State Transition Mechanism Research Articles

The thylakoid phosphatase TEF8 is involved in state transition and high light stress resistance in Chlamydomonas.

The sophisticated regulation of state transition is required to maintain optimal photosynthetic performance under fluctuating light condition, through balancing the absorbed light energy between photosystem II andphotosystem I. This exquisite process incorporates phosphorylation and dephosphorylation of light-harvesting complexes and PSII core subunits, accomplished by thylakoid membrane-localized kinases and phosphatases that have not been fully identified. In this study, one Chlamydomonas high light response gene, THYLAKOID ENRICHED FRACTION 8 (TEF8), was characterized. The Chlamydomonas tef8 mutant showed high light sensitivity and defective state transition. The enzymatic activity assays showed that TEF8 is a bona fide phosphatase localized in thylakoid membranes. Biochemical assays, including BN-PAGE, pull-down, and phosphopeptide mass spectrometry, proved that TEF8 associates with photosystem II and is involved in the dephosphorylation of D2 and CP29 subunits during state 2 to state 1 transition. Taken together, our results identified TEF8 as a thylakoid phosphatase with multiple dephosphorylation targets on photosystem II, and provide new insight into the regulatory mechanism of state transition and high light resistance in Chlamydomonas.

Aquatic environment drives the emergence of cell wall-deficient dormant forms in Listeria

Stressed bacteria can enter a dormant viable but non-culturable (VBNC) state. VBNC pathogens pose an increased health risk as they are undetectable by growth-based techniques and can wake up back into a virulent state. Although widespread in bacteria, the mechanisms governing this phenotypic switch remain elusive. Here, we investigate the VBNC state transition in the human pathogen Listeria monocytogenes. We show that bacteria starved in mineral water become VBNC by converting into osmotically stable cell wall-deficient coccoid forms, a phenomenon that occurs in other Listeria species. We reveal the bacterial stress response regulator SigB and the autolysin NamA as major actors of VBNC state transition. We lastly show that VBNC Listeria revert to a walled and virulent state after passage in chicken embryos. Our study provides more detail on the VBNC state transition mechanisms, revealing wall-free bacteria naturally arising in aquatic environments as a potential survival strategy in hypoosmotic and oligotrophic conditions.

Shedding light on blue-green photosynthesis: A wavelength-dependent mathematical model of photosynthesis in Synechocystis sp. PCC 6803.

Cyanobacteria hold great potential to revolutionize conventional industries and farming practices with their light-driven chemical production. To fully exploit their photosynthetic capacity and enhance product yield, it is crucial to investigate their intricate interplay with the environment including the light intensity and spectrum. Mathematical models provide valuable insights for optimizing strategies in this pursuit. In this study, we present an ordinary differential equation-based model for the cyanobacterium Synechocystis sp. PCC 6803 to assess its performance under various light sources, including monochromatic light. Our model can reproduce a variety of physiologically measured quantities, e.g. experimentally reported partitioning of electrons through four main pathways, O2 evolution, and the rate of carbon fixation for ambient and saturated CO2. By capturing the interactions between different components of a photosynthetic system, our model helps in understanding the underlying mechanisms driving system behavior. Our model qualitatively reproduces fluorescence emitted under various light regimes, replicating Pulse-amplitude modulation (PAM) fluorometry experiments with saturating pulses. Using our model, we test four hypothesized mechanisms of cyanobacterial state transitions for ensemble of parameter sets and found no physiological benefit of a model assuming phycobilisome detachment. Moreover, we evaluate metabolic control for biotechnological production under diverse light colors and irradiances. We suggest gene targets for overexpression under different illuminations to increase the yield. By offering a comprehensive computational model of cyanobacterial photosynthesis, our work enhances the basic understanding of light-dependent cyanobacterial behavior and sets the first wavelength-dependent framework to systematically test their producing capacity for biocatalysis.

Mechanistic insights into P-glycoprotein ligand transport and inhibition revealed by enhanced molecular dynamics simulations

P-glycoprotein (P-gp) plays a crucial role in cellular detoxification and drug efflux processes, transitioning between inward-facing (IF) open, occluded, and outward-facing (OF) states to facilitate substrate transport. Its role is critical in cancer therapy, where P-gp contributes to the multidrug resistance phenotype. In our study, classical and enhanced molecular dynamics (MD) simulations were conducted to dissect the structural and functional features of the P-gp conformational states. Our advanced MD simulations, including kinetically excited targeted MD (ketMD) and adiabatic biasing MD (ABMD), provided deeper insights into state transition and translocation mechanisms. Our findings suggest that the unkinking of TM4 and TM10 helices is a prerequisite for correctly achieving the outward conformation. Simulations of the IF-occluded conformations, characterized by kinked TM4 and TM10 helices, consistently demonstrated altered communication between the transmembrane domains (TMDs) and nucleotide binding domain 2 (NBD2), suggesting the implication of this interface in inhibiting P-gp's efflux function. A particular emphasis was placed on the unstructured linker segment connecting the NBD1 to TMD2 and its role in the transporter's dynamics. With the linker present, we specifically noticed a potential entrance of cholesterol (CHOL) through the TM4-TM6 portal, shedding light on crucial residues involved in accommodating CHOL. We therefore suggest that this entry mechanism could be employed for some P-gp substrates or inhibitors. Our results provide critical data for understanding P-gp functioning and developing new P-gp inhibitors for establishing more effective strategies against multidrug resistance.

Experimental Study on the Self-Initiated Static-Dynamic State Transition Mechanism of Strain Rockburst

During the entire processes of rockburst catastrophe, the self-initiated static-dynamic state transition behaviour is a turning point in the development of strain rockburst from static deformation to dynamic failure. However, its dynamic process and triggering mechanism have not yet received sufficient attention and accurate revelation. This study focuses on the scientific issue, by designing experiment we have successfully achieved the physical simulation of rockburst under quasi-static displacement loading conditions. Then, the static-dynamic state transition process was studied on a millisecond time scale, and rockburst triggering mechanism was investigated from mechanical perspectives. Through experiment, the self-initiated elastic rebound dynamic behaviour of the surrounding rock is discovered and identified as the key to triggering rockburst. Mechanically, this behaviour directly causes an impact load on the burst rock, which can be considered the direct cause of rockburst.

Polyphenol‐Copper Derived Self‐Cascade Nanozyme Hydrogel in Boosting Oxygenation and Robust Revascularization for Tissue Regeneration

AbstractThe regeneration of hypoxia‐impaired chronic tissue defects has long been challenging, mainly due to the inefficiency of oxygenation and the limited biological activity of existing oxygen delivery systems in regulating dynamic tissue regeneration process. Herein, a novel polyphenol‐copper coordination strategy to fabricate bioactive superoxide dismutase‐catalase self‐cascade nanozymes (SalB‐CuNCs) is reported, which can serve as an in situ oxygenator and induce angiogenesis simultaneously. The copper‐phenolic hydroxyl coordination structure in SalB‐CuNCs plays a critical role in promoting the enzyme‐like cascade reaction via catechol‐mediated Cu valence state transition and substrate capture mechanism. Furthermore, after incorporating SalB‐CuNCs into a Schiff base hydrogel (COC@SalB‐Cu), the resulting system exhibits outstanding antioxidant and robust oxygenation effect in mitigating the hypoxic microenvironment. Benefiting from the intrinsic angiogenic activity of SalB and copper, COC@SalB‐Cu hydrogel can induce a more complete tube formation by up‐regulating the expression level of vascular endothelial growth factor (VEGF), platelet‐endothelial cell adhesion molecule‐1 (CD31), and endothelial nitric oxide synthase (eNOS). In vivo experiments further demonstrate that the COC@SalB‐Cu hydrogel can significantly restore the oxygen and blood supply, leading to fast tissue regeneration. The present strategy holds enormous promise for the treatment of hypoxia‐related chronic tissue defects and vascular injury in the field of regenerative medicine.

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.

The underlying mechanism of the strain-induced reversible magnetic state transition in MXene nanosheets

MXenes have emerged as one of the most promising members among the two-dimensional (2D) material family owing to their unique physical-chemical properties and outstanding application prospects. Here, we investigated the unique magnetic properties of Ti2C MXenes monolayer by means of the first-principles calculations combining with the Heisenberg spin Hamiltonian approach. A strain-induced reversible magnetic transition for the Ti2C monolayer could be found in our study. When the strain is applied to the Ti2C monolayer, the magnetic configuration of the Ti2C monolayer transforms from the A-type antiferromagnetic (A-AFM) phase to the ferromagnetic (FM) phase with the biaxial tensile strain larger than 6%. Furthermore, the transition from the A-AFM phase to the FM phase was found to be closely related with the change of the Ti and C atomic magnetic moments. The direct magnetic coupling between Ti and C atoms with the increased Ti and C magnetic moments is able to stabilize the structure due to the weakened superexchange interaction between Ti and C atoms under large tensile strain. This is the underlying driving force for the transition from the A-AFM phase to the FM phase. This work provides a promising pathway to better understand the existence of magnetic reversal behavior in the Ti2C MXenes monolayer and opens the door to future magnetic applications of MXenes.

Reciprocating sliding friction behavior and wear state transition mechanism of cylinder liner and piston ring

To investigate the wear time-varying characteristics of cylinder liner cast iron, dry sliding wear experiments were conducted on the cylinder liner and piston ring at room temperature. The surface and interface morphologies of the cylinder liner wear scar were analyzed using a white light interferometer, scanning electron microscope (SEM), and transmission electron microscope (TEM) at different wear times. The results indicate that the wear state of cylinder liner cast iron changes over time. Based on the change in friction coefficient, the wear process can be divided into three stages: low wear stage, stable wear stage, and severe wear stage. The shedding of graphite and its self-lubricating effect contributes to the low friction coefficient and wear rate observed in the low wear stage. The formation of a friction layer leads to the wear entering a stable stage, where adhesive wear of pearlite occurs. As the friction layer and the phosphorus eutectic wear and fall off, the wear process of the cylinder liner intensifies, with delamination wear becoming the primary wear mechanism. This study reveals the wear degradation mechanism of cylinder liner cast iron from the perspective of microstructures, offering valuable information for predicting wear and optimizing the microstructure of cylinder liners and piston rings.

Unveiling the State Transition Mechanisms of Ras Proteins through Enhanced Sampling and QM/MM Simulations.

In cells, wild-type RasGTP complexes exist in two distinct states: active State 2 and inactive State 1. These complexes regulate their functions by transitioning between the two states. However, the mechanisms underlying this state transition have not been clearly elucidated. To address this, we conducted a detailed simulation study to characterize the energetics of the stable states involved in the state transitions of the HRasGTP complex, specifically from State 2 to State 1. This was achieved by employing multiscale quantum mechanics/molecular mechanics and enhanced sampling molecular dynamics methods. Based on the simulation results, we constructed the two-dimensional free energy landscapes that provide crucial information about the conformational changes of the HRasGTP complex from State 2 to State 1. Furthermore, we also explored the conformational changes from the intermediate state to the product state during guanosine triphosphate hydrolysis. This study on the conformational changes involved in the HRas state transitions serves as a valuable reference for understanding the corresponding events of both KRas and NRas as well.

Polarization state transition mechanism of light through turbid media by Monte Carlo simulation

We study the propagation of polarized light through turbid media with high scattering coefficient (μ s = 50 cm−1) and disclose the physical processes involved in the evolution of Stokes vector. The results show that the components of the Stokes vector can be expressed as the superimposition of the generalized divergence and the generalized curl of the two orthogonal electric field vectors. The components I, Q, and U can be represented as the superimposition of the generalized divergence. The components V can be conveyed as the superimposition of the generalized curl omitting the direction. Further, the depolarization of the linearly polarized light corresponds to the alteration of the generalized divergence, while the depolarization of the circularly polarized light coincides with the variability of the generalized curl omitting the direction. The evolutions of the scattering electric fields arise from the scattering of the particles, followed by the polarization state transition of the incident light and the change of the scattering phase function. Further, the circularly polarized light can preserve the polarization state better than that of the linearly polarized light with an increase of the thickness of the scattering volume.

Agent-based modeling methodology and temporal simulation for Natech events in chemical clusters

In chemical clusters, technological accidents triggered by natural events (Natech events) can lead to the failure of large number of units and cause catastrophic consequences. Previous studies on Natech events give primary concerns on the static analysis at small-scale region with few thoughts the impact of various safety barriers. From the perspective of the large-scale region and temporal characteristics, an agent-based modeling methodology is proposed to present the dynamic evolution process. All the units associated with the evolution are divided into natural hazard agent, hazard-affected facility agent, safety barrier agent, and escalation factor agent to reduce the computational complexity. The time-dependent state transition mechanism of each agent is analyzed to represent the evolution process, simultaneously, the temporal effect of natural hazards, safety barrier and multi escalation factors is considered by utilization of time steps. Moreover, Monte Carlo simulation is adopted to quantify the uncertainties accompanying an accident evolution. The proposed methodology is demonstrated on a complex chemical system in the large-scale region. The results of simulation show that the number of failed hazard-affected facilities in the Natech events scenario will cyclically increase and keep constant, with strong temporal characteristic. The intensity of natural hazards and the probability of failure of safety barriers make a big impact on the temporal characteristic. The results are valuable to support natural hazard prevention in chemical cluster.

