DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data.

Single-cell RNA sequencing has enabled to capture the gene activities at single-cell resolution, thus allowing reconstruction of cell-type-specific gene regulatory networks (GRNs). The available algorithms for reconstructing GRNs are commonly designed for bulk RNA-seq data, and few of them are applicable to analyze scRNA-seq data by dealing with the dropout events and cellular heterogeneity. In this paper, we represent the joint gene expression distribution of a gene pair as an image and propose a novel supervised deep neural network called DeepDRIM which utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRN from scRNA-seq data. Due to the consideration of TF-gene pair's neighborhood context, DeepDRIM can effectively eliminate the false positives caused by transitive gene-gene interactions. We compared DeepDRIM with nine GRN reconstruction algorithms designed for either bulk or single-cell RNA-seq data. It achieves evidently better performance for the scRNA-seq data collected from eight cell lines. The simulated data show that DeepDRIM is robust to the dropout rate, the cell number and the size of the training data. We further applied DeepDRIM to the scRNA-seq gene expression of B cells from the bronchoalveolar lavage fluid of the patients with mild and severe coronavirus disease 2019. We focused on the cell-type-specific GRN alteration and observed targets of TFs that were differentially expressed between the two statuses to be enriched in lysosome, apoptosis, response to decreased oxygen level and microtubule, which had been proved to be associated with coronavirus infection.

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.

Transcriptome analysis of Cinnamomum migao seed germination in medicinal plants of Southwest China.

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.

Effects of rhizosphere fungi on the chemical composition of fruits of the medicinal plant Cinnamomum migao endemic to southwestern China.

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.

Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units.

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 (FiO2, PEEP and SpO2) 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.

Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV.

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.

Detection of Insertions/Deletions Within SIRT1, SIRT2 and SIRT3 Genes and Their Associations with Body Measurement Traits in Cattle.

Growth traits are complex quantitative traits controlled by numerous candidate genes, and they can be well-evaluated using body measurement traits. As the members of the nicotinamide adenine dinucleotide-dependent family of histone deacetylases, class I sirtuin genes (including SIRT1, SIRT2 and SIRT3) play crucial roles in regulating lipid metabolism, cellular growth and metabolism, suggesting that they are potential candidate genes affecting body measurement traits in animals. Hence, the objective of this work aimed to detect novel insertions/deletions (indels) of SIRT1, SIRT2 and SIRT3 genes in 955 cattle belonging to five breeds, as well as to evaluate their effects on body measurement traits. Herein, the novel 12-bp indel of SIRT1 gene, the 7-bp indel of SIRT2 gene and the 26-bp indel of SIRT3 gene were firstly reported, respectively. The association analysis indicated that the indels within SIRT1 and SIRT2 genes were significantly associated with body measurement traits such as body weight, chest circumference, height at hip cross, hip width, body height, etc. (P < 0.05 or P < 0.01). Therefore, based on these findings, the two novel indel variants within bovine SIRT1 and SIRT2 genes could be considered as potential molecular markers for growth traits in cattle selection practices and breeding.

Overexpression of Long Non-Coding RNA MEG3 Inhibits Proliferation of Hepatocellular Carcinoma Huh7 Cells via Negative Modulation of miRNA-664.

Evidence is accumulating that long non-coding RNAs (lncRNAs) are involved in human tumorigenesis and dysregulated in many cancers, including hepatocellular carcinoma (HCC). Because lncRNAs can regulate essential pathways that contribute to tumor initiation and progression with their tissue specificity, lncRNAs are valuable biomarkers and therapeutic targets. Maternally expressed gene 3 (MEG3) is a lncRNA overexpressed in HCC cells that inhibits HCC progression, however, the mechanism remains largely unknown. Recently, a novel regulatory mechanism has been proposed in which RNAs can cross-talk with each other via competing for shared microRNAs (miRNAs). The proposed competitive endogenous RNAs could mediate the bioavailability of miRNAs on their targets, thus imposing another level of post-transcriptional regulation. In the current study, we demonstrated that MEG3 is down-regulated in HCC tissues. MEG3 over-expression imposes another level of post-transcriptional regulation, whereas MEG3 overexpression increase the expression of the miR-664 target gene, ADH4, through competitive "sponging" miR-664. In addition, NF-κB may affect transcription of MEG3 by directly binding to the promoter region. Our data revealed that NF-κB may affect the transcript of MEG3. MEG3 overexpression inhibited the proliferation of HCC cells, at least in part by affecting miR-664mediated regulation of ADH4. Together, these results suggest that MEG3 is a suppressor of tumor which acts in part through "sponging" miR-664. J. Cell. Biochem. 118: 3713-3721, 2017. © 2017 Wiley Periodicals, Inc.

NFATc1 releases BCL6-dependent repression of CCR2 agonist expression in peritoneal macrophages from Saccharomyces cerevisiae infected mice.

The link between the extensive usage of calcineurin (CN) inhibitors cyclosporin A and tacrolimus (FK506) in transplantation medicine and the increasing rate of opportunistic infections within this segment of patients is alarming. Currently, how peritoneal infections are favored by these drugs, which impair the activity of several signaling pathways including the Ca(++) /CN/NFAT, Ca(++) /CN/cofilin, Ca(++) /CN/BAD, and NF-κB networks, is unknown. Here, we show that Saccharomyces cerevisiae infection of peritoneal resident macrophages triggers the transient nuclear translocation of NFATc1ß isoforms, resulting in a coordinated, CN-dependent induction of the Ccl2, Ccl7, and Ccl12 genes, all encoding CCR2 agonists. CN inhibitors block the CCR2-dependent recruitment of inflammatory monocytes (IM) to the peritoneal cavities of S. cerevisiae infected mice. In myeloid cells, NFATc1/ß proteins represent the most prominent NFATc1 isoforms. NFATc1/ß ablation leads to a decrease of CCR2 chemokines, impaired mobilization of IMs, and delayed clearance of infection. We show that, upon binding to a composite NFAT/BCL6 regulatory element within the Ccl2 promoter, NFATc1/ß proteins release the BCL6-dependent repression of Ccl2 gene in macrophages. These findings suggest a novel CN-dependent cross-talk between NFAT and BCL6 transcription factors, which may affect the outcome of opportunistic fungal infections in immunocompromised patients.

Predicting protein function via downward random walks on a gene ontology.

BACKGROUND: High-throughput bio-techniques accumulate ever-increasing amount of genomic and proteomic data. These data are far from being functionally characterized, despite the advances in gene (or gene's product proteins) functional annotations. Due to experimental techniques and to the research bias in biology, the regularly updated functional annotation databases, i.e., the Gene Ontology (GO), are far from being complete. Given the importance of protein functions for biological studies and drug design, proteins should be more comprehensively and precisely annotated. RESULTS: We proposed downward Random Walks (dRW) to predict missing (or new) functions of partially annotated proteins. Particularly, we apply downward random walks with restart on the GO directed acyclic graph, along with the available functions of a protein, to estimate the probability of missing functions. To further boost the prediction accuracy, we extend dRW to dRW-kNN. dRW-kNN computes the semantic similarity between proteins based on the functional annotations of proteins; it then predicts functions based on the functions estimated by dRW, together with the functions associated with the k nearest proteins. Our proposed models can predict two kinds of missing functions: (i) the ones that are missing for a protein but associated with other proteins of interest; (ii) the ones that are not available for any protein of interest, but exist in the GO hierarchy. Experimental results on the proteins of Yeast and Human show that dRW and dRW-kNN can replenish functions more accurately than other related approaches, especially for sparse functions associated with no more than 10 proteins. CONCLUSION: The empirical study shows that the semantic similarity between GO terms and the ontology hierarchy play important roles in predicting protein function. The proposed dRW and dRW-kNN can serve as tools for replenishing functions of partially annotated proteins.

