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Background: Leaves, which are the most important organs of plants, can not only fix carbon sources through photosynthesis, but also absorb nutrients through transpiration. Leaf development directly determines the growth, flowering and fruiting of plants. There are many factors that affect leaf development, such as the growth environment, gene expression, and hormone synthesis. In this study, tomatoes were used to study the role of the transcription factor Solanum lycopersicum salt-related MYB1-like (SlSRM1-like) in the development of tomato leaves. Results: Loss-of-function of the SlSRM1-like gene mediated by clustered, regularly interspaced, short palindromic repeat (CRISPR)/CRISPR-associated 9 (Cas9) resulted in abnormal tomato leaf morphology, including thinner leaves, wrinkled edges, raised veins, disordered edge veins, and left and right asymmetry. An analysis of the transcription levels of genes related to leaf development revealed that the expression of these genes was significantly altered in the SlSRM1-like mutants (SlSRM1-like-Ms). Moreover, the SlSRM1-like gene was expressed at higher transcription levels in young tissues than in old tissues, and its expression was also induced in response to auxin. In addition, the transcription levels of genes related to the auxin pathway, which regulates tomato growth and development, were severely affected in the SlSRM1-like-Ms. Therefore, it is hypothesized that the SlSRM1-like gene functions in the regulation of tomato leaf development through the auxin-related pathway. Conclusions: In this study, we successfully knocked out the SlSRM1-like gene in the tomato variety Ailsa Craig using CRISPR technology and found that knockout of the SlSRM1-like gene resulted in abnormal development of tomato leaves. Further research indicated that SlSRM1-like regulated tomato leaf development through auxin-related pathways. The results provide an important reference for the functional study of other SRM1-like genes in plants and provide new insights into the regulation of leaf development in tomato and other plants. [ABSTRACT FROM AUTHOR]

Community detection in complex networks is of great importance in analyzing the interaction patterns and group behaviors. However, the traditional method of community division divide each node in the network into a specific community, while may ignore its internal connection. In this paper, a new strategy that selects a fuzzy function and fuzzy threshold (FF-FT) was presented to discover community structure. Edge dense degree coefficient was introduced to calculate fuzzy relation between nodes, and Fast–Warshall algorithm was used to reduce the complexity of FF-FT. Through the theoretical analysis and the comparison of eight current well-known community detection algorithms on seven real networks and artificial networks with different parameters, the results show that the FF-FT algorithm has a good community detection performance. [ABSTRACT FROM AUTHOR]

Sentiment analysis has become one of the mainstream researches in social network analysis. Its impact can be seen in many practical applications, ranging from public opinion analysis to marketing of public praise and information prediction. However, most of the existing research has been performed in the sentiment classification for subjective text, the emotional evolution analysis for complex interactive text (e.g., online reviews) has not yet been thoroughly targeted by the research community. This paper is concerned on short-text Chinese online reviews collected from Tianya forum. First, an efficient affective computing framework is proposed to capture the underlying emotions of Chinese online reviews. It can accurately calculate the semantic orientation of the entire review, without requiring expensive manual labeling of seed words. As users’ attitudes might influence with each other, predicting their future emotional behaviors that only relying on the emotional values of historical reviews is very one-sided. Therefore, we propose a game theory based emotional evolution prediction algorithm combining the affective computing, in which the mixed nash equilibrium strategies are calculated as the future emotional behavior of interactive users. Then, experimental results on the large-scaled review dataset are provided to demonstrate the effectiveness and accurateness of our approaches. Finally, by applying the research results on the pairwise happiness-popularity coordination evaluation, we have revealed some interesting phenomenon on the “World View” board in Tianya forum. [ABSTRACT FROM AUTHOR]

Detecting overlapping communities is essential for analyzing the structure and function of complex networks. However, most existing approaches only consider network topology and overlook the benefits of attribute information. In this paper, we propose a novel attribute-information non-negative matrix factorization approach that integrates sparse constraints and optimizes an objective function for detecting communities in directed weighted networks. Our algorithm updates the basic non-negative matrix adaptively, incorporating both network topology and attribute information. We also add a sparsity constraint term of graph regularization to maintain the intrinsic geometric structure between nodes. Importantly, we provide strict proof of convergence for the multiplication update rule used in our algorithm. We apply our proposed algorithm to various artificial and real-world networks and show that it is more effective for detecting overlapping communities. Furthermore, our study uncovers the intricate iterative process of system evolution toward convergence and investigates the impact of various variables on network detection. These findings provide insights into building more robust and operable complex systems. [ABSTRACT FROM AUTHOR]

In this article, the authors analyzed the nonlinear effects of projective synchronization between coupled memristive neural networks (MNNs) and their applications. Since the complete signal transmission is difficult under parameter mismatch and different projective factors, the delays, which are time-varying, and uncertainties have been taken to realize the projective synchronization of MNNs with multi-links under the nonlinear control method. Through the extended comparison principle and a new approach to dealing with the mismatched parameters, sufficient criteria have been determined under different types of projective factors and the framework of the Lyapunov–Krasovskii functional (LKF) for projective convergence of the coupled MNNs. Instead of the classical treatment for secure communication, the concept of error of synchronization between the drive and response systems has been applied to solve the signal encryption/decryption problem. Finally, the simulations in numerical form have been demonstrated graphically to confirm the adaptability of the theoretical results. [ABSTRACT FROM AUTHOR]

Vocational status and living conditions are key factors that determine the quality of life of workers. In light of a rural-urban migration wave during the past decade, this study examined migrants’ experiences of occupational and housing inequality in major urban centers. Based on data from original household-level surveys conducted in four large cities, the study investigated the vocational and tenurial situations of different social groups. Estimates obtained using a binary logistic model indicated that apart from socioeconomic factors similar to those impacting Western societies, specific institutional factors such as the Hukou system, state-associated vocations, and residential status, were significant determinants of housing inequality. The study confirmed that there were significant disparities in the vocational status and housing conditions of urban residents and migrant workers in major Chinese cities. Along with excessive privatization of urban housing, the predominance of the Hukou urban housing system has created inequalities in employment, and in the housing market, in major Chinese urban centers, leading to housing poverty among migrant households. [ABSTRACT FROM AUTHOR]

As a basic industry in the country's development, the transportation industry has a significant relationship to its normal operation for developing and constructing the national economy. The increase in carbon emissions from transport is an increasingly growing problem, and countries worldwide are also taking measures to reduce emissions. Using time series data over the period from 1990 to 2016, this paper applies the visibility graph approach to transform it into a complex network and excavate some information about the data, then evaluates all countries based on the TOPSIS method. We find that the development of transportation is an important symbol to measure the degree of modernization of a country's transportation, and low-income countries have lower carbon emissions due to slower transportation development. The results of transportation carbon emissions are especially encouraging for the Chinese government given its long-term and sustained efforts to expand railway and waterway infrastructure, and provide a new perspective for further research on the development trend of global transportation carbon emissions. Meanwhile, it is urgent to speed up the development and use of clean energy for economically developed countries. [ABSTRACT FROM AUTHOR]

The refugee problem is one of the most important issues facing the international community today. It not only troubles the countries where refugees are generated but also has a great impact on the countries where refugees are influx. With the continuous development of globalization, the refugee problem is no longer a problem of a country or a region, but a global problem faced by the international community. To cope with the global refugee problem, this paper analyzes the number of refugees in 156 countries from 1990 to 2020 and transforms the refugee population data of these countries into a complex network through a time series visibility graph (VG) method. First, we categorize the income level of 156 countries and analyze the impact of income level on the increase of refugee numbers. Then, the evaluation index of the number of refugees is obtained through the VG method. Finally, a TOPSIS comprehensive evaluation method based on the entropy weight approach is employed to analyze the data. This paper includes two main contributions. First, the application of the VG method provides a new perspective for enriching the modeling of the global refugee population growth trend. Second, this paper shows that the TOPSIS evaluation method based on the entropy weight method is effective, which provides a new method for further research on the global refugee population growth trend. [ABSTRACT FROM AUTHOR]