Using the dynamics of productivity and precipitation-use efficiency to detect state transitions in Eurasian grasslands

In the face of accelerated global dryland expansion and grassland degradation, signaling grassland ecosystem state transitions is an ongoing challenge in ecology. However, there is still a lack of effective indicators and understanding of the mechanisms of grassland ecosystem state transitions at the continental scale. Here, we propose a framework that links ecosystem function-based indicators and critical slowing down (CSD) theory to reveal grassland state transitions. Across precipitation gradients, we quantified the statistical characteristics and spatial patterns in ANPP and PUE dynamics (variability, asymmetry, and sensitivity to precipitation and temperature) in Eurasian grasslands. We show that the CVANPP, CVPUE, AANPP, APUE, SPUE-P, and SANPP-P of temperate steppes were significantly higher than those of alpine steppes, while the SPUE-T and SANPP-T were the inverse. In temperate grasslands, AANPP, APUE, and SANPP-P indicated the transition of typical steppes, and CVANPP, APUE, and SPUE-T indicated the transition from meadow to typical steppes. In alpine grasslands, APUE indicated the transition between alpine deserts and alpine steppes, and AANPP and SANPP-P indicated the transition between alpine steppes and meadow steppes. The interannual variability of precipitation strongly affected xerophyte proportion and demographic processes, which control state transitions in low-resilience grasslands. Community structures and limiting factors (nutrient, light, and/or temperature) regulate state transitions in high-resilience grasslands. Our results demonstrate that function-based indicators are predictive of impending state transitions of temperate and alpine grasslands, highlighting the complementation of ANPP and PUE dynamics that have the potential for predicting grassland ecosystem regime shifts and their underlying mechanisms.

State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions.

Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, they are often treated as black-box models and as such it is difficult to understand what exactly they learn as well as how they arrive at a particular prediction. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term state-regularization, makes RNNs transition between a finite set of learnable states. We evaluate state-regularized RNNs on (1) regular languages for the purpose of automata extraction; (2) non-regular languages such as balanced parentheses and palindromes where external memory is required; and (3) real-word sequence learning tasks for sentiment analysis, visual object recognition and text categorisation. We show that state-regularization (a) simplifies the extraction of finite state automata that display an RNN's state transition dynamic; (b) forces RNNs to operate more like automata with external memory and less like finite state machines, which potentiality leads to a more structural memory; (c) leads to better interpretability and explainability of RNNs by leveraging the probabilistic finite state transition mechanism over time steps.

Triple-network analysis of Alzheimer's disease based on the energy landscape.

Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer's disease (AD) affects the state transitions of functional networks in the resting state. Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state. AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects' dynamic features are correlated with clinical index. The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients.

Improved Ant Colony Optimization for the Operational Aircraft Maintenance Routing Problem with Cruise Speed Control

The operational aircraft maintenance routing problem (OAMRP) plays a critical part in producing considerable cost reductions for airlines, since its solution directly influences the number of operating leased aircraft. To reduce the quantity of required aircraft, adopting cruise speed control in OAMRP is a good strategy. In this paper, we investigate the OAMRP with cruise speed control. The objective is to minimize the required quantity of aircraft by finding the optimal aircraft routes through cruise time optimization. The focus is on solving two issues simultaneously: (i) optimization of cruise times and (ii) determination of aircraft routes. Since the combination of two intricate sets of decisions poses significant methodological challenges, the difficulty lies in how to efficiently solve it. Accordingly, the goal of this study is twofold: (i) to design a preprocessing step to reduce the network size and (ii) to develop an improved ant colony optimization (IACO) algorithm with a new state transition mechanism to provide the guidance for cruise times optimization and a new pheromone updating mechanism to enhance the search efficiency and precision. Using data from the Bureau of Transportation Statistics (BTS), we demonstrate the computational efficiency of the preprocessing step and the IACO algorithm.