Piecewise-constant and low-rank approximation for identification of recurrent copy number variations.

MOTIVATION: The post-genome era sees urgent need for more novel approaches to extracting useful information from the huge amount of genetic data. The identification of recurrent copy number variations (CNVs) from array-based comparative genomic hybridization (aCGH) data can help understand complex diseases, such as cancer. Most of the previous computational methods focused on single-sample analysis or statistical testing based on the results of single-sample analysis. Finding recurrent CNVs from multi-sample data remains a challenging topic worth further study. RESULTS: We present a general and robust method to identify recurrent CNVs from multi-sample aCGH profiles. We express the raw dataset as a matrix and demonstrate that recurrent CNVs will form a low-rank matrix. Hence, we formulate the problem as a matrix recovering problem, where we aim to find a piecewise-constant and low-rank approximation (PLA) to the input matrix. We propose a convex formulation for matrix recovery and an efficient algorithm to globally solve the problem. We demonstrate the advantages of PLA compared with alternative methods using synthesized datasets and two breast cancer datasets. The experimental results show that PLA can successfully reconstruct the recurrent CNV patterns from raw data and achieve better performance compared with alternative methods under a wide range of scenarios. AVAILABILITY AND IMPLEMENTATION: The MATLAB code is available at http://bioinformatics.ust.hk/pla.zip.

Mining geographic variations of Plasmodium vivax for active surveillance: a case study in China.

BACKGROUND: Geographic variations of an infectious disease characterize the spatial differentiation of disease incidences caused by various impact factors, such as environmental, demographic, and socioeconomic factors. Some factors may directly determine the force of infection of the disease (namely, explicit factors), while many other factors may indirectly affect the number of disease incidences via certain unmeasurable processes (namely, implicit factors). In this study, the impact of heterogeneous factors on geographic variations of Plasmodium vivax incidences is systematically investigate in Tengchong, Yunnan province, China. METHODS: A space-time model that resembles a P. vivax transmission model and a hidden time-dependent process, is presented by taking into consideration both explicit and implicit factors. Specifically, the transmission model is built upon relevant demographic, environmental, and biophysical factors to describe the local infections of P. vivax. While the hidden time-dependent process is assessed by several socioeconomic factors to account for the imported cases of P. vivax. To quantitatively assess the impact of heterogeneous factors on geographic variations of P. vivax infections, a Markov chain Monte Carlo (MCMC) simulation method is developed to estimate the model parameters by fitting the space-time model to the reported spatial-temporal disease incidences. RESULTS: Since there is no ground-truth information available, the performance of the MCMC method is first evaluated against a synthetic dataset. The results show that the model parameters can be well estimated using the proposed MCMC method. Then, the proposed model is applied to investigate the geographic variations of P. vivax incidences among all 18 towns in Tengchong, Yunnan province, China. Based on the geographic variations, the 18 towns can be further classify into five groups with similar socioeconomic causality for P. vivax incidences. CONCLUSIONS: Although this study focuses mainly on the transmission of P. vivax, the proposed space-time model is general and can readily be extended to investigate geographic variations of other diseases. Practically, such a computational model will offer new insights into active surveillance and strategic planning for disease surveillance and control.

Understanding self-organized regularities in healthcare services based on autonomy oriented modeling.

Self-organized regularities in terms of patient arrivals and wait times have been discovered in real-world healthcare services. What remains to be a challenge is how to characterize those regularities by taking into account the underlying patients' or hospitals' behaviors with respect to various impact factors. This paper presents a case study to address such a challenge. Specifically, it models and simulates the cardiac surgery services in Ontario, Canada, based on the methodology of Autonomy-Oriented Computing (AOC). The developed AOC-based cardiac surgery service model (AOC-CSS model) pays a special attention to how individuals' (e.g., patients and hospitals) behaviors and interactions with respect to some key factors (i.e., geographic accessibility to services, hospital resourcefulness, and wait times) affect the dynamics and relevant patterns of patient arrivals and wait times. By experimenting with the AOC-CSS model, we observe that certain regularities in patient arrivals and wait times emerge from the simulation, which are similar to those discovered from the real world. It reveals that patients' hospital-selection behaviors, hospitals' service-adjustment behaviors, and their interactions via wait times may potentially account for the self-organized regularities of wait times in cardiac surgery services.

Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range.

BACKGROUND: In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. METHODS: In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.'s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. RESULTS: We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. CONCLUSIONS: In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations.

Learning to improve medical decision making from imbalanced data without a priori cost.

BACKGROUND: In a medical data set, data are commonly composed of a minority (positive or abnormal) group and a majority (negative or normal) group and the cost of misclassifying a minority sample as a majority sample is highly expensive. This is the so-called imbalanced classification problem. The traditional classification functions can be seriously affected by the skewed class distribution in the data. To deal with this problem, people often use a priori cost to adjust the learning process in the pursuit of optimal classification function. However, this priori cost is often unknown and hard to estimate in medical decision making. METHODS: In this paper, we propose a new learning method, named RankCost, to classify imbalanced medical data without using a priori cost. Instead of focusing on improving the class-prediction accuracy, RankCost is to maximize the difference between the minority class and the majority class by using a scoring function, which translates the imbalanced classification problem into a partial ranking problem. The scoring function is learned via a non-parametric boosting algorithm. RESULTS: We compare RankCost to several representative approaches on four medical data sets varying in size, imbalanced ratio, and dimension. The experimental results demonstrate that unlike the currently available methods that often perform unevenly with different priori costs, RankCost shows comparable performance in a consistent manner. CONCLUSIONS: It is a challenging task to learn an effective classification model based on imbalanced data in medical data analysis. The traditional approaches often use a priori cost to adjust the learning of the classification function. This work presents a novel approach, namely RankCost, for learning from medical imbalanced data sets without using a priori cost. The experimental results indicate that RankCost performs very well in imbalanced data classification and can be a useful method in real-world applications of medical decision making.

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.

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.

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.

Effects of geodemographic profiles on healthcare service utilization: a case study on cardiac care in Ontario, Canada.

BACKGROUND: Although literature has associated geodemographic factors with healthcare service utilization, little is known about how these factors - such as population size, age profile, service accessibility, and educational profile - interact to influence service utilization. This study fills this gap in the literature by examining both the direct and the moderating effects of geodemographic profiles on the utilization of cardiac surgery services. METHODS: We aggregated secondary data obtained from Statistics Canada and Cardiac Care Network of Ontario to derive the geodemographic profiles of Ontario and the corresponding cardiac surgery service utilization in the years between 2004 and 2007. We conducted a two-step test using Partial Least Squares-based structural equation modeling to investigate the relationships between geodemographic profiles and healthcare service utilization. RESULTS: Population size and age profile have direct positive effects on service utilization (ß = 0.737, p < 0.01; ß = 0.284, p < 0.01, respectively), whereas service accessibility is negatively associated with service utilization (ß = -0.210, p < 0.01). Service accessibility decreases the effect of population size on service utilization (ß = -0.606, p < 0.01), and educational profile weakens the effects of population size and age profile on service utilization (ß = -0.595, p < 0.01; ß = -0.286, p < 0.01, respectively). CONCLUSIONS: In this study, we found that (1) service accessibility has a moderating effect on the relationship between population size and service utilization, and (2) educational profile has moderating effects on both the relationship between population size and service utilization, and the relationship between age profile and service utilization. Our findings suggest that reducing regional disparities in healthcare service utilization should take into account the interaction of geodemographic factors such as service accessibility and education. In addition, the allocation of resources for a particular healthcare service in one area should consider the geographic distribution of the same services in neighboring areas, as patients may be willing to utilize these services in areas not far from where they reside.