Recent Advances in Modeling Stellar Interiors

Advances in stellar interior modeling are being driven by new data from large-scale surveys and high-precision photometric and spectroscopic observations. Here we focus on single stars in normal evolutionary phases; we will not discuss the many advances in modeling star formation, interacting binaries, supernovae, or neutron stars. We review briefly: 1) updates to input physics of stellar models; 2) progress in two and three-dimensional evolution and hydrodynamic models; 3) insights from oscillation data used to infer stellar interior structure and validate model predictions (asteroseismology). We close by highlighting a few outstanding problems, e.g., the driving mechanisms for hybrid gamma Dor/delta Sct star pulsations, the cause of giant eruptions seen in luminous blue variables such as eta Car and P Cyg, and the solar abundance problem.Comment: Proceedings for invited talk at conference High Energy Density Laboratory Astrophysics 2010, Caltech, March 2010, submitted for special issue of Astrophysics and Space Science; 7 pages; 5 figure

A Mouse Stromal Response to Tumor Invasion Predicts Prostate and Breast Cancer Patient Survival

Primary and metastatic tumor growth induces host tissue responses that are believed to support tumor progression. Understanding the molecular changes within the tumor microenvironment during tumor progression may therefore be relevant not only for discovering potential therapeutic targets, but also for identifying putative molecular signatures that may improve tumor classification and predict clinical outcome. To selectively address stromal gene expression changes during cancer progression, we performed cDNA microarray analysis of laser-microdissected stromal cells derived from prostate intraepithelial neoplasia (PIN) and invasive cancer in a multistage model of prostate carcinogenesis. Human orthologs of genes identified in the stromal reaction to tumor progression in this mouse model were observed to be expressed in several human cancers, and to cluster prostate and breast cancer patients into groups with statistically different clinical outcomes. Univariate Cox analysis showed that overexpression of these genes is associated with shorter survival and recurrence-free periods. Taken together, our observations provide evidence that the expression signature of the stromal response to tumor invasion in a mouse tumor model can be used to probe human cancer, and to provide a powerful prognostic indicator for some of the most frequent human malignancies

The Kondo Resonance in Electron Spectroscopy

The Kondo resonance is the spectral manifestation of the Kondo properties of the impurity Anderson model, and also plays a central role in the dynamical mean-field theory (DMFT) for correlated electron lattice systems. This article presents an overview of electron spectroscopy studies of the resonance for the 4f electrons of cerium compounds, and for the 3d electrons of V_2O_3, including beginning efforts at using angle resolved photoemission to determine the k-dependence of the resonance. The overview includes the comparison and analysis of spectroscopy data with theoretical spectra as calculated for the impurity model and as obtained by DMFT, and the Kondo volume collapse calculation of the cerium alpha-gamma phase transition boundary, with its spectroscopic underpinnings.Comment: 32 pages, 11 figures, 151 references; paper for special issue of J. Phys. Soc. Jpn. on "Kondo Effect--40 Years after the Discovery

Exposing the Contradictory Claims, Myths and Illusions of the “Secrets of Business Success and Company Longevity†Genre

Over the last three decades, several management consultants, academics and business practitioners have laid claim to identifying “the secrets†of business success and company longevity. However, a systematic analysis of 24 studies in this genre revealed fundamental disagreements over the elements these authors claim are the primary drivers of business performance and longevity, and demonstrates that they share eight methodological and analytical flaws. Furthermore, many of the claims they made about “the secrets†of business success have not stood the test of time. The paper explains why business practitioners will find little in these studies to help their companies become more successful now and in the future, and also speculates why several of these studies became international best-sellers during the 1980s, 1990s and 2000s. It concludes by suggesting some new avenues for future research in this domain, and highlights the practical implications of these findings for business practitioners

Predictive social perception: Towards a unifying framework from action observation to person knowledge

Action observation is central to human social interaction. It allows people to derive what mental states drive others' behaviour and coordinate (and compete) effectively with them. Although previous accounts have conceptualised this ability in terms of bottom-up (motoric or conceptual) matching processes, more recent evidence suggests that such mechanisms cannot account for the complexity and uncertainty of the sensory input, even in cases where computations should be much simpler (i.e., low-level vision). It has therefore been argued that perception in general, and social perception in particular, is better described as a process of top–down hypothesis testing. In such models, any assumption about others—their goals, attitudes, and beliefs—is translated into predictions of expected sensory input and compared with incoming stimulation. This allows perception and action to be based on these expectations or—in case of a mismatch—for one's prior assumptions to be revised until they are better aligned with the individual's behaviour. This article will give a (selective) review of recent research from experimental psychology and (social) neuroscience that supports such views, discuss the relevant underlying models, and current gaps in research. In particular, it will argue that much headway can be made when current research on predictive social perception is integrated with classic findings from social psychology, which have already shown striking effects of prior knowledge on the processing of other people's behaviour

The Things You Do:Internal Models of Others' Expected Behaviour Guide Action Observation

Predictions allow humans to manage uncertainties within social interactions. Here, we investigate how explicit and implicit person models-how different people behave in different situations-shape these predictions. In a novel action identification task, participants judged whether actors interacted with or withdrew from objects. In two experiments, we manipulated, unbeknownst to participants, the two actors action likelihoods across situations, such that one actor typically interacted with one object and withdrew from the other, while the other actor showed the opposite behaviour. In Experiment 2, participants additionally received explicit information about the two individuals that either matched or mismatched their actual behaviours. The data revealed direct but dissociable effects of both kinds of person information on action identification. Implicit action likelihoods affected response times, speeding up the identification of typical relative to atypical actions, irrespective of the explicit knowledge about the individual's behaviour. Explicit person knowledge, in contrast, affected error rates, causing participants to respond according to expectations instead of observed behaviour, even when they were aware that the explicit information might not be valid. Together, the data show that internal models of others' behaviour are routinely re-activated during action observation. They provide first evidence of a person-specific social anticipation system, which predicts forthcoming actions from both explicit information and an individuals' prior behaviour in a situation. These data link action observation to recent models of predictive coding in the non-social domain where similar dissociations between implicit effects on stimulus identification and explicit behavioural wagers have been reported

A connectome of the adult drosophila central brain

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions. Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain