Optimal Uncertainty Quantification

We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront. This framework, which we call \emph{Optimal Uncertainty Quantification} (OUQ), is based on the observation that, given a set of assumptions and information about the problem, there exist optimal bounds on uncertainties: these are obtained as values of well-defined optimization problems corresponding to extremizing probabilities of failure, or of deviations, subject to the constraints imposed by the scenarios compatible with the assumptions and information. In particular, this framework does not implicitly impose inappropriate assumptions, nor does it repudiate relevant information. Although OUQ optimization problems are extremely large, we show that under general conditions they have finite-dimensional reductions. As an application, we develop \emph{Optimal Concentration Inequalities} (OCI) of Hoeffding and McDiarmid type. Surprisingly, these results show that uncertainties in input parameters, which propagate to output uncertainties in the classical sensitivity analysis paradigm, may fail to do so if the transfer functions (or probability distributions) are imperfectly known. We show how, for hierarchical structures, this phenomenon may lead to the non-propagation of uncertainties or information across scales. In addition, a general algorithmic framework is developed for OUQ and is tested on the Caltech surrogate model for hypervelocity impact and on the seismic safety assessment of truss structures, suggesting the feasibility of the framework for important complex systems. The introduction of this paper provides both an overview of the paper and a self-contained mini-tutorial about basic concepts and issues of UQ.Comment: 90 pages. Accepted for publication in SIAM Review (Expository Research Papers). See SIAM Review for higher quality figure

a comprehensive and efficient analysis pipeline designed for ChIP-nexus

Background ChIP-nexus, an extension of the ChIP-exo protocol, can be used to map the borders of protein-bound DNA sequences at nucleotide resolution, requires less input DNA and enables selective PCR duplicate removal using random barcodes. However, the use of random barcodes requires additional preprocessing of the mapping data, which complicates the computational analysis. To date, only a very limited number of software packages are available for the analysis of ChIP-exo data, which have not yet been systematically tested and compared on ChIP-nexus data. Results Here, we present a comprehensive software package for ChIP-nexus data that exploits the random barcodes for selective removal of PCR duplicates and for quality control. Furthermore, we developed bespoke methods to estimate the width of the protected region resulting from protein-DNA binding and to infer binding positions from ChIP-nexus data. Finally, we applied our peak calling method as well as the two other methods MACE and MACS2 to the available ChIP-nexus data. Conclusions The Q-nexus software is efficient and easy to use. Novel statistics about duplication rates in consideration of random barcodes are calculated. Our method for the estimation of the width of the protected region yields unbiased signatures that are highly reproducible for biological replicates and at the same time very specific for the respective factors analyzed. As judged by the irreproducible discovery rate (IDR), our peak calling algorithm shows a substantially better reproducibility. An implementation of Q-nexus is available at http://charite.github.io/Q/

Human Resources and the Resource Based View of the Firm

The resource-based view (RBV) of the firm has influenced the field of strategic human resource management (SHRM) in a number of ways. This paper explores the impact of the RBV on the theoretical and empirical development of SHRM. It explores how the fields of strategy and SHRM are beginning to converge around a number of issues, and proposes a number of implications of this convergence

Computational Design of Artificial RNA Molecules For Gene Regulation

This volume provides an overview of RNA bioinformatics methodologies, including basic strategies to predict secondary and tertiary structures, and novel algorithms based on massive RNA sequencing. Interest in RNA bioinformatics has rapidly increased thanks to the recent high-throughput sequencing technologies allowing scientists to investigate complete transcriptomes at single nucleotide resolution. Adopting advanced computational technics, scientists are now able to conduct more in-depth studies and present them to you in this book. Written in the highly successful Methods of Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and equipment, step-by-step, readily reproducible bioinformatics protocols, and key tips to avoid known pitfalls.Authoritative and practical, RNA Bioinformatics seeks to aid scientists in the further study of bioinformatics and computational biology of RNA

Placing relationships in the foreground: the role of workplace friendships in engagement

We explore the role of workplace friendships as a lens for understanding the emotional element and relational context for personal engagement (Kahn, 1990). The review of engagement theory differentiates personal engagement, recognising the role emotions play in enabling individuals’ ‘preferred selves’. Workplace relationships and friendship provide a conceptual discussion of individuals in social and workplace roles in engagement, drawing on friendship, emotion, attachment theories, particularly Kahn’s work. A case study drawn from recent research illustrates our discussion before concluding with ideas for the development of a future research agenda in answer to recent calls for work on the social context of engagement

Gendering the careers of young professionals: some early findings from a longitudinal study. in Organizing/theorizing: developments in organization theory and practice

Wonders whether companies actually have employees best interests at heart across physical, mental and spiritual spheres. Posits that most organizations ignore their workforce – not even, in many cases, describing workers as assets! Describes many studies to back up this claim in theis work based on the 2002 Employment Research Unit Annual Conference, in Cardiff, Wales

E2F6 initiates stable epigenetic silencing of germline genes during embryonic development.

