Optimal eco-driving scheme for reducing energy consumption and carbon emissions on curved roads.

Energy consumption and emissions of a vehicle are highly influenced by road contexts and driving behavior. Especially, driving on horizontal curves often necessitates a driver to brake and accelerate, which causes additional fuel consumption and emissions. This paper proposes a novel optimal ecological (eco) driving scheme (EDS) using nonlinear model predictive control (MPC) considering various road contexts, i.e., curvatures and surface conditions. Firstly, a nonlinear optimization problem is formulated considering a suitable prediction horizon and an objective function based on factors affecting fuel consumption, emissions, and driving safety. Secondly, the EDS dynamically computes the optimal velocity trajectory for the host vehicle considering its dynamics model, the state of the preceding vehicle, and information of road contexts that reduces fuel consumption and carbon emissions. Finally, we analyze the effect of different penetration rates of the EDS on overall traffic performance. The effectiveness of the proposed scheme is demonstrated using microscopic traffic simulations under dense and mixed traffic environment, and it is found that the proposed EDS substantially reduces the fuel consumption and carbon emissions of the host vehicle compared to the traditional (human-based) driving system (TDS), while ensuring driving safety. The proposed scheme can be employed as an advanced driver assistance system (ADAS) for semi-autonomous vehicles.

Gap junction-mediated contraction of myoepithelial cells induces the peristaltic transport of sweat in human eccrine glands.

Eccrine sweat glands play an essential role in regulating body temperature. Sweat is produced in the coiled secretory portion of the gland, which is surrounded by obliquely aligned myoepithelial cells; the sweat is then peristaltically transported to the skin surface. Myoepithelial cells are contractile and have been implicated in sweat transport, but how myoepithelial cells contract and transport sweat remains unexplored. Here, we perform ex vivo live imaging of an isolated human eccrine gland and demonstrate that cholinergic stimulation induces dynamic contractile motion of the coiled secretory duct that is driven by gap junction-mediated contraction of myoepithelial cells. The contraction of the secretory duct occurs segmentally, and it is most prominent in the region surrounded by nerve fibers, followed by distension-contraction sequences of the excretory duct. Overall, our ex vivo live imaging approach provides evidence of the contractile function of myoepithelial cells in peristaltic sweat secretion from human eccrine glands.

Stability and Stabilization of Nash Equilibrium for Uncertain Noncooperative Dynamical Systems With Zero-Sum Tax/Subsidy Approach.

A zero-sum tax/subsidy approach for stabilizing unstable Nash equilibria in pseudo-gradient-based noncooperative dynamical systems is proposed without the information of agents' personal sensitivity parameters. Specifically, we first present several sufficient conditions for guaranteeing stability of an unstable Nash equilibrium in the face of uncertainty. Furthermore, we develop a framework where a system manager constructs a zero-sum tax/subsidy incentive structure by collecting taxes from some agents and giving the same amount of subsidy in total to other agents so that the agents' payoff structure is properly modified. Finally, we present several numerical examples to illustrate the utility of the zero-sum tax/subsidy approach.

Establishment and characterization of immortalized sweat gland myoepithelial cells.

Sweat glands play an important role in thermoregulation via sweating, and protect human vitals. The reduction in sweating may increase the incidence of hyperthermia. Myoepithelial cells in sweat glands exhibit stemness characteristics and play a major role in sweat gland homeostasis and sweating processes. Previously, we successfully passaged primary myoepithelial cells in spheroid culture systems; however, they could not be maintained for long under in vitro conditions. No myoepithelial cell line has been established to date. In this study, we transduced two immortalizing genes into primary myoepithelial cells and developed a myoepithelial cell line. When compared with primary sweat gland cells, the immortalized myoepithelial cells (designated "iEM") continued to form spheroids after the 4th passage and expressed α-smooth muscle actin and other proteins that characterize myoepithelial cells. Furthermore, treatment with small compounds targeting the Wnt signaling pathways induced differentiation of iEM cells into luminal cells. Thus, we successfully developed an immortalized myoepithelial cell line having differentiation potential. As animal models are not useful for studying human sweat glands, our cell line will be helpful for studying the mechanisms underlying the pathophysiology of sweating disorders.

Bi-Level Coordinated Merging of Connected and Automated Vehicles at Roundabouts.

Traditional uncoordinated traffic flows in a roundabout can lead to severe traffic congestion, travel delay, and the increased fuel consumption of vehicles. An interesting way to mitigate this would be through cooperative control of connected and automated vehicles (CAVs). In this paper, we propose a novel solution, which is a roundabout control system (RCS), for CAVs to attain smooth and safe traffic flows. The RCS is essentially a bi-level framework, consisting of higher and lower levels of control, where in the higher level, vehicles in the entry lane approaching the roundabout will be made to form clusters based on traffic flow volume, and in the lower level, the vehicles' optimal sequences and roundabout merging times are calculated by solving a combinatorial optimization problem using a receding horizon control (RHC) approach. The proposed RCS aims to minimize the total time taken for all approaching vehicles to enter the roundabout, whilst minimally affecting the movement of circulating vehicles. Our developed strategy ensures fast optimization, and can be implemented in real-time. Using microscopic simulations, we demonstrate the effectiveness of the RCS, and compare it to the current traditional roundabout system (TRS) for various traffic flow scenarios. From the results, we can conclude that the proposed RCS produces significant improvement in traffic flow performance, in particular for the average velocity, average fuel consumption, and average travel time in the roundabout.

