The nucleus accumbens (NAc) and the dynorphinergic system are widely implicated in motivated behaviors. Prior studies have shown that activation of the dynorphin-kappa opioid receptor (KOR) system ...leads to aversive, dysphoria-like behavior. However, the endogenous sources of dynorphin in these circuits remain unknown. We investigated whether dynorphinergic neuronal firing in the NAc is sufficient to induce aversive behaviors. We found that photostimulation of dynorphinergic cells in the ventral NAc shell elicits robust conditioned and real-time aversive behavior via KOR activation, and in contrast, photostimulation of dorsal NAc shell dynorphin cells induced a KOR-mediated place preference and was positively reinforcing. These results show previously unknown discrete subregions of dynorphin-containing cells in the NAc shell that selectively drive opposing behaviors. Understanding the discrete regional specificity by which NAc dynorphinerigic cells regulate preference and aversion provides insight into motivated behaviors that are dysregulated in stress, reward, and psychiatric disease.
•Optogenetic excitation of nucleus accumbens dynorphin cells elicits dynorphin release•Discrete accumbens shell dynorphinergic populations drive either aversion or reward•These two nucleus accumbens subregions can be bi-directionally controlled•Both aversive and rewarding behaviors require kappa opioid receptors
Al-Hasani et al. show that dynorphin is necessary to drive opposing motivational states within subregions of the nucleus accumbens shell. Dynorphinergic neurons in the ventral shell drive aversion whereas in the dorsal shell they drive preference and reward seeking.
In addition to the large systematic differences arising from assumptions about the stellar mass-to-light ratio, the massive end of the stellar mass function is rather sensitive to how one fits the ...light profiles of the most luminous galaxies. We quantify this by comparing the luminosity and stellar mass functions based on the Sloan Digital Sky Survey (SDSS) cmodel magnitudes, and PyMorph single-Sérsic and Sérsic-exponential fits to the surface brightness profiles of galaxies in the SDSS. The PyMorph fits return more light, so that the predicted masses are larger than when cmodel magnitudes are used. As a result, the total stellar mass density at z ∼ 0.1 is about 1.2 times larger than in our previous analysis of the SDSS. The differences are most pronounced at the massive end, where the measured number density of objects having M
* ≥ 6 × 1011 M is approximately five times larger. Alternatively, at number densities of 10−6 Mpc−3, the limiting stellar mass is two times larger. The differences with respect to fits by other authors, typically based on Petrosian-like magnitudes, are even more dramatic, although some of these differences are due to sky-subtraction problems, and are sometimes masked by large differences in the assumed M
*/L (even after scaling to the same initial mass function). Our results impact studies of the growth and assembly of stellar mass in galaxies, and of the relation between stellar and halo mass, so we provide simple analytic fits to these new luminosity and stellar mass functions and quantify how they depend on morphology, as well as the binned counts in electronic format. While these allow one to quantify the differences which arise because of the assumed light profile, and we believe our Sérsic-exponential based results to be the most realistic of the models we have tested, we caution that which profile is the most appropriate at the high-mass end is still debated.
Several commercial automotive battery suppliers have developed lithium ion cells which use cathodes that consist of a mixture of two different active materials. This approach is intended to take ...advantage of the unique properties of each material and optimize the performance of the battery with respect to the automotive operating requirements. Certain cathode materials have high coulombic capacity and good cycling characteristics, but are costly and exhibit poor thermal stability (e.g., LiNi sub(x)Co sub(1-x-)y Al sub(y)O sub(2)). Alternately, other cathode materials exhibit good thermal stability, high voltage and high rate capability, but have low capacity (e.g., LiMn sub(2)O sub(4)). By blending two cathode materials the shortcomings of the parent materials could be minimized and the resultant blend can be tailored to have a higher energy or power density coupled with enhanced stability and lower cost. In this review, we survey the developing field of blended cathode materials from a new perspective. Targeting a range of cathode materials, we survey the advances in the field in the current review. Limitations, such as capacity decay due to metal dissolution are also discussed, as well as how the appropriate balance of characteristics of the blended materials can be optimized for hybrid- and electric-vehicle applications.
Abstract
We present a morphological catalogue for ∼670 000 galaxies in the Sloan Digital Sky Survey in two flavours: T-type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) ...classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-types and a series of GZ2 type questions (disc/features, edge-on galaxies, bar signature, bulge prominence, roundness, and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-type model is not so efficient. For the T-type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (>97 per cent), precision and recall values (>90 per cent), when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.