In mouse development, long-term silencing by CpG island DNA methylation is specifically targeted to germline genes; however, the molecular mechanisms of this specificity remain unclear. Here, we demonstrate that the transcription factor E2F6, a member of the polycomb repressive complex 1.6 (PRC1.6), is critical to target and initiate epigenetic silencing at germline genes in early embryogenesis. Genome-wide, E2F6 binds preferentially to CpG islands in embryonic cells. E2F6 cooperates with MGA to silence a subgroup of germline genes in mouse embryonic stem cells and in embryos, a function that critically depends on the E2F6 marked box domain. Inactivation of E2f6 leads to a failure to deposit CpG island DNA methylation at these genes during implantation. Furthermore, E2F6 is required to initiate epigenetic silencing in early embryonic cells but becomes dispensable for the maintenance in differentiated cells. Our findings elucidate the mechanisms of epigenetic targeting of germline genes and provide a paradigm for how transient repression signals by DNA-binding factors in early embryonic cells are translated into long-term epigenetic silencing during mouse development

To defer or not to defer? A German longitudinal multicentric assessment of clinical practice in urology during the COVID-19 pandemic

Introduction After the outbreak of COVID-19 unprecedented changes in the healthcare systems worldwide were necessary resulting in a reduction of urological capacities with postponements of consultations and surgeries. Material and methods An email was sent to 66 urological hospitals with focus on robotic surgery (RS) including a link to a questionnaire (e.g. bed/staff capacity, surgical caseload, protection measures during RS) that covered three time points: a representative baseline week prior to COVID-19, the week of March 16th-22nd and April 20th-26th 2020. The results were evaluated using descriptive analyses. Results 27 out of 66 questionnaires were analyzed (response rate: 41%). We found a decrease of 11% in hospital beds and 25% in OR capacity with equal reductions for endourological, open and robotic procedures. Primary surgical treatment of urolithiasis and benign prostate syndrome (BPS) but also of testicular and penile cancer dropped by at least 50% while the decrease of surgeries for prostate, renal and urothelial cancer (TUR-B and cystectomies) ranged from 15 to 37%. The use of personal protection equipment (PPE), screening of staff and patients and protection during RS was unevenly distributed in the different centers\u2013however, the number of COVID-19 patients and urologists did not reach double digits. Conclusion The German urological landscape has changed since the outbreak of COVID-19 with a significant shift of high priority surgeries but also continuation of elective surgical treatments. While screening and staff protection is employed heterogeneously, the number of infected German urologists stays low

Determination of intrinsic switching field distributions in perpendicular recording media: numerical study of the ΔH(M,ΔM)\Delta H(M, \Delta M) method

We present a numerical study of the $\Delta H(M,\Delta M)$ method and its ability to accurately determine intrinsic switching field distributions in interacting granular magnetic materials such as perpendicular recording media. In particular, we study how this methodology fails for large ferromagnetic inter-granular interactions, at which point the associated strongly correlated magnetization reversal cannot be properly represented by the mean-field approximation, upon which the $\Delta H(M,\Delta M)$ method is based. In this study, we use a 2-dimensional array of symmetric hysterons that have an intrinsic switching field distribution of standard deviation $\sigma$ and ferromagnetic nearest-neighbor interactions $J$. We find the $\Delta H(M,\Delta M)$ method to be very accurate for small $J/\sigma$ values, while substantial errors develop once the effective exchange field becomes comparable with $\sigma$, corroborating earlier results from micromagnetic simulations. We furthermore demonstrate that this failure is correlated with deviations from data set redundancy, which is a key property of the mean-field approximation. Thus, the $\Delta H(M,\Delta M)$ method fails in a well defined and quantifiable manner that can be easily assessed from the data sets alone.Comment: 13 pages, 9 figure

Targeting KDM1A in neuroblastoma with NCL-1 induces a less aggressive phenotype and suppresses angiogenesis

BACKGROUND: The KDM1A histone demethylase regulates the cellular balance between proliferation and differentiation, and is often deregulated in human cancers including the childhood tumor neuroblastoma. We previously showed that KDM1A is strongly expressed in undifferentiated neuroblastomas and correlates with poor patient prognosis, suggesting a possible clinical benefit from targeting KDM1A. METHODS: Here, we tested the efficacy of NCL-1, a small molecule specifically inhibiting KDM1A in preclinical models for neuroblastoma. RESULTS: NCL-1 mimicked the effects of siRNA-mediated KDM1A knockdown and effectively inhibited KDM1A activity in four neuroblastoma cell lines and a patient-representative cell model. KDM1A inhibition shifted the aggressive tumor cell phenotypes towards less aggressive phenotypes. The proliferation and cell viability was reduced, accompanied by the induction of markers of neuronal differentiation. Interventional NCL-1 treatment of nude mice harboring established neuroblastoma xenograft tumors reduced tumor growth and inhibited cell proliferation. Reduced vessel density and defects in blood vessel construction also resulted, and NCL-1 inhibited the growth and tube formation of HUVEC-C cells in vitro. CONCLUSIONS: Inhibiting KDM1A could attack aggressive neuroblastomas two-fold, by re-directing tumor cells toward a less aggressive, slower-growing phenotype and by preventing or reducing the vascular support of large tumors