Ophthalmic Genet

We report on a young female patient with the clinical features of blepharophimosis-ptosis-epicanthus inversus syndrome (BPES, OMIM 110100) and a balanced chromosome translocation 46, XX, t(2;3)(q33;q23)dn.BPES is a rare autosomal dominant congenital disorder characterized by the eponymous oculo-facial features that are, in female patients, associated either with (type 1 BPES) or without (type 2 BPES) premature ovarian failure. Both types of BPES are caused by heterozygous mutations in the FOXL2 gene, which is located in chromosome band 3q23. Chromosome aberrations such as balanced rearrangements have only rarely been observed in BPES patients but can provide valuable information about regulatory regions of FOXL2. The translocation in this patient broadens our knowledge of pathogenic mechanisms in BPES and highlights the importance of conventional cytogenetic investigations in patients with negative results of FOXL2 mutation screening as a prerequisite for optimal management and genetic counseling

Institutional Herding in Financial Markets: New Evidence Through the Lens of a Simulated Model

Due to data limitations and the absence of testable, model-based predictions, theory and evidence on herd behavior are only loosely connected. This paper contributes towards closing this gap in the herding literature. We use numerical simulations of a herd model to derive new, theory-based predictions for aggregate herding intensity. Using high-frequency, investor-specific trading data we confirm the predicted impact of information risk on herding. In contrast, the increase in buy herding measured for the financial crisis period cannot be explained by the herd model

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Nonstandard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

High frequency of submicroscopic genomic aberrations detected by tiling path array comparative genome hybridisation in patients with isolated congenital heart disease

BACKGROUND: Congenital heart disease (CHD) is the most common birth defect and affects nearly 1% of newborns. The aetiology of CHD is largely unknown and only a small percentage can be assigned to environmental risk factors such as maternal diseases or exposure to mutagenic agents during pregnancy. Chromosomal imbalances have been identified in many forms of syndromic CHD, but very little is known about the impact of DNA copy number changes in non-syndromic CHD. METHOD: A sub-megabase resolution array comparative genome hybridisation (CGH) screen was carried out on 105 patients with CHD as the sole abnormality at the time of diagnosis. RESULTS: There were 18 chromosomal changes detected, which do not coincide with common DNA copy number variants, including one de novo deletion, two de novo duplications and eight familial copy number variations (one deletion and seven duplications). CONCLUSIONS: Our data show that submicroscopic deletions and duplications play an important role in the aetiology of this condition, either as direct causes or as genetic risk factors for CHD. These findings have immediate consequences for genetic counselling and should pave the way for the elucidation of the pathogenetic mechanisms underlying CHD

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants

Nonstandard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants