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A copper foam supported N-doped carbon nanowire array confined CoP nanoparticle hybrid (CoP@NCNWAs/CF) as an efficient bifunctional electrocatalyst for overall water splitting.

This paper uses educational ethnography to examine in depth the emotional labor of the development of the particular professional identity of young international Chinese teachers and the underlying mechanisms behind emotions. The study found that the performance and strategy of young international Chinese teachers' emotional labor was dominated by deep acting, and that professional self-esteem, professional constraint, and cross-cultural conflict were important factors contributing to the emotional labor overload of this professional group. Although emotional labor constructs the meaning of daily life, it also reinforces this group's sense of self-alienation and professional burnout. Based on this, it is important to pay attention to the professional needs of young international Chinese teachers, establish a community mechanism for international Chinese teachers, and improve the professional assessment system for young international Chinese teachers in order to promote the maintenance of emotional labor at a normal and reasonable level, reduce emotional exhaustion, and enhance job satisfaction and professional gain.

In the modern society where the pressure of interpersonal communication is increasing, the ritualistic interactions represented by mutual praise reinterprets the interpersonal communication behavior of youth groups to some extent. In traditional interpersonal interaction, the connotation of sincerity, affirmation and identification represented by "praise" has been deprived and replaced. According to the needs of social interaction and individual emotional needs, college students have given new connotation to "praise", showing a changing trend from sincere praise to deliberate flattering. Based on this, this paper puts forward the concept of "ritualistic praise", and analyzes the causes and influences of this phenomenon from the perspective of interactive ritual chains, to get a glimpse of the emotional life patterns of young people in the context of modern interpersonal communication.

Messenger RNA (mRNA) therapies have recently gained tremendous traction with the approval of mRNA vaccines for the prevention of SARS-CoV-2 infection. However, manufacturing challenges have complicated large scale mRNA production, which is necessary for the clinical viability of these therapies. Not only can the incorporation of the required 5’ 7-methylguanosine cap analog be inefficient and costly, in vitro transcription (IVT) using wild-type T7 RNA polymerase generates undesirable double-stranded RNA (dsRNA) byproducts that elicit adverse host immune responses and are difficult to remove at large scale. To overcome these challenges, we have engineered a novel RNA polymerase, T7-68, that co-transcriptionally incorporates both di- and tri-nucleotide cap analogs with high efficiency, even at reduced cap analog concentrations. We also demonstrate that IVT products generated with T7-68 have reduced dsRNA content.

In The House of Mirth, modern American female writer Edith Wharton depicts a young woman named Lily Bart, who is struggling in the upper class of old New York society, but is eventually abandoned b...

The emergence of new therapeutic modalities requires complementary tools for their efficient syntheses. Availability of methodologies for site-selective modification of biomolecules remains a long-standing challenge, given the inherent complexity and the presence of repeating residues that bear functional groups with similar reactivity profiles. We describe a bioconjugation strategy for modification of native peptides relying on high site selectivity conveyed by enzymes. We engineered penicillin G acylases to distinguish among free amino moieties of insulin (two at amino termini and an internal lysine) and manipulate cleavable phenylacetamide groups in a programmable manner to form protected insulin derivatives. This enables selective and specific chemical ligation to synthesize homogeneous bioconjugates, improving yield and purity compared to the existing methods, and generally opens avenues in the functionalization of native proteins to access biological probes or drugs.

Recently, it was revealed that uniformly positive and negative couplings perform complementary roles in the signal amplification of coupled bistable oscillators. However, the coupling weights between oscillators are usually nonuniform due to adaptivity in many nonlinear systems. For this reason, we introduce a weight ratio of positive and negative couplings and investigate how it affects the response of globally coupled bistable oscillators to a subthreshold signal. We show that the weight ratio can significantly optimize the system response to the subthreshold signal compared to the case of uniform couplings. Finally, we propose a reduced model and compare its analytical approximations with the numerical results.

Inspired by the coexistence of excitatory and inhibitory neurons in real neural networks, we propose a motif of three coupled nodes, one with positive coupling and two with negative couplings, for signal amplification. Utilizing the bistable overdamped oscillator as well as the excitable neuron models, we show that the response of the motif is optimized for an intermediate range of coupling strength, i.e., coupling-induced resonance. Through theoretical analyses, we find that the underlying mechanism for the resonance is an abrupt pitchfork bifurcation caused by the mixed positive and negative couplings.

Synaptic noise plays a major role in setting up coexistence of various firing patterns, but the precise mechanisms whereby these synaptic noise contributes to coexisting firing activities are subtle and remain elusive. To investigate these mechanisms, neurons with hybrid synaptic interaction in a balanced neuronal networks have been recently put forward. Here we show that both synaptic noise intensity and excitatory weights can make a greater contribution than variance of synaptic noise to the coexistence of firing states with slight modification parameters. The resulting statistical analysis of both voltage trajectories and their spike trains reveals two forms of coexisting firing patterns: time-varying and parameter-varying multistability. The emergence of time-varying multistability as a format of metstable state has been observed under suitable parameters settings of noise intensity and excitatory synaptic weight. While the parameter-varying multistability is accompanied by coexistence of synchrony state and metastable (or asynchronous firing state) with slightly varying noise intensity and excitatory weights. Our results offer a series of precise statistical explanation of the intricate effect of synaptic noise in neural multistability. This reconciles previous theoretical and numerical works, and confirms the suitability of various statistical methods to investigate multistability in a hybrid synaptic interacting neuronal networks., 15 pages,8 figures

Spreading dynamics is a common yet sophisticated phenomenon in real life, and percolation theory is widely applied in analysis of this dynamics due to its conciseness and efficiency. With the development of information technology, the quality and quantity of available data are being improved. Although this offers a chance to describe and understand empirical spreading phenomena more comprehensively and accurately, complicated dynamics brought by massive data pose new challenges to the study of social contagion based on percolation theory. In this prospective, we show, by analyzing examples, how the percolation theory is used to describe the information transmission on social networks driven by big data. We also explore the indirect influence mechanism behind the spread of scientific research behavior, and develop a new algorithm to quantify the global influence of nodes from the local topology. Finally, we propose, based on these example studies, several possible new directions of percolation theory in the study of social contagion driven by big data.

The three-node feedforward motif has been revealed to function as a weak signal amplifier. In this motif, two nodes (input nodes) receive a weak input signal and send it unidirectionally to the third node (output node). Here, we change the motif’s unidirectional couplings (feedforward) to bidirectional couplings (feedforward and feedback working together). We find that a small asymmetric coupling, in which the feedforward effect is stronger than the feedback effect, may enable the three-node motif to go through two distinct dynamic transitions, giving rise to a double resonant signal response. We present an analytical description of the double resonance, which agrees with the numerical findings.

The process of vaccinating the world population against COVID-19 is expected to take well over a year to complete. As vaccination progresses and population immunity increases, a counteracting relaxation of social distancing measures is observed. The result will be a prolonged period of high disease prevalence combined with a fitness advantage for vaccine-resistant variants, implying a considerably increased probability that a resistant variant will spread in the population. In this paper we propose a spatial vaccination strategy that has the potential to dramatically reduce this risk. Instead of spreading the vaccination effort equally throughout a country, distinct geographic regions of the country are sequentially vaccinated, quickly bringing each to effective herd immunity. Regions with high vaccination rates will then have low infection rates and vice versa. Since people primarily interact with others in their own region, spatial vaccination will reduce the number of encounters between infected people (the source of mutations) and vaccinated people (who facilitate the spread of vaccine-resistant strains). Thus with proper logistic preparations, a spatial vaccination campaign could be highly effective in reducing the global risk of vaccine-resistant variants.

We study the maximum response of network-coupled bistable units to subthreshold signals focusing on the effect of phase disorder. We find that for signals with large levels of phase disorder, the network exhibits an enhanced response for intermediate coupling strength, while generating a damped response for low levels of phase disorder. We observe that the large phase-disorder-enhanced response depends mainly on the signal intensity but not on the signal frequency or the network topology. We show that a zero average activity of the units caused by large phase disorder plays a key role in the enhancement of the maximum response. With a detailed analysis, we demonstrate that large phase disorder can suppress the synchronization of the units, leading to the observed resonancelike response. Finally, we examine the robustness of this phenomenon to the unit bistability, the initial phase distribution, and various signal waveform. Our result demonstrates a potential benefit of phase disorder on signal amplification in complex systems.

In modern society, new communication channels and social platforms remarkably change the way of people receiving and sharing information, but the influences of these channels on information spreading dynamics have not been fully explored, especially in the aspects of outbreak patterns. To this end, based on a susceptible–accepted–recovered model, we examined the outbreak patterns of information spreading in a two-layered network with two coexisting channels: the intra-links within a layer and the inter-links across layers. Depending on the inter-layer coupling strength, i.e., average node degree and transmission probability between the two layers, we observed three different spreading patterns: (i) a localized outbreak with weak inter-layer coupling, (ii) two peaks with a time-delay outbreak appear for an intermediate coupling, and (iii) a synchronized outbreak for a strong coupling. Moreover, we showed that even though the average degree between the two layers is small, a large transmission probability still can compensate and promote the information spread from one layer to another, indicating by that the critical average degree decreases as a power law with transmission probability between the two layers. Additionally, we found that a large gap closed to the critical inter-layer average degree appears in the phase space of theoretical analysis, which indicates the emergence of a global large-scope outbreak. Our findings may, therefore, be of significance for understanding the outbreak behaviors of information spreading in real world.

Battling a widespread pandemic is an arms race between our mitigation efforts, e.g., social distancing or vaccination, and the pathogen's evolving persistence. This is being observed firsthand during the current COVID-19 crisis, as novel mutations are constantly challenging our global vaccination race. To address this, we introduce here a general framework for epidemic spreading under pathogen evolution, which shows that mutations can fundamentally alter the projection of the spread. Specifically, we detect a new pandemic phase - the mutated phase - in which, despite the fact that the pathogen is initially non-pandemic (R0 < 1), it may still spread due to the emergence of a critical mutation. The boundaries of this phase portray a balance between the epidemic and the evolutionary time-scales. If the mutation rate is too low, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, a broad range of conditions exists in which an initially sub-pandemic pathogen will eventually gain prevalence. This is especially relevant during vaccination, which creates, as it progresses, increasing selection pressure towards vaccine-resistance. To overcome this, we show that vaccination campaigns must be accompanied by fierce mitigation efforts, to suppress the potential rise of a resistant mutant strain.

