Common variants near TARDBP and EGR2 are associated with susceptibility to Ewing sarcoma

Ewing sarcoma, a pediatric tumor characterized by EWSR1-ETS fusions, is predominantly observed in populations of European ancestry. We performed a genome-wide association study (GWAS) of 401 French individuals with Ewing sarcoma, 684 unaffected French individuals and 3,668 unaffected individuals of European descent and living in the United States. We identified candidate risk loci at 1p36.22, 10q21 and 15q15. We replicated these loci in two independent sets of cases and controls. Joint analysis identified associations with rs9430161 (P = 1.4 × 10(-20); odds ratio (OR) = 2.2) located 25 kb upstream of TARDBP, rs224278 (P = 4.0 × 10(-17); OR = 1.7) located 5 kb upstream of EGR2 and, to a lesser extent, rs4924410 at 15q15 (P = 6.6 × 10(-9); OR = 1.5). The major risk haplotypes were less prevalent in Africans, suggesting that these loci could contribute to geographical differences in Ewing sarcoma incidence. TARDBP shares structural similarities with EWSR1 and FUS, which encode RNA binding proteins, and EGR2 is a target gene of EWSR1-ETS. Variants at these loci were associated with expression levels of TARDBP, ADO (encoding cysteamine dioxygenase) and EGR2.     

A laser frequency comb that enables radial velocity measurements with a precision of 1 cm s(-1)

Searches for extrasolar planets using the periodic Doppler shift of stellar spectral lines have recently achieved a precision of 60 cm s(-1) (ref. 1), which is sufficient to find a 5-Earth-mass planet in a Mercury-like orbit around a Sun-like star. To find a 1-Earth-mass planet in an Earth-like orbit, a precision of approximately 5 cm s(-1) is necessary. The combination of a laser frequency comb with a Fabry-Pérot filtering cavity has been suggested as a promising approach to achieve such Doppler shift resolution via improved spectrograph wavelength calibration, with recent encouraging results. Here we report the fabrication of such a filtered laser comb with up to 40-GHz (approximately 1-A) line spacing, generated from a 1-GHz repetition-rate source, without compromising long-term stability, reproducibility or spectral resolution. This wide-line-spacing comb, or 'astro-comb', is well matched to the resolving power of high-resolution astrophysical spectrographs. The astro-comb should allow a precision as high as 1 cm s(-1) in astronomical radial velocity measurements.     

The impact of parameter and model uncertainty on market risk predictions from GARCH‐type models

We study the effect of parameter and model uncertainty on the left‐tail of predictive densities and in particular on VaR forecasts. To this end, we evaluate the predictive performance of several GARCH‐type models estimated via Bayesian and maximum likelihood techniques. In addition to individual models, several combination methods are considered, such as Bayesian model averaging and (censored) optimal pooling for linear, log or beta linear pools. Daily returns for a set of stock market indexes are predicted over about 13 years from the early 2000s. We find that Bayesian predictive densities improve the VaR backtest at the 1% risk level for single models and for linear and log pools. We also find that the robust VaR backtest exhibited by linear and log pools is better than the backtest of single models at the 5% risk level. Finally, the equally weighted linear pool of Bayesian predictives tends to be the best VaR forecaster in a set of 42 forecasting techniques.     

Using the bootstrap to evaluate forecasting equations

The bootstrap, like the jack-knife, is a technique for estimating standard errors. The idea is to use Monte Carlo simulation, based on a non-parametric estimate of the underlying error distribution. The bootstrap will be applied to an econometric equation describing the demand for energy by industry, to determine multi-period forecasting error and choose among competing specifications. The delta method for estimating forecast errors turns out to be too optimistic by a factor of 2.     

Forecasting time series with increasing seasonal variation

Four options for modeling and forecasting time series data containing increasing seasonal variation are discussed, including data transformations, double seasonal difference models and two kinds of transfer function-type ARIMA models employing seasonal dummy variables. An explanation is given for the typical ARIMA model identification analysis failing to identify double seasonal difference models for this kind of data. A logical process of selecting one option for a particular case is outlined, focusing on issues of linear versus non-linear increasing seasonal variation, and the level of stochastic versus deterministic behavior in a time series. Example models for the various options are presented for six time series, with point forecast and interval forecast comparisons. Interval forecasts from data-transformation models are found to generally be too wide and sometimes illogical in the dependence of their width on the point forecast level. Suspicion that maximum likelihood estimation of ARIMA models leads to excessive indications of unit roots in seasonal moving-average operators is reported.     

Curie’s principle and causal graphs

Curie’s Principle says that any symmetry property of a cause must be found in its effect. In this article, I consider Curie’s Principle from the point of view of graphical causal models, and demonstrate that, under one definition of a symmetry transformation, the causal modeling framework does not require anything like Curie’s Principle to be true. On another definition of a symmetry transformation, the graphical causal modeling formalism does imply a version of Curie’s Principle. These results yield a better understanding of the logical landscape with respect to the relationship between Curie’s Principle and graphical causal modeling.     

Quantum metaphysical indeterminacy and the ontological foundations of orthodoxy

This paper develops quantum state individualism, a fundamental ontology for what is usually known as ‘orthodox quantum mechanics.’ The central import of this ontology is that allows for a systematic evaluation of some of the main conclusions of the recent literature on quantum metaphysical indeterminacy. In particular, quantum state individualism supports the ‘gappy’ version of Jessica Wilson's determinable-based account of metaphysical indeterminacy; it implies that fundamental reality is perfectly precise; and third, it provides a non-disjunctive definition of determinables and thereby shields Wilson's account against the charge that it requires either a departure from classical logic or a revision of the quantum formalism.     

The futility of decision making research

We have each spent more than 50 years doing research that has had little impact. Even more lamentable is that our field, judgment and decision making (JDM), has on the whole had little impact during that span. We attribute that failure to the use of methodologies that emphasize testing models rather than looking for differences in behavior. The “cognitive revolution” led the field astray, toward the goal of studying model fit rather than comparing observable results. With modeling as the goal, experimentation was stultified. Simple tasks became dominant. Although a poor metaphor for real decision making, the gambling paradigm has lasted forever because the inputs to the decision are known to the researcher and thus easily modeled.