Conceptual and Experimental Tools to Understand Spatial Effects and Transport Phenomena in Nonlinear Biochemical Networks Illustrated with Patchy Switching.

Many biochemical systems are spatially heterogeneous and exhibit nonlinear behaviors, such as state switching in response to small changes in the local concentration of diffusible molecules. Systems as varied as blood clotting, intracellular calcium signaling, and tissue inflammation are all heavily influenced by the balance of rates of reaction and mass transport phenomena including flow and diffusion. Transport of signaling molecules is also affected by geometry and chemoselective confinement via matrix binding. In this review, we use a phenomenon referred to as patchy switching to illustrate the interplay of nonlinearities, transport phenomena, and spatial effects. Patchy switching describes a change in the state of a network when the local concentration of a diffusible molecule surpasses a critical threshold. Using patchy switching as an example, we describe conceptual tools from nonlinear dynamics and chemical engineering that make testable predictions and provide a unifying description of the myriad possible experimental observations. We describe experimental microfluidic and biochemical tools emerging to test conceptual predictions by controlling transport phenomena and spatial distribution of diffusible signals, and we highlight the unmet need for in vivo tools.

HMM for discovering decision-making dynamics using reinforcement learning experiments.

Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing (e.g. reward sensitivity) to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task within the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel RL-HMM (hidden Markov model) framework for analyzing reward-based decision-making. Our model accommodates decision-making strategy switching between two distinct approaches under an HMM: subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient Expectation-maximization (EM) algorithm for parameter estimation and use a nonparametric bootstrap for inference. Extensive simulation studies validate the finite-sample performance of our method. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.

Autophagy mediates temporary reprogramming and dedifferentiation in plant somatic cells.

Somatic cells acclimate to changes in the environment by temporary reprogramming. Much has been learned about transcription factors that induce these cell-state switches in both plants and animals, but how cells rapidly modulate their proteome remains elusive. Here, we show rapid induction of autophagy during temporary reprogramming in plants triggered by phytohormones, immune, and danger signals. Quantitative proteomics following sequential reprogramming revealed that autophagy is required for timely decay of previous cellular states and for tweaking the proteome to acclimate to the new conditions. Signatures of previous cellular programs thus persist in autophagy-deficient cells, affecting cellular decision-making. Concordantly, autophagy-deficient cells fail to acclimatize to dynamic climate changes. Similarly, they have defects in dedifferentiating into pluripotent stem cells, and redifferentiation during organogenesis. These observations indicate that autophagy mediates cell-state switches that underlie somatic cell reprogramming in plants and possibly other organisms, and thereby promotes phenotypic plasticity.

Profiling the sleep architecture of ageing adults using a seven-state continuous-time Markov model.

Sleep is a complex biological process regulated by networks of neurons and environmental factors. As one falls asleep, neurotransmitters from sleep-wake regulating neurones work in synergy to control the switching of different sleep states throughout the night. As sleep disorders or underlying neuropathology can manifest as irregular switching, analysing these patterns is crucial in sleep medicine and neuroscience. While hypnograms represent the switching of sleep states well, current analyses of hypnograms often rely on oversimplified temporal descriptive statistics (TDS, e.g., total time spent in a sleep state), which miss the opportunity to study the sleep state switching by overlooking the complex structures of hypnograms. In this paper, we propose analysing sleep hypnograms using a seven-state continuous-time Markov model (CTMM). This proposed model leverages the CTMM to depict the time-varying sleep-state transitions, and probes three types of insomnia by distinguishing three types of wake states. Fitting the proposed model to data from 2056 ageing adults in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study, we profiled sleep architectures in this population and identified the various associations between the sleep state transitions and demographic factors and subjective sleep questions. Ageing, sex, and race all show distinctive patterns of sleep state transitions. Furthermore, we also found that the perception of insomnia and restless sleep are significantly associated with critical transitions in the sleep architecture. By incorporating three wake states in a continuous-time Markov model, our proposed method reveals interesting insights into the relationships between objective hypnogram data and subjective sleep quality assessments.

Overcoming Thermal Quenching in X-ray Scintillators through Multi-Excited State Switching.