ABSTRACT
We present the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17) and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the final data release of the MaNGA survey, ...which is part of the SDSS Data Release 17 (DR17). The MPP-VAC-DR17 provides photometric parameters from Sérsic and Sérsic+Exponential fits to the two-dimensional surface brightness profiles of the MaNGA DR17 galaxy sample in the g, r, and i bands (e.g. total fluxes, half-light radii, bulge-disc fractions, ellipticities, position angles, etc.). The MDLM-VAC-DR17 provides deep-learning-based morphological classifications for the same galaxies. The MDLM-VAC-DR17 includes a number of morphological properties, for example, a T-Type, a finer separation between elliptical and S0, as well as the identification of edge-on and barred galaxies. While the MPP-VAC-DR17 simply extends the MaNGA PyMorph photometric VAC published in the SDSS Data Release 15 (MPP-VAC-DR15) to now include galaxies that were added to make the final DR17, the MDLM-VAC-DR17 implements some changes and improvements compared to the previous release (MDLM-VAC-DR15): Namely, the low end of the T-Types is better recovered in this new version. The catalogue also includes a separation between early or late type, which classifies the two populations in a complementary way to the T-Type, especially at the intermediate types (−1 < T-Type < 2), where the T-Type values show a large scatter. In addition, k-fold-based uncertainties on the classifications are also provided. To ensure robustness and reliability, we have also visually inspected all the images. We describe the content of the catalogues and show some interesting ways in which they can be combined.
This paper is of methodological nature, and deals with the foundations of Risk Assessment. Several international guidelines have recently recommended to select appropriate/relevant Hazard Scenarios ...in order to tame the consequences of (extreme) natural phenomena. In particular, the scenarios should be multivariate, i.e., they should take into account the fact that several variables, generally not independent, may be of interest. In this work, it is shown how a Hazard Scenario can be identified in terms of (i) a specific geometry and (ii) a suitable probability level. Several scenarios, as well as a Structural approach, are presented, and due comparisons are carried out. In addition, it is shown how the Hazard Scenario approach illustrated here is well suited to cope with the notion of Failure Probability, a tool traditionally used for design and risk assessment in engineering practice. All the results outlined throughout the work are based on the Copula Theory, which turns out to be a fundamental theoretical apparatus for doing multivariate risk assessment: formulas for the calculation of the probability of Hazard Scenarios in the general multidimensional case (
d≥2) are derived, and worthy analytical relationships among the probabilities of occurrence of Hazard Scenarios are presented. In addition, the Extreme Value and Archimedean special cases are dealt with, relationships between dependence ordering and scenario levels are studied, and a counter‐example concerning Tail Dependence is shown. Suitable indications for the practical application of the techniques outlined in the work are given, and two case studies illustrate the procedures discussed in the paper.
Key Points:
Methodological approach to multivariate risk assessment via copulas
Probabilistically consistent definition of multivariate hazard scenario
Calculation of the failure probability of different multivariate hazard scenarios
We quantify the systematics in the size–luminosity relation of galaxies in the Sloan Digital Sky Survey main sample (i.e. at z ∼ 0.1) which arise from fitting different one- and two-component model ...profiles to the r-band images. For objects brighter than L
*, fitting a single Sérsic profile to what is really a two-component SerExp system leads to biases: the half-light radius is increasingly overestimated as n of the fitted single component increases; it is also overestimated at B/T ∼ 0.6. For such objects, the assumption of a single Sérsic component is particularly misleading. However, the net effect on the R-L relation is small, except for the most luminous tail. We then study how this relation depends on morphology. Our analysis is one of the first to use Bayesian-classifier-derived weights, rather than hard cuts, to define morphology. For the R-L relation Es, S0s and Sas are early types, whereas Sbs and Scds are late, although S0s tend to be 15 per cent smaller than Es of the same luminosity, and faint Sbs are more than 25 per cent smaller than faint Scds. Neither the early- nor the late-type relations are pure power laws: both show significant curvature, which we quantify. This curvature confirms that two mass scales are special for both early- and late-type galaxies: M
* ∼ 3 × 1010 and 2 × 1011 M⊙. Also, although the R
disc-L
disc and R
disc-M
*disc relations of discs of disc-dominated galaxies run parallel to the corresponding relations for the total light in late types (i.e. they are significantly curved), R
bulge-L
bulge and R
bulge-M
*bulge for bulge-dominated systems show almost no curvature (i.e. unlike for the total light of early-type galaxies). Finally, the intrinsic scatter in the R-L relation decreases at large L and/or M
* and should provide additional constraints on models of how the most massive galaxies formed.
We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is ...the compact star-forming phase in the central regions of many growing galaxies that follows an earlier phase of gas compaction and is followed by a central quenching phase. We train a convolutional neural network (CNN) with mock "observed" images of simulated galaxies at three phases of evolution- pre-BN, BN, and post-BN-and demonstrate that the CNN successfully retrieves the three phases in other simulated galaxies. We show that BNs are identified by the CNN within a time window of ∼0.15 Hubble times. When the trained CNN is applied to observed galaxies from the CANDELS survey at z = 1-3, it successfully identifies galaxies at the three phases. We find that the observed BNs are preferentially found in galaxies at a characteristic stellar mass range, 109.2-10.3 M at all redshifts. This is consistent with the characteristic galaxy mass for BNs as detected in the simulations and is meaningful because it is revealed in the observations when the direct information concerning the total galaxy luminosity has been eliminated from the training set. This technique can be applied to the classification of other astrophysical phenomena for improved comparison of theory and observations in the era of large imaging surveys and cosmological simulations.
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