Fluid Jet Stimulation of Auditory Hair Bundles Reveal Spatial Non-uniformities and Two Viscoelastic-Like Mechanisms

Hair cell mechanosensitivity resides in the sensory hair bundle, an apical protrusion of actin-filled stereocilia arranged in a staircase pattern. Hair bundle deflection activates mechano-electric transduction (MET) ion channels located near the tops of the shorter rows of stereocilia. The elicited macroscopic current is shaped by the hair bundle motion so that the mode of stimulation greatly influences the cell’s output. We present data quantifying the displacement of the whole outer hair cell bundle using high-speed imaging when stimulated with a fluid jet. We find a spatially non-uniform stimulation that results in splaying, where the hair bundle expands apart. Based on modeling, the splaying is predominantly due to fluid dynamics with a small contribution from hair bundle architecture. Additionally, in response to stimulation, the hair bundle exhibited a rapid motion followed by a slower motion in the same direction (creep) that is described by a double exponential process. The creep is consistent with originating from a linear passive system that can be modeled using two viscoelastic processes. These viscoelastic mechanisms are integral to describing the mechanics of the mammalian hair bundle.

Discrete Bismut formula: Conditional integration by parts and a representation for delta hedging process

The paper gives discrete conditional integration by parts formula using a Malliavin calculus approach in discrete-time setting. Then the discrete Bismut formula is introduced for asymmetric random walk model and asymmetric exponential process. In particular, a new formula for delta hedging process is obtained as an extension of the Malliavin derivative representation of the delta where the conditional integration by parts formula plays a role in the proof.

Interplay between cell proliferation and recruitment controls the duration of growth and final size of the Drosophila wing

How organs robustly attain a final size despite perturbations in cell growth and proliferation rates is a fundamental question in developmental biology. Since organ growth is an exponential process driven mainly by cell proliferation, even small variations in cell proliferation rates, when integrated over a relatively long time, will lead to large differences in size, unless intrinsic control mechanisms compensate for these variations. Here we use a mathematical model to consider the hypothesis that in the developing wing of Drosophila, cell recruitment, a process in which undifferentiated neighboring cells are incorporated into the wing primordium, determines the time in which growth is arrested in this system. Under this assumption, our model shows that perturbations in proliferation rates of wing-committed cells are compensated by an inversely proportional duration of growth. This mechanism ensures that the final size of the wing is robust in a range of cell proliferation rates. Furthermore, we predict that growth control is lost when fluctuations in cell proliferation affects both wing-committed and recruitable cells. Our model suggests that cell recruitment may act as a temporal controller of growth to buffer fluctuations in cell proliferation rates, offering a solution to a long-standing problem in the field.

A Bayesian Control Chart for Monitoring Process Variance

Automation in the service industry is emerging as a new wave of industrial revolution. Standardization and consistency of service quality is an important part of the automation process. The quality control methods widely used in the manufacturing industry can provide service quality measurement and service process monitoring. In particular, the control chart as an online monitoring technique can be used to quickly detect whether a service process is out of control. However, the control of the service process is more difficult than that of the manufacturing process because the variability of the service process comes from widespread and complex factors. First of all, the distribution of the service process is usually non-normal or unknown. Moreover, the skewness of the process distribution can be time-varying, even if the process is in control. In this study, a Bayesian procedure is applied to construct a Phase II exponential weighted moving average (EWMA) control chart for monitoring the variance of a distribution-free process. We explore the sampling properties of the new monitoring statistic, which is suitable for monitoring the time-varying process distribution. The average run lengths (ARLs) of the proposed Bayesian EWMA variance chart are calculated, and they show that the chart performs well. The simulation studies for a normal process, exponential process, and the mixed process of normal and exponential distribution prove that our chart can quickly detect any shift of a process variance. Finally, a numerical example of bank service time is used to illustrate the application of the proposed Bayesian EWMA variance chart and confirm the performance of the process control.

A conversion from slow to fast memory in response to passive motion

When the same perturbation is experienced consecutively, learning is accelerated on the second attempt. This savings is a central property of sensorimotor adaptation. Current models suggest that these improvements in learning are due to changes in the brain’s sensitivity to error. Here, we tested whether these increases in error sensitivity could be facilitated by passive movement experiences. In each experimental group, the robot moved the arm passively in the direction that solved the upcoming rotation, but no visual feedback was provided. Then, following a break in time, participants adapted to a visuomotor rotation. Prior passive movements substantially improved motor learning, increasing total compensation in each group by approximately 30%. Similar to savings, a state-space model suggested that this improvement in learning was due to an increase in error sensitivity, but not memory retention. Thus, passive memories appeared to increase the motor learning system’s sensitivity to error. However, some features in the observed behavior were not captured by this model, nor by similar empirical models, which assumed that learning was due a single exponential process. When we considered the possibility that learning was supported by parallel fast and slow adaptive processes, a striking pattern emerged; whereas initial improvements in learning were driven by a slower adaptive state, increases in error sensitivity gradually transferred to a faster learning system with the passage of time.