The Risk Contagion between Chinese and Mature Stock Markets: Evidence from a Markov-Switching Mixed-Clayton Copula Model

Exploring the risk spillover between Chinese and mature stock markets is a promising topic. In this study, we propose a Markov-switching mixed-Clayton (Ms-M-Clayton) copula model that combines a state transition mechanism with a weighted mixed-Clayton copula. It is applied to investigate the dynamic risk dependence between Chinese and mature stock markets in the Americas, Europe, and Asia-Oceania regions. Additionally, the conditional value at risk (CoVaR) is applied to analyze the risk spillovers between these markets. The empirical results demonstrate that there is mainly a time-varying but stable positive risk dependence structure between Chinese and mature stock markets, where the upside and downside risk correlations are asymmetric. Moreover, the risk contagion primarily spills over from mature stock markets to the Chinese stock market, and the downside effect is stronger. Finally, the risk contagion from Asia-Oceania to China is weaker than that from Europe and the Americas. The study provides insights into the risk association between emerging markets, represented by China, and mature stock markets in major regions. It is significant for investors and risk managers, enabling them to avoid investment risks and prevent risk contagion.

Characterization of the preferred cation cofactors of chloroplast protein kinases in Arabidopsis thaliana.

Chloroplasts sense a variety of stimuli triggering several acclimation responses. One prominent response is the mechanism of state transitions, which enables rapid adaption to changes in illumination. Here, we investigated the link between divalent cations (calcium, magnesium, and manganese) and protein kinase activity in Arabidopsis chloroplasts. Our results show that manganese ions are the strongest activator of kinase activity in chloroplasts followed by magnesium ions, whereas calcium ions are not able to induce kinase activity. Additionally, the phosphorylation of specific protein bands is strongly reduced in chloroplasts of a cmt1 mutant, which is impaired in manganese import into chloroplasts, as compared to the wild-type. These findings provide insights for the future characterization of chloroplast protein kinase activity and potential target proteins.

Oligomer-to-monomer transition underlies the chaperone function of AAGAB in AP1/AP2 assembly

Assembly of protein complexes is facilitated by assembly chaperones. Alpha and gamma adaptin-binding protein (AAGAB) is a chaperone governing the assembly of the heterotetrameric adaptor complexes 1 and 2 (AP1 and AP2) involved in clathrin-mediated membrane trafficking. Here, we found that before AP1/2 binding, AAGAB exists as a homodimer. AAGAB dimerization is mediated by its C-terminal domain (CTD), which is critical for AAGAB stability and is missing in mutant proteins found in patients with the skin disease punctate palmoplantar keratoderma type 1 (PPKP1). We solved the crystal structure of the dimerization-mediating CTD, revealing an antiparallel dimer of bent helices. Interestingly, AAGAB uses the same CTD to recognize and stabilize the γ subunit in the AP1 complex and the α subunit in the AP2 complex, forming binary complexes containing only one copy of AAGAB. These findings demonstrate a dual role of CTD in stabilizing resting AAGAB and binding to substrates, providing a molecular explanation for disease-causing AAGAB mutations. The oligomerization state transition mechanism may also underlie the functions of other assembly chaperones.

The spectral-timing analysis of Cygnus X-1 with Insight-HXMT

Cygnus X-1, as the first discovered black hole binary, is a key source for understanding the mechanisms of state transitions and the scenarios of accretion in extreme gravity fields. We present a spectral-timing analysis of observations taken with the Insight–Hard X-ray Modulation Telescope (HXMT) mission, focusing on the spectral-state-dependent timing properties in the broad energy range of 1−150 keV, thus extending previous studies based on Rossi X-ray Timing Explorer (RXTE) to both lower and higher energies. Our main results are the following: (a) We successfully use a simple empirical model to fit all spectra, confirming that the reflection component is stronger in the soft state than in the hard state. (b) The evolution of the total fractional root mean square (rms) depends on the selected energy band and the spectral shape, which is a direct result of the evolution of the power spectral densities (PSDs). (c) In the hard/intermediate state, we see clear short-term variability features and a positive correlation between the central frequencies of the variability components and the soft photon index Γ1, which we also see at energies above 15 keV. In the soft state, the power spectrum is instead dominated by red noise. These behaviors can be traced to at least 90 keV. (d) Finally, the coherence and the phase-lag spectra show different behaviors, depending on the different spectral shapes.