PRISMA: A novel approach for deriving probabilistic surrogate safety measures for risk evaluation.

Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk, restricting their applicability to scenarios where these assumptions are valid. In response to this limitation, we present the novel Probabilistic RISk Measure derivAtion (PRISMA) method. The objective of the PRISMA method is to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). The PRISMA method adopts a data-driven approach to predict the possible future traffic participant trajectories, thereby reducing the reliance on specific assumptions regarding these trajectories. Since the PRISMA is not bound to specific assumptions, the PRISMA method offers the ability to derive multiple SSMs for various scenarios. The occurrence probability of the specified event is based on simulations and combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. Since there is no known method to objectively estimate risk from first principles, i.e., there is no known risk ground truth, it is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users.

Plasma Endothelial and Oxidative Stress Biomarkers Associated with Late Mortality in Hospitalized COVID-19 Patients.

BACKGROUND: Coronavirus infectious disease 2019 (COVID-19) is a significant public health problem worldwide. COVID-19 increases the risk of non-pulmonary complications such as acute myocardial injury, renal failure, thromboembolic events, and multi-organic damage. Several studies have documented increased inflammation molecules, endothelial dysfunction biomarkers, and dysregulation of coagulation factors in COVID-19 patients. In addition, endothelium dysfunction is exacerbated by the oxidative stress (OxS) promoted by endocrine and cardiovascular molecules. Our objective was to evaluate whether endothelial and OxS biomarkers were associated with mortality in hospitalized COVID-19 patients. METHODS: A prospective cohort study was performed. Patients ≥18 years old with confirmed COVID-19 that required hospitalization were included in a prospective cohort study. Endothelium and oxidative stress biomarkers were collected between 3 and 5 days after admission. RESULTS: A total of 165 patients were evaluated; 56 patients succumbed. The median follow-up was 71 days [23-129]. Regarding endothelial dysfunction and OxS biomarkers, patients who did not survive had higher levels of nitrates (0.4564 [0.1817-0.6761] vs. 0.2817 [0.0517-0.5], p = 0.014), total nitrates (0.0507 [-0.0342-0.1809] vs. -0.0041 [-0.0887-0.0909], p = 0.016), sE-Selectin (1.095 [0.86-1.495] vs. 0.94 [0.71-1.19], p = 0.004), and malondialdehyde (MDA) (0.50 [0.26-0.72] vs. 0.36 [0.23-0.52], p = 0.010) compared to patients who survived. Endothelial and OxS biomarkers independently associated with mortality were sE-selectin (HR:2.54, CI95%; from 1.11 to 5.81, p = 0.027), nitrates (HR:4.92, CI95%; from 1.23 to 19.63, p = 0.024), and MDA (HR: 3.05, CI95%; from 1.14 to 8.15, p = 0.025). CONCLUSIONS: Endothelial dysfunction (sE-selectin and nitrates) and OxS (MDA) are independent indicators of a worse prognosis in COVID-19 patients requiring hospitalization.

The role of Poisson's binomial distribution in the analysis of TEM images.

Frank's observation that a TEM bright-field image acquired under non-stationary conditions can be modeled by the time integral of the standard TEM image model [J. Frank, Nachweis von objektbewegungen im lichtoptis- chen diffraktogramm von elektronenmikroskopischen auf- nahmen, Optik 30 (2) (1969) 171-180.] is re-derived here using counting statistics based on Poisson's binomial distribution. The approach yields a statistical image model that is suitable for image analysis and simulation.

Introducing measure-by-wire, the systematic use of systems and control theory in transmission electron microscopy.

Transmission electron microscopes (TEMs) are the tools of choice for academic and industrial research at the nano-scale. Due to their increasing use for routine, repetitive measurement tasks (e.g., quality control in production lines) there is a clear need for a new generation of high-throughput microscopes designed to autonomously extract information from specimens (e.g., particle size distribution, chemical composition, structural information, etc.). To aid in their development, a new engineering perspective on TEM design, based on principles from systems and control theory, is proposed here: measure-by-wire (not to be confused with remote microscopy). Under this perspective, the TEM operator yields the direct control of the microscope's internal processes to a hierarchy of feedback controllers and high-level supervisors. These make use of dynamical models of the main TEM components together with currently available measurement techniques to automate processes such as defocus correction or specimen displacement. Measure-by-wire is discussed in depth, and its methodology is illustrated through a detailed example: the design of a defocus regulator, a type of feedback controller that is akin to existing autofocus procedures.