Transient cytosolic calcium ([Ca²⁺]_cyt) elevation is an ubiquitous denominator of the signaling network when plants are exposed to literally every known abiotic and biotic stress. These stress-induced [Ca²⁺]_cyt elevations vary in magnitude, frequency, and shape, depending on the severity of the stress as well the type of stress experienced. This creates a unique stress-specific calcium “signature” that is then decoded by signal transduction networks. While most published papers have been focused predominantly on the role of Ca²⁺ influx mechanisms to shaping [Ca²⁺]_cyt signatures, restoration of the basal [Ca²⁺]_cyt levels is impossible without both cytosolic Ca²⁺ buffering and efficient Ca²⁺ efflux mechanisms removing excess Ca²⁺ from cytosol, to reload Ca²⁺ stores and to terminate Ca²⁺ signaling. This is the topic of the current review. The molecular identity of two major types of Ca²⁺ efflux systems, Ca²⁺-ATPase pumps and Ca²⁺/H⁺ exchangers, is described, and their regulatory modes are analyzed in detail. The spatial and temporal organization of calcium signaling networks is described, and the importance of existence of intracellular calcium microdomains is discussed. Experimental evidence for the role of Ca²⁺ efflux systems in plant responses to a range of abiotic and biotic factors is summarized. Contribution of Ca²⁺-ATPase pumps and Ca²⁺/H⁺ exchangers in shaping [Ca²⁺]_cyt signatures is then modeled by using a four-component model (plasma- and endo-membrane-based Ca²⁺-permeable channels and efflux systems) taking into account the cytosolic Ca²⁺ buffering. It is concluded that physiologically relevant variations in the activity of Ca²⁺-ATPase pumps and Ca²⁺/H⁺ exchangers are sufficient to fully describe all the reported experimental evidence and determine the shape of [Ca²⁺]_cyt signatures in response to environmental stimuli, emphasizing the crucial role these active efflux systems play in plant adaptive responses to environment.

We present a model describing the evolution of Fanaroff–Riley type I and II radio active galactic nuclei (AGNs) and the transition between these classes. We quantify galactic environments using a semianalytic galaxy formation model and apply our model to a volume-limited low-redshift () sample of observed AGNs to determine the distribution of jet powers and active lifetimes at the present epoch. Radio sources in massive galaxies are found to remain active for longer, spend less time in the quiescent phase, and inject more energy into their hosts than their less massive counterparts. The jet power is independent of the host stellar mass within uncertainties, consistent with maintenance-mode AGN feedback paradigm. The environments of these AGNs are in or close to long-term heating–cooling balance. We also examine the properties of high- and low-excitation radio galactic subpopulations. The HERGs are younger than LERGs by an order of magnitude, whereas their jet powers are greater by a factor of four. The Eddington-scaled accretion rates and jet production efficiencies of these populations are consistent with LERGs being powered by radiatively inefficient advection-dominated accretion flows, whereas HERGs are fed by a radiatively efficient accretion mechanism.

We present results from the first 12 months of operation of Radio Galaxy Zoo, which upon completion will enable visual inspection of over 170 000 radio sources to determine the host galaxy of the radio emission and the radio morphology. Radio Galaxy Zoo uses 1.4 GHz radio images from both the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) and the Australia Telescope Large Area Survey (ATLAS) in combination with mid-infrared images at 3.4 μm from the Wide-field Infrared Survey Explorer (WISE) and at 3.6 μm from the Spitzer Space Telescope. We present the early analysis of the WISE mid-infrared colours of the host galaxies. For images in which there is >75 per cent consensus among the Radio Galaxy Zoo cross-identifications, the project participants are as effective as the science experts at identifying the host galaxies. The majority of the identified host galaxies reside in the mid-infrared colour space dominated by elliptical galaxies, quasi-stellar objects and luminous infrared radio galaxies. We also find a distinct population of Radio Galaxy Zoo host galaxies residing in a redder mid-infrared colour space consisting of star-forming galaxies and/or dust-enhanced non-star-forming galaxies consistent with a scenario of merger-driven active galactic nuclei (AGN) formation. The completion of the full Radio Galaxy Zoo project will measure the relative populations of these hosts as a function of radio morphology and power while providing an avenue for the identification of rare and extreme radio structures. Currently, we are investigating candidates for radio galaxies with extreme morphologies, such as giant radio galaxies, late-type host galaxies with extended radio emission and hybrid morphology radio sources.

We use a volume- and flux-limited sample of local (0.03 ≤z≤ 0.1) radio galaxies with optical counterparts to address the question of how long a typic

The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification through visual inspection unfeasible. We present Claran — Classifying Radio sources Automatically with Neural networks — a proof-of-concept radio source morphology classifier based upon the Faster Region-based Convolutional Neutral Networks method. Specifically, we train and test Claran on the FIRST and WISE (Wide-field Infrared Survey Explorer) images from the Radio Galaxy Zoo Data Release 1 catalogue. Claran provides end users with automated identification of radio source morphology classifications from a simple input of a radio image and a counterpart infrared image of the same region. Claran is the first open-source, end-to-end radio source morphology classifier that is capable of locating and associating discrete and extended components of radio sources in a fast (<200 ms per image) and accurate (≥90 per cent) fashion. Future work will improve Claran’s relatively lower success rates in dealing with multisource fields and will enable Claran to identify sources on much larger fields without loss in classification accuracy.