The influence of leg-to-body ratio, arm-to-body ratio and intra-limb ratio on male human attractiveness.

Human mate choice is influenced by limb proportions. Previous work has focused on leg-to-body ratio (LBR) as a determinant of male attractiveness and found a preference for limbs that are close to, or slightly above, the average. We investigated the influence of two other key aspects of limb morphology: arm-to-body ratio (ABR) and intra-limb ratio (IR). In three studies of heterosexual women from the USA, we tested the attractiveness of male physiques that varied in LBR, ABR and IR, using figures that ranged from -3 to +3 standard deviations from the population mean. We replicated previous work by finding that the optimally attractive LBR is approximately 0.5 standard deviations above the baseline. We also found a weak effect of IR, with evidence of a weak preference for the baseline proportions. In contrast, there was no effect of ABR on attractiveness, and no interactions between the effects of LBR, ABR and IR. Our results indicate that ABR is not an important determinant of human mate choice for this population, and that IR may exert some influence but that this is much smaller than the effects of LBR. We discuss possible reasons for these results, including the limited variability in upper limb proportions and the potentially weak fitness-signal provided by this aspect of morphology

Testing for Episodic Predictability in Stock Returns

Standard tests based on predictive regressions estimated over the full available sample data have tended to find little evidence of predictability in stock returns. Recent approaches based on the analysis of subsamples of the data have been considered, suggesting that predictability where it occurs might exist only within so-called 'pockets of predictability' rather than across the entire sample. However, these methods are prone to the criticism that the sub-sample dates are endogenously determined such that the use of standard critical values appropriate for full sample tests will result in incorrectly sized tests leading to spurious findings of stock returns predictability. To avoid the problem of endogenously-determined sample splits, we propose new tests derived from sequences of predictability statistics systematically calculated over sub-samples of the data. Specifically, we will base tests on the maximum of such statistics from sequences of forward and backward recursive, rolling, and double-recursive predictive sub-sample regressions. We develop our approach using the over-identified instrumental variable-based predictability test statistics of Breitung and Demetrescu (2015). This approach is based on partial-sum asymptotics and so, unlike many other popular approaches including, for example, those based on Bonferroni corrections, can be readily adapted to implementation over sequences of subsamples. We show that the limiting distributions of our proposed tests are robust to both the degree of persistence and endogeneity of the regressors in the predictive regression, but not to any heteroskedasticity present even if the sub-sample statistics are based on heteroskedasticity-robust standard errors. We therefore develop fixed regressor wild bootstrap implementations of the tests which we demonstrate to be first-order asymptotically valid. Finite sample behaviour against a variety of temporarily predictable processes is considered. An empirical application to US stock returns illustrates the usefulness of the new predictability testing methods we propose

Semi-parametric seasonal unit root tests

We extend the M class of unit root tests introduced by Stock (1999, Cointegration, Causality and Forecasting. A Festschrift in Honour of Clive W.J. Granger. Oxford University Press), Perron and Ng (1996, Review of Economic Studies 63, 435–463) and Ng and Perron (2001, Econometrica 69, 1519–1554) to the seasonal case, thereby developing semi-parametric alternatives to the regression-based augmented seasonal unit root tests of Hylleberg, Engle, Granger, and Yoo (1990, Journal of Econometrics 44, 215–238). The success of this class of unit root tests to deliver good finite sample size control even in the most problematic (near-cancellation) case where the shocks contain a strong negative moving average component is shown to carry over to the seasonal case as is the superior size/power trade-off offered by these tests relative to other available tests

Testing for Episodic Predictability in Stock Returns

Standard tests based on predictive regressions estimated over the full available sample data have tended to find little evidence of predictability in stock returns. Recent approaches based on the analysis of subsamples of the data suggest in fact that predictability where it occurs might exist only within so-called \pockets of predictability" rather than across the entire sample. However, these methods are prone to the criticism that the subsample dates are endogenously determined such that the use of standard critical values appropriate for full sample tests will result in incorrectly sized tests leading to spurious findings of stock returns predictability. To avoid the problem of endogenously-determined sample splits, we propose new tests derived from sequences of predictability statistics systematically calculated over subsamples of the data. Specifically, we will base tests on the maximum of such statistics from sequences of forward and backward recursive, rolling, and double-recursive predictive subsample regressions. We develop our approach using the over-identified instrumental variable-based predictability test statistics of Breitung and Demetrescu (2015). This approach is based on partial-sum asymptotics and so, unlike many other popular approaches including, for example, those based on Bonferroni corrections, can be readily adapted to implementation over sequences of subsamples. We show that the limiting null distributions of our proposed test statistics depend in general on whether the putative predictor is strongly or weakly persistent and on any heteroskedasticity present (indeed on any timevariation present in the unconditional variance matrix of the innovations), the latter even if the subsample statistics are based on heteroskedasticity-robust standard errors. As a consequence, we develop fixed regressor wild bootstrap implementations of the tests which we demonstrate to be first-order asymptotically valid. Finite sample behaviour against a variety of temporarily predictable processes is considered. An empirical application to US stock returns illustrates the usefulness of the new predictability testing methods we propose

