A mechanism-based theory of cellular and tissue plasticity.

Plastic deformation in cells and tissues has been found to play crucial roles in collective cell migration, cancer metastasis, and morphogenesis. However, the fundamental question of how plasticity is initiated in individual cells and then propagates within the tissue remains elusive. Here, we develop a mechanism-based theory of cellular and tissue plasticity that accounts for all key processes involved, including the activation and development of active contraction at different scales as well as the formation of endocytic vesicles on cell junctions and show that this theory achieves quantitative agreement with all existing experiments. Specifically, it reveals that, in response to optical or mechanical stimuli, the myosin contraction and thermal fluctuation-assisted formation and pinching of endocytic vesicles could lead to permanent shortening of cell junctions and that such plastic constriction can stretch neighboring cells and trigger their active contraction through mechanochemical feedbacks and eventually their plastic deformations as well. Our theory predicts that endocytic vesicles with a size around 1 to 2 µm will most likely be formed and a higher irreversible shortening of cell junctions could be achieved if a long stimulation is split into multiple short ones, all in quantitative agreement with experiments. Our analysis also shows that constriction of cells in tissue can undergo elastic/unratcheted to plastic/ratcheted transition as the magnitude and duration of active contraction increases, ultimately resulting in the propagation of plastic deformation waves within the monolayer with a constant speed which again is consistent with experimental observations.

Size-dependent response of cells in epithelial tissue modulated by contractile stress fibers.

Although cells with distinct apical areas have been widely observed in epithelial tissues, how the size of cells affects their behavior during tissue deformation and morphogenesis as well as key physical factors modulating such influence remains elusive. Here, we showed that the elongation of cells within the monolayer under anisotropic biaxial stretching increases with their size because the strain released by local cell rearrangement (i.e., T1 transition) is more significant for small cells that possess higher contractility. On the other hand, by incorporating the nucleation, peeling, merging, and breakage dynamics of subcellular stress fibers into classical vertex formulation, we found that stress fibers with orientations predominantly aligned with the main stretching direction will be formed at tricellular junctions, in good agreement with recent experiments. The contractile forces generated by stress fibers help cells to resist imposed stretching, reduce the occurrence of T1 transitions, and, consequently, modulate their size-dependent elongation. Our findings demonstrate that epithelial cells could utilize their size and internal structure to regulate their physical and related biological behaviors. The theoretical framework proposed here can also be extended to investigate the roles of cell geometry and intracellular contraction in processes such as collective cell migration and embryo development.

Quantifying the Influence of Delay in Opinion Transmission of COVID-19 Information Propagation: Modeling Study.

BACKGROUND: In a fast-evolving public health crisis such as the COVID-19 pandemic, multiple pieces of relevant information can be posted sequentially on a social media platform. The interval between subsequent posting times may have a different impact on the transmission and cross-propagation of the old and new information that results in a different peak value and a final size of forwarding users of the new information, depending on the content correlation and whether the new information is posted during the outbreak or quasi-steady-state phase of the old information. OBJECTIVE: This study aims to help in designing effective communication strategies to ensure information is delivered to the maximal number of users. METHODS: We developed and analyzed two classes of susceptible-forwarding-immune information propagation models with delay in transmission to describe the cross-propagation process of relevant information. A total of 28,661 retweets of typical information were posted frequently by each opinion leader related to COVID-19 with high influence (data acquisition up to February 19, 2020). The information was processed into discrete points with a frequency of 10 minutes, and the real data were fitted by the model numerical simulation. Furthermore, the influence of parameters on information dissemination and the design of a publishing strategy were analyzed. RESULTS: The current epidemic outbreak situation, epidemic prevention, and other related authoritative information cannot be timely and effectively browsed by the public. The ingenious use of information release intervals can effectively enhance the interaction between information and realize the effective diffusion of information. We parameterized our models using real data from Sina Microblog and used the parameterized models to define and evaluate mutual attractiveness indexes, and we used these indexes and parameter sensitivity analyses to inform optimal strategies for new information to be effectively propagated in the microblog. The results of the parameter analysis showed that using different attractiveness indexes as the key parameters can control the information transmission with different release intervals, so it is considered as a key link in the design of an information communication strategy. At the same time, the dynamic process of information was analyzed through index evaluation. CONCLUSIONS: Our model can carry out an accurate numerical simulation of information at different release intervals and achieve a dynamic evaluation of information transmission by constructing an indicator system so as to provide theoretical support and strategic suggestions for government decision making. This study optimizes information posting strategies to maximize communication efforts for delivering key public health messages to the public for better outcomes of public health emergency management.

COVID-19 information contact and participation analysis and dynamic prediction in the Chinese Sina-microblog.

The outbreak of a novel coronavirus (COVID-19) aroused great public opinion in the Chinese Sina-microblog. To help in designing effective communication strategies during a major public health emergency, we analyze the real data of COVID-19 information and propose a comprehensive susceptible-reading-forwarding-immune (SRFI) model to understand the patterns of key information propagation considering both public contact and participation. We develop the SRFI model, based on the public reading quantity and forwarding quantity that denote contact and participation respectively, and take into account the behavior that users may re-enter another related topic during the attention phase or the participation phase freely. Data fitting using the real data of both reading quantity and forwarding quantity obtained from Chinese Sina-microblog can parameterize the model to make an accurate prediction of the COVID-19 public opinion trend until the next major news item occurs, and the sensitivity analysis provides the basic strategies for communication.

Tension- and Adhesion-Regulated Retraction of Injured Axons.

Damage-induced retraction of axons during traumatic brain injury is believed to play a key role in the disintegration of the neural network and to eventually lead to severe symptoms such as permanent memory loss and emotional disturbances. However, fundamental questions such as how axon retraction progresses and what physical factors govern this process still remain unclear. Here, we report a combined experimental and modeling study to address these questions. Specifically, a sharp atomic force microscope probe was used to transect axons and trigger their retraction in a precisely controlled manner. Interestingly, we showed that the retracting motion of a well-developed axon can be arrested by strong cell-substrate attachment. However, axon retraction was found to be retriggered if a second transection was conducted, albeit with a lower shrinking amplitude. Furthermore, disruption of the actin cytoskeleton or cell-substrate adhesion significantly altered the retracting dynamics of injured axons. Finally, a mathematical model was developed to explain the observed injury response of neural cells in which the retracting motion was assumed to be driven by the pre-tension in the axon and progress against neuron-substrate adhesion as well as the viscous resistance of the cell. Using realistic parameters, model predictions were found to be in good agreement with our observations under a variety of experimental conditions. By revealing the essential physics behind traumatic axon retraction, findings here could provide insights on the development of treatment strategies for axonal injury as well as its possible interplay with other neurodegenerative diseases.

Distinct relaxation timescales of neurites revealed by rate-dependent indentation, relaxation and micro-rheology tests.

Although the dynamic response of neurites is believed to play crucial roles in processes like axon outgrowth and formation of the neural network, the dynamic mechanical properties of such protrusions remain poorly understood. In this study, by using AFM (atomic force microscopy) indentation, we systematically examined the dynamic behavior of well-developed neurites on primary neurons under different loading modes (step loading, oscillating loading and ramp loading). Interestingly, the response was found to be strongly rate-dependent, with an apparent initial and long-term elastic modulus around 800 and 80 Pa, respectively. To better analyze the measurement data and extract information of key interest, the finite element simulation method (FEM) was also conducted where the neurite was treated as a viscoelastic solid consisting of multiple characteristic relaxation times. It was found that a minimum of three relaxation timescales, i.e. ∼0.01, 0.1 and 1 seconds, are needed to explain the observed relaxation curve as well as fit simulation results to the indentation and rheology data under different loading rates and driving frequencies. We further demonstrated that these three characteristic relaxation times likely originate from the thermal fluctuations of the microtubule, membrane relaxation and cytosol viscosity, respectively. By identifying key parameters describing the time-dependent behavior of neurites, as well as revealing possible physical mechanisms behind, this study could greatly help us understand how neural cells perform their biological duties over a wide spectrum of timescales.

Differential evolution for population diversity mechanism based on covariance matrix.

Differential evolution (DE) is a heuristic global search algorithm based on population. It has exhibited great adaptability in solving continuous-domain problems, but sometimes suffered from insufficient local search ability and being trapped in local optimum when dealing with complicated optimization problems. To solve these problems, an improved differential evolution algorithm with population diversity mechanism based on covariance matrix (CM-DE) is proposed. First, a new parameter adaptation strategy is used to adapt the control parameters, in which the scale factor F is updated according to the improved wavelet basis function in the early stage and Cauchy distribution in the later stage and the crossover rate CR is generated according to normal distribution. The diversity of population and convergence speed are improved by employing the method above. Second, the perturbation strategy is incorporated into crossover operator to enhance the search ability of DE. Finally, the covariance matrix of the population is constructed, where the variance in the covariance matrix is used as indicator to measure the similarity between individuals in the population in order to prevent the algorithm from falling into local optimum resulted by low population diversity. The CM-DE is compared with the state-of-art DE variants including LSHADE (Tanabe and Fukunaga, 2014), jSO [1], LPalmDE [2], PaDE [3] and LSHADE-cnEpSin [4] under 88 test functions from CEC2013 [5], CEC2014 [6] and CEC2017 (Wu et al., 2017) test suites. From the experiment results, it is obvious that among 30 benchmark functions from CEC2017 on 50D optimization, the CM-DE algorithm has 22, 20, 24, 23, 28 better performances comparing with LSHADE, jSO, LPalmDE, PaDE, and LSHADE-cnEpsin. For CEC2017 on 30D optimization, the proposed algorithm secures better performance on 19 out of 30 benchmark functions in terms of convergence speed. In addition, a real-world application is also used to verify the feasibility of the proposed algorithm. The experiment results validate the highly competitive performance in terms of solution accuracy and convergence speed.

FD-DE: Differential Evolution with fitness deviation based adaptation in parameter control.

Differential Evolution (DE) is arguably one of the most powerful stochastic optimization algorithms for different optimization applications, however, even the state-of-the-art DE variants still have many weaknesses. In this study, a new powerful DE variant for single-objective numerical optimization is proposed, and there are several contributions within it: First, an enhanced wavelet basis function is proposed to generate scale factor F of each individual in the first stage of the evolution; Second, a hybrid trial vector generation strategy with perturbation and t-distribution is advanced to generate different trial vectors regarding different stages of the evolution; Third, a fitness deviation based parameter control is proposed for the adaptation of control parameters; Fourth, a novel diversity indicator is proposed and a restart scheme can be launched if necessary when the quality of the individuals is detected bad. The novel algorithm is validated using a large test suite containing 130 benchmarks from the universal test suites on single-objective numerical optimization, and the results approve the big improvement in comparison with several well-known state-of-the-art DE variants. Moreover, our algorithm is also validated under real-world optimization applications, and the results also support its superiority.

Rapid Plastic Deformation of Cancer Cells Correlates with High Metastatic Potential.

Metastasis plays a crucial role in tumor development, however, lack of quantitative methods to characterize the capability of cells to undergo plastic deformations has hindered the understanding of this important process. Here, a microfluidic system capable of imposing precisely controlled cyclic deformation on cells and therefore probing their viscoelastic and plastic characteristics is developed. Interestingly, it is found that significant plastic strain can accumulate rapidly in highly invasive cancer cell lines and circulating tumor cells (CTCs) from late-stage lung cancer patients with a characteristic time of a few seconds. In constrast, very little irreversible deformation is observed in the less invasive cell lines and CTCs from early-stage lung cancer patients, highlighting the potential of using the plastic response of cells as a novel marker in future cancer study. Furthermore, author showed that the observed irreversible deformation should originate mainly from cytoskeleton damage, rather than plasticity of the cell nucleus.

An AIEgen/graphene oxide nanocomposite (AIEgen@GO)-based two-stage "turn-on" nucleic acid biosensor for rapid detection of SARS-CoV-2 viral sequence.

The ongoing outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has posed significant challenges in early viral diagnosis. Hence, it is urgently desirable to develop a rapid, inexpensive, and sensitive method to aid point-of-care SARS-CoV-2 detection. In this work, we report a highly sequence-specific biosensor based on nanocomposites with aggregation-induced emission luminogens (AIEgen)-labeled oligonucleotide probes on graphene oxide nanosheets (AIEgen@GO) for one step-detection of SARS-CoV-2-specific nucleic acid sequences (Orf1ab or N genes). A dual "turn-on" mechanism based on AIEgen@GO was established for viral nucleic acids detection. Here, the first-stage fluorescence recovery was due to dissociation of the AIEgen from GO surface in the presence of target viral nucleic acid, and the second-stage enhancement of AIE-based fluorescent signal was due to the formation of a nucleic acid duplex to restrict the intramolecular rotation of the AIEgen. Furthermore, the feasibility of our platform for diagnostic application was demonstrated by detecting SARS-CoV-2 virus plasmids containing both Orf1ab and N genes with rapid detection around 1 h and good sensitivity at pM level without amplification. Our platform shows great promise in assisting the initial rapid detection of the SARS-CoV-2 nucleic acid sequence before utilizing quantitative reverse transcription-polymerase chain reaction for second confirmation.

Force-mediated cellular anisotropy and plasticity dictate the elongation dynamics of embryos.

We developed a unified dynamic model to explain how cellular anisotropy and plasticity, induced by alignment and severing/rebundling of actin filaments, dictate the elongation dynamics of Caenorhabditis elegans embryos. It was found that the gradual alignment of F-actins must be synchronized with the development of intracellular forces for the embryo to elongate, which is then further sustained by muscle contraction-triggered plastic deformation of cells. In addition, we showed that preestablished anisotropy is essential for the proper onset of the process while defects in the integrity or bundling kinetics of actin bundles result in abnormal embryo elongation, all in good agreement with experimental observations.

Modeling and analyzing cross-transmission dynamics of related information co-propagation.

