAC and PKD catalytic activity, respectively. We describe unanticipated effects of these compounds. MC1568 failed to inhibit class IIa HDAC catalytic activity in vitro, and exerted divergent effects on skeletal muscle differentiation compared to a bona fide inhibitor of these HDACs. In cardiomyocytes, Gö-6976 triggered calcium signaling and activated stress-inducible kinases. Based on these findings, caution is warranted when employing MC1568 and Gö-6976 as pharmacological tool compounds to assess functions of class IIa HDACs and PKD

Methane Emissions from Process Equipment at Natural Gas Production Sites in the United States: Liquid Unloadings

Methane emissions from liquid unloadings were measured at 107 wells in natural gas production regions throughout the United States. Liquid unloadings clear wells of accumulated liquids to increase production, employing a variety of liquid lifting mechanisms. In this work, wells with and without plunger lifts were sampled. Most wells without plunger lifts unload less than 10 times per year with emissions averaging 21 000–35 000 scf methane (0.4–0.7 Mg) per event (95% confidence limits of 10 000–50 000 scf/event). For wells with plunger lifts, emissions averaged 1000–10 000 scf methane (0.02–0.2 Mg) per event (95% confidence limits of 500–12 000 scf/event). Some wells with plunger lifts are automatically triggered and unload thousands of times per year and these wells account for the majority of the emissions from all wells with liquid unloadings. If the data collected in this work are assumed to be representative of national populations, the data suggest that the central estimate of emissions from unloadings (270 Gg/yr, 95% confidence range of 190–400 Gg) are within a few percent of the emissions estimated in the EPA 2012 Greenhouse Gas National Emission Inventory (released in 2014), with emissions dominated by wells with high frequencies of unloadings