Metabolic activity diffusion imaging (MADI): I. Metabolic,cytometric modeling and simulations

Evidence mounts that the steady-state cellular water efflux (unidirectional) first-order rate constant (k_io [s⁻¹]) magnitude reflects the ongoing, cellular metabolic rate of the cytolemmal Na⁺, K⁺-ATPase (NKA), ^cMR_NKA (pmol [ATP consumed by NKA]/s/cell), perhaps biology's most vital enzyme. Optimal ¹H₂O MR k_io determinations require paramagnetic contrast agents (CAs) in model systems. However, results suggest that the homeostatic metabolic k_io biomarker magnitude in vivo is often too large to be reached with allowable or possible CA living tissue distributions. Thus, we seek a noninvasive (CA-free) method to determine k_io in vivo. Because membrane water permeability has long been considered important in tissue water diffusion, we turn to the well-known diffusion-weighted MRI (DWI) modality. To analyze the diffusion tensor magnitude, we use a parsimoniously primitive model featuring Monte Carlo simulations of water diffusion in virtual ensembles comprising water-filled and -immersed randomly sized/shaped contracted Voronoi cells. We find this requires two additional, cytometric properties: the mean cell volume (V [pL]) and the cell number density ( ρ [cells/μL]), important biomarkers in their own right. We call this approach metabolic activity diffusion imaging (MADI). We simulate water molecule displacements and transverse MR signal decays covering the entirety of b-space from pure water ( ρ = V = 0; k_io undefined; diffusion coefficient, D₀) to zero diffusion. The MADI model confirms that, in compartmented spaces with semipermeable boundaries, diffusion cannot be described as Gaussian: the nanoscopic D (D_n) is diffusion time-dependent, a manifestation of the “diffusion dispersion”. When the “well-mixed” (steady-state) condition is reached, diffusion becomes limited, mainly by the probabilities of (1) encountering ( ρ , V), and (2) permeating (k_io) cytoplasmic membranes, and less so by D_n magnitudes. Importantly, for spaces with large area/volume (A/V; claustrophobia) ratios, this can happen in less than a millisecond. The model matches literature experimental data well, with implications for DWI interpretations.     

基于多性能参数的民用航空发动机实时性能可靠性预测

以民用航空发动机为研究对象，运用性能退化可靠性理论，对发动机的性能可靠性进行了研究。通过分析发动机性能退化过程，利用状态空间方法建立了时变性能退化模型，并通过卡尔曼滤波对性能趋势进行预测；然后考虑各性能参数之间的相关性，运用随机过程理论建立了基于多性能参数的实时性能可靠性预测模型，从而对发动机的退化时间进行实时预测；最后通过实例证明了该方法的有效性，并且易于工程实现，为航空公司进行发动机机队科学管理提供了基础。     

Optimal operation of a grid-connected battery energy storage system over its lifetime

This paper deals with the optimal control of grid-connected Battery Energy Storage Systems (BESSs) operating for energy arbitrage. An important issue is that BESSs degrade over time, according to their use, and thus they are usable only for a limited number of cycles. Therefore, the time horizon of the optimization depends on the actual operation of the BESS. We focus on Li-ion batteries and use an empirical model to describe battery degradation. The BESS model includes an equivalent circuit for the battery and a simplified model for the power converter. In order to model the energy price variations, we use a linear stochastic model that includes the effect of the time-of-the-day. The problem of maximizing the revenues obtained over the BESS lifetime is formulated as a stochastic optimal control problem with a long, operation-dependent time horizon. First, we divide this problem into a finite set of subproblems, such that for each one of them, the State of Health (SoH) of the battery is approximately constant. Next, we reformulate approximately every subproblem into the minimization of the ratio of two long-time average-cost criteria and use a value-iteration-type algorithm to derive the optimal policy. Finally, we present some numerical results and investigate the effects of the energy loss parameters, degradation parameters, and price dynamics on the optimal policy.     

Kinematic synergies in over-ground slip recovery outcomes: Distinct strategies or a single strategy?

BackgroundAfter experiencing an unexpected slip perturbation, individuals’ behavioral performance can be classified into three categories: recovery, feet-forward fall, and split fall. Researchers are uncertain whether these differences in slip outcomes are due to distinct strategies or part of a single strategy.Research questionWhether older adults with different behavioral outcomes during their novel slip have different kinematic synergies?MethodsThe kinematic synergies were extracted from segment angles in 87 participants using principal component analysis (PCA). The first two principal components (PC1 and PC2) in pre-slip, early-reactive, and late-reactive phases were compared across different slip outcomes.ResultsResults showed that the kinematic synergies in pre-slip and early-reactive phases are highly consistent among the three outcomes (recovery, split fall, and feet-forward fall). For the late-reactive phase, both split falls and feet-forward falls showed different kinematics synergies from recoveries.SignificanceOur findings indicated that a single strategy might be used for different slip outcomes in the pre-slip and early-reactive phases, while distinct strategies were used by fallers compared to recovered individuals. Specifically, larger trunk flexion in pre-slip phase, larger knee flexion and plantar flexion of the slipping limb in both early-reactive and late-reactive phase, and larger knee extension of the recovery limb in late-reactive phase would lower the fall risk. This study would help to assess the vulnerabilities in control strategy, according to which individualized treatment could be provided to reduce predisposition to specific types of falls.     