X-ray scintillators have gained significant attention in medical diagnostics and industrial applications. Despite their widespread utility, scintillator development faces a significant hurdle when exposed to elevated temperatures, as it usually results in reduced scintillation efficiency and diminished luminescence output. Here we report a molecular design strategy based on a hybrid perovskite (TpyBiCl5) that overcomes thermal quenching through multi-excited state switching. The structure of perovskite provides a platform to modulate the luminescence centers. The rigid framework constructed by this perovskite structure stabilized its triplet states, resulting in TpyBiCl5 exhibiting an approximately 12 times higher (45 % vs. 3.8 %) photoluminescence quantum yield of room temperature phosphorescence than that of its organic ligand (Tpy). Most importantly, the interactions between the components of this perovskite enable the mixing of different excited states, which has been revealed by experimental and theoretical investigations. The TpyBiCl5 scintillator exhibits a detection limit of 38.92 nGy s-1 at 213 K and a detection limit of 196.31 nGy s-1 at 353 K through scintillation mode switching between thermally activated delayed fluorescence and phosphorescence. This work opens up the possibility of solving the thermal quenching in X-ray scintillators by tuning different excited states.

Spin-State Modulation in FeII -Based Hofmann-Type Coordination Polymers: From Molecules to Materials.

Spin crossover complexes that reversibly interconvert between two stable states imitate a binary state of 0 and 1, delivering a promising possibility to address the data processing concept in smart materials. Thus, a comprehensive understanding of the modulation of magnetic transition between high spin and low spin and the factors responsible for stabilizing the spin states is an essential theme in modern materials design. In this context, the present review attempts to provide a concise outline of the design strategy employed at the molecular level for fine-tuning the spin-state switching in FeII -based Hofmann-type coordination polymers and their effects on the optical and magnetic response. In addition, development towards the nanoscale architectures of HCPs, i. e., in terms of nanoparticles and thin films, are emphasized to bridge the gap between the laboratory and reality.

Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter.

An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is overused. Based on the innovation covariance theory, the algorithm designs an improved basis for judging filtering anomalies and makes the timing of the introduction of the fading factor more reasonable by switching the filtering state. Different from the traditional basis of filter abnormality judgment, the improved judgment basis adopts a recursive way to continuously update the estimated value of the innovation covariance to improve the estimation accuracy of the innovation covariance, and an empirical reserve factor for the judgment basis is introduced to adapt to practical engineering applications. By establishing an inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation model, the results show that the average positioning accuracy of the proposed algorithm is improved by 26.52% and 7.48%, respectively, compared with the KF and MFKF, and shows better robustness and self-adaptability.

Vapochromism and Magnetochemical Switching of a Nickel(II) Paddlewheel Complex by Reversible NH3 Uptake and Release.

Reaction of [NiCl2 (PnH)4 ] (1) (PnH=6-tert-butyl-pyridazine-3-thione) with NiCl2 affords the binuclear paddlewheel (PW) complex [Ni2 (Pn)4 ] (2). Diamagnetic complex 2 is the first example of a PW complex capable of reversibly binding and releasing NH3 . The NH3 ligand in [Ni2 (Pn)4 (NH3 )] (2⋅NH3 ) enforces major spectroscopic and magnetic susceptibility changes, thus displaying vapochromic properties (λmax (2)=532 nm, λmax (2⋅NH3 )=518 nm) and magnetochemical switching (2: S=0; 2⋅NH3 : S=1). Upon repeated adsorption/desorption cycles of NH3 the PW core remains intact. Compound 2 can be embedded into thin polyurethane films (2P ) under retention of its sensing abilities. Therefore, 2 qualifies as reversible optical probe for ammonia. The magnetochemical switching of 2 and 2⋅NH3 was studied in detail by SQUID measurements showing that in 2⋅NH3 , solely the Ni atom coordinated the NH3 molecule is responsible for the paramagnetic behavior.

Noise-Induced Transitions in a Nonsmooth Producer-Grazer Model with Stoichiometric Constraints.

Stoichiometric producer-grazer models are nonsmooth due to the Liebig's Law of Minimum and can generate new dynamics such as bistability for producer-grazer interactions. Environmental noises can be extremely important and change dynamical behaviors of a stoichiometric producer-grazer model. In this paper, we consider a stochastically forced producer-grazer model and study the phenomena of noise-induced state switching between two stochastic attractors in the bistable zone. Namely, there is a frequent random hopping of phase trajectories between attracting basins of the attractors. In addition, by applying the stochastic sensitivity function technique, we construct the confidence ellipse and confidence band to find the configurational arrangement of equilibria and a limit cycle, respectively.

Metal-Organic Frameworks as Hosts for Fluorinated Azobenzenes: A Path towards Quantitative Photoswitching with Visible Light.

Fifteen new photochromic hybrid materials were synthesized by gas phase loading of fluorinated azobenzenes, namely ortho-tetrafluoroazobenzene (tF-AZB), 4H,4H'-octafluoroazobenzene (oF-AZB), and perfluoroazobenzene (pF-AZB), into the pores of the well-known metal-organic frameworks MOF-5, MIL-53(Al), MIL-53(Ga), MIL-68(Ga), and MIL-68(In). Their composition was analysed by elemental (CHNS) and DSC/TGA. For pF-AZB0.34 @MIL-53(Al), a structural model based on high-resolution synchrotron powder diffraction data was developed and the host-guest and guest-guest interactions were elucidated from this model. These interactions of O-H⋅⋅⋅F and π⋅⋅⋅π type were confirmed by significant shifts of the O-H frequencies in loaded and unloaded MOFs of the MIL-53 and MIL-68 series. Most remarkably, all of the synthesized F-AZB@MOF systems can be switched with visible light, and some of them show almost quantitative (>95 %) photo-isomerization between its E and Z forms with no significant fatigue after repeated switching cycles.

Guest-Dependent Spin-Transition Behavior of Porous Coordination Polymers.

The host-guest composites of Hofmann-type iron(II) spin-transition (ST) porous coordination polymers incorporating guest molecules show guest-dependent ST behavior in accordance with the respective guest species, which may be a gas, solvent, halogen, or organic molecule. The guest also works as a chemical stimulant to switch the spin state of the host between high and low spin at room temperature. In this review, we discuss guest properties including size, shape, flexibility, chemical properties, and pore loading content, which impact the spin states of the host framework and the ST behavior exhibited by the host-guest composites.

On the Use of Mixed Markov Models for Intensive Longitudinal Data.

Markov modeling presents an attractive analytical framework for researchers who are interested in state-switching processes occurring within a person, dyad, family, group, or other system over time. Markov modeling is flexible and can be used with various types of data to study observed or latent state-switching processes, and can include subject-specific random effects to account for heterogeneity. We focus on the application of mixed Markov models to intensive longitudinal data sets in psychology, which are becoming ever more common and provide a rich description of each subject's process. We examine how specifications of a Markov model change when continuous random effect distributions are included, and how mixed Markov models can be used in the intensive longitudinal research context. Advantages of Bayesian estimation are discussed and the approach is illustrated by two empirical applications.

Methanol-Triggered Vapochromism Coupled with Solid-State Spin Switching in a Nickel(II)-Quinonoid Complex.

A highly methanol-selective vapochromic response has been realized in a NiII -quinonoid complex, [Ni(HLMe )2 ] (H2 LMe =4-methylamino-6-methyliminio-3-oxocyclohexa-1,4-dien-1-olate) which exhibits a reversible structural transformation including a coordination geometrical change between the square-planar and octahedral structure by the selective uptake of methanol vapor. This was accompanied by a remarkable color change between purple and orange, as well as temperature-robust spin-state switching in the solid state under ambient conditions. It is remarkable that the properties are derived by the fine structural modification of the quinonoid ligand such as methyl or ethyl analogues. Such a system has high potential for applications in memory devices as well as chemical sensors and smart responsive materials.

Disorder, Promiscuous Interactions, and Stochasticity Regulate State Switching in the Unstable Prostate.