Effects of the reservoir bag disconnection on inspired gases during general anesthesia: a simulator-based study

Background Fresh gas decoupling is a feature of the modern anesthesia workstation, where the fresh gas flow (FGF) is diverted into the reservoir bag and is not added to the delivered tidal volume, which thus remains constant. The present study aimed to investigate the entraining of the atmospheric air into the anesthesia breathing circuit in case the reservoir bag was disconnected. Methods We conducted a simulator-based study, where the METI HPS simulator was connected to the anesthesia workstation. The effect of the disconnected reservoir bag was evaluated using oxygen (O2) and air or oxygen and nitrous oxide (N2O) as a carrier gas at different FGF rates. We disconnected the reservoir bag for 10 min during the maintenance phase. We recorded values for inspiratory O2, N2O, and sevoflurane. The time constant of the exponential process was estimated during reservoir bag disconnection. Results The difference of O2, N2O and sevoflurane concentrations, before, during, and after reservoir bag disconnection was statistically significant at 0.5, 1, and 2 L/min of FGF (p < 0.001). The largest decrease of the inspired O2 concentrations (FIO2) was detected in the case of oxygen and air as the carrier gas and an FGF of 1 L/min, when oxygen decreased from median [25th–75th percentile] 55.00% [54.00–56.00] to median 39.50% [38.00–42.50] (p < 0.001). The time constant for FIO2 during reservoir bag disconnection in oxygen and air as the carrier gas, were median 2.5, 2.5, and 1.5 min in FGF of 0.5, 1.0, and 2 L/min respectively. Conclusions During the disconnection of the anesthesia reservoir bag, the process of pharmacokinetics takes place faster compared to the wash-in and wash-out pharmacokinetic properties in the circle breathing system. The time constant was affected by the FGF rate, as well as the gradient of anesthetic gases between the anesthesia circle system and atmospheric air.

COVID-19 scaling dynamics in growth and decline phases

The definition of optimal COVID-19 mitigation strategies remains worldwide on the top of public health agendas, particularly when facing a second wave. It requires a better understanding and a refined modelling of its dynamics. We emphasise the fact that epidemic models are phenomenologically based on the paradigm of a cascade of contacts that propagates infection. However, the introduction of ad-hoc characteristic times and corresponding rates spuriously break their scale symmetry.Here we theoretically argue and empirically demonstrate that COVID-19 dynamics, during both growth and decline phases, is a cascade with a rather universal scale symmetry whose power-law statistics drastically differ from those of an exponential process. This involves slower but longer phases which are furthermore linked by a fairly simple symmetry. These results explain biases of epidemic models and help to improve them. Due to their generality, these results pave the way to a renewed approach to epidemics, and more generally to growth phenomena.

Polygenic Adaptation in a Population of Finite Size

Polygenic adaptation in response to selection on quantitative traits has become an important topic in evolutionary biology. Here we review the recent literature on models of polygenic adaptation. In particular, we focus on a model that includes mutation and both directional and stabilizing selection on a highly polygenic trait in a population of finite size (thus experiencing random genetic drift). Assuming that a sudden environmental shift of the fitness optimum occurs while the population is in a stochastic equilibrium, we analyze the adaptation of the trait to the new optimum. When the shift is not too large relative to the equilibrium genetic variance and this variance is determined by loci with mostly small effects, the approach of the mean phenotype to the optimum can be approximated by a rapid exponential process (whose rate is proportional to the genetic variance). During this rapid phase the underlying changes to allele frequencies, however, may depend strongly on genetic drift. While trait-increasing alleles with intermediate equilibrium frequencies are dominated by selection and contribute positively to changes of the trait mean (i.e., are aligned with the direction of the optimum shift), alleles with low or high equilibrium frequencies show more of a random dynamics, which is expected when drift is dominating. A strong effect of drift is also predicted for population size bottlenecks. Our simulations show that the presence of a bottleneck results in a larger deviation of the population mean of the trait from the fitness optimum, which suggests that more loci experience the influence of drift.