The dynamics of epidemic spreading is often reduced to the single control parameter R0 (reproduction-rate), whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, R0 may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of this pandemic phase, we introduce here a modeling framework to couple the inter-host network spreading patterns with the intra-host evolutionary dynamics. We find that even in the extreme case when these two process are driven by mutually independent selection forces, mutations can still fundamentally alter the pandemic phase-diagram. The pandemic transitions, we show, are now shaped, not just by R0, but also by the balance between the epidemic and the evolutionary timescales. If mutations are too slow, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, we identify a broad range of conditions in which an initially sub-pandemic pathogen can breakthrough to gain widespread prevalence.

A fundamental problem in new field of Network Physiology is how organ systems in the human body dynamically interact to coordinate and synchronize their functions, and integrate as a network to generate distinct states and behaviours at the organism level. Physiological systems exhibit complex dynamics, operate at different time scales, and are regulated by multi-component mechanisms, which poses challenges to studying physiologic coupling and network interactions. We present a method based on the concept of time delay stability to probe transient physiologic network interactions in a group of healthy subjects during sleep. We investigate the multi-layer network structure and dynamics of interactions among (i) physiologically relevant brain rhythms within and across cortical locations, (ii) brain rhythms and key peripheral organ systems, and (iii) organ systems with each other. We demonstrate that each physiologic state (sleep stage) is characterized by a specific network structure and link strength distribution, and that the entire physiological network undergoes hierarchical reorganization across layers with transition from one stage to another. Our findings are consistent across subjects, and indicate a robust association of network structure and dynamics with physiologic state and function. The presented approach provides a new framework to explore physiologic states through networks of organ interactions.

An intriguing question in the new field of Network Physiology is how organ systems in the human body dynamically interact to coordinate functions, to maintain healthy homeostasis, and to generate distinct physiological states and behaviors at the organism level. Physiological systems exhibit complex dynamics, operate at different time scales and are regulated by multi-component mechanisms, which poses challenges to studying physiologic coupling and network interactions among systems with diverse dynamics. We present a conceptual framework and a method based on the concept of time delay stability to probe transient physiologic network interactions in a group of healthy subjects during sleep. We investigate the multi-layer network structure and dynamics of interactions among (i) physiologically relevant brain rhythms within and across cortical locations, (ii) brain rhythms and key peripheral organ systems, and (iii) the network structure and dynamics among peripheral organ systems across distinct physiological states. We demonstrate that each physiologic state (sleep stage) is characterized by a specific network structure and link strength distribution. The entire physiological network undergoes hierarchical reorganization across layers with the transition from one stage to another. Our findings are consistent across subjects and indicate a robust association of organ network structure and dynamics with physiologic state and function. The presented Network Physiology approach provides a new framework to explore physiologic states under health and disease through networks of organ interactions.

The threshold model as a classical paradigm for studying information spreading processes has been well studied. The main focuses are on how the underlying social network structure or the size of initial seeds can affect the cascading dynamics. However, the influence of node characteristics has been largely ignored. Here, inspired by empirical observations, we extend the threshold model by taking into account lurking nodes, who rarely interact with their neighbors. In particular, we consider two different scenarios: (i) Lurkers are absolutely silent and never interact with others and (ii) lurkers intermittently interact with their neighborhood with an activity rate $p$. In the first case, we demonstrate that lurkers may reduce the effective average degree of the underlying network, playing a dual role in spreading dynamics. In the latter case, we find that the stochastic dynamic behavior of lurkers could significantly promote the spread of information. Concretely, slightly raising the activity rate $p$ of lurkers may result in a remarkable increase in the final cascade size. Further increasing $p$ could make nodes become more stable on average, while it is still easy to observe global cascades due to the fluctuations of the effective degree of nodes.

Recent research has revealed that a system of coupled units with a certain degree of parameter diversity can generate an enhanced response to a subthreshold signal compared to that without diversity, exhibiting a diversity-induced resonance. We here show that diversity-induced resonance can also respond to a suprathreshold signal in a system of globally coupled bistable oscillators or excitable neurons, when the signal amplitude is in an optimal range close to the threshold amplitude. We find that such diversity-induced resonance for optimally suprathreshold signals is sensitive to the signal period for the system of coupled excitable neurons, but not for the coupled bistable oscillators. Moreover, we show that the resonance phenomenon is robust to the system size. Furthermore, we find that intermediate degrees of parameter diversity and coupling strength jointly modulate either the waveform or the period of collective activity of the system, giving rise to the resonance for optimally suprathreshold signals. Finally, with low-dimensional reduced models, we explain the underlying mechanism of the observed resonance. Our results extend the scope of the diversity-induced resonance effect.