Effects of carbon limitation and carbon fertilization on karst lake-reservoir productivity.

Nitrogen and phosphorus are universally recognized as limiting elements in the eutrophication processes affecting the majority of the world's lakes, reservoirs, and coastal ecosystems. However, despite extensive research spanning several decades, critical questions in eutrophication science remain unanswered. For example, there is still much to understand about the interactions between carbon limitation and ecosystem stability, and the availability of carbon components adds significant complexity to aquatic resource management. Mounting evidence suggests that aqueous CO2 could be a limiting factor, influencing the structure and succession of aquatic plant communities, especially in karstic lake and reservoir ecosystems. Moreover, the fertilization effect of aqueous CO2 has the potential to enhance carbon sequestration and phosphorus removal. Therefore, it is important to address these uncertainties to achieve multiple positive outcomes, including improved water quality and increased carbon sinks in karst lakes and reservoirs.

Control of carbon dioxide exchange fluxes by rainfall and biological carbon pump in karst river-lake systems.

As an important component of inland water, the primary factors affecting the carbon cycle in karst river-lake systems require further investigation. In particular, the impacts of climatic factors and the biological carbon pump (BCP) on carbon dioxide (CO2) exchange fluxes in karst rivers and lakes deserve considerable attention. Using quarterly sampling, field monitoring, and meteorological data collection, the spatiotemporal characteristics of CO2 exchange fluxes in Erhai Lake (a typical karst lake in Yunnan, SW China) and its inflow rivers were investigated and the primary influencing factors were analyzed. The average river CO2 exchange flux reached 346.80 mg m-2 h-1, compared to -6.93 mg m-2 h-1 for the lake. The carbon cycle in rivers was strongly influenced by land use within the basin; cultivated and construction land were the main contributors to organic carbon (OC) in the river (r = 0.66, p < 0.01) and the mineralization of OC was a major factor in CO2 oversaturation in most rivers (r = 0.76, p < 0.01). In addition, the BCP effect of aquatic plants and the high pH in karst river-lake systems enhance the ability of water body to absorb CO2, resulting in undersaturated CO2 levels in the lake. Notably, under rainfall regulation, riverine OC and dissolved inorganic carbon (DIC) flux inputs controlled the level of CO2 exchange fluxes in the lake (rOC = 0.78, p < 0.05; rDIC = 0.97, p < 0.01). We speculate that under future climate and human activity scenarios, the DIC and OC input from rivers may alleviate the CO2 limitation of BCP effects in karst eutrophication lakes, possibly enabling aquatic plants to convert more CO2 into OC for burial. The results of this research can help advance our understanding of CO2 emissions and absorption mechanisms in karst river-lake systems.

Long noncoding RNAs in human reproductive processes and diseases.

Infertility has become a global disease burden. Although assisted reproductive technologies are widely used, the assisted reproduction birth rate is no more than 30% worldwide. Therefore, understanding the mechanisms of reproduction can provide new strategies to improve live birth rates and clinical outcomes of enhanced implantation. Long noncoding RNAs (lncRNAs) have been reported to exert regulatory roles in various biological processes and diseases in many species. In this review, we especially focus on the role of lncRNAs in human reproduction. We summarize the function and mechanisms of lncRNAs in processes vital to reproduction, such as spermatogenesis and maturation, sperm motility and morphology, follicle development and maturation, embryo development and implantation. Then, we highlight the importance and diverse potential of lncRNAs as good diagnostic molecular biomarkers and therapeutic targets for infertility treatment.

A mathematical model to simulate the release of Fe and Mn from sediments in a drinking water reservoir.

Fe and Mn release from sediments promotes the release of other chemicals and jointly affects downstream water safety, especially in drinking water reservoirs. Quantitative research on release processes and flux estimation methods for endogenous Fe and Mn in reservoirs is still limited. Static incubation experiments were designed to systematically explore the effects of water temperature (WT), dissolved oxygen (DO), pH, carbon sources, and microbial activity on Fe and Mn release. The results showed that increased WT and carbon source addition promoted the release of acid-extractable Fe and Mn from the sediments; hypoxia and acidification promoted the dissolution of reducible sediment Fe and Mn; and microorganisms participated in the cycling of Fe and Mn. Based on the experimental results, first-order kinetic equations for sediment Fe and Mn release to overlying water were proposed, and the relationships between release rate and environmental factors were mathematically represented by a surface equation (R2 = 0.88 and 0.86, respectively). A diffusion gradients in thin films (DGT) device based on the diffusion model was used in situ to obtain the diffusion fluxes of Fe (JFe = 13.93 mg m-2 d-1) and Mn (JMn = 3.48 mg m-2 d-1). When environmental factors obtained in the field were introduced into the established mathematical model, the modeled release fluxes of Fe and Mn were RFe = 20.92 mg m-2 d-1 and RMn = 13.12 mg m-2 d-1, respectively. The established model filled gaps in the diffusion model, which does not account for differences in release fluxes under changing physicochemical water conditions. This work serves as a reference for studying the release fluxes of endogenous chemicals in sediments.