The dissemination of one public hot event is usually affected by some related information, and the implication of co-propagation by different information is critical for the integrated analysis. To help in designing effective communication strategies during the whole event, we propose the cross-transmission susceptible-forwarding-immune (CT-SFI) model to describe the dynamics of co-propagation particularly with focus on the cross-transmission effects. This model is based on the forwarding quantity and takes into account the behavior that users may have a strong attraction or continuous attraction within or without an active time after contacting one information. Data fitting using the real data of Chinese Sina-microblog can accurately parameterize the model and parameter sensitivity analysis gives some strategies for co-propagation.

The role of proactive behavior on COVID-19 infordemic in the Chinese Sina-Microblog: a modeling study.

In order to avoid forming an information cocoon, the information propagation of COVID-19 is usually created through the action of "proactive search", an important behavior other than "reactive follow". This behavior has been largely ignored in modeling information dynamics. Here, we propose to fill in this gap by proposing a proactive-reactive susceptible-discussing-immune (PR-SFI) model to describe the patterns of co-propagation on social networks. This model is based on the forwarding quantity and takes into account both proactive search and reactive follow behaviors. The PR-SFI model is parameterized by data fitting using real data of COVID-19 related topics in the Chinese Sina-Microblog, and the model is calibrated and validated using the prediction accuracy of the accumulated forwarding users. Our sensitivity analysis and numerical experiments provide insights about optimal strategies for public health emergency information dissemination.

Maturation of Neural Cells Leads to Enhanced Axon-Extracellular Matrix Adhesion and Altered Injury Response.

Although it is known that stronger cell-extracellular matrix interactions will be developed as neurons mature, how such change influences their response against traumatic injury remains largely unknown. In this report, by transecting axons with a sharp atomic force microscope tip, we showed that the injury-induced retracting motion of axon can be temporarily arrested by tight NCAM (neural cell adhesion molecule) mediated adhesion patches, leading to a retraction curve decorated with sudden bursts. Interestingly, although the size of adhesion clusters (~0.5-1 µm) was found to be more or less the same in mature and immature neurons (after 7- and 3-days of culturing, respectively), the areal density of such clusters is three times higher in mature axons resulting in a much reduced retraction in response to injury. A physical model was also adopted to explain the observed retraction trajectories which suggested that apparent adhesion energy between axon and the substrate increases from ~0.12 to 0.39 mJ/m2 as neural cell matures, in good agreement with our experiments.

On-Demand 3D Printing of Nanowire Probes for High-Aspect-Ratio Atomic Force Microscopy Imaging.

With the growing importance of three-dimensional (3D) nanomaterials and devices, there has been a great demand for high-fidelity, full profile topographic characterizations in a nondestructive manner. A promising route is to employ a high-aspect-ratio (HAR) probe in atomic force microscopy (AFM) imaging. However, the fabrication of HAR-AFM probes continues to suffer from extravagant cost, limited material choice, and complicated manufacturing steps. Here, we report one-step, on-demand electrohydrodynamic 3D printing of metallic HAR-AFM probes with tailored dimensions. Our additive fabrication approach yields a freestanding metallic nanowire with an aspect ratio over 30 directly on a cantilever within tens of seconds, producing a HAR-AFM probe. Furthermore, the benefits associated with unprecedented simplicity in the probe's dimension control, material selection, and regeneration are provided. The 3D-printed HAR-AFM probe exhibits a better fidelity in deep trench AFM imaging than a standard pyramidal probe. We expect this approach to find facile, material-saving manufacturing routes in particular for customizing functional nanoprobes.

Insight into the unique role of silver single-atom in atomic-thickness ZnIn2S4/g-C3N4 Van der Waals heterojunction for photocatalytic hydrogen evolution.

The construction of ultra-close 2D atomic-thickness Van der Waals heterojunctions with high-speed charge transfer still faces challenges. Here, we synthesized single-layer ZnIn2S4 and g-C3N4, and introduced silver single atoms to regulate Van der Waals heterojunctions at the atomic level to optimize charge transfer and catalytic activity. At the atomic scale, the impact of detailed structural differences between the two characteristic surfaces of ZnIn2S4 ([Zn-S4] and [In-S4]) on catalytic performance has been first proposed. Experiments combined with the DFT study demonstrate that single atom Ag not only acts as a charge transfer bridge but also regulates the energy band and intrinsic catalytic activity. Benefiting from the enhanced electron delocalization, the synthesized catalyst ZIS/Ag@CN exhibits excellent photocatalytic performance, with a hydrogen production rate of 5.50 mmol·g-1·h-1, which is much higher than the reported Ag-based single-atom catalysts so far. This work provides a new understanding of atomic-level heterojunction interface regulation and modification.