The First Detection of Blue Straggler Stars in the Milky Way Bulge

We report the first detections of Blue Straggler Stars (BSS) in the bulge of the Milky Way galaxy. Proper motions from extensive space-based observations along a single sight-line allow us to separate a sufficiently clean and well-characterized bulge sample that we are able to detect a small population of bulge objects in the region of the color-magnitude diagram commonly occupied young objects and blue strgglers. However, variability measurements of these objects clearly establish that a fraction of them are blue stragglers. Out of the 42 objects found in this region of the color-magnitude diagram, we estimate that at least 18 are genuine BSS. We normalize the BSS population by our estimate of the number of horizontal branch stars in the bulge in order to compare the bulge to other stellar systems. The BSS fraction is clearly discrepant from that found in stellar clusters. The blue straggler population of dwarf spheroidals remains a subject of debate; some authors claim an anticorrelation between the normalised blue straggler fraction and integrated light. If this trend is real, then the bulge may extend it by three orders of magnitude in mass. Conversely, we find that the genuinely young (~5Gy or younger) population in the bulge, must be at most 3.4% under the most conservative scenario for the BSS population.Comment: ApJ in press; 25 pages, 6 figures, 2 table

Stakeholder Capitalism and Implications for How We Think About Leadership

MAD statement The intention of this leading article is to help reframe our take on capitalism and leadership. Rather than presenting a linear, one-solution approach, it promotes an often messy, uncertain approach based on purpose, co-creation, creativity, courage and action delivering on a multitude of stakeholders’ needs and interests.publishedVersio

PARTNERSHIPS-LIMITED-FAILURE TO COMPLY WITH STATUTES AS BASIS FOR UNLIMITED LIABILITY

The recent decision of the Eighth Circuit Court of Appeals in Kistler v. Gingles, that a limited partner under the Arkansas Limited Partnership Act fails to avoid unlimited liability if the terms of the statute are not complied with, illustrates the inherent danger of the limited partnership. This statute, which is typical of the limited partnership statutes antedating the Uniform Limited Partnership Act, provides, in part, for an affidavit by one of the general partners stating that the sums which each limited partner proposes to contribute to the enterprise have actually and in good faith been paid into the business in cash by the date of its registration; and that if any false statement be made in the affidavit, all the persons interested in such partnership shall be liable for all the engagements of the firm as general partners. The uncontested facts in the above case show that while the would-be limited partners had not actually paid over in cash their full contributions as had been stated in the affidavit, they were at all times ready, willing, and able to so do. Instead of losing only their initial investment in the enterprise, the investors were held liable for the concern\u27 s entire indebtedness as a result of their failure to follow the precise terms of the statute. Herein lies the danger of the limited partnership and the reason this type of business association has been largely neglected in those states not adopting the Uniform Limited Partnership Act

Information transmission in oscillatory neural activity

Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al, 1999; Brenner et al, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.Comment: 18 pages, 8 figures, to appear in Biological Cybernetic

Multivariate characterization of white matter heterogeneity in autism spectrum disorder

The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders

Telegram from Ed and Lois Freeman to the family of Amon Carter

Telegram from Ed and Lois Freeman of The Nashville Tennessean upon the death of Amon Giles Carter. The telegram expresses condolences and sympathy about his death.https://mavmatrix.uta.edu/specialcollections_meachamcarterpapers/1194/thumbnail.jp

Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure

Amplification of asynchronous inhibition-mediated synchronization by feedback in recurrent networks

Synchronization of 30-80 Hz oscillatory activity of the principle neurons in the olfactory bulb (mitral cells) is believed to be important for odor discrimination. Previous theoretical studies of these fast rhythms in other brain areas have proposed that principle neuron synchrony can be mediated by short-latency, rapidly decaying inhibition. This phasic inhibition provides a narrow time window for the principle neurons to fire, thus promoting synchrony. However, in the olfactory bulb, the inhibitory granule cells produce long lasting, small amplitude, asynchronous and aperiodic inhibitory input and thus the narrow time window that is required to synchronize spiking does not exist. Instead, it has been suggested that correlated output of the granule cells could serve to synchronize uncoupled mitral cells through a mechanism called "stochastic synchronization", wherein the synchronization arises through correlation of inputs to two neural oscillators. Almost all work on synchrony due to correlations presumes that the correlation is imposed and fixed. Building on theory and experiments that we and others have developed, we show that increased synchrony in the mitral cells could produce an increase in granule cell activity for those granule cells that share a synchronous group of mitral cells. Common granule cell input increases the input correlation to the mitral cells and hence their synchrony by providing a positive feedback loop in correlation. Thus we demonstrate the emergence and temporal evolution of input correlation in recurrent networks with feedback. We explore several theoretical models of this idea, ranging from spiking models to an analytically tractable model. © 2010 Marella, Ermentrout

The XMM Cluster Survey: Forecasting cosmological and cluster scaling-relation parameter constraints

We forecast the constraints on the values of sigma_8, Omega_m, and cluster scaling relation parameters which we expect to obtain from the XMM Cluster Survey (XCS). We assume a flat Lambda-CDM Universe and perform a Monte Carlo Markov Chain analysis of the evolution of the number density of galaxy clusters that takes into account a detailed simulated selection function. Comparing our current observed number of clusters shows good agreement with predictions. We determine the expected degradation of the constraints as a result of self-calibrating the luminosity-temperature relation (with scatter), including temperature measurement errors, and relying on photometric methods for the estimation of galaxy cluster redshifts. We examine the effects of systematic errors in scaling relation and measurement error assumptions. Using only (T,z) self-calibration, we expect to measure Omega_m to +-0.03 (and Omega_Lambda to the same accuracy assuming flatness), and sigma_8 to +-0.05, also constraining the normalization and slope of the luminosity-temperature relation to +-6 and +-13 per cent (at 1sigma) respectively in the process. Self-calibration fails to jointly constrain the scatter and redshift evolution of the luminosity-temperature relation significantly. Additional archival and/or follow-up data will improve on this. We do not expect measurement errors or imperfect knowledge of their distribution to degrade constraints significantly. Scaling-relation systematics can easily lead to cosmological constraints 2sigma or more away from the fiducial model. Our treatment is the first exact treatment to this level of detail, and introduces a new `smoothed ML' estimate of expected constraints.Comment: 28 pages, 17 figures. Revised version, as accepted for publication in MNRAS. High-resolution figures available at http://xcs-home.org (under "Publications"

Neuroinflammation, Mast Cells, and Glia: Dangerous Liaisons

The perspective of neuroinflammation as an epiphenomenon following neuron damage is being replaced by the awareness of glia and their importance in neural functions and disorders. Systemic inflammation generates signals that communicate with the brain and leads to changes in metabolism and behavior, with microglia assuming a pro-inflammatory phenotype. Identification of potential peripheral-to-central cellular links is thus a critical step in designing effective therapeutics. Mast cells may fulfill such a role. These resident immune cells are found close to and within peripheral nerves and in brain parenchyma/meninges, where they exercise a key role in orchestrating the inflammatory process from initiation through chronic activation. Mast cells and glia engage in crosstalk that contributes to accelerate disease progression; such interactions become exaggerated with aging and increased cell sensitivity to stress. Emerging evidence for oligodendrocytes, independent of myelin and support of axonal integrity, points to their having strong immune functions, innate immune receptor expression, and production/response to chemokines and cytokines that modulate immune responses in the central nervous system while engaging in crosstalk with microglia and astrocytes. In this review, we summarize the findings related to our understanding of the biology and cellular signaling mechanisms of neuroinflammation, with emphasis on mast cell-glia interactions