A causal link between benign prostatic hyperplasia (BPH) and prostate cancer has long been suspected but not widely accepted. A new model is proposed that supports such a connection. In contrast to the prevailing wisdom, our model, that draws on dynamical systems theory, suggests that in response to stress, epithelial cells in the unstable gland can give rise to both types of diseases via a phenotypic switching mechanism. The central idea is that phenotypic switching is a stochastic process which exploits the plasticity of the epithelial cell. It is driven by 'noise' contributed by the conformational dynamics of proteins that are intrinsically disordered. In a system that is noisy when stressed, disorder promotes promiscuity, unmasks latent information, and rewires the network to cause phenotypic switching. Cells with newly acquired phenotypes can transcend the traditional zonal boundaries to give rise to BPH or prostate cancer depending on the microenvironment. Establishing causality between the two diseases may provide us with an opportunity to better understand their etiology and guide prevention and treatment strategies. J. Cell. Biochem. 117: 2235-2240, 2016. © 2016 Wiley Periodicals, Inc.

Default mode network abnormalities during state switching in attention deficit hyperactivity disorder.

BACKGROUND: Individuals with attention deficit hyperactivity disorder (ADHD) display excess levels of default mode network (DMN) activity during goal-directed tasks, which are associated with attentional disturbances and performance decrements. One hypothesis is that this is due to attenuated down-regulation of this network during rest-to-task switching. A second related hypothesis is that it may be associated with right anterior insula (rAI) dysfunction - a region thought to control the actual state-switching process. METHOD: These hypotheses were tested in the current fMRI study in which 19 adults with ADHD and 21 typically developing controls undertook a novel state-to-state switching paradigm. Advance cues signalled upcoming switches between rest and task periods and switch-related anticipatory modulation of DMN and rAI was measured. To examine whether rest-to-task switching impairments may be a specific example of a more general state regulation deficit, activity upon task-to-rest cues was also analysed. RESULTS: Against our hypotheses, we found that the process of down-regulating the DMN when preparing to switch from rest to task was unimpaired in ADHD and that there was no switch-specific deficit in rAI modulation. However, individuals with ADHD showed difficulties up-regulating the DMN when switching from task to rest. CONCLUSIONS: Rest-to-task DMN attenuation seems to be intact in adults with ADHD and thus appears unrelated to excess DMN activity observed during tasks. Instead, individuals with ADHD exhibit attenuated up-regulation of the DMN, hence suggesting disturbed re-initiation of a rest state.

Disorder, oscillatory dynamics and state switching: the role of c-Myc.

In this paper, using the intrinsically disordered oncoprotein Myc as an example, we present a mathematical model to help explain how protein oscillatory dynamics can influence state switching. Earlier studies have demonstrated that, while Myc overexpression can facilitate state switching and transform a normal cell into a cancer phenotype, its downregulation can reverse state-switching. A fundamental aspect of the model is that a Myc threshold determines cell fate in cells expressing p53. We demonstrate that a non-cooperative positive feedback loop coupled with Myc sequestration at multiple binding sites can generate bistable Myc levels. Normal quiescent cells with Myc levels below the threshold can respond to mitogenic signals to activate the cyclin/cdk oscillator for limited cell divisions but the p53/Mdm2 oscillator remains nonfunctional. In response to stress, the p53/Mdm2 oscillator is activated in pulses that are critical to DNA repair. But if stress causes Myc levels to cross the threshold, Myc inactivates the p53/Mdm2 oscillator, abrogates p53 pulses, and pushes the cyclin/cdk oscillator into overdrive sustaining unchecked proliferation seen in cancer. However, if Myc is downregulated, the cyclin/cdk oscillator is inactivated and the p53/Mdm2 oscillator is reset and the cancer phenotype is reversed.

Electromyography-Based Quantitative Representation Method for Upper-Limb Elbow Joint Angle in Sagittal Plane.

