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We examine a series of articles on honeybee odometry and navigation published between 1996 and 2010, and find inconsistencies in results, duplicated figures, indications of data manipulation, and incorrect calculations. This suggests that redoing the experiments in question is warranted., Comment: 16 pages

Safety analyses in terms of adverse events (AEs) are an important aspect of benefit-risk assessments of therapies. Compared to efficacy analyses AE analyses are often rather simplistic. The probability of an AE of a specific type is typically estimated by the incidence proportion, sometimes the incidence density or the Kaplan-Meier estimator are proposed. But these analyses either do not account for censoring, rely on a too restrictive parametric model, or ignore competing events. With the non-parametric Aalen-Johansen estimator as the gold-standard, these potential sources of bias are investigated in a data example from oncology and in simulations, both in the one-sample and in the two-sample case. As the estimators may have large variances at the end of follow-up, the estimators are not only compared at the maximal event time but also at two quantiles of the observed times. To date, consequences for safety comparisons have hardly been investigated in the literature. The impact of using different estimators for group comparisons is unclear, as, for example, the ratio of two both underestimating or overestimating estimators may or may not be comparable to the ratio of the gold-standard estimator. Therefore, the ratio of the AE probabilities is also calculated based on different approaches. By simulations investigating constant and non-constant hazards, different censoring mechanisms and event frequencies, we show that ignoring competing events is more of a problem than falsely assuming constant hazards by use of the incidence density and that the choice of the AE probability estimator is crucial for group comparisons., Comment: 27 pages, 5 figures

Background: State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features play an important role in certain diseases. Results: We present Netboost, a three-step dimension reduction technique. First, a boosting-based filter is combined with the topological overlap measure to identify the essential edges of the network. Second, sparse hierarchical clustering is applied on the selected edges to identify modules and finally module information is aggregated by the first principal components. The primary analysis is than carried out on these summary measures instead of the original data. We demonstrate the application of the newly developed Netboost in combination with CoxBoost for survival prediction of DNA methylation and gene expression data from 180 acute myeloid leukemia (AML) patients and show, based on cross-validated prediction error curve estimates, its prediction superiority over variable selection on the full dataset as well as over an alternative clustering approach. The identified signature related to chromatin modifying enzymes was replicated in an independent dataset of AML patients in the phase II AMLSG 12-09 study. In a second application we combine Netboost with Random Forest classification and improve the disease classification error in RNA-sequencing data of Huntington's disease mice. Conclusion: Netboost improves definition of predictive variables for survival analysis and classification. It is a freely available Bioconductor R package for dimension reduction and hypothesis generation in high-dimensional omics applications.

We compute the transverse momentum dependent (TMD) soft function for the production of a color-neutral final state at the LHC within the rapidity renormalization group (RRG) framework to next-to-next-to-leading order (NNLO). We use this result to extract the universal renormalized TMD beam functions (aka TMDPDFs) in the same scheme and at the same order from known results in another scheme. We derive recurrence relations for the logarithmic structure of the soft and beam functions, which we use to cross check our calculation. We also explicitly confirm the non-Abelian exponentiation of the TMD soft function in the RRG framework at two loops. Our results provide the ingredients for resummed predictions of pT-differential cross sections at NNLL' in the RRG formalism. The RRG provides a systematic framework to resum large (rapidity) logarithms through (R)RG evolution and to assess the associated perturbative uncertainties., Comment: 37 pages, 3 figures; v2: journal version; v3: typos in eq.(6.19) corrected

We describe the perturbative calculation of the transverse parton distribution functions in all partonic channels up to next-to-next-to-leading order based on a gauge invariant operator definition. We demonstrate the cancellation of light-cone divergences and show that universal process-independent transverse parton distribution functions can be obtained through a refactorization. Our results serve as the first explicit higher-order calculation of these functions starting from first principles, and can be used to perform next-to-next-to-next-to-leading logarithmic $q_T$ resummation for a large class of processes at hadron colliders., Comment: 34 pages, 2 figures, FORM file of results included to source; fixed a typo in I_{q/q}^{(2)}

We present a perturbative calculation of the transverse parton distribution functions in all partonic channels up to next-to-next-to-leading order based on a gauge invariant operator definition. We demonstrate for the first time that such a definition works beyond the first non-trivial order. We extract the coefficient functions relevant for a next-to-next-to-next-to-leading logarithmic $q_T$ resummation in a large class of processes at hadron colliders., Comment: 10 pages, 1 figure, presented at the 11th International Symposium on Radiative Corrections, RADCOR 2013, 22.-27. September 2013, Lumley Castle, Durham, UK

We present a calculation of the perturbative quark-to-quark transverse parton distribution function at next-to-next-to-leading order based on a gauge invariant operator definition. We demonstrate for the first time that such a definition works beyond the first non-trivial order. We extract from our calculation the coefficient functions relevant for a next-to-next-to-next-to-leading logarithmic $Q_T$ resummation in a large class of processes at hadron colliders., Comment: 4 pages, 1 figure, revised version, accepted for publication in PRL