An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses.

In this paper, we provide an overview of recent research efforts on networked control systems under denial-of-service attacks. Our goal is to discuss the utility of different attack modeling and analysis techniques proposed in the literature for addressing feedback control, state estimation, and multi-agent consensus problems in the face of jamming attacks in wireless channels and malicious packet drops in multi-hop networks. We discuss several modeling approaches that are employed for capturing the uncertainty in denial-of-service attack strategies. We give an outlook on deterministic constraint-based modeling ideas, game-theoretic and optimization-based techniques and probabilistic modeling approaches. A special emphasis is placed on tail-probability based failure models, which have been recently used for describing jamming attacks that affect signal to interference-plus-noise ratios of wireless channels as well as transmission failures on multi-hop networks due to packet-dropping attacks and non-malicious issues. We explain the use of attack models in the security analysis of networked systems. In addition to the modeling and analysis problems, a discussion is provided also on the recent developments concerning the design of attack-resilient control and communication protocols.

Possible associations of personality traits representing harm avoidance and self-directedness with medication adherence in Japanese patients with type 2 diabetes.

BACKGROUND: Insufficient medication adherence in diabetes patients, of which numbers continue to increase globally, remains a critical issue. Medication adherence is multifactorial and determined by interactions among factors including socioeconomic status, health care team and system, condition, therapy, and patient-specific factors. On the other hand, personality traits have been studied in adherence other than to medication. Using the instruments of Temperament and Character Inventory (TCI), Harm Avoidance (TCI-HA) and Self-directedness (TCI-SD) showed distinguishing associations with adherence of health-related programs. However, few studies have been performed to elucidate psychometric properties related to medication adherence. We investigated how TCI-HA and TCI-SD of patients with diabetes are related to medication adherence. METHOD: A cross-sectional survey was conducted among type 2 diabetes patients recruited at medical institutions or via an online research company. Medication adherence was measured using the 8-item Morisky Medication Adherence Scale (MMAS-8). Personality traits were assessed using the established scales of TCI-HA and TCI-SD. Univariate and multivariate regression analyses of the MMAS-8 scores were performed in addition to assessing demographic and disease characteristics and TCI-HA and TCI-SD. RESULTS: A total of 358 responses were analyzed. Multivariate regression analysis of MMAS-8 scores revealed that higher TCI-SD was related to better adherence and experiencing drug-related side effects was related to poor adherence. Aging was significantly associated with better medication adherence in univariate regression analysis but became insignificant in multivariate regression. CONCLUSIONS: In diabetes patients, the anxiety reflected in TCI-HA tends to lower and the self-control reflected in TCI-SD tends to promote medication adherence. TCI-SD has a greater effect than TCI-HA.

Possible involvement of distinct phylogenetic clusters of HIV-1 variants in the discrepancies between coreceptor tropism predictions based on viral RNA and proviral DNA.

BACKGROUND: The coreceptor tropism testing should be conducted prior to commencing a regimen containing a CCR5 antagonist for treatment of HIV-1 infection. For aviremic patients on long antiretroviral therapy, proviral DNA is often used instead of viral RNA in genotypic tropism testing. However, the tropism predictions from RNA and DNA are sometimes different. We examined the cause of the discrepancies between HIV-1 tropism predictions based on viral RNA and proviral DNA. METHODS: The nucleotide sequence of the env C2V3C3 region was determined using pair samples of plasma RNA and peripheral blood mononuclear cell DNA from 50 HIV-1 subtype B-infected individuals using population-based sequencing. The samples with discrepant tropism assessments between RNA and DNA were further analyzed using deep sequencing, followed by phylogenetic analysis. The tropism was assessed using the algorithm geno2pheno with a false-positive rate cutoff of 10 %. RESULTS: In population-based sequencing, five of 50 subjects showed discrepant tropism predictions between their RNA and DNA samples: four exhibited R5 tropism in RNA and X4 tropism in DNA, while one exhibited the opposite pattern. In the deep sequencing and phylogenetic analysis, three subjects had single clusters comprising of RNA- and DNA-derived sequences that were a mixture of R5 and X4 sequences. The other two subjects had two and three distinct phylogenetic clusters of sequences, respectively, each of which was dominated by R5 or X4 sequences; sequences of the R5-dominated cluster were mostly found in RNA, while sequences of the X4-dominated cluster were mostly in DNA. CONCLUSIONS: Some of HIV-1 tropism discrepancies between viral RNA and proviral DNA seem to be caused by phylogenetically distinct clusters which resides in plasma and cells in different frequencies. Our findings suggest that the tropism testing using PBMC DNA or deep sequencing may be required when the viral load is not suppressed or rebounds in the course of a CCR5 antagonist-containing regimen.