A total of 30 isolates of Cladosporium fulvum was collected from three provinces (Liaoning, Jilin and Heilongjiang) and the Beijing area in 2013. All isolates were tested on a set of tomato cultivars with differential genotypes against C. fulvum. New races 2.5 and 2.4.5 were identified, which have not been previously reported in China. Race 2.4.9, which had previously only been reported in the Beijing area in 2005, was identified for the first time in Liaoning, Jilin, and Heilongjiang Provinces. [ABSTRACT FROM AUTHOR]

The efficient use of livestock and poultry manure waste has become a global challenge, with microorganisms playing an important role. To investigate the impact of novel ammonifying microorganism cultures (NAMC) on microbial community dynamics and carbon and nitrogen metabolism, five treatments [5% (v/w) sterilized distilled water, Amm-1, Amm-2, Amm-3, and Amm-4] were applied to cow manure compost. Inoculation with NAMC improved the structure of bacterial and fungal communities, enriched the populations of the functional microorganisms, enhanced the role of specific microorganisms, and promoted the formation of tight modularity within the microbial network. Further functional predictions indicated a significant increase in both carbon metabolism (CMB) and nitrogen metabolism (NMB). During the thermophilic phase, inoculated NAMC treatments boosted carbon metabolism annotation by 10.55%–33.87% and nitrogen metabolism annotation by 26.69%–63.11. Structural equation modeling supported the NAMC-mediated enhancement of NMB and CMB. In conclusion, NAMC inoculation, particularly with Amm-4, enhanced the synergistic interaction between bacteria and fungi. This collaboration promoted enzymatic catabolic and synthetic processes, resultng in positive feedback loops with the endogenous microbial community. Understanding these mechanisms not only unravels how ammonifying microorganisms influence microbial communities but also paves the way for the development of the composting industry and global waste management practices. [Display omitted] • Novel ammonifying microorganism cultures (NAMC) increased the stability of the composting • Inoculation with NAMC improved the structure of bacterial and fungal communities. • Inoculation with NAMC enhanced the complementary effects of bacteria and fungi in compost • Inoculated NAMC promoted carbon metabolism and nitrogen metabolism in composting. [ABSTRACT FROM AUTHOR]

• The historical stage division of air quality information via network science is investigated. • A novel dividing technique via community detection algorithm is proposed. • Historical information of air quality is reasonably divided to obtain the statistical results. • Some critical events or periods affecting air quality can be identified effectively. Urban air quality is related to human health in modern life. The statistical features of urban air quality highly depend on the division of historical stages. Conventional division methods that use a fixed period (e.g., month) can result in confusion during statistical analysis. In this study, we propose a novel analysis technique based on time series complex network theories to divide the historical information of urban air quality by using flexible periods. First, air quality information is converted into time series complex networks via a multilayer visibility model. Thereafter, an improved community detection algorithm is proposed on the basis of network characteristics. In particular, the centrality of nodes is increased using a kernel density estimation model. An improved bidirectional search pattern results in the optimal modularity. Finally, the historical curves of urban air quality are divided into several stages in accordance with the optimal clustering results. The simulation experiments demonstrate important conclusions. The clustering accuracy of the proposed algorithm is superior to those of other evaluated methods on actual air quality networks. The number of historical stages is decreased constantly in accordance with clustering results, and this condition is beneficial for statistics. Our results can reasonably explain the relationship between valid time and air quality features. The proposed technique can provide effective and reliable division results of historical stages. [ABSTRACT FROM AUTHOR]