Characterization of the long noncoding RNA transcriptome in human preimplantation embryo development.

PURPOSE: Infertility remains a human health burden globally. Only a fraction of embryos produced via assisted reproductive technologies (ARTs) develop to the blastocyst stage in vitro. lncRNA abundance changes significantly during human early embryonic development, indicating vital regulatory roles of lncRNAs in this process. The aim of this study is to obtain insights into the transcriptional basis of developmental events. METHODS: scRNA-seq data and SUPeR-seq data were used to investigate the lncRNA profiles of human preimplantation embryos. The top 50 highly expressed unique and shared lncRNAs in each stage of preimplantation development were identified. Comparative analysis of the two datasets was used to verify the consistent expression patterns of the lncRNAs. Differentially expressed lncRNAs were identified and subjected to functional enrichment analysis. RESULTS: The lncRNA profiles of human preimplantation embryos in the E-MTAB-3929 dataset were similar to those in the GSE71318 dataset. The ratios of overlap among the top 50 highly expressed lncRNAs between two pairs of stages (2-cell stage vs. 4-cell stage and 8-cell stage vs. morula) were aberrantly low compared with those between other stages. Each stage of preimplantation development exhibited unique and shared lncRNAs among the top 50 highly expressed lncRNAs. Among the between-group comparisons, the 2-cell stage vs. 4-cell stage showed the highest number of differentially expressed lncRNAs. Functional enrichment analysis revealed that differentially expressed lncRNAs and their associated super enhancers and RNA binding proteins (RBPs) are closely involved in regulating embryonic development. These lncRNAs could function as important cell markers for distinguishing fetal germ cells. CONCLUSIONS: Our study paves the way for understanding the regulation of developmental events, which might be beneficial for improved reproductive outcomes.

DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome.

Silencers are noncoding DNA sequence fragments located on the genome that suppress gene expression. The variation of silencers in specific cells is closely related to gene expression and cancer development. Computational approaches that exclusively rely on DNA sequence information for silencer identification fail to account for the cell specificity of silencers, resulting in diminished accuracy. Despite the discovery of several transcription factors and epigenetic modifications associated with silencers on the genome, there is still no definitive biological signal or combination thereof to fully characterize silencers, posing challenges in selecting suitable biological signals for their identification. Therefore, we propose a sophisticated deep learning framework called DeepICSH, which is based on multiple biological data sources. Specifically, DeepICSH leverages a deep convolutional neural network to automatically capture biologically relevant signal combinations strongly associated with silencers, originating from a diverse array of biological signals. Furthermore, the utilization of attention mechanisms facilitates the scoring and visualization of these signal combinations, whereas the employment of skip connections facilitates the fusion of multilevel sequence features and signal combinations, thereby empowering the accurate identification of silencers within specific cells. Extensive experiments on HepG2 and K562 cell line data sets demonstrate that DeepICSH outperforms state-of-the-art methods in silencer identification. Notably, we introduce for the first time a deep learning framework based on multi-omics data for classifying strong and weak silencers, achieving favorable performance. In conclusion, DeepICSH shows great promise for advancing the study and analysis of silencers in complex diseases. The source code is available at https://github.com/lyli1013/DeepICSH.

Research on the purification enhancement of ecological ponds: Integrating water cycle optimization and plants layout.