SIRT1 suppresses the senescence-associated secretory phenotype through epigenetic gene regulation.

Senescent cells develop a pro-inflammatory response termed the senescence-associated secretory phenotype (SASP). As many SASP components affect surrounding cells and alter their microenvironment, SASP may be a key phenomenon in linking cellular senesence with individual aging and age-related diseases. We herein demonstrated that the expression of Sirtuin1 (SIRT1) was decreased and the expression of SASP components was reciprocally increased during cellular senescence. The mRNAs and proteins of SASP components, such as IL-6 and IL-8, quickly accumulated in SIRT1-depleted cells, and the levels of these factors were also higher than those in control cells, indicating that SIRT1 negatively regulated the expression of SASP factors at the transcriptional level. SIRT1 bound to the promoter regions of IL-8 and IL-6, but dissociated from them during cellular senescence. The acetylation of Histone H3 (K9) and H4 (K16) of the IL-8 and IL-6 promoter regions gradually increased during cellular senescence. In SIRT1-depleted cells, the acetylation levels of these regions were already higher than those in control cells in the pre-senescent stage. Moreover, these acetylation levels in SIRT1-depleted cells were significantly higher than those in control cells during cellular senescence. These results suggest that SIRT1 repressed the expression of SASP factors through the deacetylation of histones in their promoter regions.

A stochastic mean field model for an excitatory and inhibitory synaptic drive cortical neuronal network.

With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state, that is, lack of responsiveness to noxious stimuli. In this paper, we develop a mean field synaptic drive firing rate cortical neuronal model and demonstrate how the induction of general anesthesia can be explained using multistability; the property whereby the solutions of a dynamical system exhibit multiple attracting equilibria under asymptotically slowly changing inputs or system parameters. In particular, we demonstrate multistability in the mean when the system initial conditions or the system coefficients of the neuronal connectivity matrix are random variables. Uncertainty in the system coefficients is captured by representing system uncertain parameters by a multiplicative white noise model wherein stochastic integration is interpreted in the sense of Itô. Modeling a priori system parameter uncertainty using a multiplicative white noise model is motivated by means of the maximum entropy principle of Jaynes and statistical analysis.

Neural network adaptive control for a class of nonlinear uncertain dynamical systems with asymptotic stability guarantees.

In this paper, a neuroadaptive control framework for continuous- and discrete-time nonlinear uncertain dynamical systems with input-to-state stable internal dynamics is developed. The proposed framework is Lyapunov based and unlike standard neural network (NN) controllers guaranteeing ultimate boundedness, the framework guarantees partial asymptotic stability of the closed-loop system, that is, asymptotic stability with respect to part of the closed-loop system states associated with the system plant states. The neuroadaptive controllers are constructed without requiring explicit knowledge of the system dynamics other than the assumption that the plant dynamics are continuously differentiable and that the approximation error of uncertain system nonlinearities lie in a small gain-type norm bounded conic sector. This allows us to merge robust control synthesis tools with NN adaptive control tools to guarantee system stability. Finally, two illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach.

Neural network adaptive output feedback control for intensive care unit sedation and intraoperative anesthesia.

The potential applications of neural adaptive control for pharmacology, in general, and anesthesia and critical care unit medicine, in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for nonlinear uncertain nonnegative and compartmental systems with nonnegative control inputs. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state space. Finally, the proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery.

Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems.

The potential clinical applications of adaptive neural network control for pharmacology in general, and anesthesia and critical care unit medicine in particular, are clearly apparent. Specifically, monitoring and controlling the depth of anesthesia in surgery is of particular importance. Nonnegative and compartmental models provide a broad framework for biological and physiological systems, including clinical pharmacology, and are well suited for developing models for closed-loop control of drug administration. In this paper, we develop a neural adaptive output feedback control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. The approach is applicable to nonlinear nonnegative systems with unmodeled dynamics of unknown dimension and guarantees that the physical system states remain in the nonnegatiye orthant of the state-space for nonnegative initial conditions. Finally, a numerical example involving the infusion of the anesthetic drug midazolam for maintaining a desired constant level of depth of anesthesia for noncardiac surgery is provided to demonstrate the efficacy of the proposed approach.

Neural network adaptive control for nonlinear nonnegative dynamical systems.

Nonnegative and compartmental dynamical system models are derived from mass and energy balance considerations that involve dynamic states whose values are nonnegative. These models are widespread in engineering and life sciences and typically involve the exchange of nonnegative quantities between subsystems or compartments wherein each compartment is assumed to be kinetically homogeneous. In this paper, we develop a full-state feedback neural adaptive control framework for adaptive set-point regulation of nonlinear uncertain nonnegative and compartmental systems. The proposed framework is Lyapunov-based and guarantees ultimate boundedness of the error signals corresponding to the physical system states and the neural network weighting gains. In addition, the neural adaptive controller guarantees that the physical system states remain in the nonnegative orthant of the state-space for nonnegative initial conditions.