Time-Series Modeling and Forecasting of Cerebral Pressure-Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature.

The modeling and forecasting of cerebral pressure-flow dynamics in the time-frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure-flow and oxygen delivery data streams obtained from invasive/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications.

Airborne particulate matter measurement and prediction with machine learning techniques.

Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated.

Estimated Incidence of Hospitalizations Attributable to RSV Infection Among Adults in Ontario, Canada, Between 2013 and 2019.

INTRODUCTION: Adult respiratory syncytial virus (RSV) burden is underestimated due to non-specific symptoms, limited standard-of-care and delayed testing, reduced diagnostic test sensitivity-particularly when using single diagnostic specimen-when compared to children, and variable test sensitivity based on the upper airway specimen source. We estimated RSV-attributable hospitalization incidence among adults aged ≥ 18 years in Ontario, Canada, using a retrospective time-series model-based approach. METHODS: The Institute for Clinical Evaluative Sciences data repository provided weekly numbers of hospitalizations (from 2013 to 2019) for respiratory, cardiovascular, and cardiorespiratory disorders. The number of hospitalizations attributable to RSV was estimated using a quasi-Poisson regression model that considered probable overdispersion and was based on periodic and aperiodic time trends and viral activity. As proxies for viral activity, weekly counts of RSV and influenza hospitalizations in children under 2 years and adults aged 60 years and over, respectively, were employed. Models were stratified by age and risk group. RESULTS: In patients ≥ 60 years, RSV-attributable incidence rates were high for cardiorespiratory hospitalizations (range [mean] in 2013-2019: 186-246 [215] per 100,000 person-years, 3‒4% of all cardiorespiratory hospitalizations), and subgroups including respiratory hospitalizations (144-192 [167] per 100,000 person-years, 5‒7% of all respiratory hospitalizations) and cardiovascular hospitalizations (95-126 [110] per 100,000 person-years, 2‒3% of all cardiovascular hospitalizations). RSV-attributable cardiorespiratory hospitalization incidence increased with age, from 14-18 [17] hospitalizations per 100,000 person-years (18-49 years) to 317-411 [362] per 100,000 person-years (≥ 75 years). CONCLUSIONS: Estimated RSV-attributable respiratory hospitalization incidence among people ≥ 60 years in Ontario, Canada, is comparable to other incidence estimates from high-income countries, including model-based and pooled prospective estimates. Recently introduced RSV vaccines could have a substantial public health impact.

Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network.

Modeling microbial metabolic dynamics is important for the rational optimization of both biosynthetic systems and industrial processes to facilitate green and efficient biomanufacturing. Classical approaches utilize explicit equation systems to represent metabolic networks, enabling the quantification of pathway fluxes to identify metabolic bottlenecks. However, these white-box models, despite their diverse applications, have limitations in simulating metabolic dynamics and are intrinsically inaccurate for industrial strains that lack information on network structures and kinetic parameters. On the other hand, black-box models do not rely on prior mechanistic knowledge of strains but are built upon observed time-series trajectories of biosynthetic systems in action. In practice, these observations are typically irregular, with discontinuously observed time points across multiple independent batches, each time point potentially containing missing measurements. Learning from such irregular data remains challenging for existing approaches. To address this issue, we present the Bidirectional Time-Series State Transfer Network (BTSTN) for modeling metabolic dynamics directly from irregular observations. Using evaluation data sets derived from both ideal dynamic systems and a real-world fermentation process, we demonstrate that BTSTN accurately reconstructs dynamic behaviors and predicts future trajectories. This approach exhibits enhanced robustness against missing measurements and noise, as compared to the state-of-the-art methods.IMPORTANCEIndustrial biosynthetic systems often involve strains with unclear genetic backgrounds, posing challenges in modeling their distinct metabolic dynamics. In such scenarios, white-box models, which commonly rely on inferred networks, are thereby of limited applicability and accuracy. In contrast, black-box models, such as statistical models and neural networks, are directly fitted or learned from observed time-series trajectories of biosynthetic systems in action. These methods typically assume regular observations without missing time points or measurements. If the observations are irregular, a pre-processing step becomes necessary to obtain a fully filled data set for subsequent model training, which, at the same time, inevitably introduces errors into the resulting models. BTSTN is a novel approach that natively learns from irregular observations. This distinctive feature makes it a unique addition to the current arsenal of technologies modeling metabolic dynamics.