The IGF1 small dog haplotype is derived from Middle Eastern gray wolves

Abstract Background A selective sweep containing the insulin-like growth factor 1 (IGF1) gene is associated with size variation in domestic dogs. Intron 2 of IGF1 contains a SINE element and single nucleotide polymorphism (SNP) found in all small dog breeds that is almost entirely absent from large breeds. In this study, we surveyed a large sample of grey wolf populations to better understand the ancestral pattern of variation at IGF1 with a particular focus on the distribution of the small dog haplotype and its relationship to the origin of the dog. Results We present DNA sequence data that confirms the absence of the derived small SNP allele in the intron 2 region of IGF1 in a large sample of grey wolves and further establishes the absence of a small dog associated SINE element in all wild canids and most large dog breeds. Grey wolf haplotypes from the Middle East have higher nucleotide diversity suggesting an origin there. Additionally, PCA and phylogenetic analyses suggests a closer kinship of the small domestic dog IGF1 haplotype with those from Middle Eastern grey wolves. Conclusions The absence of both the SINE element and SNP allele in grey wolves suggests that the mutation for small body size post-dates the domestication of dogs. However, because all small dogs possess these diagnostic mutations, the mutations likely arose early in the history of domestic dogs. Our results show that the small dog haplotype is closely related to those in Middle Eastern wolves and is consistent with an ancient origin of the small dog haplotype there. Thus, in concordance with past archeological studies, our molecular analysis is consistent with the early evolution of small size in dogs from the Middle East. See associated opinion by Driscoll and Macdonald: http://jbiol.com/content/9/2/1

Big data analyses reveal patterns and drivers of the movements of southern elephant seals

The growing number of large databases of animal tracking provides an opportunity for analyses of movement patterns at the scales of populations and even species. We used analytical approaches, developed to cope with big data, that require no a priori assumptions about the behaviour of the target agents, to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina) in the Southern Ocean, that was comprised of >500,000 location estimates collected over more than a decade. Our analyses showed that the displacements of these seals were described by a truncated power law distribution across several spatial and temporal scales, with a clear signature of directed movement. This pattern was evident when analysing the aggregated tracks despite a wide diversity of individual trajectories. We also identified marine provinces that described the migratory and foraging habitats of these seals. Our analysis provides evidence for the presence of intrinsic drivers of movement, such as memory, that cannot be detected using common models of movement behaviour. These results highlight the potential for big data techniques to provide new insights into movement behaviour when applied to large datasets of animal tracking.Comment: 18 pages, 5 figures, 6 supplementary figure

Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.

BACKGROUND: The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists. METHODS: Using vignettes of actual clinical cases, clinician testers generated a differential diagnosis before and after using diagnostic decision support software. The evaluation used the SimulConsult® DDSS tool, based on Bayesian pattern matching with temporal onset of each finding in each disease. The tool covered 5405 diseases (averaging 22 findings per disease). Rheumatology content in the database was developed using both primary references and textbooks. The frequency, timing, age of onset and age of disappearance of findings, as well as their incidence, treatability, and heritability were taken into account in order to guide diagnostic decision making. These capabilities allowed key information such as pertinent negatives and evolution over time to be used in the computations. Efficacy was measured by comparing whether the correct condition was included in the differential diagnosis generated by clinicians before using the software ( unaided ), versus after use of the DDSS ( aided ). RESULTS: The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p \u3c 0.0001). Improvement was greatest for emergency medicine physicians (p = 0.013) and clinicians in practice for less than 10 years (p = 0.012). This error reduction occurred despite the fact that testers employed an open book approach to generate their initial lists of potential diagnoses, spending an average of 8.6 min using printed and electronic sources of medical information before using the diagnostic software. CONCLUSIONS: These findings suggest that decision support can reduce diagnostic errors and improve use of relevant information by generalists. Such assistance could potentially help relieve the shortage of experts in pediatric rheumatology and similarly underserved specialties by improving generalists\u27 ability to evaluate and diagnose patients presenting with musculoskeletal complaints. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02205086

Effect of Monthly, High-Dose, Long-Term Vitamin D on Lung Function: A Randomized Controlled Trial.

Although observational studies suggest positive vitamin D-lung function associations, randomized trials are inconsistent. We examined effects of vitamin D supplementation on lung function. We recruited 442 adults (50-84 years, 58% male) into a randomized, double-blinded, placebo-controlled trial. Participants received, for 1.1 years (median; range = 0.9-1.5 years), either (1) vitamin D₃ 200,000 IU, followed by monthly 100,000 IU doses (n = 226); or (2) placebo monthly (n = 216). At baseline and follow-up, spirometry yielded forced expiratory volume in 1 s (FEV1; primary outcome). Mean (standard deviation) 25-hydroxyvitamin D increased from 61 (24) nmol/L at baseline to 119 (45) nmol/L at follow-up in the vitamin D group, but was unchanged in the placebo group. There were no significant lung function improvements (vitamin D versus placebo) in the total sample, vitamin D-deficient participants or asthma/chronic obstructive pulmonary disease (COPD) participants. However, among ever-smokers (n = 217), the mean (95% confidence interval) FEV1 increase in the vitamin D versus placebo was 57 (4, 109) mL (p = 0.03). FEV1 increases were larger among vitamin D-deficient ever-smokers (n = 54): 122 (8, 236) mL (p = 0.04). FEV1 improvements were largest among ever-smokers with asthma/COPD (n = 60): 160 (53, 268) mL (p = 0.004). Thus, vitamin D supplementation did not improve lung function among everyone, but benefited ever-smokers, especially those with vitamin D deficiency or asthma/COPD

Approximation algorithms for hard variants of the stable marriage and hospitals/residents problems

When ties and incomplete preference lists are permitted in the Stable Marriage and Hospitals/Residents problems, stable matchings can have different sizes. The problem of finding a maximum cardinality stable matching in this context is known to be NP-hard, even under very severe restrictions on the number, size and position of ties. In this paper, we describe polynomial-time 5/3-approximation algorithms for variants of these problems in which ties are on one side only and at the end of the preference lists. The particular variant is motivated by important applications in large scale centralised matching schemes