Dynamic encoding of temperature in the central circadian circuit coordinates physiological activities.

The circadian clock regulates animal physiological activities. How temperature reorganizes circadian-dependent physiological activities remains elusive. Here, using in-vivo two-photon imaging with the temperature control device, we investigated the response of the Drosophila central circadian circuit to temperature variation and identified that DN1as serves as the most sensitive temperature-sensing neurons. The circadian clock gate DN1a's diurnal temperature response. Trans-synaptic tracing, connectome analysis, and functional imaging data reveal that DN1as bidirectionally targets two circadian neuronal subsets: activity-related E cells and sleep-promoting DN3s. Specifically, behavioral data demonstrate that the DN1a-E cell circuit modulates the evening locomotion peak in response to cold temperature, while the DN1a-DN3 circuit controls the warm temperature-induced nocturnal sleep reduction. Our findings systematically and comprehensively illustrate how the central circadian circuit dynamically integrates temperature and light signals to effectively coordinate wakefulness and sleep at different times of the day, shedding light on the conserved neural mechanisms underlying temperature-regulated circadian physiology in animals.

A study of the transient gas flow affected ion transmission in atmospheric pressure interfaces based on large eddy simulation for electrospray ionization mass spectrometry.

Modern atmosphere pressure interface (API) enables high-efficiency coupling between mass analyzers in high vacuum and atmosphere ionization sources such as electrospray ionization (ESI) source. The transient gas flow entering API possesses strong compressibility and turbulent characteristics, which exerts a huge impact on ion transmission. However, the instantaneous nature and vortical morphology of the turbulence in API and its affection in ion transmission were hardly covered in the reported research. Here we conduct a transient turbulent flow-affected ion transmission evaluation for two typical APIs, the ion funnel and the S-lens, based on scale-resolving large eddy simulation and electro-hydrodynamical ion tracing simulation. In our simulation, the transient properties of the gas flow in the two APIs are illustrated and analyzed in-depth. After experimentally validated on a homemade ESI-TOF-MS platform, the results suggest that the ion funnel can achieve a higher droplet desolvation rate by introducing a unique droplet recirculation mechanism. Meanwhile, the less-dispersed gas flow in S-lens is beneficial in actuating ions axially. In conclusion, the application of the scale-resolving turbulence model helps us to understand the complicated fluid-ion interaction mechanism in APIs and is promising in the development of mass spectrometry instruments of higher performance.

Event-Triggered Multiasynchronous H∞ Control for Markov Jump Systems With Transmission Delay.

In this article, the issue of event-triggered multiasynchronous H∞ control for Markov jump systems with transmission delay is concerned. In order to reduce sampling frequency, multiple event-triggered schemes (ETSs) are introduced. Then hidden Markov model (HMM) is employed to describe multiasynchronous jumps among subsystems, ETSs, and controller. Based on the HMM, the time-delay closed-loop model is constructed. In particular, when triggered data are transmitted over networks, a large transmission delay may cause disorder of transmission data such that the time-delay closed-loop model cannot be developed directly. To overcome this difficulty, a packet loss schedule is presented and the unified time-delay closed-loop system is obtained. By the use of the Lyapunov-Krasovskii functional method, sufficient conditions with the controller design are formulated for guaranteeing the H∞ performance of the time-delay closed-loop system. Finally, the effectiveness of the proposed control strategy is demonstrated by two numerical examples.

The High-Efficiency Calculation and Analysis of Different Quadrupole Fields Based on the Method of Fundamental Solution.

The calculation and analysis of electric fields are indispensable steps in the design of mass spectrometry. In this work, an approach for this calculation was established based on the method of fundamental solution (MFS). It was proved to be much faster and more accurate than the other popular methods, and its optimum parameters were found for the calculation of different quadrupole fields. After this, quadrupole fields with round rods and different shielding covers were computed to investigate the impact of shielding covers, and a strategy of nonequilibrium allocation in the MFS was proposed to further improve the calculational efficiency. Moreover, through field calculation and mass analysis, the performances of the quadrupole fields with rectangular rods and different electrode cross section lengths were demonstrated, and their optimum sizes were also found. The proposed method and results of analysis in this work provided a highly efficient calculational approach and useful instruction for the design of a quadrupole mass filter.