In this paper, a Caputo fractional-order HCV Periodic immune response model with saturation incidence, cell-to-cell and drug control was proposed. We derive two different basic reproductive numbers and their relation with infection-free equilibrium and the immune-exhausted equilibrium. Moreover, there exists some symmetry in the relationship between the two equilibria and the basic reproduction numbers. We obtain the global stability of the infection-free equilibrium by using Lyapunov function and the local stability of the immune-exhausted equilibrium. The optimal control problem is also considered and two control strategies are given; one is for ITX5061 monotherapy, the other is for ITX5061 and DAAs combination therapy. Matlab numerical simulation shows that combination therapy has lower objective function value; therefore, it is worth trying to use combination therapy to treat HCV infection. [ABSTRACT FROM AUTHOR]

[Display omitted] • Scaling-up methods from lab-scale to stack-scale are systematically investigated. • Fow-related transport and electrochemical characteristics are disclosed. • Key factors degerming the performance of stack-scale flow field are analyzed. • A high-performance multiple-parallel channel scaling-up method is discovered. Flow fields are key competent to distribute electrolytes onto electrodes at maximum uniformity while maintaining a minimum pumping loss for redox flow batteries. Previously, efforts are mainly made to develop lab-scale flow fields (＜100 cm2) with varying patterns, but due to the lack of reasonable scaling-up methods, a huge gap between lab-scale and stack-scale (＞1000 cm2) flow fields exists, limiting the application of designed flow fields for commercialized kW-class battery stacks. In this work, six scaling-up methods, including channel length extension, geometric similarity, multi-parallel channels, splitting subzone, channel multi-parallel length extension and split-area multi-parallel channels, are proposed and evaluated by adopting a rotary serpentine flow field as an example to unravel the flow-related concentration distribution and electrochemical characteristics from lab-scale to stack-scale. Results show that shortening the supply path and enhancing the supply rate by utilizing flow channel structures within the unit area are key factors determining the uniform distribution of active materials at stack-scale flow fields. More remarkably, the flow filed with the multiple-parallel channel scaling-up method shows the lowest charge voltage (1.49 V), the highest discharge voltage (1.32 V), the highest uniformity factor (0.812) and the lowest pressure drop (100.1 kPa) among methods investigated at 100 mA cm−2, leading to a high energy efficiency of 88.4 %, which is almost identical to that of lab-scale counterpart (88.9 %). This work provides an in-depth analysis of flow field scaling-up methods, which is expected to guide the design of kW-class VRFB stacks. [ABSTRACT FROM AUTHOR]

• The time series data on power systems in the USA have been obtained. • The visibility graph method can be used to visualize and analyze these time series data. • The correlation among 50 states and resulting relationship networks are constructed. • Policies for power market reform are identified based on the community detection. Through the market-oriented reform and operation in the recent 30 years, the organizational structure of power systems in the U.S. has formed a relatively stable situation, where various levels of power companies can supervise and restrict each other to create a set of perfect management, plan and approval mechanisms. In this paper, by use of the time series data of electricity price, renewable energy proportion and CO 2 emission at each state from 1990 to 2014, we analyze these data from 50 states based on the approach of network science. Firstly, we perform the time series visualization according to the vision graph method. Subsequently, the correlation among these 50 states is calculated regarding the related indicators, and henceforth the relationship network between them is constructed. Finally, the community detection is used to cluster the specific policies carried out at various states. After the detailed analyses, it is indicated that all these 50 states can be divided into four categories as far as the policy of electric reform and power market is concerned. The current results provide some clues to deeply understand the electricity marketing reform in the U.S., and also offer some valuable hints for other countries to optimize the operation or reform of power systems. [ABSTRACT FROM AUTHOR]

The new energy industry gains more and more attention since the problem of resource scarcity and utilization of the renewable energy has become a global highlight issue. In this paper, we propose a new load forecasting model under the development of new energy industry by choosing the typical wind power as the key subject, which is also an important reference for other energy industries. The wind power load forecasting model is built based on optimized combination, which is forecasted and analyzed by the time series, the Markov and the gray forecasting models individually, and then combined by the optimized weighting coefficients. The method has overcome the limitations of poor adaptability of the single forecasting models and come out with an ideal result. Experimental results show our method has better performance compared with other related algorithms in different datasets. [ABSTRACT FROM AUTHOR]

Background: Leaf mold disease caused by Cladosporium fulvum is a serious threat affecting the global production of tomato. Cf genes are associated with leaf mold resistance, including Cf-16, which confers effective resistance to leaf mold in tomato. However, the molecular mechanism of the Cf-16-mediated resistance response is largely unknown. Results: We performed a comparative transcriptome analysis of C. fulvum-resistant (cv. Ontario7816) and C. fulvum-susceptible (cv. Moneymaker) tomato cultivars to identify differentially expressed genes (DEGs) at 4 and 8 days post inoculation (dpi) with C. fulvum. In total, 1588 and 939 more DEGs were found in Cf-16 tomato than in Moneymaker at 4 and 8 dpi, respectively. Additionally, 1350 DEGs were shared between the 4- and 8-dpi Cf-16 groups, suggesting the existence of common core DEGs in response to C. fulvum infection. The up-regulated DEGs in Cf-16 tomato were primarily associated with defense processes and phytohormone signaling, including salicylic acid (SA) and jasmonic acid (JA). Moreover, SA and JA levels were significantly increased in Cf-16 tomato at the early stages of C. fulvum infection. Contrary to the previous study, the number of up-regulated genes in Cf-16 compared to Cf-10 and Cf-12 tomatoes was significantly higher at the early stages of C. fulvum infection. Conclusion: Our results provide new insight into the Cf-mediated mechanism of resistance to C. fulvum, especially the unique characteristics of Cf-16 tomato in response to this fungus. [ABSTRACT FROM AUTHOR]

• ATG4A SNPrs807185 was at a higher frequency in MPA cases in Chinese population. • Rs807185 allele could be a predictive factor for MPA. • Further study of rs807185 could elucidate potential mechanisms of MPA pathogenesis. Microscopic polyangiitis is a type of antineutrophil cytoplasmic antibody-associated vasculitis, characterized by a systemic, pauci-immune, necrotizing, small-vessel vasculitis without clinical or pathological evidence of necrotizing granulomatous inflammation. Many studies have linked autophagy-related gene polymorphisms to the development of immune diseases. However, the association between autophagy-related genes and microscopic polyangiitis remains unclear. We investigated the association between microscopic polyangiitis and the single nucleotide polymorphism rs807185 in autophagy-associated gene 4A (ATG4A) in the Chinese population. ATG4A single nucleotide polymorphism rs807185 frequency was identified using multiplex polymerase chain reaction and high-throughput sequencing in a cohort with microscopic polyangiitis (n = 181) and a healthy control group (n = 209). We found that the control cohort had a higher prevalence of allele A of rs807185 compared to the microscopic polyangiitis cohort. The rs807185 A allele was therefore linked to a lower risk of microscopic polyangiitis. The single nucleotide polymorphism rs807185 allele A in ATG4A may have a protective effect against microscopic polyangiitis in the Chinese population, but the molecular mechanisms remain unclear. [ABSTRACT FROM AUTHOR]

Synchronization is a powerful basic rule in nature that regulates a large variety of complex processes, and global synchronization phenomena have been extensively studied for modeling and analyzing the dynamic of systems. Distinct from the existing studies, the work presented here explores the notion of local synchronization phenomenon and their intrinsic properties from the dynamics of a stochastic model defined on a complex network. The relationship between the hierarchical community structure of the network and the local synchronization properties of such a stochastic model has been established based on the famous Kuramoto Model. Critical topological information regarding to the community structures of the network can be inferred from their spectral signatures. Utilizing a novel dynamic system, we have developed an efficient two stages model which can efficiently uncover natural communities hidden in networks in a scalable manner. The effectiveness and efficiency of the algorithm have been theoretically analyzed as well as experimentally validated and it overcomes the inefficiency of the existing methods. [ABSTRACT FROM AUTHOR]