Advances in understanding the graft healing mechanism: a review of factors and regulatory pathways.

Grafting is a widely used technique for asexual plant reproduction, especially in agriculture and forestry. This procedure is used to shorten the seedling period, improve the structure of scion branches, and help plants adapt to difficult environments. Although grafting has numerous benefits, several obstacles remain to be overcome. The connection between scion and rootstock is regulated by various factors, including phytohormones and molecular mechanisms, which are crucial for graft healing. This review provides an overview of recent advances in the field of grafting, with a specific focus on the factors and regulatory pathways that influence graft healing. The ultimate goal is to aid understanding of how to achieve successful grafting between plants and create desirable grafting chimeras. We provide an overview of the latest developments in plant grafting, covering aspects related to morphology, physiology, and molecular biology. We also discuss research directions in polyploid breeding and long-distance transfer of small molecules in grafted plants.

Enhanced vascularization and osseointegration under osteoporotic conditions through functional peptide coating on implant surfaces.

Patients with osteoporosis face challenges such as decreased bone density, a sparse trabecular structure, weakened osteogenic ability, and impaired angiogenesis, leading to poor osseointegration and implant failure. Surface modification of implants with biologically active molecules possessing various functions is an effective strategy to improve osseointegration. In this study, we constructed a simple multifunctional coating interface that significantly improves osseointegration. In brief, a multifunctional coating interface was constructed by coupling the Rgd adhesive peptide, Ogp osteogenic peptide, and Ang angiogenic peptide to Lys6 (k6), which self-assembled layer by layer with TA to form the (TA-Rgd@ogp@ang)n composite membrane. This study characterized the surface morphology and biomechanical properties of the coating under both gas and liquid phases and monitored the deposition process and reaction rate of the two peptides with TA using a quartz crystal microbalance. Moreover, (TA-Rgd@ogp@ang)n exhibited a triple synergistic effect on cell migration and adhesion, osteogenic differentiation, and angiogenesis. It also ameliorated the high ROS environment characteristic of osteoporosis pathology, promoted angiogenic bone defect regeneration in osteoporosis, thereby avoiding poor osseointegration. This work provides a new approach for the prevention of implant failure in pathological environments by constructing multifunctional coatings on implants, with tremendous potential applications in the fields of orthopedics and dentistry.

Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases.

Background: Emerging infectious diseases pose a significant threat to global public health. Timely detection and response are crucial in mitigating the spread of such epidemics. Inferring the onset time and epidemiological characteristics is vital for accelerating early interventions, but accurately predicting these parameters in the early stages remains challenging. Methods: We introduce a Bayesian inference method to fit epidemic models to time series data based on state-space modeling, employing a stochastic Susceptible-Exposed-Infectious-Removed (SEIR) model for transmission dynamics analysis. Our approach uses the particle Markov chain Monte Carlo (PMCMC) method to estimate key epidemiological parameters, including the onset time, the transmission rate, and the recovery rate. The PMCMC algorithm integrates the advantageous aspects of both MCMC and particle filtering methodologies to yield a computationally feasible and effective means of approximating the likelihood function, especially when it is computationally intractable. Results: To validate the proposed method, we conduct case studies on COVID-19 outbreaks in Wuhan, Shanghai and Nanjing, China, respectively. Using early-stage case reports, the PMCMC algorithm accurately predicted the onset time, key epidemiological parameters, and the basic reproduction number. These findings are consistent with empirical studies and the literature. Conclusion: This study presents a robust Bayesian inference method for the timely investigation of emerging infectious diseases. By accurately estimating the onset time and essential epidemiological parameters, our approach is versatile and efficient, extending its utility beyond COVID-19.

Phylodynamics unveils invading and diffusing patterns of dengue virus serotype-1 in Guangdong, China from 1990 to 2019 under a global genotyping framework.

BACKGROUND: The strong invasiveness and rapid expansion of dengue virus (DENV) pose a great challenge to global public health. However, dengue epidemic patterns and mechanisms at a genetic scale, particularly in term of cross-border transmissions, remain poorly understood. Importation is considered as the primary driver of dengue outbreaks in China, and since 1990 a frequent occurrence of large outbreaks has been triggered by the imported cases and subsequently spread to the western and northern parts of China. Therefore, this study aims to systematically reveal the invasion and diffusion patterns of DENV-1 in Guangdong, China from 1990 to 2019. METHODS: These analyses were performed on 179 newly assembled genomes from indigenous dengue cases in Guangdong, China and 5152 E gene complete sequences recorded in Chinese mainland. The genetic population structure and epidemic patterns of DENV-1 circulating in Chinese mainland were characterized by phylogenetics, phylogeography, phylodynamics based on DENV-1 E-gene-based globally unified genotyping framework. RESULTS: Multiple serotypes of DENV were co-circulating in Chinese mainland, particularly in Guangdong and Yunnan provinces. A total of 189 transmission clusters in 38 clades belonging to 22 subgenotypes of genotype I, IV and V of DENV-1 were identified, with 7 Clades of Concern (COCs) responsible for the large outbreaks since 1990. The epidemic periodicity was inferred from the data to be approximately 3 years. Dengue transmission events mainly occurred from Great Mekong Subregion-China (GMS-China), Southeast Asia (SEA), South Asia Subcontinent (SASC), and Oceania (OCE) to coastal and land border cities respectively in southeastern and southwestern China. Specially, Guangzhou was found to be the most dominant receipting hub, where DENV-1 diffused to other cities within the province and even other parts of the country. Genome phylogeny combined with epidemiological investigation demonstrated a clear local consecutive transmission process of a 5C1 transmission cluster (5C1-CN4) of DENV-1 in Guangzhou from 2013 to 2015, while the two provinces of Guangdong and Yunnan played key roles in ongoing transition of dengue epidemic patterns. In contextualizing within Invasion Biology theories, we have proposed a derived three-stage model encompassing the stages of invasion, colonization, and dissemination, which is supposed to enhance our understanding of dengue spreading patterns. CONCLUSIONS: This study demonstrates the invasion and diffusion process of DENV-1 in Chinese mainland within a global genotyping framework, characterizing the genetic diversities of viral populations, multiple sources of importation, and periodic dynamics of the epidemic. These findings highlight the potential ongoing transition trends from epidemic to endemic status offering a valuable insight into early warning, prevention and control of rapid spreading of dengue both in China and worldwide.

Towards an actionable One Health approach.

BACKGROUND: Despite the increasing focus on strengthening One Health capacity building on global level, challenges remain in devising and implementing real-world interventions particularly in the Asia-Pacific region. Recognizing these gaps, the One Health Action Commission (OHAC) was established as an academic community for One Health action with an emphasis on research agenda setting to identify actions for highest impact. MAIN TEXT: This viewpoint describes the agenda of, and motivation for, the recently formed OHAC. Recognizing the urgent need for evidence to support the formulation of necessary action plans, OHAC advocates the adoption of both bottom-up and top-down approaches to identify the current gaps in combating zoonoses, antimicrobial resistance, addressing food safety, and to enhance capacity building for context-sensitive One Health implementation. CONCLUSIONS: By promoting broader engagement and connection of multidisciplinary stakeholders, OHAC envisions a collaborative global platform for the generation of innovative One Health knowledge, distilled practical experience and actionable policy advice, guided by strong ethical principles of One Health.

Identification of keystone taxa in rhizosphere microbial communities using different methods and their effects on compounds of the host Cinnamomum migao.

Exploring keystone taxa affecting microbial community stability and host function is crucial for understanding ecosystem functions. However, identifying keystone taxa from humongous microbial communities remains challenging. We collected 344 rhizosphere and bulk soil samples from the endangered plant C. migao for 2 years consecutively. Used high-throughput sequencing 16S rDNA and ITS to obtain the composition of bacterial and fungal communities. We explored keystone taxa and the applicability and limitations of five methods (SPEC-OCCU, Zi-Pi, Subnetwork, Betweenness, and Module), as well as the impact of microbial community domain, time series, and rhizosphere boundary on the identification of keystone taxa in the communities. Our results showed that the five methods, identified abundant keystone taxa in rhizosphere and bulk soil microbial communities. However, the keystone taxa shared by the rhizosphere and bulk soil microbial communities over time decreased rapidly decrease in the five methods. Among five methods on the identification of keystone taxa in the rhizosphere community, Module identified 113 taxa, SPEC-OCCU identified 17 taxa, Betweenness identified 3 taxa, Subnetwork identified 3 taxa, and Zi-Pi identified 4 taxa. The keystone taxa are mainly conditionally rare taxa, and their ecological functions include chemoheterotrophy, aerobic chemoheterotrophy, nitrate reduction, and anaerobic photoautotrophy. The results of the random forest model and structural equation model predict that keystone taxa Mortierella and Ellin6513 may have an effects on the accumulation of 1, 4, 7, - Cycloundecatriene, 1, 5, 9, 9-tetramethyl-, Z, Z, Z-, beta-copaene, bicyclogermacrene, 1,8-Cineole in C. migao fruits, but their effects still need further evidence. Our study evidence an unstable microbial community in the bulk soil, and the definition of microbial boundary and ecologically functional affected the identification of keystone taxa in the community. Subnetwork and Module are more in line with the definition of keystone taxa in microbial ecosystems in terms of maintaining community stability and hosting function.

Composition, functional, and niche differentiation of fungal communities in the rhizosphere and root endosphere of Cinnamomum migao in southwestern China.

AIMS: Roots are key parts of plant material circulation and energy flow, creating two distinct niches for the plant microbiome, such as the rhizosphere and root endosphere, which interact to maintain plant growth and health. In this study, two niches of plant rhizosphere and root endosphere were selected to study the composition and differences of fungi communities in order to better understand how differences in the structure and function of plant fungi communities affect plant health. METHODS AND RESULTS: The community structure, diversity, and ecological function of fungi in the rhizosphere and root endosphere of Cinnamomum migao were studied using high-throughput sequencing techniques, traditional culture methods, and the FUNGuild database. The results of the analysis of alpha diversity showed that the diversity of rhizosphere fungal communities in C. migao was much higher than that of root endosphere communities. Some similarities and differences were noted between the two niche fungal communities, and some fungi had niche preferences. Functional prediction results demonstrated that fungi in the rhizosphere and root endosphere adopt multiple trophic modes, mostly saprophytic fungi. CONCLUSIONS: This study provided a basis for an in-depth understanding of the structural variation, niche differentiation, and function of plant root-related fungal microbiota. We believe that it could provide guidance on the subsequent development of beneficial fungi.

Construction of multifunctional coating with cationic amino acid-coupled peptides for osseointegration of implants.

Osseointegration is an important indicator of implant success. This process can be improved by coating modified bioactive molecules with multiple functions on the surface of implants. Herein, a simple multifunctional coating that could effectively improve osseointegration was prepared through layer-by-layer self-assembly of cationic amino acids and tannic acid (TA), a negatively charged molecule. Osteogenic growth peptide (OGP) and the arginine-glycine-aspartic acid (RGD) functional polypeptides were coupled with Lys6 (K6), the two polypeptides then self-assembled with TA layer by layer to form a composite film, (TA-OGP@RGD)n. The surface morphology and biomechanical properties of the coating were analyzed in gas and liquid phases, and the deposition process and kinetics of the two peptides onto TA were monitored using a quartz crystal microbalance. In addition, the feeding consistency and adsorption ratios of the two peptides were explored by using fluorescence visualization and quantification. The (TA-OGP@RGD)n composite membrane mediated the early migration and adhesion of cells and significantly promoted osteogenic differentiation and mineralization of the extracellular matrix in vitro. Additionally, the bifunctional peptide exhibited excellent osteogenesis and osseointegration owing to the synergistic effect of the OGP and RGD peptides in vivo. Simultaneously, the (TA-OGP@RGD)n membrane regulated the balance of reactive oxygen species in the cell growth environment, thereby influencing the complex biological process of osseointegration. Thus, the results of this study provide a novel perspective for constructing multifunctional coatings for implants and has considerable application potential in orthopedics and dentistry.

Machine learning screening of high-performance single-atom electrocatalysts for two-electron oxygen reduction reaction.

Electrocatalysis has emerged as one of the most promising alternatives to conventional anthraquinone for preparing hydrogen peroxide (H2O2) with high energy consumption and pollution because of its simplicity, convenience, and environmental friendliness. However, the oxygen reduction reaction (ORR) generating H2O2viathe2e- path is acompetitive path for 4e-ORR to generate H2O. Therefore, it is crucial to identify an electrocatalyst with high selectivity and activity of 2e-ORR. Here, we established five machine learning (ML) models based on the adsorption free energy of O* (△G (O*)) of 149 single-atom catalysts (SACs) collected and the limiting potential (UL) of 31 SACs calculated using density functional theory (DFT) from the literature. We then obtained descriptors that could accurately describe SACs. Furthermore, 690 unknown SACs' 2e-ORR catalytic performance was well predicted. Four 2e-ORR materials with high selectivity and activity were screened: Zn@Pc-N3C1, Au@Pd-N4, Au@Pd-N1C3, and Au@Py-N3C1. We verified the UL of these SACs through DFT calculation, which was higher than the standard value, proving the ML model's validity. The ML-based method to predict the material properties with highly selective and active electrocatalysts provides an efficient, rapid, and low-cost method for discovering and designing more valuable SACs catalysts.

Detecting the skewness of data from the five-number summary and its application in meta-analysis.

For clinical studies with continuous outcomes, when the data are potentially skewed, researchers may choose to report the whole or part of the five-number summary (the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation. In the recent literature, it is often suggested to transform the five-number summary back to the sample mean and standard deviation, which can be subsequently used in a meta-analysis. However, if a study contains skewed data, this transformation and hence the conclusions from the meta-analysis are unreliable. Therefore, we introduce a novel method for detecting the skewness of data using only the five-number summary and the sample size, and meanwhile, propose a new flow chart to handle the skewed studies in a different manner. We further show by simulations that our skewness tests are able to control the type I error rates and provide good statistical power, followed by a simulated meta-analysis and a real data example that illustrate the usefulness of our new method in meta-analysis and evidence-based medicine.

Transfer and transformation characteristics of Zn and Cd in soil-rotation plant (Brassica napus L and Oryza sativa L) system and its influencing factors.

Rice-rape rotation is a widely practiced cropping system in China. However, changes in soil properties and management could change the bioavailability of Cd, In order to explore the occurrence state, transportation and transformation characteristics of heavy metals Cd and Zn in rice-rape rotation system in Guizhou karst area with high background value of Cd. In the karst rice-rape rotation area, the physical and chemical properties of soil, chemical specifications and activities of Cd and Zn at different soil depths and during various crop growth stages, and the bioaccumulation of Cd and Zn in different tissues of rice and rape were studied by field experiment and laboratory analysis. The bioaccumulation of Cd and Zn and the effects of physical and chemical soil properties on the activities and bioavailabilities of Cd and Zn during rice-rape rotation were explored. The findings revealed that soil particle size, composition, pH, redox potential, soil organic matter, and Cd and Zn contents varied dramatically, especially in deep soils. The physical and chemical properties of the deep and surface soils were significantly related to the bioaccumulation of Cd and Zn. Cd and Zn are activated during crop rotation. Cd was easier to be enriched in rice, while Zn was easier to be enriched in rape. The correlation between Cd and Zn contents in Brassica campestris L and their enrichment abilities were not significant, but that in Oryza sativa L were significant. During rice-rape rotation, the chemical speciations and activities of Cd and Zn changed with the changes of soil properties and waterlogging environment. This study had important basic guiding significance for the evaluation, prevention and control of heavy metal pollution, and improving soil quality in different rotation systems in karst areas, and was conducive to promoting the safe production of rape and rice.

Rhizobacterial compositions and their relationships with soil properties and medicinal bioactive ingredients in Cinnamomum migao.

Introduction: Rhizobacterial communities and their metabolites can affect plant growth, development, and stress resistance, as well as the biosynthesis and accumulation of bioactive compounds in medicinal plants. This relationship has been well-characterized in many medicinal herbs, although much less commonly in medicinal trees. Methods: Here, we analyzed the composition and structure of Cinnamomum migao rhizobacterial communities across nine growing regions in Yunnan, Guizhou and Guangxi, China, as well as differences in soil properties and fruit bioactive compounds. Results: Results showed that the C. migao rhizobacterial communities exhibited high species richness, but location-specific differences in structure. Site-specific differences in soil properties and bioactive compounds were also observed. Furthermore, rhizobacterial community compositions were correlated with both soil properties and fruit bioactive compounds, metabolism-related functions were most common in C. migao rhizobacteria. Discussion: Several bacterial genera, including Acidothermus, Acidibacter, Bryobacter, Candidatus_Solibacter, and Acidimicrobiales, potentially promote the biosynthesis and accumulation of 1,8-cineole, cypressene, limonene, and α-terpineol, Nitrospira and Alphaproteobacteria may play an inhibitory role. Finally, our results emphasized the critical role that soil pH and nitrogen levels play in driving rhizobacterial community structure, and specific functional bacteria can also counteract with soil properties, Acidibacter and Nitrospira can affect soil pH and nitrogen effectiveness. Overall, this study provides additional insight into the complex correlation of rhizosphere microorganisms with bioactive ingredients and soil properties of medicinal plants.

TransCode: Uncovering COVID-19 transmission patterns via deep learning.

BACKGROUND: The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning. METHODS: We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available. RESULTS: The TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level. CONCLUSIONS: The fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.

Arbuscular Mycorrhizal Fungi Improve the Growth, Water Status, and Nutrient Uptake of Cinnamomum migao and the Soil Nutrient Stoichiometry under Drought Stress and Recovery.

Drought greatly influences the growth and ecological stoichiometry of plants in arid and semi-arid regions such as karst areas, where Cinnamomum migao (C. migao) is an endemic tree species that is used as a bioenergy resource. Arbuscular mycorrhizal fungi (AMF) play a key role in nutrient uptake in the soil-plant continuum, increasing plant tolerance to drought. However, few studies have examined the contribution of AMF in improving the growth of C. migao seedlings and the soil nutrient stoichiometry under drought-stress conditions. A pot experiment was conducted under natural light in a plastic greenhouse to investigate the effects of individual inoculation and Co-inoculation of AMF [Funneliformis mosseae (F. mosseae) and Claroideoglomus etunicatum (C. etunicatum)] on the growth, water status, and nutrient uptake of C. migao as well as the soil nutrient stoichiometry under well-watered (WW) and drought-stress (DS) conditions. The results showed that compared with non-AMF control (CK), AM symbiosis significantly stimulated plant growth and had higher dry mass. Mycorrhizal plants had better water status than corresponding CK plants. AMF colonization notably increased the total nitrogen and phosphorus content of C. migao seedlings compared with CK. Mycorrhizal plants had higher leaf and stem total carbon concentrations than CK. The results indicated that AM symbiosis protects C. migao seedlings against drought stress by improving growth, water status, and nutrient uptake. In general, the C. migao seedlings that formed with C. etunicatum showed the most beneficial effect on plant growth, water status, and nutrient uptake among all treatments. In the future, we should study more about the biological characteristics of each AMF in the field study to understand more ecological responses of AMF under drought stress, which can better provide meaningful guidance for afforestation projects in karst regions.

Assessing the spatiotemporal malaria transmission intensity with heterogeneous risk factors: A modeling study in Cambodia.

Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity, which needs to incorporate spatiotemporally varying risk factors. In this study, we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective, where nodes capture the local transmission intensities resulting from dominant vector species, the population density, and land cover, and edges describe the cross-region human mobility patterns. The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations. Our study focuses on malaria-severe districts in Cambodia. The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics: the risks increase in the rainy season and decrease in the dry season; remote and sparsely populated areas generally show higher transmission intensities than other areas. Our findings suggest that: the human mobility (e.g., in planting/harvest seasons), environment (e.g., temperature), and contact risk (coexistences of human and vector occurrence) contribute to malaria transmission in spatiotemporally varying degrees; quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.

Complex Network Evolution Model Based on Turing Pattern Dynamics.

Complex network models are helpful to explain the evolution rules of network structures, and also are the foundations of understanding and controlling complex networks. The existing studies (e.g., scale-free model, small-world model) are insufficient to uncover the internal mechanisms of the emergence and evolution of communities in networks. To overcome the above limitation, in consideration of the fact that a network can be regarded as a pattern composed of communities, we introduce Turing pattern dynamic as theory support to construct the network evolution model. Specifically, we develop a Reaction-Diffusion model according to Q-Learning technology (RDQL), in which each node regarded as an intelligent agent makes a behavior choice to update its relationships, based on the utility and behavioral strategy at every time step. Extensive experiments indicate that our model not only reveals how communities form and evolve, but also can generate networks with the properties of scale-free, small-world and assortativity. The effectiveness of the RDQL model has also been verified by its application in real networks. Furthermore, the depth analysis of the RDQL model provides a conclusion that the proportion of exploration and exploitation behaviors of nodes is the only factor affecting the formation of communities. The proposed RDQL model has potential to be the basic theoretical tool for studying network stability and dynamics.

Spatial Heterogeneity of Soil Bacterial Community Structure and Enzyme Activity along an Altitude Gradient in the Fanjingshan Area, Northeastern Guizhou Province, China.

Changes in altitude can cause regional microclimate changes, leading to the spatial heterogeneity of environmental factors and soil bacteria. However, the internal driving process and mechanism remain unclear. Here, we selected Fanjingshan, a typical nature reserve in the subtropical region of south China with a clear altitudinal belt, to reveal the response mechanisms of microbial populations with altitude changes. We examined the physiochemical and biological properties (pH and soil enzyme activities) of 0~10 cm soil layers, soil bacterial diversity, and community structure across the 2.1 km belt (consisting of six altitude ranges). Our results showed that soil pH was highest at the altitude range below 900 m and decreased with altitude thereafter. Soil enzyme activities showed an overall decreasing trend with altitude rising. The soil sucrase and catalase activity was highest (48.35 mg.g-1.d-1 and 23.75 µmol.g-1, respectively) at altitudes below 900 m; the soil urease activity was highest (704.24 µg.g-1.d-1) at 900~1200 m; and the soil acid phosphatase activity was highest (57.18 µmol.g-1) at 1200~1500 m. In addition, the soil bacterial community diversity showed a linear increasing trend, with the maximum abundance at 1500~1800 m. Soil pH was correlated with enzyme activity and bacterial community composition and structure, and the correlation was the strongest between pH and the distribution of bacterial diversity at altitudes below 900 m. Overall, soil enzyme activities and soil bacterial diversity showed spatial heterogeneity along the altitude gradient, and their community structure and composition were affected by altitude as a result of changes in soil physicochemical factors. This study provides a better and deeper understanding of the spatial succession of soil in the Fanjingshan area and the distribution pattern of soil microorganisms in central subtropical mountain ecosystems.

Commonality and Individuality-Based Subspace Learning.

Subspace learning (SL) plays a key role in various learning tasks, especially those with a huge feature space. When processing multiple high-dimensional learning tasks simultaneously, it is of great importance to make use of the subspace extracted from some tasks to help learn others, so that the learning performance of all tasks can be enhanced together. To achieve this goal, it is crucial to answer the following question: How can the commonality among different learning tasks and, of equal importance, the individuality of each single learning task, be characterized and extracted from the given datasets, so as to benefit the subsequent learning, for example, classification? Existing multitask SL methods usually focused on the commonality among the given tasks, while neglecting the individuality of the learning tasks. In order to offer a more general and comprehensive framework for multitask SL, in this article, we propose a novel method dubbed commonality and individuality-based SL (CISL). First, we formally define the notions and objective functions of both commonality and individuality with respect to multiple SL tasks. Then, we design an iterative algorithm to solve the formulated objective functions, with the convergence of the algorithm being guaranteed. To show the generality of the proposed method, we theoretically analyze its connections to existing single-task and multitask SL methods. Finally, we demonstrate the necessity and effectiveness of incorporating both commonality and individuality by interpreting the learned subspaces and comparing the performance of CISL (in terms of the subsequent classification accuracy) with that of classical and state-of-the-art SL approaches on both synthetic and real-world multitask datasets. The empirical evaluation validates the effectiveness of the proposed method in characterizing the commonality and individuality for multitask SL.

A unified global genotyping framework of dengue virus serotype-1 for a stratified coordinated surveillance strategy of dengue epidemics.

BACKGROUND: Dengue is the fastest spreading arboviral disease, posing great challenges on global public health. A reproduceable and comparable global genotyping framework for contextualizing spatiotemporal epidemiological data of dengue virus (DENV) is essential for research studies and collaborative surveillance. METHODS: Targeting DENV-1 spreading prominently in recent decades, by reconciling all qualified complete E gene sequences of 5003 DENV-1 strains with epidemiological information from 78 epidemic countries/areas ranging from 1944 to 2018, we established and characterized a unified global high-resolution genotyping framework using phylogenetics, population genetics, phylogeography, and phylodynamics. RESULTS: The defined framework was discriminated with three hierarchical layers of genotype, subgenotype and clade with respective mean pairwise distances 2-6%, 0.8-2%, and ≤ 0.8%. The global epidemic patterns of DENV-1 showed strong geographic constraints representing stratified spatial-genetic epidemic pairs of Continent-Genotype, Region-Subgenotype and Nation-Clade, thereby identifying 12 epidemic regions which prospectively facilitates the region-based coordination. The increasing cross-transmission trends were also demonstrated. The traditional endemic countries such as Thailand, Vietnam and Indonesia displayed as persisting dominant source centers, while the emerging epidemic countries such as China, Australia, and the USA, where dengue outbreaks were frequently triggered by importation, showed a growing trend of DENV-1 diffusion. The probably hidden epidemics were found especially in Africa and India. Then, our framework can be utilized in an accurate stratified coordinated surveillance based on the defined viral population compositions. Thereby it is prospectively valuable for further hampering the ongoing transition process of epidemic to endemic, addressing the issue of inadequate monitoring, and warning us to be concerned about the cross-national, cross-regional, and cross-continental diffusions of dengue, which can potentially trigger large epidemics. CONCLUSIONS: The framework and its utilization in quantitatively assessing DENV-1 epidemics has laid a foundation and re-unveiled the urgency for establishing a stratified coordinated surveillance platform for blocking global spreading of dengue. This framework is also expected to bridge classical DENV-1 genotyping with genomic epidemiology and risk modeling. We will promote it to the public and update it periodically.