The hydrodynamic conditions of ponds are generally poor, which seriously affects the long-term water quality guarantee. In this research, the numerical simulation method was used to establish an integrated model of hydrodynamics and water quality for the simulation of the plant purification effect in ponds. Based on the flushing time using the tracer method, the purification rate of plants was introduced to consider the purification effect of plants on water quality. In-situ monitoring was carried out at the Luxihe pond in Chengdu, and the model parameters such as the purification rate of typical plants were calibrated. The degradation coefficient of NH3-N in the non-vegetated area was 0.014 d-1 in August and 0.010 d-1 in November. In areas with vegetation, the purification rate of NH3-N was 0.10-0.20 g/(m2·d) in August and 0.06-0.12 g/(m2·d) in November. The comparison of the results in August and November showed that due to the higher temperature in August, the plant growth effect was better, and the degradation rate of pollutants and the purification rate of pollutants by plants were higher. The flushing time distribution of the proposed Baihedao pond under the conditions of terrain reconstruction, water replenishment, and plant layout was simulated, and the frequency distribution curve of flushing time was used to evaluate the results. Terrain reconstruction and water replenishment can significantly improve the water exchange capacity of ponds. The reasonable planting of plants can reduce the variability of the water exchange capacity. Based on this combined with the purification effect of plants on NH3-N, the layout plan of Canna, Cattails, and Thalia in ponds was proposed.

DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.

MOTIVATION: Enhancers are vital cis-regulatory elements that regulate gene expression. Enhancer RNAs (eRNAs), a type of long noncoding RNAs, are transcribed from enhancer regions in the genome. The tissue-specific expression of eRNAs is crucial in the regulation of gene expression and cancer development. The methods that identify eRNAs based solely on genomic sequence data have high error rates because they do not account for tissue specificity. Specific histone modifications associated with eRNAs offer valuable information for their identification. However, identification of eRNAs using histone modification data requires the use of both RNA-seq and histone modification data. Unfortunately, many public datasets contain only one of these components, which impedes the accurate identification of eRNAs. RESULTS: We introduce DeepITEH, a deep learning framework that leverages RNA-seq data and histone modification data from multiple samples of the same tissue to enhance the accuracy of identifying eRNAs. Specifically, deepITEH initially categorizes eRNAs into two classes, namely, regularly expressed eRNAs and accidental eRNAs, using histone modification data from multiple samples of the same tissue. Thereafter, it integrates both sequence and histone modification features to identify eRNAs in specific tissues. To evaluate the performance of DeepITEH, we compared it with four existing state-of-the-art enhancer prediction methods, SeqPose, iEnhancer-RD, LSTMAtt, and FRL, on four normal tissues and four cancer tissues. Remarkably, seven of these tissues demonstrated a substantially improved specific eRNA prediction performance with DeepITEH, when compared with other methods. Our findings suggest that DeepITEH can effectively predict potential eRNAs on the human genome, providing insights for studying the eRNA function in cancer. AVAILABILITY AND IMPLEMENTATION: The source code and dataset of DeepITEH have been uploaded to https://github.com/lyli1013/DeepITEH.

Alleviating eutrophication by reducing the abundance of Cyanophyta due to dissolved inorganic carbon fertilization: Insights from Erhai Lake, China.

The eutrophication of lakes is a global environmental problem. Regulating nitrogen (N) and phosphorus (P) on phytoplankton is considered to be the most important basis of lake eutrophication management. Therefore, the effects of dissolved inorganic carbon (DIC) on phytoplankton and its role in mitigating lake eutrophication have often been overlooked. In this study, the relationships between phytoplankton and DIC concentrations, carbon isotopic composition, nutrients (N and P), and hydrochemistry in the Erhai Lake (a karst lake) were investigated. The results showed that when the dissolved carbon dioxide (CO2(aq)) concentrations in the water were higher than 15 µmol/L, the productivity of phytoplankton was controlled by the concentrations of TP and TN, especially by that of TP. When the N and P were sufficient and the CO2(aq) concentrations were lower than 15 µmol/L, the phytoplankton productivity was controlled by the concentrations of TP and DIC, especially by that of DIC. Additionally, DIC significantly affected the composition of the phytoplankton community in the lake (p<0.05). When the CO2(aq) concentrations were higher than 15 µmol/L, the relative abundance of Bacillariophyta and Chlorophyta was much higher than those of harmful Cyanophyta. Thus, high concentrations of CO2(aq) can inhibit harmful Cyanophyta blooms. During lake eutrophication, when controlling N and P, an appropriate increase in CO2(aq) concentrations by land-use changes or pumping of industrial CO2 into water may reduce the proportion of harmful Cyanophyta and promote the growth of Chlorophyta and Bacillariophyta, which may provide effectively assist in mitigating water quality deterioration in surface waters.

Field investigation on the change process of microbial community structure in large-deep reservoir during the initial impoundment.

