Do language change rates depend on population size?

An earlier study (Nettle 1999b) concluded, based on computer simulations and some inferences from empirical data, that languages will change the more slowly the larger the population gets. We replicate this study using a more complete language model for simulations (the Schulze model combined with a Barabasi-Albert net- work) and a richer empirical dataset (the World Atlas of Language Structures edited by Haspelmath et al. 2005). Our simulations show either a weak or stronger dependence of language change on population sizes depending on the parameter settings, and empirical data, like some of the simulations, show a weak dependence.Comment: 20 pages including all figures for a linguistic journa

Population Size and Rates of Language Change

Previous empirical studies of population size and language change have produced equivocal results. We therefore address the question with a new set of lexical data from nearly one-half of the world’s languages. We first show that relative population sizes of modern languages can be extrapolated to ancestral languages, albeit with diminishing accuracy, up to several thousand years into the past. We then test for an effect of population against the null hypothesis that the ultrametric inequality is satisfied by lexical distance among triples of related languages. The test shows mainly negligible effects of population, the exception being an apparently faster rate of change in the larger of two closely related variants. A possible explanation for the exception may be the influence on emerging standard (or cross-regional) variants from speakers who shift from different dialects to the standard. Our results strongly indicate that the sizes of speaker populations do not in and of themselves determine rates of language change. Comparison of this empirical finding with previously published computer simulations suggests that the most plausible model for language change is one in which changes propagate on a local level in a type of network in which the individuals have different degrees of connectivity

Profiles of epistemological beliefs, knowledge about explanation norms, and explanation skills: changes after an intervention

In this study, we exploratively investigate the relation between students’ epistemological beliefs and their declarative knowledge about scientific explanations and their practical skills to explain psychological phenomena drawing on scientific theories before and after a training intervention using a person-centered approach. We theoretically derive profiles of epistemological beliefs that should be beneficial for constructing scientific explanations. We those having higher explanation skills show a profile of epistemological beliefs that is beneficial for explanations skills. Using a latent profile transition analysis and a sample with N  =  108 students, we explore which profiles of epistemological beliefs, declarative knowledge about explanations, and explanation skills empirically emerge before and after an intervention that aimed and fostering students’ skills to construct scientific explanations. Before the intervention, two profiles emerged that differed in epistemological beliefs and explanation skills, but both did not in declarative knowledge about explanation. The intervention, in general, yielded a gain in declarative knowledge about explanations and explanation skills. After the intervention, again, two profiles emerged. However, these profiles did not differ in their epistemological beliefs but only in declarative knowledge about explanations and explanation skills. Thus, the intervention seems to level out the effects of epistemological beliefs. Additionally, the pattern of change in epistemological beliefs is consistent with theoretical expectations about which epistemological beliefs are beneficial for explanations. We discuss the results and their implications, as well as their limitations. Finally, we provide an outlook of using the person-oriented approach and this study’s type of intervention in the research on changing epistemological beliefs

A Distributed Population of Low Mass Pre-Main Sequence Stars near the Taurus Molecular Clouds

We present a drift scan survey covering a ~5 deg by 50 deg region toward the southern portion of the Taurus-Auriga molecular cloud. Data taken in the B,R,I filters with the Quest-2 camera on the Palomar 48-inch telescope were combined with 2MASS near-infrared photometry to select candidate young stars. Follow-up optical spectroscopy of 190 candidates led to identification of 42 new low mass pre-main sequence stars with spectral types M4-M8, of which approximately half exhibit surface gravity signatures similar to known Taurus stars while the other half exhibit surface gravity signatures similar to members of the somewhat older Upper Sco, TW Hya and Beta Pic associations. The pre-main sequence stars are spread over ~35 deg, and many are located well outside of previously explored regions. From assessment of the spatial and proper motion distributions, we argue that the new pre-main sequence stars identified far from the clouds cannot have originated from the vicinity of the 1-2 Myr-old subclusters which contain the bulk of the identified Taurus members, but instead represent a newly-identified area of recent star-formation near the clouds.Comment: Accepted for publication in AJ. 13 pages including 9 figures (2 in color) and 1 table. A separate file tabA1.ps contains a hard copy of a second table which will be published in electronic form onl

X-rays in the Orion Nebula Cluster: Constraints on the origins of magnetic activity in pre-main sequence stars

A recent Chandra/ACIS observation of the Orion Nebula Cluster detected 1075 sources (Feigelson et al. 2002), providing a uniquely large and well-defined sample to study the dependence of magnetic activity on bulk properties for stars descending the Hayashi tracks. The following results are obtained: (1) X-ray luminosities L_t in the 0.5-8 keV band are strongly correlated with bolometric luminosity with = -3.8 for stars with masses 0.7<M<2 Mo, an order of magnitude below the main sequence saturation level; (2) the X-ray emission drops rapidly below this level in some or all stars with 2<M<3 Mo; (3) the presence or absence of infrared circumstellar disks has no apparent relation to X-ray levels; and (4) X-ray luminosities exhibit a slight rise as rotational periods increase from 0.4 to 20 days. This last finding stands in dramatic contrast to the strong anticorrelation between X-rays and period seen in main sequence stars. The absence of a strong X-ray/rotation relationship in PMS stars, and particularly the high X-ray values seen in some very slowly rotating stars, is a clear indication that the mechanisms of magnetic field generation differ from those operating in main sequence stars. The most promising possibility is a turbulent dynamo distributed throughout the deep convection zone, but other models such as alpha-Omega dynamo with `supersaturation' or relic core fields are not immediately excluded. The drop in magnetic activity in intermediate-mass stars may reflect the presence of a significant radiative core. The evidence does not support X-ray production in large-scale star-disk magnetic fields.Comment: 51 pages, 8 figures. To appear in the Astrophysical Journa

Natural and Orthogonal Interaction framework for modeling gene-environment interactions with application to lung cancer

Objectives: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data. Methods: The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data. Results: Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested. Conclusion: The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits. Copyright (C) 2012 S. Karger AG, Base

The ETO2 transcriptional cofactor maintains acute leukemia by driving a MYB/EP300‐dependent stemness program

Transcriptional cofactors of the ETO family are recurrent fusion partners in acute leukemia. We characterized the ETO2 regulome by integrating transcriptomic and chromatin binding analyses in human erythroleukemia xenografts and controlled ETO2 depletion models. We demonstrate that beyond its well‐established repressive activity, ETO2 directly activates transcription of MYB, among other genes. The ETO2‐activated signature is associated with a poorer prognosis in erythroleukemia but also in other acute myeloid and lymphoid leukemia subtypes. Mechanistically, ETO2 colocalizes with EP300 and MYB at enhancers supporting the existence of an ETO2/MYB feedforward transcription activation loop (e.g., on MYB itself). Both small‐molecule and PROTAC‐mediated inhibition of EP300 acetyltransferases strongly reduced ETO2 protein, chromatin binding, and ETO2‐activated transcripts. Taken together, our data show that ETO2 positively enforces a leukemia maintenance program that is mediated in part by the MYB transcription factor and that relies on acetyltransferase cofactors to stabilize ETO2 scaffolding activity

Biological, clinical and population relevance of 95 loci for blood lipids.

Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in &gt;100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P &lt; 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD