DATADVANCE (France)

The exponential growth and affordability of genomics sequencing in the clinical landscape in recent years have intensified a challenge facing privacy experts. They grapple with how to evaluate and mitigate the significant risk of genomics data being used to re-identify individuals. The lack of clear genomics guidance in HIPAA has invited a wide spectrum of expert opinion from advocating for conservative limitations to be placed on genomics data sharing to ‘security’-based arguments that carefully calibrated technical, administrative, and legal safeguards may provide adequate protection in many cases. The need to weigh privacy concerns against the extraordinary value that genomics data offers to cancer and rare disease clinical research is compounded by the reality that conventional data modifications, such as redaction, aggregation, and perturbation, often critically undermine these important use cases. We present an innovative solution for de-identifying genomics data according to a conservative interpretation of HIPAA while still preserving maximal utility for clinical purposes. Our solution rejects the notion that privacy can be achieved directly via security. Instead, we introduce a reversible encryption protocol designed to be applied to specific combinations of genetic variants and their locations on the genome. The encrypted genomic data is fully de-identified, allowing researchers to perform analyses on the encrypted data in a secure Clean Room environment. The environment host is then able to decrypt and deliver variant-level summary statistics and aggregated outputs from the analysis to the recipient. As information in this format no longer links to patient-level data the solution remains de-identified end-to-end.

Since the beginning of the COVID-19 pandemic, pharmaceutical treatment hypotheses have abounded, each requiring careful evaluation. A randomized controlled trial generally provides the most credible evaluation of a treatment, but the efficiency and effectiveness of the trial depend on the existing evidence supporting the treatment. The researcher must therefore compile a body of evidence justifying the use of time and resources to further investigate a treatment hypothesis in a trial. An observational study can provide this evidence, but the lack of randomized exposure and the researcher’s inability to control treatment administration and data collection introduce significant challenges. A proper analysis of observational health care data thus requires contributions from experts in a diverse set of topics ranging from epidemiology and causal analysis to relevant medical specialties and data sources. Here we summarize these contributions as 10 rules that serve as an end-to-end introduction to retrospective pharmacoepidemiological analyses of observational health care data using a running example of a hypothetical COVID-19 study. A detailed supplement presents a practical how-to guide for following each rule. When carefully designed and properly executed, a retrospective pharmacoepidemiological analysis framed around these rules will inform the decisions of whether and how to investigate a treatment hypothesis in a randomized controlled trial. This work has important implications for any future pandemic by prescribing what we can and should do while the world waits for global vaccine distribution.

Abstract Installation of subsea pipelines using reeling process is an attractive method. The pipeline is welded in long segments, typically several kilometers in length, and reeled onto a large diameter drum. The pipeline is then transported onto such reel to the offshore site where it is unreeled and lowered on the seabed. The deformation imposed on the pipeline while spooled onto the drum needs to be controlled so that local buckling is avoided. Mitigation of such failure is generally provided by proper pipeline design & reeling operation parameters. Buckling stems from excessive strain concentration near the circumferential weld area resulting from strength discontinuity at pipeline joints, mainly depending on steel wall thickness and yield strength. This requires the characterization of critical mismatches obtained by trial and error. Such method is a long process since each “trial” requires a complete Finite Element Analysis run. Such simulations are complex and lengthy. Occasionally, this can drive the selection of the pipeline minimum wall thickness, which is a key parameter for progressing the project. The timeframe of such method is therefore not compatible with such a key decision. The paper discusses the use of approximation models to capitalize on the data and alleviate the design cost. To do so, design of experiments and automation of the computational tool chain are implemented. It is demonstrated that initial complex chain of FEA computational process can be replaced using design space description and exploration techniques such as design of experiments combined with advanced statistical regression techniques in order to provide an approximation model. This paper presents the implementation of such methodology and the results are discussed.

The processes of dynamic deformation of shells are actively studied by both domestic and foreign scientists [4; 11; 13; 14]. The work describes the ray method for solving systems of partial differential equations of hyperbolic type. This method consists in constructing the equations of discontinuity transfer along the propagation of perturbations on moving surfaces, as well as in representing the solution in the form of a Taylor power series in the variable distance behind the perturbation front while preserving a sufficient number of terms. The implementation of the method is shown by the example of a system of partial differential equations of hyperbolic type, which describe the process of dynamic deformation of thin cylindrical shells of revolution from elastoviscoplastic materials [10]. An algorithm for constructing a solution up to the required order is given.