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Abstract Tumor vaccines, a crucial immunotherapy, have gained growing interest because of their unique capability to initiate precise anti-tumor immune responses and establish enduring immune memory. Injected tumor vaccines passively diffuse to the adjacent draining lymph nodes, where the residing antigen-presenting cells capture and present tumor antigens to T cells. This process represents the initial phase of the immune response to the tumor vaccines and constitutes a pivotal determinant of their effectiveness. Nevertheless, the granularity paradox, arising from the different requirements between the passive targeting delivery of tumor vaccines to lymph nodes and the uptake by antigen-presenting cells, diminishes the efficacy of lymph node-targeting tumor vaccines. This study addressed this challenge by employing a vaccine formulation with a tunable, controlled particle size. Manganese dioxide (MnO2) nanoparticles were synthesized, loaded with ovalbumin (OVA), and modified with A50 or T20 DNA single strands to obtain MnO2/OVA/A50 and MnO2/OVA/T20, respectively. Administering the vaccines sequentially, upon reaching the lymph nodes, the two vaccines converge and simultaneously aggregate into MnO2/OVA/A50-T20 particles through base pairing. This process enhances both vaccine uptake and antigen delivery. In vitro and in vivo studies demonstrated that, the combined vaccine, comprising MnO2/OVA/A50 and MnO2/OVA/T20, exhibited robust immunization effects and remarkable anti-tumor efficacy in the melanoma animal models. The strategy of controlling tumor vaccine size and consequently improving tumor antigen presentation efficiency and vaccine efficacy via the DNA base-pairing principle, provides novel concepts for the development of efficient tumor vaccines. Graphical Abstract

Abstract Condensation has long been a closely studied problem in statistical physics but little attention has been paid to neural science. Here, we discuss this problem in brain networks and discover the condensation of a functional brain network whereby all its eigenmodes are condensed only into a few or even a single eigenmode of the structural brain network. We show that the condensation occurs due to the emergence of both chimera states and brain functions from the structure of the brain network. Furthermore, the condensation only appears in the regions of chimera and the condensed eigenmodes are only limited to the lower ones. Condensation is confirmed across different levels of brain subnetworks, including hemispheres, cognitive subnetworks, and isolated cognitive subnetworks, which are further supported by resting-state functional connectivity from empirical data. Our results indicate that condensation could be a potential mechanism for performing brain functions.

Abstract Spontaneous activity of the human brain provides a window to explore intrinsic principles of functional organization. However, most studies have focused on interregional functional connectivity. The principles underlying rich repertoires of instantaneous activity remain largely unknown. We apply a recently proposed eigen-microstate analysis to three resting-state functional MRI datasets to identify basic modes that represent fundamental activity patterns that coexist over time. We identify five leading basic modes that dominate activity fluctuations. Each mode exhibits a distinct functional system-dependent coactivation pattern and corresponds to specific cognitive profiles. In particular, the spatial pattern of the first leading basis mode shows the separation of activity between the default-mode and primary and attention regions. Based on theoretical modelling, we further reconstruct individual functional connectivity as the weighted superposition of coactivation patterns corresponding to these leading basic modes. Moreover, these leading basic modes capture sleep deprivation-induced changes in brain activity and interregional connectivity, primarily involving the default-mode and task-positive regions. Our findings reveal a dominant set of basic modes of spontaneous activity that reflect multiplexed interregional coordination and drive conventional functional connectivity, furthering the understanding of the functional significance of spontaneous brain activity.

To understand how the long connections of a brain functional network come from the short connections of its corresponding structural network, remote synchronization (RS) was recently studied in star graph networks. However, the motif of the star graph cannot completely characterize the features of brain networks as the leaf nodes of a star graph may also be connected to each other to some extent in real brain networks. Especially, the dynamics of a star motif in a brain network will be seriously influenced by its surrounding nodes, i.e., other parts of the brain network. To study RS of real brain networks, we here present a model of star-like networks by considering both the partial connections among leaf nodes and the influence of other parts of the brain network. We find that RS will not appear in all leaf nodes and instead appears only in the group of indirectly connected leaf nodes when the frequency difference between the hub and leaf nodes is not large enough, resulting in the concept of partial RS (PRS). Further, we find that the partial connections among leaf nodes favor PRS, implying that PRS can more easily appear in real brain networks than RS and thus provides a different way to understand the mechanism of long connections in brain functional networks. Moreover, we find another kind of PRS, i.e., double PRS, and discuss the dependence of PRS on system parameters. Finally, a brief theoretical analysis is provided to explain the results.

Summary: Amino acid (aa) metabolism is closely correlated with the pathogenesis of psoriasis; however, details on aa transportation during this process are barely known. Here, we find that SLC38A5, a sodium-dependent neutral aa transporter that counter-transports protons, is markedly upregulated in the psoriatic skin of both human patients and mouse models. SLC38A5 deficiency significantly ameliorates the pathogenesis of psoriasis, indicating a pathogenic role of SLC38A5. Surprisingly, SLC38A5 is almost exclusively expressed in dendritic cells (DCs) when analyzing the psoriatic lesion and mainly locates on the lysosome. Mechanistically, SLC38A5 potentiates lysosomal acidification, which dictates the cleavage and activation of TLR7 with ensuing production of pro-inflammatory cytokines such as interleukin-23 (IL-23) and IL-1β from DCs and eventually aggravates psoriatic inflammation. In summary, this work uncovers an auxiliary mechanism in driving lysosomal acidification, provides inspiring insights for DC biology and psoriasis etiology, and reveals SLC38A5 as a promising therapeutic target for treating psoriasis.

Summary: γδ T cells make key contributions to tissue physiology and immunosurveillance through two main functionally distinct subsets, γδ T1 and γδ T17. m6A methylation plays critical roles in controlling numerous aspects of mRNA metabolism that govern mRNA turnover, gene expression, and cellular functional specialization; however, its role in γδ T cells remains less well understood. Here, we find that m6A methylation controls the functional specification of γδ T17 vs. γδ T1 cells. Mechanistically, m6A methylation prevents the formation of endogenous double-stranded RNAs and promotes the degradation of Stat1 transcripts, which converge to prevent over-activation of STAT1 signaling and ensuing inhibition of γδ T17. Deleting Mettl3, the key enzyme in the m6A methyltransferases complex, in γδ T cells reduces interleukin-17 (IL-17) production and ameliorates γδ T17-mediated psoriasis. In summary, our work shows that METTL3-mediated m6A methylation orchestrates mRNA stability and double-stranded RNA (dsRNA) contents to equilibrate γδ T1 and γδ T17 cells.

Abstract Dendritic cells (DCs)‐based tumor vaccines have the advantages of high safety and rapid activation of T cells, and have been approved for clinical tumor treatment. However, the conventional DC vaccines have some severe problems, such as poor activation of DCs in vitro, low level of antigen presentation, reduced cell viability, and difficulty in targeting lymph nodes in vivo, resulting in poor clinical therapeutic effects. In this research, magnetic nanoparticles Fe3O4@Ca/MnCO3 were prepared and used to actively and efficiently deliver antigens to the cytoplasm of DCs, promote antigen cross‐presentation and DC activation, and finally enhance the cellular immune response of DC vaccines. The results show that the magnetic nanoparticles can actively and quickly deliver antigens to the cytoplasm of DCs by regulating the magnetic field, and achieve cross‐presentation of antigens. At the same time, the nanoparticles degradation product Mn2+ enhanced immune stimulation through the interferon gene stimulating protein (STING) pathway, and another degradation product Ca2+ ultimately promoted cellular immune response by increasing autophagy. The DC vaccine constructed with the magnetic nanoparticles can more effectively migrate to the lymph nodes, promote the proliferation of CD8+ T cells, prolong the time of immune memory, and produce higher antibody levels. Compared with traditional DC vaccines, cytoplasmic antigen delivery with the magnetic nanoparticles provides a new idea for the construction of novel DC vaccines.

Abstract Liver fibrosis represents a severe stage of liver damage, with hallmarks of inflammation, hepatic stellate cell activation, and extracellular matrix accumulation. Although previous studies demonstrated γδ T cells are involved in liver fibrosis, the precise role and mechanisms of γδ T cells migrating to fibrotic liver have not been elucidated. Here, we aim to investigate the functional subsets of γδ T cells in hepatic fibrosis and to further explore the underlying causes and drivers of migration. In this study, we observed that γδ T cells accumulate in fibrotic liver. Adoptive transfer of γδ T, especially Vγ4 γδ T subset, can significantly alleviate liver fibrosis. In addition, CCl4 treatment also leads to activation of mTOR signaling in γδ T cells. Genetic deletion of the Rictor gene, but not Raptor, in γδ T cells markedly exacerbated liver fibrosis. Mechanistically, CCl4-induced liver injury causes macrophage accumulation in the liver, and IL-1β produced by macrophages promotes mTORC2 signaling activation in γδ T cells, which upregulates T-bet expression and eventually promotes CXCR3 transcription to drive γδ T cell migration. Moreover, hepatic γδ T cells ameliorated liver fibrosis by cytotoxicity against activated hepatic stellate cells in FasL-dependent manner, and secrete IFN-γ to inhibit the differentiation of pro-fibrotic Th17 cells. Thus, IL-1β-activated mTORC2 signaling in γδ T cells upregulates CXCR3 expression, which is critical for IFN-γ+ γδ T cells migration into the liver and amelioration of liver fibrosis. Our findings indicate that targeting the mTORC2 or CXCR3 in γδ T cells could be considered as a promising approach for γδ T cell immunotherapy against liver fibrosis.

Southern corn rust (SCR) is a destructive maize disease caused by Puccinia polysora Underw. To investigate the mechanism of SCR resistance in maize, a highly resistant inbred line, L119A, and a highly susceptible line, Lx9801, were subjected to gene mapping and transcriptome analysis. Bulked-segregant analysis coupled with whole-genome sequencing revealed several quantitative trait loci (QTL) on chromosomes 1, 6, 8, and 10. A set of 25 genes, including two coiled-coil nucleotide-binding site leucine-rich repeat (CC-NBS-LRR) genes, were identified as candidate genes for a major-effect QTL on chromosome 10. To investigate the mechanism of SCR resistance in L119A, RNA-seq of P. polysora-inoculated and non-inoculated plants of L119A and Lx9801 was performed. Unexpectedly, the number of differentially expressed genes in inoculated versus non-inoculated L119A plants was about 10 times that of Lx9801, with only 29 common genes identified in both lines, suggesting extensive gene expression changes in the highly resistant but not in the susceptible line. Based on the transcriptome analysis, one of the CC-NBS-LRR candidate genes was confirmed to be upregulated in L119A relative to Lx9801 independently of P. polysora inoculation. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that transcription factors, as well as genes involved in defense responses and metabolic processes, were dominantly enriched, with the phenylpropanoid biosynthesis pathway most specifically activated. Consistently, accumulation of phenylpropanoid-derived lignin, especially S lignin, was drastically increased in L119A after P. polysora inoculation, but remained unchanged in Lx9801, suggesting a critical role of lignin in SCR resistance. A regulatory network of defense activation and metabolic change in SCR-resistant maize upon P. polysora infection is described.

Tumor vaccines can inhibit or eliminate tumors by vaccinating hosts with tumor antigens to activate antigen-specific immune responses and have gained wild attention. However, their clinical application efficacy is often comprised due to the low safety and poor efficiency of vaccine adjuvants/carriers. Specifically, the adjuvants/carriers usually could not efficiently recruit antigen presenting cells (APCs) to capture the vaccines or directly damage these cells. Therefore, ideal tumor vaccine adjuvants/carriers should effectively recruit APCs and be friendly to the cells for well keeping their bio-functions. In this work, injectable natural blood plasma hydrogel was used for the first time to encapsulate tumor antigens and adjuvant (Mn2+) for the construction of a personalized tumor vaccine. This kind of natural hydrogel with extremely high bio-safety has great potential to friendly recruit APCs in a biomimetic manner by simulating the natural degradation process of subcutaneous blood stasis. The obtained results show that the natural blood plasma hydrogel-based tumor vaccines could significantly promote the recruitment of APCs, well maintain the immuno-functions of the cells, and finally induce efficient anti-tumor immune responses. Compared with traditional tumor vaccines, this natural blood plasma-based hydrogel provides a new strategy for the development of safe and effective tumor vaccines.

Abstract Developing efficient strategy to regulate heat conduction is a challenging problem, with potential implication in the field of thermal materials. We here focus on a potential thermal material, i.e. complex networks of nanowires and nanotubes, and propose a model where the mass of each node is assigned proportional to its degree with $$m_i\sim k_i^{\alpha }$$ m i ∼ k i α , to investigate how distributed nodes masses can impact the heat flow in a network. We find that the heat conduction of complex network can be either increased or decreased, depending on the controlling parameter $$\alpha$$ α . Especially, there is an optimal heat conduction at $$\alpha =1$$ α = 1 and it is independent of network topologies. Moreover, we find that the temperature distribution within a complex network is also strongly influenced by the controlling parameter $$\alpha$$ α . A brief theoretical analysis is provided to explain these results. These findings may open up appealing applications in the cases of demanding either increasing or decreasing heat conduction, and our approach of regulating heat conduction by distributed nodes masses may be also valuable to the challenge of controlling waste heat dissipation in highly integrated and miniaturized modern devices.

Abstract Background Maize haploid breeding technology can be used to rapidly develop homozygous lines, significantly shorten the breeding cycle and improve breeding efficiency. Rapid and accurate sorting haploid kernels is a prerequisite for the large-scale application of this technology. At present, the automatic haploid sorting based on nuclear magnetic resonance (NMR) using a single threshold method has been realized. However, embryo-aborted (EmA) kernels are usually produced during in vivo haploid induction, and both haploids and EmA kernels have lower oil content and are separated together using a single threshold method based on NMR. This leads to a higher haploid false discrimination rate (FDR) and requires secondary manual sorting to select the haploid kernels from the mixtures, which increases the sorting cost and decreases the haploid sorting efficiency. In order to improve the correct discrimination rate (CDR) in sorting haploids, a method to distinguish EmA kernels is required. Results Single kernel weight and oil content were measured for the diploid, haploid, and EmA kernels derived from three maize hybrids and nine inbred lines by in vivo induction. The results showed that the distribution of oil content showed defined boundaries between the three types of kernels, while the single kernel weight didn't. According to the distribution of oil content in the three types of kernels, a double-threshold method was proposed to distinguish the embryo-aborted kernels, haploid and diploid kernels based on NMR and their oil content. The double thresholds were set based on the minimum oil content of diploid kernels and the maximum content of EmA kernels as the upper and lower boundary values, respectively. The CDR of EmA kernels in different maize materials was > 97.8%, and the average FDR was reduced by 27.9 percent. Conclusions The oil content is an appropriate indicator to discriminate diploid, haploid and EmA kernels. An oil content double-threshold method based on NMR was first developed in this study to identify the three types of kernels. This methodology could reduce the FDR of haploids and improve the sorting efficiency of automated sorting system. Thus, this technique represents a potentially efficient method for haploid sorting and provides a reference for the process of automated sorting of haploid kernels with high efficiency using NMR.

Understanding failure propagation dynamics in complex networks with recovery processes is vital to realizing networks that are resistant to large scale failures. Here, the authors report a model for general failure propagation dynamics in complex networks with non-Markovian recovery processes.

It is evident that increasing the intensive-care-unit (ICU) capacity and giving priority to admitting and treating patients will reduce the number of COVID-19 deaths, but the quantitative assessment of these measures has remained inadequate. We develop a comprehensive, non-Markovian state transition model, which is validated through the accurate prediction of the daily death toll for two epicenters: Wuhan, China and Lombardy, Italy. The model enables prediction of COVID-19 deaths in various scenarios. For example, if appropriate treatment priorities had been used, the death toll in Wuhan and Lombardy would have been reduced by about 10% and 7%, respectively. The strategy depends on the epidemic scale and is more effective in countries with a younger population structure. Analyses of data from China, South Korea, Italy, and Spain suggest that countries with less per capita ICU medical resources should implement this strategy in the early stage of the pandemic to reduce mortalities. We emphasize that the results of this paper should be interpreted purely from a scientific and a quantitative-analysis point of view. No ethical implications are intended and meaningful.

We give evidence that consecutive explosive transitions may occur in two-layered networks, when a dynamical layer made of an ensemble of networking phase oscillators interacts with an environmental layer of oscillators which are in a state of approximate synchronization. Under these conditions, the interlayer coupling induces two consecutive explosive transitions in the dynamical layer, each one associated with a hysteresis loop. We also show that the same phenomenon can be observed when the environmental layer is simplified into a single node with phase lag. Theoretical arguments unveil that the mechanisms at the basis of the two transitions are in fact different, with the former originating from a coupling-amplified disorder and the latter originating from a coupling-induced synchronization. We discuss the relevance of the observed state in brain dynamics and show how it may emerge in a real brain network.

Stock price crashes have occurred frequently in the Chinese security market during the last three decades. They have not only caused substantial economic losses to market investors but also seriously threatened the stability and financial safety of the security market. To protect against the price crash risk of individual stocks, a prediction and explanation approach has been proposed by combining eXtreme Gradient Boosting (XGBoost), the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and SHapley Additive exPlanations (SHAP). We assume that financial indicators can be adopted for stock crash risk prediction, and they are utilized as prediction variables. In the proposed method, XGBoost is used to classify the stock crash and non-crash samples, while NSGA-II is employed to optimize the hyperparameters of XGBoost. To obtain the essential features for stock crash prediction, the importance of each financial indicator is calculated, and the outputs of the prediction model are explained by SHAP. Compared with the results of benchmarks using traditional machine learning methods, we found that the proposed method performed best in terms of both prediction accuracy and efficiency. Especially for the small market capitalization samples, the accuracy of classifying all samples reached 78.41%, and the accuracy of identifying the crash samples was up to 81.31%. In summary, the performance of the proposed method demonstrates that it could be employed as a valuable reference for market regulators engaged in the Chinese security market.

Abstract Background Immunochromatographic strips (ICSs) are a practical tool commonly used in point-of-care testing (POCT) applications. However, ICSs that are currently available have low sensitivity and require expensive equipment for quantitative analysis. These limitations prohibit their extensive use in areas where medical resources are scarce. Methods We developed a novel POCT platform by integrating a gas generation biosensor with Au@Pt Core/Shell nanoparticle (Au@PtNPs)-based ICSs (G-ICSs). The resulting G-ICSs enabled the convenient and quantitative assessment of a target protein using the naked eye, without the need for auxiliary equipment or complicated computation. To assess this platform, C-reactive protein (CRP), a biomarker commonly used for the diagnosis of acute, infectious diseases was chosen as a proof-of-concept test. Results The linear detection range (LDR) of the G-ICSs for CRP was 0.05–6.25 μg/L with a limit of detection (LOD) of 0.041 μg/L. The G-ICSs had higher sensitivity and wider LDR when compared with commonly used AuNPs and fluorescent-based ICSs. When compared with results from a chemiluminescent immunoassay, G-ICS concordance rates for CRP detection in serum samples ranged from 93.72 to 110.99%. Conclusions These results demonstrated that G-ICSs have wide applicability in family diagnosis and community medical institutions, especially in areas with poor medical resources.

Resting-state networks usually show time-limited self-sustaining oscillatory patterns (TLSOPs) with the characteristic features of multiscaled rhythms and frequent switching between different rhythms, but the underlying mechanisms remain unclear. To reveal the mechanisms of multiscaled rhythms, we present a simplified reaction-diffusion model of activation propagation to reproduce TLSOPs in real brain networks. We find that the reproduced TLSOPs do show multiscaled rhythms, depending on the activating threshold and initially chosen activating nodes. To understand the frequent switching between different rhythms, we present an approach of dominant activation paths and find that the multiscaled rhythms can be separated into individual rhythms denoted by different core networks, and the switching between them can be implemented by a time-dependent activating threshold. Further, based on the microstates of TLSOPs, we introduce the concept of a return loop to study the distribution of the return times of microstates in TLSOPs and find that it satisfies the Weibull distribution. Then, to check it for real data, we present a method of a shifting window to transform a continuous time series into a discrete two-state time series and interestingly find that the Weibull distribution also exists in resting-state EEG and fMRI data. Finally, we show that the TLSOP lifetime depends exponentially on the core network size and can be explained by a theory of the complete graphs.

Understanding the human brain and its functions has always been an interesting and challenging problem. Recently, a significant progress on this problem has been achieved on the aspect of chimera state where a coexistence of synchronized and unsynchronized states can be sustained in identical oscillators. This counterintuitive phenomenon is closely related to the unihemispheric sleep in some marine mammals and birds and has recently gotten a hot attention in neural systems, except the previous studies in non-neural systems such as phase oscillators. This review will briefly summarize the main results of chimera state in neuronal systems and pay special attention to the network of cerebral cortex, aiming to accelerate the study of chimera state in brain networks. Some outlooks are also discussed.

Non-alcoholic fatty liver disease (NAFLD) is one of the main causes of cirrhosis and major risk factors for hepatocellular carcinoma and liver-related death. Despite substantial clinical and basic research, the pathogenesis of obesity-related NAFLD remains poorly understood. In this study, we show that perforin can act as an immune regulator to prevent the progression of NAFLD. Aged perforin-deficient (Prf−/−) mice have increased lipid accumulation in the liver compared to WT mice. With high-fat diet (HFD) challenge, Prf−/− mice have increased liver weight, more severe liver damage, and increased liver inflammation when compared with WT controls. Mechanistic studies revealed that perforin specifically regulates intrinsic IFN-γ production in CD4 T cells, not CD8 T cells. We found that CD4 T cell depletion reduces liver injury and ameliorates the inflammation and metabolic morbidities in Prf−/− mice. Furthermore, improved liver characteristics in HFD Prf−/− and IFN-γR−/− double knockout mice confirmed that IFN-γ is a key factor for mediating perforin regulation of NAFLD progression. Overall, our findings reveal the important regulatory role perforin plays in the progression of obesity-related NAFLD and highlight novel strategies for treating NAFLD.

Green tea could reportedly modify gut microbiota and benefit on High-Fat-Diet (HFD) induced obesity. However, most of the studies usually gave green tea (GT) to animals while feeding them HFD. When thin or obese mice, drink GT continuously or only for a period of time, what kind of benefit will them get, is still need to evaluate. In this study, we found that preventive GT consumption by lean mice could prevent HFD-induced-obesity event after stop drinking GT, while the mice with pre-existing obesity couldn’t get such benefit. Gut microbiota structure analysis indicated that the anti-obesity effect by GT consumption always accompanied with microbiota structure modification. We hypothesized that this difference depended upon the status of gut microbiota. Therefore, to gain the persistently protective effect from GT, the precondition is drinking GT before getting fat; otherwise, GT could only slowdown the weight gain of the pre-obese mice by continuously consumption.

Kernel abortion is common phenomenon in vivo haploid induction and closely linked with haploid induction rate, but little information of kernel abortion is available and its genetic basis still unclear. We used two mapping populations including 186 and 263 F2.3 family lines to analyze the different degree of kernel abortion and identify quantitative trait loci (QTL) responsible for kernel abortion during haploid induction. In total 62 putative QTL, accounting for 3.27-14.70% of the phenotypic variation in kernel abortion traits, were detected across all 10 chromosomes. Ten QTL with over 10% contribution to phenotypic variation were affecting the fifth level of endosperm abortion (EnA5th), endosperm abortion (EnA) and total abortion (TA). Co-localization among kernel abortion traits QTL was observed in both populations and among different kernel abortion types. Five overlaps were indentified in the QTL for kernel abortion traits and HIR traits. Maize chromosome bins 3.01-3.02, 3.04-3.06, 4.05-4.06, 5.03-5.04, 8.06 were QTL hotspots for three or four traits related to the kernel abortion during haploid induction. Total kernel abortion rate (TAR) and HIR showed highly significant positive correlation. These findings may help to reveal haploid induction mechanisms and improve haploid production efficiency.

Abstract It is recently revealed from amounts of real data of recurrent epidemics that there is a phenomenon of hysteresis loop in the state space. To understand it, an indirect investigation from the parameter space has been given to qualitatively explain its mechanism but a more convincing study to quantitatively explain the phenomenon directly from the state space is still missing. We here study this phenomenon directly from the state space and find that there is a positive correlation between the size of outbreak and the size of hysteresis loop, implying that the hysteresis is a nature feature of epidemic outbreak in real case. Moreover, we surprisingly find a paradox on the dependence of the size of hysteresis loop on the two parameters of the infectious rate increment and the transient time, i.e. contradictory behaviors between the two spaces, when the evolutionary time of epidemics is long enough. That is, with the increase of the infectious rate increment, the size of hysteresis loop will decrease in the state space but increase in the parameter space. While with the increase of the transient time, the size of hysteresis loop will increase in the state space but decrease in the parameter space. Furthermore, we find that this paradox will disappear when the evolutionary time of epidemics is limited in a fixed period. Some theoretical analysis are presented to both the paradox and other numerical results.

Stroke has a large physical, psychological, and financial burden on patients, their families, and society. Based on functional networks (FNs) constructed from resting state fMRI data, network connectivity after stroke is commonly conjectured to be more randomly reconfigured. We find that this hypothesis depends on the severity of stroke. Head movement-corrected, resting-state fMRI data were acquired from 32 patients after stroke, and 37 healthy volunteers. We constructed anomaly FNs, which combine time series information of a patient with the healthy control group. We propose data-driven techniques to automatically identify regions of interest that are stroke relevant. Graph analysis based on anomaly FNs suggests consistently that strong connections in healthy controls are broken down specifically and characteristically for brain areas that are related to sensorimotor functions and frontoparietal control systems, but new links in stroke patients are rebuilt randomly from all possible areas. Entropic measures of complexity are proposed for characterizing the functional connectivity reorganization patterns, which are correlated with hand and wrist function assessments of stroke patients and show high potential for clinical use. Keywords: Brain networks, Random reorganization hypothesis, Connectivity complexity, Stroke

Abstract Epidemic spreading has been studied for a long time and most of them are focused on the growing aspect of a single epidemic outbreak. Recently, we extended the study to the case of recurrent epidemics (Sci. Rep. 5, 16010 (2015)) but limited only to a single network. We here report from the real data of coupled regions or cities that the recurrent epidemics in two coupled networks are closely related to each other and can show either synchronized outbreak pattern where outbreaks occur simultaneously in both networks or mixed outbreak pattern where outbreaks occur in one network but do not in another one. To reveal the underlying mechanism, we present a two-layered network model of coupled recurrent epidemics to reproduce the synchronized and mixed outbreak patterns. We show that the synchronized outbreak pattern is preferred to be triggered in two coupled networks with the same average degree while the mixed outbreak pattern is likely to show for the case with different average degrees. Further, we show that the coupling between the two layers tends to suppress the mixed outbreak pattern but enhance the synchronized outbreak pattern. A theoretical analysis based on microscopic Markov-chain approach is presented to explain the numerical results. This finding opens a new window for studying the recurrent epidemics in multi-layered networks.

Abstract Many studies of synchronization properties of coupled oscillators, based on the classical Kuramoto approach, focus on ensembles coupled via a mean field. Here we introduce a setup of Kuramoto-type phase oscillators coupled via two mean fields. We derive stability properties of the incoherent state and find traveling wave solutions with different locking patterns; stability properties of these waves are found numerically. Mostly nontrivial states appear when the two fields compete, i.e. one tends to synchronize oscillators while the other one desynchronizes them. Here we identify normal branches which bifurcate from the incoherent state in a usual way, and anomalous branches, appearance of which cannot be described as a bifurcation. Furthermore, hybrid branches combining properties of both are described. In the situations where no stable traveling wave exists, modulated quasiperiodic in time dynamics is observed. Our results indicate that a competition between two coupling channels can lead to a complex system behavior, providing a potential generalized framework for understanding of complex phenomena in natural oscillatory systems.