Exploring the causes of augmentation in restless legs syndrome.

Long-term drug treatment for Restless Legs Syndrome (RLS) patients can frequently result in augmentation, which is the deterioration of symptoms with an increased drug dose. The cause of augmentation, especially derived from dopamine therapy, remains elusive. Here, we review recent research and clinical progress on the possible mechanism underlying RLS augmentation. Dysfunction of the dopamine system highly possibly plays a role in the development of RLS augmentation, as dopamine agonists improve desensitization of dopamine receptors, disturb receptor interactions within or outside the dopamine receptor family, and interfere with the natural regulation of dopamine synthesis and release in the neural system. Iron deficiency is also indicated to contribute to RLS augmentation, as low iron levels can affect the function of the dopamine system. Furthermore, genetic risk factors, such as variations in the BTBD9 and MEIS1 genes, have been linked to an increased risk of RLS initiation and augmentation. Additionally, circadian rhythm, which controls the sleep-wake cycle, may also contribute to the worsening of RLS symptoms and the development of augmentation. Recently, Vitamin D deficiency has been suggested to be involved in RLS augmentation. Based on these findings, we propose that the progressive reduction of selective receptors, influenced by various pathological factors, reverses the overcompensation of the dopamine intensity promoted by short-term, low-dose dopaminergic therapy in the development of augmentation. More research is needed to uncover a deeper understanding of the mechanisms underlying the RLS symptom and to develop effective RLS augmentation treatments.

Adaptive Neural Event-Triggered Control of Networked Markov Jump Systems Under Hybrid Cyberattacks.

This article is concerned with the neural network (NN)-based event-triggered control problem for discrete-time networked Markov jump systems with hybrid cyberattacks and unmeasured states. The event-triggered mechanism (ETM) is used to reduce the communication load, and a Luenberger observer is introduced to estimate the unmeasured states. Two kinds of cyberattacks, denial-of-service (DoS) attacks and deception attacks, are investigated due to the vulnerability of cyberlayer. For the sake of mitigating the impact of these two types of cyberattacks on system performance, the ETM under DoS jamming attacks is discussed first, and a new estimation of such mechanism is given. Then, the NN technique is applied to approximate the injected false information. Some sufficient conditions are derived to guarantee the boundedness of the closed-loop system, and the observer and controller gains are presented by solving a set of matrix inequalities. The effectiveness of the presented control method is demonstrated by a numerical example.

Event-Triggered and Self-Triggered L∞ Control for Markov Jump Stochastic Nonlinear Systems Under DoS Attacks.

This article investigates event-triggered and self-triggered L∞ control problems for the Markov jump stochastic nonlinear systems subject to denial-of-service (DoS) attacks. When attacks prevent system devices from obtaining valid information over networks, a new switched model with unstable subsystems is constructed to characterize the effect of DoS attacks. On the basis of the switched model, a multiple Lyapunov function method is utilized and a set of sufficient conditions incorporating the event-triggering scheme (ETS) and restriction of DoS attacks are provided to preserve L∞ performance. In particular, considering that ETS based on mathematical expectation is difficult to be implemented on a practical platform, a self-triggering scheme (STS) without mathematical expectation is presented. Meanwhile, to avoid the Zeno behavior resulted from general exogenous disturbance, a positive lower bound is fixed in STS in advance. In addition, the exponent parameters are designed in STS to reduce triggering frequency. Based on the STS, the mean-square asymptotical stability and almost sure exponential stability are both discussed when the system is in the absence of exogenous disturbance. Finally, two examples are given to substantiate the effectiveness of the proposed method.

Adaptive Neural State Estimation of Markov Jump Systems Under Scheduling Protocols and Probabilistic Deception Attacks.

The neural-network (NN)-based state estimation issue of Markov jump systems (MJSs) subject to communication protocols and deception attacks is addressed in this article. For relieving communication burden and preventing possible data collisions, two types of scheduling protocols, namely: 1) the Round-Robin (RR) protocol and 2) weighted try-once-discard (WTOD) protocol, are applied, respectively, to coordinate the transmission sequence. In addition, considering that the communication channel may suffer from mode-dependent probabilistic deception attacks, a hidden Markov-like model is proposed to characterize the relationship between the malicious signal and system mode. Then, a novel adaptive neural state estimator is presented to reconstruct the system states. By taking the influence of deception attacks into performance analysis, sufficient conditions under two different scheduling protocols are derived, respectively, so as to ensure the ultimately boundedness of the estimate error. In the end, simulation results testify the correctness of the adaptive neural estimator design method proposed in this article.

