Inference of Natural Selection from Interspersed Genomic Elements Based on Polymorphism and Divergence

Complete genome sequences contain valuable information about natural selection, but extracting this information for short, widely scattered noncoding elements remains a challenging problem. Here we introduce a new computational method for addressing this problem called Inference of Natural Selection from Interspersed Genomically coHerent elemenTs (INSIGHT). INSIGHT uses a generative probabilistic model to contrast patterns of polymorphism and divergence in the elements of interest with those in flanking neutral sites, pooling weak information from many short elements in a manner that accounts for variation among loci in mutation rates and genealogical backgrounds. The method is able to disentangle the contributions of weak negative, strong negative, and positive selection based on their distinct effects on patterns of polymorphism and divergence. Information about divergence is obtained from multiple outgroup genomes using a full phylogenetic model. The model is efficiently fitted to genome-wide data by decomposing the maximum likelihood estimation procedure into three straightforward stages. The key selection-related parameters are estimated by expectation maximization. Using simulations, we show that INSIGHT can accurately estimate several parameters of interest even in complex demographic scenarios. We apply our methods to noncoding RNAs, promoter regions, and transcription factor binding sites in the human genome, and find clear evidence of natural selection. We also present a detailed analysis of particular nucleotide positions within GATA2 binding sites and primary micro-RNA transcripts.Comment: 21 page manuscript, 4 figure, 4 tables + 3 supp figures + 3 supp tables + supp methods. V4: additional results on human noncoding RNAs annotated by GENCODE + refinement of previous versions + additional supplementary material included to main document. V5: some minor modifications. V6: this is an electronic version of an article published in Mol Biol Evol, 201

The scientific payload of the Ultraviolet Transient Astronomy Satellite (ULTRASAT)

The Ultraviolet Transient Astronomy Satellite (ULTRASAT) is a space-borne near UV telescope with an unprecedented large field of view (200 sq. deg.). The mission, led by the Weizmann Institute of Science and the Israel Space Agency in collaboration with DESY (Helmholtz association, Germany) and NASA (USA), is fully funded and expected to be launched to a geostationary transfer orbit in Q2/3 of 2025. With a grasp 300 times larger than GALEX, the most sensitive UV satellite to date, ULTRASAT will revolutionize our understanding of the hot transient universe, as well as of flaring galactic sources. We describe the mission payload, the optical design and the choice of materials allowing us to achieve a point spread function of ~10arcsec across the FoV, and the detector assembly. We detail the mitigation techniques implemented to suppress out-of-band flux and reduce stray light, detector properties including measured quantum efficiency of scout (prototype) detectors, and expected performance (limiting magnitude) for various objects.Comment: Presented in the SPIE Astronomical Telescopes + Instrumentation 202

Not only what but also when : a theory of dynamic voluntary disclosure

We examine a dynamic model of voluntary disclosure of multiple pieces of private information. In our model, a manager of a firm who may learn multiple signals over time interacts with a competitive capital market and maximizes payoffs that increase in both period prices. We show (perhaps surprisingly) that in equilibrium later disclosures are interpreted more favorably even though the time the manager obtains the signals is independent of the value of the firm. We also provide sufficient conditions for the equilibrium to be in threshold strategies

The effect of exogenous information on voluntary disclosure and market quality

We analyze a model in which information may be voluntarily disclosed by a firm and/or by a third party, e.g., financial analysts. Due to its strategic nature, corporate voluntary disclosure is qualitatively different from third-party disclosure. Greater analyst coverage crowds out (crowds in) corporate voluntary disclosure when analysts mostly discover information that is available (unavailable) to the firm. Nevertheless, greater analyst coverage always improves the overall quality of public information. We base this claim on two market quality measures: price efficiency, which is statistical in nature, and liquidity, which is derived in a trading stage that follows the disclosure stage

Sequential Reporting Bias

Firms with correlated fundamentals often issue reports sequentially, leading to information spillovers. The theoretical literature has investigated multi-firm reporting, but only when firms report simultaneously. We examine the implications of sequential reporting, where firms aim to maximize their market price and can manipulate their reports. Our model demonstrates that the introduction of sequentiality in the presence of information spillovers significantly alters the biasing behavior of firms and the resulting informational environment relative to simultaneous reporting. In particular, a lead firm always manipulates more when reports are issued sequentially. Interestingly, this occurs because follower firms, who benefit from information spillovers, place less weight on their own private information when issuing a report. This information loss leads the market to place greater weight on the leader’s report, which increases the incentive of the lead manager to manipulate her report. Moreover, the information loss from sequentiality leads to less efficient and less volatile prices. Additionally, we find that stronger correlation in firm fundamentals can amplify the lead firm's incentive for manipulation under sequentiality, in contrast to simultaneous reporting. We offer additional results regarding, for example, market response coefficients, and provide a number of empirical implications

Regression, Correlation, and the Time Interval: Additive-Multiplicative Framework

When two random variables are both additive or multiplicative, the effect of the way one "slices" the available period to subperiods (time intervals) is well documented in the literature. In this paper, we investigate the time interval effect when one of the variables is additive and one is multiplicative. We prove that the squared multiperiod correlation coefficient (\rho 2 n) decreases monotonically as n increases, and approaches zero when n goes to infinity. However, for relevant data corresponding to the U.S. stock market index, when shifting from weekly parameters to quarterly parameters the decrease in \rho 2 n is negligible. The effect on the regression coefficient is much more dramatic and even a shift from weekly data to quarterly data affects the regression coefficient substantially. The regression slope generally approaches zero, minus infinity or plus infinity, as the number of periods increases. Montonicity, however, exists only in certain cases.Correlation Coefficient, Regression Coefficient (Beta), Time Interval

Dividend Stickiness and Strategic Pooling

We argue that dividend stickiness, the tendency of managers to keep dividends unchanged, implies that managers use a partially pooling dividend policy. We offer a model that demonstrates how such a policy can evolve endogenously in equilibrium. An informed manager who cares about the firm's intrinsic value as well as short-term stock price allocates earnings between investments and dividends. We show that there is a continuum of equilibria in which the dividend is constant for a range of realized earnings. Compared with the standard separating equilibrium, this partial pooling behavior induces higher firm value and lower underinvestment. We offer new empirical implications relating the pooling nature of dividend stickiness to the information environment of the firm, dividend prediction models, managerial incentives, and investment. The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.