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For many rare diseases with no approved preventive interventions, promising interventions exist. However, it has proven difficult to conduct a pivotal phase 3 trial that could provide direct evidence demonstrating a beneficial effect of the intervention on the target disease outcome. When a promising putative surrogate endpoint(s) for the target outcome is available, surrogate-based provisional approval of an intervention may be pursued. Following the general Causal Roadmap rubric, we describe a surrogate endpoint-based provisional approval causal roadmap. Based on an observational study data set and a phase 3 randomized trial data set, this roadmap defines an approach to analyze the combined data set to draw a conservative inference about the treatment effect (TE) on the target outcome in the phase 3 study population. The observational study enrolls untreated individuals and collects baseline covariates, surrogate endpoints, and the target outcome, and is used to estimate the surrogate index—the regression of the target outcome on the surrogate endpoints and baseline covariates. The phase 3 trial randomizes participants to treated vs. untreated and collects the same data but is much smaller and hence very underpowered to directly assess TE, such that inference on TE is based on the surrogate index. This inference is made conservative by specifying 2 bias functions: one that expresses an imperfection of the surrogate index as a surrogate endpoint in the phase 3 study, and the other that expresses imperfect transport of the surrogate index in the untreated from the observational to the phase 3 study. Plug-in and nonparametric efficient one-step estimators of TE, with inferential procedures, are developed. The finite-sample performance of the estimators is evaluated in simulation studies. The causal roadmap is motivated by and illustrated with contemporary Group B Streptococcus vaccine development. 

Hollow graphitic porous carbon nanosphere (CNS) materials are synthesized from polymerization of resorcinol (R) and formaldehyde (F) in the presence of templating iron polymeric complex (IPC), followed by carbonization treatment. The effect of rapid heating in the carbonization process is investigated for the formation of hollow graphitic carbon nanospheres. The resulting CNS from rapid heating was characterized for its structure and properties by transmission electron microscope (TEM), x-ray diffraction (XRD), Raman spectroscopy, bulk conductivity measurement and Brunauer-Emmett-Teller (BET) surface area. Hollow graphitic CNS with reduced degree of agglomeration is observed under rapid heating during the carbonization process when compared to the CNS synthesized using the standard slow heating approach. Key words: carbon nanosphere (CNS), rapid pyrolytic carbonization, agglomeration 

Abstract The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules.During Run 2 (2015–2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb -1 to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector.Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2.It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%.Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules.