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Abstract Inactivating mutations in SMARCB1 confer an oncogenic dependency on EZH2 in atypical teratoid rhabdoid tumors (ATRTs), but the underlying mechanism has not been fully elucidated. We found that the sensitivity of ATRTs to EZH2 inhibition (EZH2i) is associated with the viral mimicry response. Unlike other epigenetic therapies targeting transcriptional repressors, EZH2i-induced viral mimicry is not triggered by cryptic transcription of endogenous retroelements, but rather mediated by increased expression of genes enriched for intronic inverted-repeat Alu (IR-Alu) elements. Interestingly, interferon-stimulated genes (ISGs) are highly enriched for dsRNA-forming intronic IR-Alu elements, suggesting a feedforward loop whereby these activated ISGs may reinforce dsRNA formation and viral mimicry. EZH2i also upregulates the expression of full-length LINE-1s, leading to genomic instability and cGAS/STING signaling in a process dependent on reverse transcriptase activity. Co-depletion of dsRNA sensing and cytoplasmic DNA sensing completely rescues the viral mimicry response to EZH2i in SMARCB1-deficient tumors.

Abstract Temperature prediction over decades provides crucial information for quantifying the expected effects of future climate changes. However, such predictions are extremely challenging due to the chaotic nature of temperature variations. Here we devise a prediction method involving an information tracking mechanism that aims to track and adapt to changes in temperature dynamics during the prediction phase by providing probabilistic feedback on the prediction error of the next step based on the current prediction. We integrate this information tracking mechanism, which can be considered as a model calibrator, into the objective function of the proposed method to obtain the corrections needed to avoid error accumulation. Experimental results on the task of global weekly land surface temperature prediction over a decade validate the effectiveness of the proposed method.

Abstract 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. Graphical Abstract

Abstract 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. Graphical Abstract

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.

Farmland shelterbelts are aimed at farmland protection and productivity improvement, environmental protection and ecological balance, as well as land use planning and management. Farmland shelterbelts play a vital role in determining the structural integrity and overall effectiveness of farmland, and assessing the dynamic changes within these protective forests accurately and swiftly is essential to maintaining their protective functions as well as for policy formulation and effectiveness evaluation in relevant departments. Traditional methods for extracting farmland shelterbelt information have faced significant challenges due to the large workload required and the inconsistencies in the accuracy of existing methods. For example, the existing vegetation index extraction methods often have significant errors, which remain unresolved. Therefore, developing a more efficient extraction method with greater accuracy is imperative. This study focused on Youyi Farm in Heilongjiang Province, China, utilizing satellite data with spatial resolutions ranging from 0.8 m (GF-7) to 30 m (Landsat). By taking into account the growth cycles of farmland shelterbelts and variations in crop types, the optimal temporal window for extraction is identified based on phenological analysis. The study introduced a new index—the Re-Modified Anthocyanin Reflectance Index (RMARI)—which is an improvement on existing vegetation indexes, such as the NDVI and the improved original ARI. Both the accuracy and extraction results showed significant improvements, and the feasibility of the RMARI was confirmed. The study proposed four extraction schemes for farmland shelterbelts: (1) spectral feature extraction, (2) extraction using vegetation indexes, (3) random forest extraction, and (4) RF combined with characteristic index bands. The extraction process was implemented on the GEE platform, and results from different spatial resolutions were compared. Results showed that (1) the bare soil period in May is the optimal time period for extracting farmland shelterbelts; (2) the RF method combined with characteristic index bands produces the best extraction results, effectively distinguishing shelterbelts from other land features; (3) the RMARI reduces background noise more effectively than the NDVI and ARI, resulting in more comprehensive extraction outcomes; and (4) among the satellite images analyzed—GF-7, Planet, Sentinel-2, and Landsat OLI 8—GF-7 achieves the highest extraction accuracy (with a Kappa coefficient of 0.95 and an OA of 0.97), providing the most detailed textural information. However, comprehensive analysis suggests that Sentinel-2 is more suitable for large-scale farmland shelterbelt information extraction. This study provides new approaches and technical support for periodic dynamic forestry surveys, providing valuable reference points for agricultural ecological research.

Understanding the relationship between climate, snow cover, and vegetation Net Primary Productivity (NPP) in the Tibetan Plateau (TP) is crucial. However, the role of snow cover in influencing the NPP remains unclear. This study investigates the connection between the NPP and snow phenology (SP) across the TP from 2011 to 2020. Interannual trends were assessed using the Theil–Sen non-parametric regression approach combined with the Mann–Kendall test. Additionally, the pathways through which snow cover affects the NPP, considering various environmental factors, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Approximately 10.72% of the TP showed a significant decrease in the NPP, accompanied by advancing trends in the Snow Onset Date (SOD) and Snow End Date (SED), as well as a gradual decrease in the Snow Cover Duration (SCD). The PLS-SEM results reveal that precipitation and soil temperature significantly influenced the NPP, with total effects of 0.309 and 0.206 in the SCD structural equation. Temperature had a relatively strong indirect effect on the NPP through its influence on the SOD and SCD, contributing 16% and 10% to the total effect, respectively. Neglecting the mediating effect of SP underestimates the environmental impact on the NPP. This study highlights how environmental factors influence the NPP through snow cover changes as the biomass increases, thereby enhancing our understanding of SP’s impact on the TP.

BackgroundEmerging 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.MethodsWe 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.ResultsTo 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.ConclusionThis 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.

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.

Abstract 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.

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.

This study establishes and verifies the finite element models (FEM) of MSCCJs to analyze the mechanical performance of modular steel–concrete composite joints (MSCCJs) under monotonic static load without considering the floor slab composite effect (FSCE). Subsequently, 12 MSCCJ models incorporating the FSCE are constructed using ABAQUS. The study evaluates the effects of varying column axial compression ratios (n = 0.15, 0.25, 0.4, 0.6), floor width (w = 800, 1200 mm), floor thickness (t = 90, 110, 120 mm), and floor reinforcement ratio (r = 6 %, 8 %, 12 %) on the bending moment, initial stiffness (k0), ductility, and stress transfer of the joints. The results indicated that as n increases, the shear stress concentration in the joint core module (JCM) and the steel framework at the lower column end become more pronounced. Concurrently, the bearing moment and k0 of the joint also increase. However, once n surpasses 0.4, the joint's plastic properties diminish upon damage. With an increment in w, the k0 and ductility deformation of the joint remain stable and then decline. At w = 1000 mm, the joint's bearing capacity, stress distribution, and deformation performance reach their optimum. Since it significantly impacts stress distribution, its effect on the joint's mechanical performance is minimal. Lastly, the value of r enhances the ductility of MSCCJs and the stress concentration in the reinforcement. It is advisable to maintain r between 1.1 % and 1.7 %.

Abstract 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. Graphical Abstract

Abstract 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. Graphical Abstract

An experiment to compare the effects of land use types on soil and macrofauna characteristics was conducted in a mountainous region of southwestern China. Soil physical and chemical properties and soil macrofauna were investigated in four land use types: natural grassland, mixed pasture of Dactylis glomerata L. and Trifolium repens L., mixed pasture of Holcus lanatus L. and Trifolium repens L., and cropland planted with annual Brassica napus L. and Zea mays L. rotation. The results showed that natural grassland, mixed pasture and cropping increased soil pH (23.0%–36.0%), soil organic matter (69.1%–73.9%, except the cropland with a decrease of 18.9%), total nitrogen (346.2%–738.5%), available nitrogen (389.9%–482.7%), available phosphorus (61.9%–303.6%) and available potassium (326.2%–481.4%). The taxonomic richness of macrofaunal communities was lower in the mixed pasture and cropped land than in natural grassland, with the Shannon’s index and Menhinick index being negatively related to soil organic carbon content. The mixed pasture maintained the abundance and diversity of soil macrofauna. The short-term cessation of utilization and management facilitated the restoration of soil macrofaunal communities. This study shows that pasture/grazing or leaving fallow for a year after cropping were able to better sustain macrofaunal communities in this mountainous region.

Abstract Background The new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world, especially in highly populated regions. The healthcare capacity (especially the testing resources, vaccination coverage, and hospital capacity) is becoming extremely insufficient as the demand will far exceed the supply. To address this time-critical issue, we need to answer a key question: How can we effectively infer the daily transmission risks in different districts using machine learning methods and thus lay out the corresponding resource prioritization strategies, so as to alleviate the impact of the Omicron outbreaks? Methods We propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant. We collect the publicly available data from the official website of the Hong Kong Special Administrative Region (HKSAR) Government and the study period in this paper is from December 27, 2021 to July 17, 2022 (including a period for future prediction). First, we construct the spatiotemporal transmission intensity matrices across different districts based on infection case records. With the constructed cross-district transmission matrices, we forecast the future risks of various locations daily by means of the Gaussian process. Finally, we develop a transmission-guided resource prioritization strategy that enables effective control of Omicron outbreaks under limited capacity. Results We conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong, China. The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks, making it difficult for the public health authority to foresee the outbreaks and plan the responses accordingly. With the guidance of the inferred transmission risks, the developed prioritization strategy enables the optimal testing resource allocation for integrative case management (including case detection, quarantine, and further treatment), i.e., with the 300,000 testing capacity per day; it could reduce the infection peak by 87.1% compared with the population-based allocation strategy (case number reduces from 20,860 to 2689) and by 24.2% compared with the case-based strategy (case number reduces from 3547 to 2689), significantly alleviating the burden of the healthcare system. Conclusions Computationally characterizing spatiotemporal transmission patterns allows for the effective risk mapping and resource prioritization; such adaptive strategies are of critical importance in achieving timely outbreak control under insufficient capacity. The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants. Moreover, the investigation conducted in Hong Kong, China provides useful suggestions on how to achieve effective disease control with insufficient capacity in other highly populated countries and regions. Graphical Abstract

IntroductionRhizobacterial 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.MethodsHere, 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.ResultsResults 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.DiscussionSeveral 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.

Triterpenoid saponin are important secondary metabolites and bioactive constituents of soapberry (Sapindus mukorossi Gaertn.) and are widely used in medicine and toiletry products. However, little is known about the roles of miRNAs in the regulation of triterpenoid saponin biosynthesis in soapberry. In this study, a total of 3036 miRNAs were identified, of which 1372 miRNAs were differentially expressed at different stages of pericarp development. Important KEGG pathways, such as terpenoid backbone biosynthesis, sesquiterpenoid and triterpenoid biosynthesis, and basal transcription factors were highlighted, as well the roles of some key miRNAs, such as ath-miR5021, han-miR3630-3p, and ppe-miR858, which may play important roles in regulating triterpenoid saponin biosynthesis. In addition, 58 miRNAs might participate in saponin biosynthesis pathways by predicting the targets of those miRNAs to 53 saponin biosynthesis structural genes. And 75 miRNAs were identified to potentially play vital role in saponin accumulation by targeting transcript factor genes, bHLH, bZIP, ERF, MYB, and WRKY, respectively, which are candidate regulatory genes in the pathway of saponin biosynthesis. The results of weighted gene coexpression network analysis (WGCNA) suggested that two saponin-specific miRNA modules and 10 hub miRNAs may participate in saponin biosynthesis. Furthermore, multiple miRNA–mRNA regulatory networks potentially involved in saponin biosynthesis were generated, e.g., ath-miR5021–SmIDI2/SmGPS5/SmbAS1/SmCYP71D-3/SmUGT74G-2, han-miR3630-3p–SmCYP71A-14/SmbHLH54/SmMYB135/SmWRKY32, and ppe-miR858–SmMYB5/SmMYB32. qRT-PCR analysis validated the expression patterns of nine miRNAs and 12 corresponding target genes. This study represents the first comprehensive analysis of miRNAs in soapberry and lays the foundation for further understanding of miRNA-based regulation in triterpenoid saponin biosynthesis.

Karst landscapes typically have extremely poor soil fertility, water retention, and water holding capacity, making these landscapes extremely unusual among terrestrial ecosystems. Owing to the complexity of karst areas on a microsite level, a variety of different microhabitats have often been created in a very small area in karst areas, and the differences have facilitated the differentiation of species within the region. This study involved an experiment on Ampelocalamus luodianensis (Poaceae), a bamboo species that specializes in karst habitats, to discover the adaptation strategies this plant uses in this area. Furthermore, a random sampling method was used to measure different variables and their correlation, such as plant biomass, soil temperature, soil mechanical composition, alkali nitrogen, Olsen-K, PZOS (meaning effective phosphorus), soil water content, pH, and organic matter. The findings showed that the distribution of biomass varied in different years in the same habitat. In particular, the ratio of above-ground biomass to below-ground biomass during a quadrennium at the earthy surface was visibly lower than in the remaining three microhabitats. Also, the highest above-ground biomass to below-ground biomass ratios of annual, triennial, and quadrennial A. luodianensis were of those plants living in stone crevices, while biennial ratios of plants in stone gullies were affected by the mechanical composition of soil and by temperature. In conclusion, plant biomass allocation in A. luodianensis was affected by a combination of environmental factors. Thus, the biomass allocation strategy of A. luodianensis was in line with the isokinetic/allometric biomass allocation theory, which was relevant to soil mechanical composition and temperature, perhaps caused by the plant itself and the special karst environment.

Abstract Sapindus (Sapindus L.) is a widely distributed economically important tree genus that provides biodiesel, biomedical and biochemical products. However, with climate change, deforestation, and economic development, the diversity of Sapindus germplasms may face the risk of destruction. Therefore, utilising historical environmental data and future climate projections from the BCC-CSM2-MR global climate database, we simulated the current and future global distributions of suitable habitats for Sapindus using a Maximum Entropy (MaxEnt) model. The estimated ecological thresholds for critical environmental factors were: a minimum temperature of 0–20 °C in the coldest month, soil moisture levels of 40–140 mm, a mean temperature of 2–25 °C in the driest quarter, a mean temperature of 19–28 °C in the wettest quarter, and a soil pH of 5.6–7.6. The total suitable habitat area was 6059.97 × 104 km2, which was unevenly distributed across six continents. As greenhouse gas emissions increased over time, the area of suitable habitats contracted in lower latitudes and expanded in higher latitudes. Consequently, surveys and conservation should be prioritised in southern hemisphere areas which are in danger of becoming unsuitable. In contrast, other areas in northern and central America, China, and India can be used for conservation and large-scale cultivation in the future.

Abstract Background This study examined how rhizosphere fungi influence the accumulation of chemical components in fruits of a small population species of Cinnamomum migao. Results Ascomycota and Basidiomycota were dominant in the rhizosphere fungal community of C. migao. Pestalotiopsis and Gibellulopsis were associated with α-Terpineol and sabinene content, and Gibellulopsis was associated with crude fat and carbohydrate content. There were significant differences in rhizosphere fungal populations between watersheds, and there was no obvious change between fruiting periods. Gibberella, Ilyonectria, Micropsalliota, and Geminibasidium promoted sabinene accumulation, and Clitocybula promoted α-Terpineol accumulation. Conclusion The climate-related differentiation of rhizosphere fungal communities in watershed areas is the main driver of the chemical composition of C. migao fruit. The control of the production of biologically active compounds by the rhizosphere fungal community provides new opportunities to increase the industrial and medicinal value of the fruit of C. migao.

Cinnamomum migao plants often face different degrees of drought in karst habitats, which can lead to plants’ death, especially in the seedling stage. Widespread of arbuscular mycorrhizal (AM) fungi in karst soils have the potential to address this drought, which is a threat to C. migao seedlings. We inoculated C. migao seedlings with spores from Glomus lamellosum and Glomus etunicatum, two AM fungi widely distributed in karst soils, to observe seedling growth response after simulated drought. Our results showed that 40 g of G. lamellosum and G. etunicatum significantly promoted the growth of C. migao seedlings, 120 days after inoculation. Following a 15-day drought treatment, root colonization of the seedlings with G. lamellosum or G. etunicatum had lower the accumulation of malondialdehyde (MDA) and increased the accumulation of enzymes and osmotic substances in the seedlings. The relative water content in different organs (roots, stems, and leaves) of the drought-stressed seedlings was higher in plants with G. lamellosum or G. etunicatum than in plants without AM fungi colonization. Our results showed that inoculation with AM fungi was an effective means to improve the drought resistance of C. migao seedlings.

Abstract Background Cinnamomum migao is an endangered evergreen woody plant species endemic to China. Its fruit is used as a traditional medicine by the Miao nationality of China and has a high commercial value. However, its seed germination rate is extremely low under natural and artificial conditions. As the foundation of plant propagation, seed germination involves a series of physiological, cellular, and molecular changes; however, the molecular events and systematic changes occurring during C. migao seed germination remain unclear. Results In this study, combined with the changes in physiological indexes and transcription levels, we revealed the regulation characteristics of cell structures, storage substances, and antioxidant capacity during seed germination. Electron microscopy analysis revealed that abundant smooth and full oil bodies were present in the cotyledons of the seeds. With seed germination, oil bodies and other substances gradually degraded to supply energy; this was consistent with the content of storage substances. In parallel to electron microscopy and physiological analyses, transcriptome analysis showed that 80–90 % of differentially expressed genes (DEGs) appeared after seed imbibition, reflecting important development and physiological changes. The unigenes involved in material metabolism (glycerolipid metabolism, fatty acid degradation, and starch and sucrose metabolism) and energy supply pathways (pentose phosphate pathway, glycolysis pathway, pyruvate metabolism, tricarboxylic acid cycle, and oxidative phosphorylation) were differentially expressed in the four germination stages. Among these DEGs, a small number of genes in the energy supply pathway at the initial stage of germination maintained high level of expression to maintain seed vigor and germination ability. Genes involved in lipid metabolism were firstly activated at a large scale in the LK (seed coat fissure) stage, and then genes involved in carbohydrates (CHO) metabolism were activated, which had their own species specificity. Conclusions Our study revealed the transcriptional levels of genes and the sequence of their corresponding metabolic pathways during seed germination. The changes in cell structure and physiological indexes also confirmed these events. Our findings provide a foundation for determining the molecular mechanisms underlying seed germination.

Abstract Soapberry (Sapindus mukorossi Gaertn.) is a multi-functional tree with widespread application in toiletries, biomedicine, biomass energy, and landscaping. The pericarp of soapberry can be used as a medicine or detergent. However, there is currently no systematic study on the chemical constituents of soapberry pericarp during fruit development and ripening, and the dynamic changes in these constituents still unclear. In this study, a non-targeted metabolomics approach using ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) was used to comprehensively profile the variations in metabolites in the soapberry pericarp at eight fruit growth stages. The metabolome coverage of UHPLC-HRMS on a HILIC column was higher than that of a C18 column. A total of 111 metabolites were putatively annotated. Principal component analysis and hierarchical clustering analysis of pericarp metabolic composition revealed clear metabolic shifts from early (S1–S2) to late (S3–S5) development stages to fruit ripening stages (S6–S8). Furthermore, pairwise comparison identified 57 differential metabolites that were involved in 18 KEGG pathways. Early fruit development stages (S1–S2) were characterized by high levels of key fatty acids, nucleotides, organic acids, and phosphorylated intermediates, whereas fruit ripening stages (S6–S8) were characterized by high contents of bioactive and valuable metabolites, such as troxipide, vorinostat, furamizole, alpha-tocopherol quinone, luteolin, and sucrose. S8 (fully developed and mature stage) was the most suitable stage for fruit harvesting to utilize the pericarp. To the best of our knowledge, this was the first metabolomics study of the soapberry pericarp during whole fruit growth. The results could offer valuable information for harvesting, processing, and application of soapberry pericarp, as well as highlight the metabolites that could mediate the biological activity or properties of this medicinal plant.

Abstract Drought is the main limiting factor for plant growth in karst areas with a fragile ecological environment. Cinnamomum migao H.W. Li is an endemic medicinal woody plant present in the karst areas of southwestern China, and it is endangered due to poor drought tolerance. Arbuscular mycorrhizal fungi (AMF) are known to enhance the drought tolerance of plants. However, few studies have examined the contribution of AMF in improving the drought tolerance of C. migao seedlings. Therefore, we conducted a series of experiments to determine whether a single inoculation and coinoculation of AMF (Claroideoglomus lamellosum and Claroideoglomus etunicatum) enhanced the drought tolerance of C. migao. Furthermore, we compared the effects of single inoculation and coinoculation with different inoculum sizes (20, 40, 60, and 100 g; four replicates per treatment) on mycorrhizal colonization rate, plant growth, photosynthetic parameters, antioxidant enzyme activity, and malondialdehyde (MDA) and osmoregulatory substance contents. The results showed that compared with nonmycorrhizal plants, AMF colonization significantly improved plant growing status; net photosynthetic rate; superoxide dismutase, catalase, and peroxidase activities; and soluble sugar, soluble protein, and proline contents. Furthermore, AMF colonization increased relative water content and reduced MDA content in cells. These combined cumulative effects of AMF symbiosis ultimately enhanced the drought tolerance of seedlings and were closely related to the inoculum size. With an increase in inoculum size, the growth rate and drought tolerance of plants first increased and then decreased. The damage caused by drought stress could be reduced by inoculating 40–60 g of AMF, and the effect of coinoculation was significantly better than that of single inoculation at 60 g of AMF, while the effect was opposite at 40 g of AMF. Additionally, the interaction between AMF and inoculum sizes had a significant effect on drought tolerance. In conclusion, the inoculation of the AMF (Cl. lamellosum and Cl. etunicatum) improved photosynthesis, activated antioxidant enzymes, regulated cell osmotic state, and enhanced the drought tolerance of C. migao, enabling its growth in fragile ecological environments.

Abstract Ankylosing spondylitis cervical spine fractures (ASCFs) are particularly unstable and need special consideration when selecting appropriate internal fixation technology. However, there is a lack of related biomechanical studies. This study aimed to investigate the biomechanical influence of the pattern, length, and density of instrumentation for the treatment of ASCF. Posterior, anterior, and various combined fixation approaches were constructed using the finite element model (FEM) to mimic the surgical treatment of ASCFs at C5/6. The rate of motion change (RMC) at the fractured level and the internal stress distribution (ISD) were observed. The results showed that longer segments of fixation and combined fixation approaches provided better stability and lowered the maximal stress. The RMC decreased more significantly when the length increased from 1 to 3 levels (302% decrease under flexion, 134% decrease under extension) than from 3 to 5 levels (22% decrease under flexion, 23% decrease under extension). Longer fixation seems to be more stable with the anterior/posterior approach alone, but 3-level posterior fixation may be the most cost-effective option. It is recommended to perform surgery with combined approaches, which provide the best stability. Long skipped-screwing posterior fixation is an alternative technique for use in ASCF patients.

Discovering and mitigating potential risks in advance is essential for preventing aviation accidents on routine flights. Although anomaly detection-based explanation techniques have successfully uncovered potential risks for proactive flight safety management, explaining group-scale precursors using these methods is challenging due to the assumption that risky flights are significantly fewer in number than normal flights, as well as the reliance on non-domain knowledge for hyperparameter adjustment. To characterize the group-scale precursors more accurately, we propose a novel technique called Catalyst Mass-Based Clustering Analysis (CMCA), which employs a composite entropy-energy dissipation index during approach to evaluate the energy management performance. On this basis, an optimization objective is constructed to identify clusters exhibiting significant energy management differences during the approach phase. We successfully identify group-scale precursors with energy management issues by applying CMCA to a combination of minority-labeled and majority-unlabeled flights. Comparative experiments show that these precursors have energy levels that deviate from normal flights by 5.83% and 10.93%, respectively, 1000 ft above touchdown, demonstrating the effectiveness of our method. The analysis suggests that poor energy management awareness on the part of pilots could be responsible for these group-scale precursors. Notably, the results obtained using CMCA are comprehensible for Subject Matter Experts, making the method a valuable tool for proactive flight safety management.

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.

Abstract Background The pandemic of the coronavirus disease 2019 (COVID-19) has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases, such as malaria in sub-Saharan Africa. The aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa. Methods We present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as various non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 cases. Then, we simulate the epidemic dynamics of COVID-19 under two groups of NPIs: (1) contact restriction and social distancing, and (2) early identification and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity. Results We conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number $$R_0$$ R 0 and the duration of infection $$D_I$$ D I ) of COVID-19 in each country are estimated as follows: Ethiopia ( $$R_0=1.57$$ R 0 = 1.57 , $$D_I=5.32$$ D I = 5.32 ), Nigeria ( $$R_0=2.18$$ R 0 = 2.18 , $$D_I=6.58$$ D I = 6.58 ), Tanzania ( $$R_0=2.47$$ R 0 = 2.47 , $$D_I=6.01$$ D I = 6.01 ), and Zambia ( $$R_0=2.12$$ R 0 = 2.12 , $$D_I=6.96$$ D I = 6.96 ). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the effect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020. Conclusions By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria-endemic countries in Africa. The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential.

Sapindus is an important forest tree genus with utilization in biodiesel, biomedicine, and it harbors great potential for biochemical engineering applications. For advanced breeding of Sapindus, it is necessary to evaluate the genetic diversity and construct a rationally designed core germplasm collection. In this study, the genetic diversity and population structure of Sapindus were conducted with 18 expressed sequence tag-simple sequence repeat (EST-SSR) markers in order to establish a core germplasm collection from 161 Sapindus accessions. The population of Sapindus showed high genetic diversity and significant population structure. Interspecific genetic variation was significantly higher than intraspecific variation in the Sapindus mukorossi, Sapindus delavayi, and combined Sapindus rarak plus Sapindus rarak var. velutinus populations. S. mukorossi had abundant genetic variation and showed a specific pattern of geographical variation, whereas S. delavayi, S. rarak, and S. rarak var. velutinus showed less intraspecific variation. A core germplasm collection was created that contained 40% of genetic variation in the initial population, comprising 53 S. mukorossi and nine S. delavayi lineages, as well as single representatives of S. rarak and S. rarak var. velutinus. These results provide a germplasm basis and theoretical rationale for the efficient management, conservation, and utilization of Sapindus, as well as genetic resources for joint genomics research in the future.

Abstract Background Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series. To evaluate the severity of an epidemic, statistical methods are widely used to forecast the trend, seasonality, and the possible number of infections of a disease. However, most statistical methods are limited in revealing the underlying dynamics of disease transmission, which may be affected by various impact factors, such as environmental, meteorological, and physiological factors. In this study, we focus on investigating malaria transmission dynamics based on time series data. Methods A data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence data. Specifically, the dynamics of malaria transmission is modeled based on the notion of vectorial capacity (VCAP) and entomological inoculation rate (EIR). A particle Markov chain Monte Carlo (PMCMC) method is employed to estimate the model parameters. Accordingly, a one-step-ahead prediction method is proposed to project the number of future malaria infections. Finally, two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling, Yunnan province, China. Results The results show that the trained data-driven stochastic model can well fit the historical time series of P. vivax prevalence data in both counties from 2007 to 2010. Moreover, with well-trained model parameters, the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria infections. Conclusions By involving dynamically changing impact factors, the proposed data-driven model together with the PMCMC method can successfully (i) depict the dynamics of malaria transmission, and (ii) achieve accurate one-step-ahead prediction about malaria infections. Such a data-driven method has the potential to investigate malaria transmission dynamics in other malaria-endemic countries/regions.

Graph representation learning (GRL) has recently drawn a lot of attention due to its advantage in solving various machine learning tasks on graphs/networks, ranging from drug design to recommendation systems. One typical GRL approach is graph embedding, the purpose of which is to learn a map that encodes or represents network elements as points in a low-dimensional vector space so that downstream machine learning methods can be easily implemented. Initially, most graph embedding algorithms learn such a map independently from subsequent machine learning tasks. Therefore, they have limitations in solving supervised machine learning tasks on networks. Later, a great deal of graph convolutional networks (GCNs) have been proposed to learn node representations in an end-to-end manner based on different information aggregation mechanisms. By treating network structure as a computational layer in a GCN, the associated information of nodes with higher-order proximity can be aggregated by increasing the number of layers (i.e., depth) of the GCN. As a consequence, the computational overhead will increase and the representations will be projected towards a steady state. To solve this problem, in this paper, we propose a multi-channel graph convolutional network (MCGCN) that allows higher-order information aggregation by enriching the number of input channels. Based on the notion of Katz index, our model can further achieve an arbitrary order of information aggregation without increasing the computational overhead. Comprehensive experiments on several benchmark networks demonstrate the effectiveness of the proposed architecture by comparing it with the-state-of-art GRL methods in terms of node classification and computational efficiency.

Abstract Nitrogen (N) deposition, a major phenomenon of climate change, is increasing with industrialization and more human activity and can affect litter decomposition. Yet it remains unclear whether N deposition will accelerate or inhibit the decomposition of litter in karst regions, which are characterized by P‐limitation or N and P co‐limited stress conditions in soil. Here, to document the influence of simulated N deposition on litter decomposition in karst, the leaf litter of Cinnamomum migao was studied, this being a dominant perennial woody plant endemic to karst landforms. We conducted a 300‐d field decomposition experiment with four treatments: control (CK); addition of (+) N at 50 kg⋅ha−1⋅a−1 (N1); +N at 150 kg⋅ha−1⋅a−1 (N2), and +N at 300 kg⋅ha−1⋅a−1 (N3), in southwest Guizhou, the largest karst region in China (n = 4 treatments). From each treatment, the litter samples’ remaining mass were measured during the decomposition period, in addition to their nitrogen (N), phosphorus (P), carbon (C), and potassium (K) concentrations, as well as that of soils; the soil’s pH was also measured. The results showed that litter remaining mass increased significantly under nitrogen deposition, which, as expected, acidified the soil. Meanwhile, litter N, P, and K contents responded to nitrogen deposition, whereas C did not. Otherwise, no direct effects of nitrogen deposition upon soil nutrients were detected. The soil nutrient contents were related to the release of the elements in litter. In conclusion, nitrogen deposition suppressed the decomposition of C. migao litter in karst, but the nutrients of karst soil are mainly replenished by litter inputs.

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.

Abstract Background Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. However, existing studies simply apply naive RL algorithms in discovering optimal treatment strategies for a targeted problem. This kind of direct applications ignores the abundant causal relationships between treatment options and the associated outcomes that are inherent in medical domains. Methods This paper investigates how to integrate causal factors into an RL process in order to facilitate the final learning performance and increase explanations of learned strategies. A causal policy gradient algorithm is proposed and evaluated in dynamic treatment regimes (DTRs) for HIV based on a simulated computational model. Results Simulations prove the effectiveness of the proposed algorithm for designing more efficient treatment protocols in HIV, and different definitions of the causal factors could have significant influence on the final learning performance, indicating the necessity of human prior knowledge on defining a suitable causal relationships for a given problem. Conclusions More efficient and robust DTRs for HIV can be derived through incorporation of causal factors between options of anti-HIV drugs and the associated treatment outcomes.

Abstract Background Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. To ensure such applications, an explicit reward function encoding domain knowledge should be specified beforehand to indicate the goal of tasks. However, there is usually no explicit information regarding the reward function in medical records. It is then necessary to consider an approach whereby the reward function can be learned from a set of presumably optimal treatment trajectories using retrospective real medical data. This paper applies inverse RL in inferring the reward functions that clinicians have in mind during their decisions on weaning of mechanical ventilation and sedative dosing in Intensive Care Units (ICUs). Methods We model the decision making problem as a Markov Decision Process, and use a batch RL method, Fitted Q Iterations with Gradient Boosting Decision Tree, to learn a suitable ventilator weaning policy from real trajectories in retrospective ICU data. A Bayesian inverse RL method is then applied to infer the latent reward functions in terms of weights in trading off various aspects of evaluation criterion. We then evaluate how the policy learned using the Bayesian inverse RL method matches the policy given by clinicians, as compared to other policies learned with fixed reward functions. Results Results show that the inverse RL method is capable of extracting meaningful indicators for recommending extubation readiness and sedative dosage, indicating that clinicians pay more attention to patients’ physiological stability (e.g., heart rate and respiration rate), rather than oxygenation criteria (FiO 2, PEEP and SpO 2) which is supported by previous RL methods. Moreover, by discovering the optimal weights, new effective treatment protocols can be suggested. Conclusions Inverse RL is an effective approach to discovering clinicians’ underlying reward functions for designing better treatment protocols in the ventilation weaning and sedative dosing in future ICUs.

Contact tracking is one of the key technologies in prevention and control of infectious diseases. In the face of a sudden infectious disease outbreak, contact tracking systems can help medical professionals quickly locate and isolate infected persons and high-risk individuals, preventing further spread and a large-scale outbreak of infectious disease. Furthermore, the transmission networks of infectious diseases established using contact tracking technology can aid in the visualization of actual virus transmission paths, which enables simulations and predictions of the transmission process, assessment of the outbreak trend, and further development and deployment of more effective prevention and control strategies. Exploring effective contact tracking methods will be significant. Governments, academics, and industries have all given extensive attention to this goal. In this paper, we review the developments and challenges of current contact tracing technologies regarding individual and group contact from both static and dynamic perspectives, including static individual contact tracing, dynamic individual contact tracing, static group contact tracing, and dynamic group contact tracing. With the purpose of providing useful reference and inspiration for researchers and practitioners in related fields, directions in multi-view contact tracing, multi-scale contact tracing, and AI-based contact tracing are provided for next-generation technologies for epidemic prevention and control.

Infectious diseases pose a constant and serious threat to human life. One way to prevent infectious disease spread is through active surveillance: monitoring patients to discover disease incidences before they get out of hand. However, active surveillance can be difficult to implement, especially when the monitored area is vast and resources are limited. Incidences of infectious disease that arrive with visitors from abroad are a further challenge. When faced with imported incidences and a large region to monitor, it is critical that public health authorities precisely allocate their sparse resources to high-priority areas to maximize the efficacy of active surveillance. In this paper, the difficulties of active surveillance are considered, and we offer a computational framework to address these challenges by modeling and mining the spatiotemporal patterns of infectious risks from heterogeneous data sources. Malaria is used as an empirical case study (with real-world data) to validate our proposed method and enhance our findings.

The fundamental purpose of network embedding is to automatically encode each node in a network as a low-dimensional vector, while at the same time preserving certain characteristics of the network. Based on the nodes' embeddings, downstream network analytic tasks such as community mining, node classification, and link prediction, can then be easily implemented using traditional machine learning methods. In recent years, extensive network embedding methods have been proposed based on factorization, random walks, deep learning, and so on. However, most of them focus mainly on preserving the structural proximity of network nodes, where highly interconnected nodes in a network will be represented closely together in the embedded vector space. While in many real-world networks, existing studies have revealed that high-order organizations (e.g., network motifs and graphlets) may be related to specific network functions. In this case, nodes far apart but with a similar organization in a network (i.e., structural equivalence) may have similar network functions. Accordingly, in this paper, we present a hybrid embedding method that unifies both structural proximity and equivalence (SPaE) of a network. Specifically, we adopt the concept of graphlet degree vector (GDV) to measure structural equivalence between network nodes. Through carrying out experiments on both synthetic and real-world datasets, we evaluate the performance of the hybrid embedding method in tasks of node clustering, node classification, and visualization. The results demonstrate that the proposed SPaE method outperforms several state-of-the-art methods when the network analytic tasks are not merely related to structural proximity. Finally, we also conduct experiments to evaluate the flexibility, robustness, and parameter sensitivity of the hybrid embedding method.

The decomposition of litter plays an important role in the return of forest soil nutrients, as well as the growth and productivity of plants. With this study, we aimed to determine the impact of litter mulching on different tissues of Cinnamomum migao, a rare Chinese endemic species. In particular, seeds and pericarp are easily overlooked components of C. migao litter. In this study, we tested control (uncovered litter) and litter (leaf, branch, seed, and pericarp) mulching conditions and conducted a one-year litter decomposition experiment. The enzyme activities of urease enzyme (UE) and invertase enzyme (INV) were significantly improved by litter mulching. Catalase (CAT) enzyme activities in leaf, branch, and seed litter mulching were lower than in the control, whereas CAT activity in pericarp mulching was significantly higher than in the control. Although Mortierella, Cladophialophora, Acidothermus, Sphingomonas, and Burkholderia were the dominant microbes of topsoil in different mulching treatments, there were differences in the number and connectivity of microbial communities, and this change was correlated with soil organic carbon (SOC) and CAT enzyme activity. Compared with leaves and branches, seeds and pericarp as litter are also very important for nutrient return and affect topsoil microbes in C. migao forest, which may be of significance for the growth feedback of C. migao in biennial bearing.

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic emergency. Culture and anti-epidemic policy do not necessarily conflict with each other. All countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

Background: COVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened. The findings can help inform mitigation strategies in the disease-affected countries. Methods: In this work, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. We develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups. We divide a city's population into seven age-groups: 0–6 years old (children); 7–14 (primary and junior high school students); 15–17 (high school students); 18–22 (university students); 23–44 (young/middle-aged people); 45–64 years old (middle-aged/elderly people); and 65 or above (elderly people). We consider four representative settings of social contacts that may cause the disease spread: (1) individual households; (2) schools, including primary/high schools as well as colleges and universities; (3) various physical workplaces; and (4) public places and communities where people can gather, such as stadiums, markets, squares, and organized tours. A contact matrix is computed to describe the contact intensity between different age-groups in each of the four settings. By integrating the four contact matrices with the next-generation matrix, we quantitatively characterize the underlying transmission patterns of COVID-19 among different populations. Findings: We focus our study on 6 representative cities in China: Wuhan, the epicenter of COVID-19 in China, together with Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, which are five major cities from three key economic zones. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns as well as the associated risks (including both confirmed and unconfirmed cases). In Wuhan, the age-groups involving relatively intensive contacts in households and public/communities are dispersedly distributed. This can explain why the transmission of COVID-19 in the early stage mainly took place in public places and families in Wuhan. We estimate that Feb. 11, 2020 was the date with the highest transmission risk in Wuhan, which is consistent with the actual peak period of the reported case number (Feb. 4–14). Moreover, the surge in the number of new cases reported on Feb. 12 and 13 in Wuhan can readily be captured using our model, showing its ability in forecasting the potential/unconfirmed cases. We further estimate the disease transmission risks associated with different work resumption plans in these cities after the outbreak. The estimation results are consistent with the actual situations in the cities with relatively lenient policies, such as Beijing, and those with strict policies, such as Shenzhen. Interpretation: With an in-depth characterization of age-specific social contact-based transmission, the retrospective and prospective situations of the disease outbreak, including the past and future transmission risks, the effectiveness of different interventions, and the disease transmission risks of restoring normal social activities, are computationally analyzed and reasonably explained. The conclusions drawn from the study not only provide a comprehensive explanation of the underlying COVID-19 transmission patterns in China, but more importantly, offer the social contact-based risk analysis methods that can readily be applied to guide intervention planning and operational responses in other countries, so that the impact of COVID-19 pandemic can be strategically mitigated. Funding: General Research Fund of the Hong Kong Research Grants Council; Key Project Grants of the National Natural Science Foundation of China.

Drought stress is one of the greatest obstacles affecting field crop productivity in arid and semi-arid regions, and its severity and frequency are expected to increase due to human-induced changes to the environment and climate. Drought has led to rocky desertification in Karst regions. Cinnamomum migao is a unique, fast-growing medicinal plant of Southwest China that only thrives in Karst regions. Arbuscular mycorrhizal fungi (AMF) symbiosis alleviates drought stress in plants; however, establishment and function of the symbiotic interaction between AMF host plant in relation to the inoculation method remain unclear. Therefore, we conducted an experiment to investigate the effects of AMF species (Glomus etunicatum and Funneliformis mosseae) and two inoculation methods (seed vs. seedling inoculation) under drought stress on C. migao seedlings, and quantified mycorrhizal colonization, AMF spore density, root vigor, relative water content, C. migao growth, antioxidant enzyme activities, and osmotic adjustment. Inoculation with AMF (G. etunicatum and F. mosseae) positively affected the growth and root vigor of Cinnamomum migao under drought stress, regardless of the inoculation method. Additionally, both AMF species markedly upregulated antioxidant enzyme activities and osmotic adjustment substances, regardless of the inoculation method. Our results showed that the collective stimulatory effect of G. etunicatum is more efficient than that of F. mosseae. AMF application could promote afforestation with C. migao to prevent rocky desertification in Karst regions where water is the greatest limiting factor on plant growth and yield.

Abstract Background In China since the first human infection of avian influenza A (H7N9) virus was identified in 2013, it has caused serious public health concerns due to its wide spread and high mortality rate. Evidence shows that bird migration plays an essential role in global spread of avian influenza viruses. Accordingly, in this paper, we aim to identify key bird species and geographical hotspots that are relevant to the transmission of avian influenza A (H7N9) virus in China. Methods We first conducted phylogenetic analysis on 626 viral sequences of avian influenza A (H7N9) virus isolated in chicken, which were collected from the Global Initiative on Sharing All Influenza Data (GISAID), to reveal geographical spread and molecular evolution of the virus in China. Then, we adopted the cross correlation function (CCF) to explore the relationship between the identified influenza A (H7N9) cases and the spatiotemporal distribution of migratory birds. Here, the spatiotemporal distribution of bird species was generated based on bird observation data collected from China Bird Reports, which consists of 157 272 observation records about 1145 bird species. Finally, we employed a kernel density estimator to identify geographical hotspots of bird habitat/stopover that are relevant to the influenza A (H7N9) infections. Results Phylogenetic analysis reveals the evolutionary and geographical patterns of influenza A (H7N9) infections, where cases in the same or nearby municipality/provinces are clustered together with small evolutionary differences. Moreover, three epidemic waves in chicken along the East Asian–Australasian flyway in China are distinguished from the phylogenetic tree. The CCF analysis identifies possible migratory bird species that are relevant to the influenza A(H7N9) infections in Shanghai, Jiangsu, Zhejiang, Fujian, Jiangxi, and Guangdong in China, where the six municipality/provinces account for 91.2% of the total number of isolated H7N9 cases in chicken in GISAID. Based on the spatial distribution of identified bird species, geographical hotspots are further estimated and illustrated within these typical municipality/provinces. Conclusions In this paper, we have identified key bird species and geographical hotspots that are relevant to the spread of influenza A (H7N9) virus. The results and findings could provide sentinel signal and evidence for active surveillance, as well as strategic control of influenza A (H7N9) transmission in China.

Polymorphisms in the prion protein gene (PRNP) have been linked with the occurrence of transmissible spongiform encephalopathies and the expression of some phenotypic traits in healthy animals including cattle. The aim of this study was to verify the 23-bp insertion/deletion (indel) within promoter region and the 12-bp indel within intron 1 of PRNP, as well as to evaluate their associations with growth traits. Two loci of PRNP were genotyped in 1558 healthy Chinese cattle sampled from 6 indigenous breeds. Moreover, phenotypic records for growth were used to find the relationship between PRNP polymorphisms and phenotypic performance. The results confirmed the occurrence of these two indel polymorphisms in the analysed breeds. Association analysis showed that the 23-bp indel was significantly related to the body length and heart girth in 18-months-old Nanyang cattle. The 12-bp indel was significantly related to the growth traits in three cattle breeds, such as the body weight in Xia'nan cattle, the daily gained weight in 12-months-old Nanyang cattle and the cannon circumference and rump length in Ji'an cattle. These findings indicated that these two indels may affect bovine growth traits, which could benefit to healthy cattle selection and breeding through marker-assisted selection. Abbreviations AI: artificial insemination; ANOVA: analysis of variance; Bp: base pair; BSE: bovine spongiform encephalopathy; D: deletion; DD: deletion/deletion; GLM: generalized linear model; reaction; He: heterozygosity; Ho: homozygosity; HWE: Hardy–Weinberg equilibrium; I: insertion; ID: insertion/deletion; II: insertion/insertion; Indel: insertion/deletion; MAS: marker-assisted selection; Ne: effective allele numbers; PCR: polymerase chain reaction; PIC: polymorphism information content; PrP: Prion protein (PrP); PRNP: the prion protein; RP58: repressor protein with a predicted molecular mass of 58 kDa; SNP: single nucleotide polymorphism; Sp1: specific protein 1; SPSS: statistical product and service solutions; TSE: transmissible spongiform encephalopathies

The ecosystems across the Tibetan Plateau are changing rapidly in response to climate change, which poses unprecedented challenges for the control and mitigation of desertification on the Tibetan Plateau. Sophora moorcroftiana (Benth.) Baker is a drought-resistant plant species that has great potential to be used for desertification and soil degradation control on the Tibetan Plateau. In this study, using a maximum entropy (MaxEnt) niche model, we characterized the habitat distribution of S. moorcroftiana on the Tibetan Plateau under both current and future climate scenarios. To construct a robust model, 242 population occurrence records, gathered from our field surveys, historical data records, and a literature review, were used to calibrate the MaxEnt model. Our results showed that, under current environmental conditions, the habitat of S. moorcroftiana was concentrated in regions along the Yarlung Tsangpo, Lancang, and Jinsha rivers on the Tibetan Plateau. Elevation, isothermality, and minimal air temperature of the coldest month played a dominant role in determining the habitat distribution of S. moorcroftiana. Under future climate scenarios, the increased air temperature was likely to benefit the expansion of S. moorcroftiana over the short term, but, in the long run, continued warming may restrict the growth of S. moorcroftiana and lead to a contraction in its habitat. Importantly, the Yarlung Tsangpo River valley was found to be the core habitat of S. moorcroftiana, and this habitat moved westwards along the Yarlung Tsangpo River under future climate scenarios, but did not detach from it. This finding suggests that, with the current pace of climate change, an increase in efforts to protect and cultivate S. moorcroftiana is necessary and critical to control desertification on the Tibetan Plateau.