State-Space Model for Arrival Time Simulations and Methodology for Offline Blade Tip-Timing Software Characterization

Blade tip-timing is an extensively used technique for measuring blade vibrations in turbine and compressor stages; it is one of the preferred techniques used for characterizing their dynamic behaviors using non-contact probes. Typically, arrival time signals are acquired and processed by a dedicated measurement system. Performing a sensitivity analysis on the data processing parameters is essential for the proper design of tip-timing test campaigns. This study proposes a mathematical model for generating synthetic tip-timing signals, descriptive of specific test conditions. The generated signals were used as the controlled input for a thorough characterization of post-processing software for tip-timing analysis. This work represents the first step in quantifying the uncertainty introduced by tip-timing analysis software into user measurements. The proposed methodology can also offer essential information for further sensitivity studies on parameters that influence the accuracy of data analysis during testing.

Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements

Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.

Corrigendum: Sparse system identification of leptin dynamics in women with obesity

[This corrects the article DOI: 10.3389/fendo.2022.769951.].

Sparse System Identification of Leptin Dynamics in Women With Obesity

The prevalence of obesity is increasing around the world at an alarming rate. The interplay of the hormone leptin with the hypothalamus-pituitary-adrenal axis plays an important role in regulating energy balance, thereby contributing to obesity. This study presents a mathematical model, which describes hormonal behavior leading to an energy abnormal equilibrium that contributes to obesity. To this end, we analyze the behavior of two neuroendocrine hormones, leptin and cortisol, in a cohort of women with obesity, with simplified minimal state-space modeling. Using a system theoretic approach, coordinate descent method, and sparse recovery, we deconvolved the serum leptin-cortisol levels. Accordingly, we estimate the secretion patterns, timings, amplitudes, number of underlying pulses, infusion, and clearance rates of hormones in eighteen premenopausal women with obesity. Our results show that minimal state-space model was able to successfully capture the leptin and cortisol sparse dynamics with the multiple correlation coefficients greater than 0.83 and 0.87, respectively. Furthermore, the Granger causality test demonstrated a negative prospective predictive relationship between leptin and cortisol, 14 of 18 women. These results indicate that increases in cortisol are prospectively associated with reductions in leptin and vice versa, suggesting a bidirectional negative inhibitory relationship. As dysregulation of leptin may result in an abnormality in satiety and thereby associated to obesity, the investigation of leptin-cortisol sparse dynamics may offer a better diagnostic methodology to improve better treatments plans for individuals with obesity.

Thermal Model of an Omnimagnet for Performance Assessment and Temperature Control

An Omnimagnet is an electromagnetic device that enables remote magnetic manipulation of devices such as medical implants and microrobots. It is composed of three orthogonal nested solenoids with a ferromagnetic core at the center. Electrical current within the solenoids leads to undesired temperature increase within the Omnimagnet. If the temperature exceeds the melting point of the wire insulation, device failure may occur. Thus, a study of heat transfer within an Omnimagnet is a necessity, particularly to maximize the performance of the device. A transient heat transfer model that incorporates all three heat transfer modes is proposed and experimentally validated with an average normalized root-mean-square error of less than 4% (data normalized by temperature in degree celsius). The transient model is not computationally expensive and is applicable to Omnimagnets with different structures. The code is applied to calculate the maximum safe operational time at a fixed input current or the maximum safe input current for a fixed time interval. The maximum safe operational time and maximum safe input current depend on size and structure of the Omnimagnet and the lowest critical temperature of all the Omnimagnet materials. A parametric study shows that increasing convective heat transfer during cooling, and during heating with low input currents, is an effective method to increase the maximum operational time of the Omnimagnet. The thermal model is also presented in a state-space equation format that can be used in a real-time Kalman filter current controller to avoid device failure due to excessive heating.

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia

Temporal dynamics of gene expression inform cellular and molecular perturbations associated with disease development and evolution. Given the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Here we model temporal dynamics of the transcriptome of peripheral blood mononuclear cells in a two-dimensional state-space representing states of health and leukemia using time-sequential bulk RNA-seq data from a murine model of acute myeloid leukemia (AML). The state-transition model identified critical points that accurately predict AML development and identifies stepwise transcriptomic perturbations that drive leukemia progression. The geometry of the transcriptome state-space provided a biological interpretation of gene dynamics, aligned gene signals that are not synchronized in time across mice, and allowed quantification of gene and pathway contributions to leukemia development. Our state-transition model synthesizes information from multiple cell types in the peripheral blood and identifies critical points in the transition from health to leukemia to guide interpretation of changes in the transcriptome as a whole to predict disease progression. SIGNIFICANCE: These findings apply the theory of state transitions to model the initiation and development of acute myeloid leukemia, identifying transcriptomic perturbations that accurately predict time to disease development.See related commentary by Kuijjer, p. 3072 GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/15/3157/F1.large.jpg.

Sea ice predicts long-term trends in Adélie penguin population growth, but not annual fluctuations: Results from a range-wide multiscale analysis

Understanding the scales at which environmental variability affects populations is critical for projecting population dynamics and species distributions in rapidly changing environments. Here we used a multilevel Bayesian analysis of range-wide survey data for Adélie penguins to characterize multidecadal and annual effects of sea ice on population growth. We found that mean sea ice concentration at breeding colonies (i.e., "prevailing" environmental conditions) had robust nonlinear effects on multidecadal population trends and explained over 85% of the variance in mean population growth rates among sites. In contrast, despite considerable year-to-year fluctuations in abundance at most breeding colonies, annual sea ice fluctuations often explained less than 10% of the temporal variance in population growth rates. Our study provides an understanding of the spatially and temporally dynamic environmental factors that define the range limits of Adélie penguins, further establishing this iconic marine predator as a true sea ice obligate and providing a firm basis for projection under scenarios of future climate change. Yet, given the weak effects of annual sea ice relative to the large unexplained variance in year-to-year growth rates, the ability to generate useful short-term forecasts of Adélie penguin breeding abundance will be extremely limited. Our approach provides a powerful framework for linking short- and longer term population processes to environmental conditions that can be applied to any species, facilitating a richer understanding of ecological predictability and sensitivity to global change.

The Low-Dimensional Neural Architecture of Cognitive Complexity Is Related to Activity in Medial Thalamic Nuclei

Cognitive activity emerges from large-scale neuronal dynamics that are constrained to a low-dimensional manifold. How this low-dimensional manifold scales with cognitive complexity, and which brain regions regulate this process, are not well understood. We addressed this issue by analyzing sub-second high-field fMRI data acquired during performance of a task that systematically varied the complexity of cognitive reasoning. We show that task performance reconfigures the low-dimensional manifold and that deviations from these patterns relate to performance errors. We further demonstrate that individual differences in thalamic activity relate to reconfigurations of the low-dimensional architecture during task engagement.

Corticoamygdala Transfer of Socially Derived Information Gates Observational Learning

Observational learning is a powerful survival tool allowing individuals to learn about threat-predictive stimuli without directly experiencing the pairing of the predictive cue and punishment. This ability has been linked to the anterior cingulate cortex (ACC) and the basolateral amygdala (BLA). To investigate how information is encoded and transmitted through this circuit, we performed electrophysiological recordings in mice observing a demonstrator mouse undergo associative fear conditioning and found that BLA-projecting ACC (ACC→BLA) neurons preferentially encode socially derived aversive cue information. Inhibition of ACC→BLA alters real-time amygdala representation of the aversive cue during observational conditioning. Selective inhibition of the ACC→BLA projection impaired acquisition, but not expression, of observational fear conditioning. We show that information derived from observation about the aversive value of the cue is transmitted from the ACC to the BLA and that this routing of information is critically instructive for observational fear conditioning. VIDEO ABSTRACT.

Local epiphyte establishment and future metapopulation dynamics in landscapes with different spatiotemporal properties

Understanding the relative importance of different ecological processes on the metapopulation dynamics of species is the basis for accurately forecasting metapopulation size in fragmented landscapes. Successful local colonization depends on both species dispersal range and how local habitat conditions affect establishment success. Moreover, there is limited understanding of the effects of different spatiotemporal landscape properties on future metapopulation size. We investigate which factors drive the future metapopulation size of the epiphytic model lichen species Lobaria pulmonaria in a managed forest landscape. First, we test the importance of dispersal and local conditions on the colonization-extinction dynamics of the species using Bayesian state-space modelling of a large-scale data set collected over a 10-yr period. Second, we test the importance of dispersal and establishment limitation in explaining establishment probability and subsequent local population growth, based on a 10-yr propagule sowing experiment. Third, we test how future metapopulation size is affected by different metapopulation and spatiotemporal landscape dynamics, using simulations with the metapopulation models fitted to the empirical data. The colonization probability increased with tree inclination and connectivity, with a mean dispersal distance of 97 m (95% credible intervals, 5-530 m). Local extinctions were mainly deterministic set by tree mortality, but also by tree cutting by forestry. No experimental establishments took place on clearcuts, and in closed forest the establishment probability was higher on trees growing on moist than on dry-mesic soils. The subsequent local population growth rate increased with increasing bark roughness. The simulations showed that the restricted dispersal range estimated (compared to non-restricted dispersal range), and short tree rotation length (65 yr instead of 120) had approximately the same negative effects on future metapopulation size, while regeneration of trees creating a random tree pattern instead of an aggregated one had only some negative effect. However, using the colonization rate obtained with the experimentally added diaspores led to a considerable increase in metapopulation size, making the dispersal limitation of the species clear. The future metapopulation size is thus set by the number of host trees located in shady conditions, not isolated from occupied trees, and by the rotation length of these host trees.

A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies

An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor–response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their ability to incorporate latent variables and model direct and indirect links between state variables and capture probabilities.

Deciphering elapsed time and predicting action timing from neuronal population signals

The proper timing of actions is necessary for the survival of animals, whether in hunting prey or escaping predators. Researchers in the field of neuroscience have begun to explore neuronal signals correlated to behavioral interval timing. Here, we attempt to decode the lapse of time from neuronal population signals recorded from the frontal cortex of monkeys performing a multiple-interval timing task. We designed a Bayesian algorithm that deciphers temporal information hidden in noisy signals dispersed within the activity of individual neurons recorded from monkeys trained to determine the passage of time before initiating an action. With this decoder, we succeeded in estimating the elapsed time with a precision of approximately 1 s throughout the relevant behavioral period from firing rates of 25 neurons in the pre-supplementary motor area. Further, an extended algorithm makes it possible to determine the total length of the time-interval required to wait in each trial. This enables observers to predict the moment at which the subject will take action from the neuronal activity in the brain. A separate population analysis reveals that the neuronal ensemble represents the lapse of time in a manner scaled relative to the scheduled interval, rather than representing it as the real physical time.