Abstract We here study explosive synchronization transitions and network activity propagation in networks of coupled neurons to provide a new understanding of the relationship between network topology and explosive dynamical transitions as in epileptic seizures and their propagations in the brain. We model local network motifs and configurations of coupled neurons and analyze the activity propagations between a group of active neurons to their inactive neuron neighbors in a variety of network configurations. We find that neuronal activity propagation is limited to local regions when network is highly clustered with modular structures as in the normal brain networks. When the network cluster structure is slightly changed, the activity propagates to the entire network, which is reminiscent of epileptic seizure propagation in the brain. Finally, we analyze intracranial electroencephalography (IEEG) recordings of a seizure episode from a epilepsy patient and uncover that explosive synchronization-like transition occurs around the clinically defined onset of seizure. These findings may provide a possible mechanism for the recurrence of epileptic seizures, which are known to be the results of aberrant neuronal network structure and/or function in the brain.

Abstract Owing to the rapid development of urbanisation and industrialisation, heavy metal pollution has become a widespread environmental problem. Maize planted on mercury (Hg)-polluted soil can absorb and accumulate Hg in its edible parts, posing a potential threat to human health. To understand the genetic mechanism of Hg accumulation in maize, we performed a genome-wide association study using a mixed linear model on an association population consisting of 230 maize inbred lines with abundant genetic variation. The order of relative Hg concentrations in different maize tissues was as follows: leaves > bracts > stems > axes > kernels. Combined two locations, a total of 37 significant single-nucleotide polymorphisms (SNPs) associated with kernels, 12 with axes, 13 with stems, 27 with bracts and 23 with leaves were detected with p

According to the official report, the first case of COVID-19 and the first death in the United States occurred on January 20 and February 29, 2020, respectively. On April 21, California reported that the first death in the state occurred on February 6, implying that community spreading of COVID-19 might have started earlier than previously thought. Exactly what is time zero, i.e., when did COVID-19 emerge and begin to spread in the U.S. and other countries? We develop a comprehensive predictive modeling framework to address this question. Using available data of confirmed infections to obtain the optimal values of the key parameters, we validate the model and demonstrate its predictive power. We then carry out an inverse inference analysis to determine time zero for 10 representative states in the U.S., plus New York City, United Kingdom, Italy, and Spain. The main finding is that, in both the U.S. and Europe, COVID-19 started around the new year day.

Seed aging decreases the quality and vigor of crop seeds, thereby causing substantial agricultural and economic losses in crops. To identify genetic differences in seed aging between homozygotes and heterozygotes in maize, the seeds of a set of recombinant inbred lines (RILs) and an immortalized F2 (IF2) population were subjected to artificial aging treatments for 0, 2, 3, and 4 days under 45 ºC and 85% relative humidity and seed vigor was then evaluated in a field experiment. Seed vigor of all entries tested decreased sharply with longer aging treatment and seed vigor decreased more slowly in heterozygotes than in homozygotes. Forty-nine QTL were detected for four measured seed vigor traits in the RIL (28 QTL) and IF2 (21 QTL) populations. Only one QTL, qGP5, was detected in both populations, indicating that the genes involved in anti-aging mechanisms differed between inbred lines and hybrids. Several QTL were identified to be responsible for multiple seed vigor traits simultaneously in the RIL and IF2 populations under artificial aging conditions. These QTL may include major genes for seed vigor or seed aging. QTL qVI4b and qGE3a detected in the RIL population coincided with genes ZmLOX1 and ZmPLD1 in the same respective chromosomal regions. These QTL would be useful for screening for anti-aging genes in maize breeding.

When large ensembles of phase oscillators interact globally, and when bimodal frequency distributions are chosen for the natural frequencies of the oscillators themselves, Bellerophon states are generically observed at intermediate values of the coupling strength. These are multi-clustered states emerging in symmetric pairs. Oscillators belonging to a given cluster are not locked in their instantaneous phases or frequencies, rather they display the same long-time average frequency (a sort of effective global frequency). Moreover, Bellerophon states feature quantized traits, in that such average frequencies are all odd multiples (±(2 n − 1), n = 1, 2...) of a fundamental frequency Ω _1 . We identify and investigate (analytically and numerically) several typical bifurcation paths to synchronization, including first-order and second-order-like. Linear stability analysis allows to successfully solve the critical transition point for synchronization. Our results highlight that the spontaneous setting of higher order forms of coherence could be achieved in classical Kuramoto model.

Implementing a positive correlation between the natural frequencies of nodes and their connectivity on a single star graph leads to a pronounced explosive transition to synchronization, additionally presenting hysteresis behavior. From the viewpoint of network connectivity, a star has been considered as a building motif to generate a big graph by graph operations. On the other hand, we propose to construct complex synchronization dynamics by applying the Cartesian product of two Kuramoto models on two star networks. On the product model, the lower dimensional equations describing the ensemble dynamics in terms of collective order parameters are fully solved by the Watanabe–Strogatz method. Different graph parameter choices lead to three different interacting scenarios of the hysteresis areas of two individual factor graphs, which further change the basins of attraction of multiple fixed points. Furthermore, we obtain coupling regimes where cluster synchronization states are often present on the product graph and the number of clusters is fully controlled. More specifically, oscillators on one star graph are synchronized while those on the other star are not synchronized, which induces clustered state on the product model. The numerical results agree perfectly with the theoretic predictions.