This paper presents a quantitative representation method for the upper-limb elbow joint angle using only electromyography (EMG) signals for continuous elbow joint voluntary flexion and extension in the sagittal plane. The dynamics relation between the musculotendon force exerted by the biceps brachii muscle and the elbow joint angle is developed for a modified musculoskeletal model. Based on the dynamics model, a quadratic-like quantitative relationship between EMG signals and the elbow joint angle is built using a Hill-type-based muscular model. Furthermore, a state switching model is designed to stabilize the transition of EMG signals between different muscle contraction motions during the whole movement. To evaluate the efficiency of the method, ten subjects performed continuous experiments during a 4-day period and five of them performed a subsequent consecutive stepping test. The results were calculated in real-time and used as control reference to drive an exoskeleton device bilaterally. The experimental results indicate that the proposed method can provide suitable prediction results with root-mean-square (RMS) errors of below 10° in continuous motion and RMS errors of below 10° in stepping motion with 20° and 30° increments. It is also easier to calibrate and implement.

Anticipatory processes in brain state switching - evidence from a novel cued-switching task implicating default mode and salience networks.

The default mode network (DMN) is the core brain system supporting internally oriented cognition. The ability to attenuate the DMN when switching to externally oriented processing is a prerequisite for effective performance and adaptive self-regulation. Right anterior insula (rAI), a core hub of the salience network (SN), has been proposed to control the switching from DMN to task-relevant brain networks. Little is currently known about the extent of anticipatory processes subserved by DMN and SN during switching. We investigated anticipatory DMN and SN modulation using a novel cued-switching task of between-state (rest-to-task/task-to-rest) and within-state (task-to-task) transitions. Twenty healthy adults performed the task implemented in an event-related functional magnetic resonance imaging (fMRI) design. Increases in activity were observed in the DMN regions in response to cues signalling upcoming rest. DMN attenuation was observed for rest-to-task switch cues. Obversely, DMN was up-regulated by task-to-rest cues. The strongest rAI response was observed to rest-to-task switch cues. Task-to-task switch cues elicited smaller rAI activation, whereas no significant rAI activation occurred for task-to-rest switches. Our data provide the first evidence that DMN modulation occurs rapidly and can be elicited by short duration cues signalling rest- and task-related state switches. The role of rAI appears to be limited to certain switch types - those implicating transition from a resting state and to tasks involving active cognitive engagement.

How to account for behavioral states in step-selection analysis: a model comparison.

Step-selection models are widely used to study animals' fine-scale habitat selection based on movement data. Resource preferences and movement patterns, however, often depend on the animal's unobserved behavioral states, such as resting or foraging. As this is ignored in standard (integrated) step-selection analyses (SSA, iSSA), different approaches have emerged to account for such states in the analysis. The performance of these approaches and the consequences of ignoring the states in step-selection analysis, however, have rarely been quantified. We evaluate the recent idea of combining iSSAs with hidden Markov models (HMMs), which allows for a joint estimation of the unobserved behavioral states and the associated state-dependent habitat selection. Besides theoretical considerations, we use an extensive simulation study and a case study on fine-scale interactions of simultaneously tracked bank voles (Myodes glareolus) to compare this HMM-iSSA empirically to both the standard and a widely used classification-based iSSA (i.e., a two-step approach based on a separate prior state classification). Moreover, to facilitate its use, we implemented the basic HMM-iSSA approach in the R package HMMiSSA available on GitHub.

Dynamic Aggregation Triggering Reversible Spin-State Switching.

The manipulation of spin-state switching (SSS) under ambient conditions is of significant importance for the construction of molecular switches. Herein, we demonstrate that reversible SSS can be mediated by the aggregation state of a near-infrared (NIR)-sensitive ferrous complex. The ferrous complex was J-aggregated in a DMF suspension and with a low-spin (LS) state; however, with the addition of water, it changed to H-aggregation and reached a high-spin (HS) state, owing to the enhanced intramolecular charge transfer and metal-to-ligand charge transfer. Interestingly, the following NIR irradiation can restore the J-aggregation and LS states owing to the enhanced ligand-to-ligand charge transfer. More interestingly, the ferrous complex can be further incorporated into a hygroscopic sponge that was capable of capturing humidity effectively for all weather conditions, which displayed reversible SSS via alternating atmospheric humidity capture and NIR irradiation under ambient conditions in the sponge state. This study thus opens up a new avenue for the development of novel smart molecular switches at the device level.