Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data.

Measuring risk aversion with lists: A new bias

Various experimental procedures aimed at measuring individual risk aversion involve alist of pairs of alternative prospects. We first study the widely used method by Holt andLaury (2002), for which we find that the removal of some items from the lists yields asystematic decrease in risk aversion. This bias is quite distinct from other confoundsthat have been previously observed in the use of the Holt and Laury method. It may berelated to empirical phenomena and theoretical developments where better prospectsincrease risk aversion. Nevertheless, we have also found that the more recent elicitationmethod due to Abdellaoui et al. (2011), also based on lists, does not display anystatistically significant bias when the corresponding items of the list are removed. Ourresults suggest that methods other than the popular Holt and Laury one may bepreferable for the measurement of risk aversion.

Systematic review of the empirical evidence of study publication bias and outcome reporting bias - an updated review.

BACKGROUND: The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias and outcome reporting bias have been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. METHODOLOGY/PRINCIPAL FINDINGS: In this update, we review and summarise the evidence from cohort studies that have assessed study publication bias or outcome reporting bias in randomised controlled trials. Twenty studies were eligible of which four were newly identified in this update. Only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Fifteen of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40-62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies. CONCLUSIONS: This update does not change the conclusions of the review in which 16 studies were included. Direct empirical evidence for the existence of study publication bias and outcome reporting bias is shown. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.

Fine-Tuning Translation Kinetics Selection as the Driving Force of Codon Usage Bias in the Hepatitis A Virus Capsid

Hepatitis A virus (HAV), the prototype of genus Hepatovirus, has several unique biological characteristics that distinguish it from other members of the Picornaviridae family. Among these, the need for an intact eIF4G factor for the initiation of translation results in an inability to shut down host protein synthesis by a mechanism similar to that of other picornaviruses. Consequently, HAV must inefficiently compete for the cellular translational machinery and this may explain its poor growth in cell culture. In this context of virus/cell competition, HAV has strategically adopted a naturally highly deoptimized codon usage with respect to that of its cellular host. With the aim to optimize its codon usage the virus was adapted to propagate in cells with impaired protein synthesis, in order to make tRNA pools more available for the virus. A significant loss of fitness was the immediate response to the adaptation process that was, however, later on recovered and more associated to a re-deoptimization rather than to an optimization of the codon usage specifically in the capsid coding region. These results exclude translation selection and instead suggest fine-tuning translation kinetics selection as the underlying mechanism of the codon usage bias in this specific genome region. Additionally, the results provide clear evidence of the Red Queen dynamics of evolution since the virus has very much evolved to re-adapt its codon usage to the environmental cellular changing conditions in order to recover the original fitness.

Relation for the nonequilibrium population of the interface states: effects on the bias dependence of the ideality factors

By an analysis of the exchange of carriers through a semiconductor junction, a general relationship for the nonequilibrium population of the interface states in Schottky barrier diodes has been derived. Based on this relationship, an analytical expression for the ideality factor valid in the whole range of applied bias has been given. This quantity exhibits two different behaviours depending on the value of the applied bias with respect to a critical voltage. This voltage, which depends on the properties of the interfacial layer, constitutes a new parameter to complete the characterization of these junctions. A simple interpretation of the different behaviours of the ideality factor has been given in terms of the nonequilibrium charging properties of interface states, which in turn explains why apparently different approaches have given rise to similar results. Finally, the relevance of our results has been considered on the determination of the density of interface states from nonideal current-voltage characteristics and in the evaluation of the effects of the interfacial layer thickness in metal-insulator-semiconductor tunnelling diodes.

Detection of bias in animal model pedigree indices of heifers

Selostus: Eläinmalliin perustuvien hiehojen odotusarvojen luotettavuus jalostusarvon ennusteena

Electric-field control of exchange bias in multiferroic epitaxial heterostructures

The magnetic exchange between epitaxial thin films of the multiferroic (antiferromagnetic and ferroelectric) hexagonal YMnO3 oxide and a soft ferromagnetic (FM) layer is used to couple the magnetic response of the FM layer to the magnetic state of the antiferromagnetic one. We will show that biasing the ferroelectric YMnO3 layer by an electric field allows control of the magnetic exchange bias and subsequently the magnetotransport properties of the FM layer. This finding may contribute to paving the way towards a new generation of electric-field controlled spintronic devices.

Exchange bias between magnetoelectric YMnO3 and ferromagnetic SrRuO3 epitaxial films

Orthorhombic YMnO3 (YMO) epitaxial thin films were deposited on SrTiO3 (STO) single-crystal substrates. We show that the out-of-plane texture of the YMO films can be tailored using STO substrates having (001), (110), or (111) orientations. We report on the magnetic properties of the YMO(010) films grown on STO(001) substrates. The dependence of the susceptibility on the temperature indicates that the films are antiferromagnetic below the Néel temperature (around 35 K). Orthorhombic YMO(010) films were also deposited on an epitaxial buffer layer of ferromagnetic and metallic SrRuO3 (SRO). The magnetic hysteresis loops of SRO show exchange bias at temperatures below the Néel temperature of YMO. These results confirm that the YMO films are antiferromagnetic and demonstrate that magnetoelectric YMO can be integrated in functional epitaxial architectures.

Microscopic origin of exchange bias in core/shell nanoparticles

We report the results of Monte Carlo simulations with the aim to clarify the microscopic origin of exchange bias in the magnetization hysteresis loops of a model of individual core/shell nanoparticles. Increase of the exchange coupling across the core/shell interface leads to an enhancement of exchange bias and to an increasing asymmetry between the two branches of the loops which is due to different reversal mechanisms. A detailed study of the magnetic order of the interfacial spins shows compelling evidence that the existence of a net magnetization due to uncompensated spins at the shell interface is responsible for both phenomena and allows to quantify the loop shifts directly in terms of microscopic parameters with striking agreement with the macroscopic observed values.

Depth profile of uncompensated spins in an exchange bias system

We have used the unique spatial sensitivity of polarized neutron and soft x-ray beams in reflection geometry to measure the depth dependence of magnetization across the interface between a ferromagnet and an antiferromagnet. The net uncompensated magnetization near the interface responds to applied field, while uncompensated spins in the antiferromagnet bulk are pinned, thus providing a means to establish exchange bias.