The principle clock of mammals, named suprachiasmatic nucleus (SCN), coordinates the circadian rhythms of behavioral and physiological activity to the external 24 h light-dark cycle. In the absence of the daily cycle, the SCN acts as an endogenous clock that regulates the ~24 h rhythm of activity. Experimental and theoretical studies usually take the light-dark cycle as a main external influence, and often ignore light pollution as an external influence. However, in modern society, the light pollution such as induced by electrical lighting influences the circadian clock. In the present study, we examined the effect of external noise (light pollution) on the collective behavior of coupled circadian oscillators under constant darkness using a Goodwin model. We found that the external noise plays distinct roles in the network behavior of neurons for weak or strong coupling between the neurons. In the case of strong coupling, the noise reduces the synchronization and the period of the SCN network. Interestingly, in the case of weak coupling, the noise induces a circadian rhythm in the SCN network which is absent in noise-free condition. In addition, the noise increases the synchronization and decreases the period of the SCN network. Our findings may shed new light on the impact of the external noise on the collective behavior of SCN neurons.

To investigate the genetic mechanism of mercury accumulation in maize (Zea mays L.), a population of 194 recombinant inbred lines derived from an elite hybrid Yuyu 22, was used to identify quantitative trait loci (QTLs) for mercury accumulation at two locations. The results showed that the average Hg concentration in the different tissues of maize followed the order: leaves > bracts > stems > axis > kernels. Twenty-three QTLs for mercury accumulation in five tissues were detected on chromosomes 1, 4, 7, 8, 9 and 10, which explained 6.44% to 26.60% of the phenotype variance. The QTLs included five QTLs for Hg concentration in kernels, three QTLs for Hg concentration in the axis, six QTLs for Hg concentration in stems, four QTLs for Hg concentration in bracts and five QTLs for Hg concentration in leaves. Interestingly, three QTLs, qKHC9a, qKHC9b, and qBHC9 were in linkage with two QTLs for drought tolerance. In addition, qLHC1 was in linkage with two QTLs for arsenic accumulation. The study demonstrated the concentration of Hg in Hg-contaminated paddy soil could be reduced, and maize production maintained simultaneously by selecting and breeding maize Hg pollution-safe cultivars (PSCs).

Kernel size and weight are important determinants of grain yield in maize. In this study, multivariate conditional and unconditional quantitative trait loci (QTL), and digenic epistatic analyses were utilized in order to elucidate the genetic basis for these kernel-related traits. Five kernel-related traits, including kernel weight (KW), volume (KV), length (KL), thickness (KT), and width (KWI), were collected from an immortalized F2 (IF2) maize population comprising of 243 crosses performed at two separate locations over a span of two years. A total of 54 unconditional main QTL for these five kernel-related traits were identified, many of which were clustered in chromosomal bins 6.04-6.06, 7.02-7.03, and 10.06-10.07. In addition, qKL3, qKWI6, qKV10a, qKV10b, qKW10a, and qKW7a were detected across multiple environments. Sixteen main QTL were identified for KW conditioned on the other four kernel traits (KL, KWI, KT, and KV). Thirteen main QTL were identified for KV conditioned on three kernel-shape traits. Conditional mapping analysis revealed that KWI and KV had the strongest influence on KW at the individual QTL level, followed by KT, and then KL; KV was mostly strongly influenced by KT, followed by KWI, and was least impacted by KL. Digenic epistatic analysis identified 18 digenic interactions involving 34 loci over the entire genome. However, only a small proportion of them were identical to the main QTL we detected. Additionally, conditional digenic epistatic analysis revealed that the digenic epistasis for KW and KV were entirely determined by their constituent traits. The main QTL identified in this study for determining kernel-related traits with high broad-sense heritability may play important roles during kernel development. Furthermore, digenic interactions were shown to exert relatively large effects on KL (the highest AA and DD effects were 4.6% and 6.7%, respectively) and KT (the highest AA effects were 4.3%).

In recent years, the frequent occurrence of corporate scandals in Chinese companies, organizations have increased their efforts to curb unethical employee behavior. However, little is known about whether corporate social responsibility (CSR) inhibits unethical pro-organizational behavior. Drawing on social identity theory, this study investigates the relationship between CSR, moral identity and unethical pro-organizational behavior. Data from 319 employees of new ventures in China at two-time points are used to test hypotheses. Results show that CSR has a negative effect on unethical pro-organizational behavior, moral identity plays a partial mediating role in the negative effect of CSR on unethical pro-organizational behavior. Moreover, supervisor-employee value congruence moderates the effect of CSR on unethical pro-organizational behavior. Implications for research and practice are discussed., Author(s): Liu Zonghua [sup.1] , Liao Junyun [sup.2] , Guo Yulang [sup.3] , Zhang Ming [sup.1] , Wang Xu [sup.1] Author Affiliations: (1) grid.254148.e, 0000 0001 0033 6389, College of [...]

Grain filling during the linear phase contributes most of the dry matter accumulated in the maize kernel, which in turn determines the final grain yield. Endosperms and embryos of three elite maize hybrids (Zhengdan 958, Nongda 108, and Pioneer 335) were sampled 17, 22, 25, and 28 days after pollination, during the linear phase of grain filling, for proteomic analysis to explore the regulatory factors critical for grain filling rate. In total, 39 and 43 protein spots that showed more than 2-fold changes in abundance at P

The grain filling rate (GFR) is an important dynamic trait that determines the final grain yield and is controlled by a network of genes and environment factors. To determine the genetic basis of the GFR, a conditional quantitative trait locus (QTL) analysis method was conducted using time-related phenotypic values of the GFR collected from a set of 243 immortalized F(2) (IF(2)) population, which were evaluated at two locations over 2 years. The GFR gradually rose in the 0-15 days after pollination (DAP) and 16-22 DAP, reaching a maximum at 23-29 DAP, and then gradually decreasing. The variation of kernel weight (KW) was mainly decided by the GFR, and not by the grain filling duration (GFD). Thirty-three different unconditional QTLs were identified for the GFR at the six sampling stages over 2 years. Among them, QTLs qGFR7b, qGFR9 and qGFR6d were identified at the same stages at two locations over 2 years. In addition, 14 conditional QTLs for GFR were detected at five stages. The conditional QTL qGFR7c was identified at stage V|IV (37-43 DAP) at two locations over 2 years, and qGFR7b was detected at the sixth stage (44-50 DAP) in all four environments, except at Anyang location in 2009. QTLs qQTL7b and qQTL6f were identified by unconditional and conditional QTL mapping at the same stages, and might represent major QTLs for regulating the GFR in maize in the IF(2) population. Moreover, most of the QTLs identified were co-located with QTLs from previous studies that were associated with GFR, enzyme activities of starch synthesis, soluble carbohydrates, and grain filling related genes. These results indicated that the GFR is regulated by many genes, which are specifically expressed at different grain filling stages, and the specific expression of the genes between 16-35 DAP might be very important for deciding the final kernel weight.

Heterosis has been utilized widely in the breeding of maize and other crops, and plays an important role in increasing yield, improving quality and enhancing stresses resistance, but the molecular mechanism responsible for heterosis is far from clear. To illustrate whether miRNA-dependent gene regulation is responsible for heterosis during maize germination, a deep-sequencing technique was applied to germinating embryos of a maize hybrid, Yuyu22, which is cultivated widely in China and its parental inbred lines, Yu87-1 and Zong3. The target genes of several miRNAs showing significant expression in the hybrid and parental lines were predicted and tested using real-time PCR. A total of 107 conserved maize miRNAs were co-detected in the hybrid and parental lines. Most of these miRNAs were expressed non-additively in the hybrid compared to its parental lines. These results indicated that miRNAs might participate in heterosis during maize germination and exert an influence via the decay of their target genes. Novel miRNAs were predicted follow a rigorous criterion and only the miRNAs detected in all three samples were treated as a novel maize miRNA. In total, 34 miRNAs belonged to 20 miRNA families were predicted in germinating maize seeds. Global repression of miRNAs in the hybrid, which might result in enhanced gene expression, might be one reason why the hybrid showed higher embryo germination vigor compared to its parental lines.

The suprachiasmatic nucleus (SCN) is the master circadian clock in mammals and is composed of thousands of neuronal oscillators expressing different intrinsic periods. These oscillators form a coupled network with a free-running period around 24 h in constant darkness and entrainable to the external light-dark cycle (T cycle). Coupling plays an important role in setting the period of the network and its range of entrainment. Experiments in rats have shown that two subgroups of oscillators within the SCN, a ventrolateral (VL) subgroup that receives photic input and a dorsomedial (DM) subgroup that is coupled to VL, can be desynchronized under a short (22-h) T cycle, with VL entrained to the cycle and DM free-running. We use a modified Goodwin model to understand how entrainment of the subgroups to short (22-h) and long (26-h) T cycles is influenced by light intensity, the proportion of neurons that receives photic input, and coupling heterogeneity. We find that the model's critical value for the proportion of photically-sensitive neurons is in accord with actual experimental estimates, while the model's inclusion of dispersed coupling can account for the experimental observation that VL and DM desynchronize more readily under the 22-h than under the 26-h T cycle. Heterogeneous intercellular coupling within the SCN is likely central to the generation of complex behavioral patterns.

Complex network approaches have been recently developed as an alternative framework to study the statistical features of time-series data. We perform a visibility-graph analysis on both the daily and monthly sunspot series. Based on the data, we propose two ways to construct the network: one is from the original observable measurements and the other is from a negative-inverse-transformed series. The degree distribution of the derived networks for the strong maxima has clear non-Gaussian properties, while the degree distribution for minima is bimodal. The long-term variation of the cycles is reflected by hubs in the network that span relatively large time intervals. Based on standard network structural measures, we propose to characterize the long-term correlations by waiting times between two subsequent events. The persistence range of the solar cycles has been identified over 15–1000 days by a power-law regime with scaling exponent γ = 2.04 of the occurrence time of two subsequent strong minima. In contrast, a persistent trend is not present in the maximal numbers, although maxima do have significant deviations from an exponential form. Our results suggest some new insights for evaluating existing models.

Mitochondrial protein translation that is essential for aerobic energy production includes four essential steps of the mitochondrial ribosome cycle, namely, initiation, elongation, termination of the polypeptide, and ribosome recycling. Translation termination initiates when a stop codon enters the A site of the mitochondrial ribosome where it is recognized by a dedicated peptide release factor (RF). However, RFs and mechanisms involved in translation in plant mitochondria, especially in monocotyledons, remain largely unknown. Here, we identified a crumpled kernel (crk5 allele) mutant, with significantly decreased kernel size, 100-kernel weight, and an embryo-lethal phenotype. The Crk5 allele was isolated using map-based cloning and found to encode a mitochondrial localization RF2a. As it is an ortholog of Arabidopsis mitochondrial RF2a, we named the gene ZmmtRF2a. ZmmtRF2a is missing the 5th-7th exons in the crk5 resulting in deletion of domains containing motifs GGQ and SPF that are essential for release activity of RF, mitochondrial ribosome binding, and stop codon recognition. Western blot and qRT-PCR analyses indicate that the crk5 mutation results in abnormal mitochondrion structure and function. Intriguingly, we observed a feedback loop in the crk5 with up-regulated transcript levels detected for several mitochondrial ribosome and mitochondrial-related components, in particular mitochondrial complexes CI, CIV, and a ribosome assembly related PPR. Together, our data support a crucial role for ZmmtRF2a in regulation of mitochondrial structure and function in maize. [ABSTRACT FROM AUTHOR]