Event-Triggered Resilient L∞ Control for Markov Jump Systems Subject to Denial-of-Service Jamming Attacks.

In this article, the event-triggered resilient L∞ control problem is concerned for the Markov jump systems in the presence of denial-of-service (DoS) jamming attacks. First, a fixed lower bound-based event-triggering scheme (ETS) is presented in order to avoid the Zeno problem caused by exogenous disturbance. Second, when DoS jamming attacks are involved, the transmitted data are blocked and the old control input is kept by using the zero-order holder (ZOH). On the basis of this process, the effect of DoS attacks on ETS is further discussed. Next, by utilizing the state-feedback controller and multiple Lyapunov functions method, some criteria incorporating the restriction of DoS jamming attacks are proposed to guarantee the L∞ control performance of the event-triggered Markov closed-loop jump system. In particular, the bounded transition rates rather than the exact ones are taken into account. That is appropriate for the practical environment in which transition rates of the Markov process are difficult to measure accurately. Correspondingly, some criteria are proposed to obtain state-feedback gains and event-triggering parameters simultaneously. Finally, we provide two examples to show the effectiveness of the proposed method.

Glutamatergic and gabaergic neuronal populations in the dorsal Periacqueductual Gray have different functional roles in aversive conditioning.

The dorsal periacqueductal gray (dPAG) is a midbrain structure having an essential role in coordinating defensive behaviors in response to aversive stimulation. However, the question of whether dPAG neurons can respond to aversive conditioning and retrieval, properties involved in emergence of negative emotional state, is still under debate. Here we used calcium imaging by fiber photometry to record the activity of dPAGVGluT2+ and dPAGGAD2+ neuronal populations during unconditioned and conditioned aversive stimulation. Then, following an unconditioned stimulation we performed a retrieval experiment to quantify memory-like responses of dPAG neurons. This shown that whilst both dPAGVGluT2+ and dPAGGAD2+ neuronal populations respond to direct US stimulation, and to CS stimulation during conditioning, only the dPAGVGluT2+ population persisted in responding to the CS stimulation during retrieval. Finally to better understand these divergences in dPAGVGluT2+ and dPAGGAD2+ responses, we investigated their respective connectivity patterns by performing a cell specific monosynaptic retrograde rabies virus tracing experiment. This revealed that different patterns of fibers projects to dPAGVGluT2+ and dPAGGAD2+, which could explain part of their response specificities. This may indicate that glutamatergic subpopulation is a main contributor of aversive memories in dPAG.

Characterization of glutamatergic VTA neural population responses to aversive and rewarding conditioning in freely-moving mice.

The Ventral Tegmental Area (VTA) is a midbrain structure known to integrate aversive and rewarding stimuli, but little is known about the role of VTA glutamatergic (VGluT2) neurons in these functions. Direct activation of VGluT2 soma evokes rewarding behaviors, while activation of their downstream projections evokes aversive behaviors. To facilitate our understanding of these conflicting properties, we recorded calcium signals from VTAVGluT2+ neurons using fiber photometry in VGluT2-cre mice to investigate how this population was recruited by aversive and rewarding stimulation, both during unconditioned and conditioned protocols. Our results revealed that, as a population, VTAVGluT2+ neurons responded similarly to unconditioned-aversive and unconditioned-rewarding stimulation. During aversive and rewarding conditioning, the CS-evoked responses gradually increased across trials whilst the US-evoked response remained stable. Retrieval 24 h after conditioning, during which mice received only CS presentation, resulted in VTAVGluT2+ neurons strongly responding to CS presentation and to the expected-US but only for aversive conditioning. To help understand these differences based on VTAVGluT2+ neuronal networks, the inputs and outputs of VTAVGluT2+ neurons were investigated using Cholera Toxin B (CTB) and rabies virus. Based on our results, we propose that the divergent VTAVGluT2+ neuronal responses to aversion and reward conditioning may be partly due to the existence of VTAVGluT2+ subpopulations that are characterized by their connectivity.