A Data-Driven Approach to Support the Understanding and Improvement of Patients’ Journeys: A Case Study Using Electronic Health Records of an Emergency Department

ObjectivesGiven the increasing availability of electronic health records, it has become increasingly feasible to adopt data-driven approaches to capture a deep understanding of the patient journeys. Nevertheless, simply using data-driven techniques to depict the patient journeys without an integrated modeling and analysis approach is proving to be of little benefit for improving patients’ experiences. Indeed, a model of the journey patterns is necessary to support the improvement process.MethodsWe presented a 3-phase methodology that integrates a process mining–based understanding of patient journeys with a stochastic graphical modeling approach to derive and analyze the analytical expressions of some important performance indicators of an emergency department including mean and variance of patients’ length of stay (LOS).ResultsAnalytical expressions were derived and discussed for mean and variance of LOS times and discharge and admission probabilities. LOS differed significantly depending on whether a patient was admitted to the hospital or discharged. Moreover, multiparameter sensitivity equations are obtained to identify which activities contribute the most in reducing the LOS at given operating conditions so decision makers can prioritize their improvement initiatives.ConclusionsData-driven based approaches for understanding the patient journeys coupled with appropriate modeling techniques yield a promising tool to support improving patients’ experiences. The modeling techniques should be easy to implement and not only should be capable of deriving some key performance indicators of interest but also guide decision makers in their improvement initiatives.     

Impact of combination preventative interventions on hospitalization and death under the pandemic of SARS-CoV-2 Omicron variant in China

With a large population most susceptible to Omicron and emerging SARS-CoV-2 variants, China faces uncertain scenarios if reopening its border. Thus, we aimed to predict the impact of combination preventative interventions on hospitalization and death. An age-stratified susceptible-infectious-quarantined-hospitalized-removed-susceptible (SIQHRS) model based on the new guidelines of COVID-19 diagnosis and treatment (the ninth edition) was constructed to simulate the transmission dynamics of Omicron within 365 days. At baseline, we assumed no interventions other than 60% booster vaccination in individuals aged ≤60 years and 80% in individuals aged >60 years, quarantine and hospitalization. Oral antiviral medications for COVID-19 and nonpharmaceutical interventions (NPIs) such as social distancing and antigen self-testing were considered in subsequent scenarios. Sensitivity analyses were conducted to reflect different levels of interventions. A total of 0.73 billion cumulative quarantines (95% CI 0.53–0.83), 33.59 million hospitalizations (22.41–39.31), and 0.62 million deaths (0.40–0.75) are expected in 365 days. The case fatality rate with pneumonia symptoms (moderate, severe and critical illness) is expected to be 1.83% (1.68–1.99%) and the infected fatality rate is 0.38‰ (0.33–0.4‰). The highest existing hospitalization and ICU occupations are 3.11 (0.30–3.85) and 20.33 (2.01–25.20) times of capacity, respectively. Sensitivity analysis showed that interventions can be adjusted to meet certain conditions to reduce the total number of infections and deaths. In conclusion, after sufficient respiratory and ICU beds are prepared and the relaxed NPIs are in place, the SARS-CoV-2 Omicron variant would not seriously impact the health system.     

Quantitative Benefit-Risk Assessment in Medical Product Decision Making: A Good Practices Report of an ISPOR Task Force

Benefit-risk assessment is commonly conducted by drug and medical device developers and regulators, to evaluate and communicate issues around benefit-risk balance of medical products. Quantitative benefit-risk assessment (qBRA) is a set of techniques that incorporate explicit outcome weighting within a formal analysis to evaluate the benefit-risk balance. This report describes emerging good practices for the 5 main steps of developing qBRAs based on the multicriteria decision analysis process. First, research question formulation needs to identify the needs of decision makers and requirements for preference data and specify the role of external experts. Second, the formal analysis model should be developed by selecting benefit and safety endpoints while eliminating double counting and considering attribute value dependence. Third, preference elicitation method needs to be chosen, attributes framed appropriately within the elicitation instrument, and quality of the data should be evaluated. Fourth, analysis may need to normalize the preference weights, base-case and sensitivity analyses should be conducted, and the effect of preference heterogeneity analyzed. Finally, results should be communicated efficiently to decision makers and other stakeholders. In addition to detailed recommendations, we provide a checklist for reporting qBRAs developed through a Delphi process conducted with 34 experts.     

A Sample-Wise Data Driven Control Solver for the Stochastic Optimal Control Problem with Unknown Model Parameters

In this work, an efficient sample-wise data driven control solver will be developed to solve the stochastic optimal control problem with unknown model parameters. A direct filter method will be applied as an online parameter estimation method that dynamically estimates the target model parameters upon receiving the data, and a sample-wise optimal control solver will be provided to efficiently search for the optimal control. Then, an effective overarching algorithm will be introduced to combine the parameter estimator and the optimal control solver. Numerical experiments will be carried out to demonstrate the effectiveness and the efficiency of the sample-wise data driven control method.     

Estimation of monkeypox spread in a nonendemic country considering contact tracing and self-reporting: A stochastic modeling study

In May 2022, monkeypox started to spread in nonendemic countries. To investigate contact tracing and self-reporting of the primary case in the local community, a stochastic model is developed. An algorithm based on Gillespie's stochastic chemical kinetics is used to quantify the number of infections, contacts, and duration from the arrival of the primary case to the detection of the index case (or until there are no more local infections). Different scenarios were set considering the delay in contact tracing and behavior of infectors. We found that the self-reporting behavior of a primary case is the most significant factor affecting outbreak size and duration. Scenarios with a self-reporting primary case have an 86% reduction in infections (average: 5–7, in a population of 10 000) and contacts (average: 27–72) compared with scenarios with a non-self-reporting primary case (average number of infections and contacts: 27–72 and 197–537, respectively). Doubling the number of close contacts per day is less impactful compared with the self-reporting behavior of the primary case as it could only increase the number of infections by 45%. Our study emphasizes the importance of the prompt detection of the primary case.