During the initial impoundment of large-deep reservoir, the aquatic environment changed dramatically in various aspects such as water level, hydrological regime, and pollutants, which could alter microorganisms' community structure, break the balance of the aquatic ecosystem and even endanger the aquatic ecosystem. However, the interaction of microbial communities and water environment during the initial impoundment process of a large-deep reservoir remained unclear. To this end, in-situ monitoring and sampling analysis on water quality and microbial communities during the initial impoundment process of a typical large-deep reservoir named Baihetan were conducted so as to explore the response of microbial community structure to the changes of water environmental factors during the initial impoundment of large deep reservoir and reveal the key driving factors affecting microbial community structure. The spatio-temporal variation in water quality was analyzed, and the microbial community structure in the reservoir was investigated based on high-throughput sequencing. The results showed that the COD of each section increased slightly, and the water quality after impoundment was slightly poorer than that before the impoundment. Water temperature and pH were proved to be the key factors affecting the structure of bacterial and eukaryotic communities respectively during the initial impoundment. The research results revealed the role of microorganisms and their interaction with biogeochemical processes in the large-deep reservoir ecosystem, which was crucial for later operation and management of the reservoir and the protection of the reservoir water environment.

Non-pharmaceutical interventions for COVID-19 reduced the incidence of infectious diseases: a controlled interrupted time-series study.

BACKGROUND: Non-pharmaceutical interventions (NPIs) have been implemented worldwide to suppress the spread of coronavirus disease 2019 (COVID-19). However, few studies have evaluated the effect of NPIs on other infectious diseases and none has assessed the avoided disease burden associated with NPIs. We aimed to assess the effect of NPIs on the incidence of infectious diseases during the COVID-19 pandemic in 2020 and evaluate the health economic benefits related to the reduction in the incidence of infectious diseases. METHODS: Data on 10 notifiable infectious diseases across China during 2010-2020 were extracted from the China Information System for Disease Control and Prevention. A two-stage controlled interrupted time-series design with a quasi-Poisson regression model was used to examine the impact of NPIs on the incidence of infectious diseases. The analysis was first performed at the provincial-level administrative divisions (PLADs) level in China, then the PLAD-specific estimates were pooled using a random-effect meta-analysis. RESULTS: A total of 61,393,737 cases of 10 infectious diseases were identified. The implementation of NPIs was associated with 5.13 million (95% confidence interval [CI] 3.45‒7.42) avoided cases and USD 1.77 billion (95% CI 1.18‒2.57) avoided hospital expenditures in 2020. There were 4.52 million (95% CI 3.00‒6.63) avoided cases for children and adolescents, corresponding to 88.2% of total avoided cases. The top leading cause of avoided burden attributable to NPIs was influenza [avoided percentage (AP): 89.3%; 95% CI 84.5‒92.6]. Socioeconomic status and population density were effect modifiers. CONCLUSIONS: NPIs for COVID-19 could effectively control the prevalence of infectious diseases, with patterns of risk varying by socioeconomic status. These findings have important implications for informing targeted strategies to prevent infectious diseases.

Spatiotemporal dynamics and environmental determinants of scrub typhus in Anhui Province, China, 2010-2020.

This study aims to describe the epidemiological characteristics of scrub typhus, detect the spatio-temporal patterns of scrub typhus at county level, and explore the associations between the environmental variables and scrub typhus cases in Anhui Province. Time-series analysis, spatial autocorrelation analysis, and space-time scan statistics were used to explore the characteristics and spatiotemporal patterns of the scrub typhus in Anhui Province. Negative binomial regression analysis was used to explore the association between scrub typhus and environmental variables. A total of 16,568 clinically diagnosed and laboratory-confirmed cases were reported from 104 counties of 16 prefecture-level cities. The number of female cases was higher than male cases, with a proportion of 1.32:1. And the proportion of cases over 65 years old was the highest, accounting for 33.8% of the total cases. Two primary and five secondary high-risk clusters were detected in the northwestern, northeastern, and central-eastern parts of Anhui Province. The number of cases in primary and secondary high-risk clusters accounted for 60.27% and 3.00%, respectively. Scrub typhus incidence in Anhui Province was positively correlated with the population density, normalized difference vegetation index, and several meteorological variables. The mean monthly sunshine duration with 3 lags (SSD_lag3), mean monthly ground surface temperature with 1 lag (GST_lag1), and mean monthly relative humidity with 3 lags (RHU_lag3) had the most significant association with increased cases of scrub typhus. Our findings indicate that public health interventions need to be focused on the elderly farmers in north of the Huai River in Anhui Province.

Dispersed cobalt embedded nitrogen-rich carbon framework activates peroxymonosulfate for carbamazepine degradation: cobalt leaching restriction and mechanism investigation.

Metal leaching is a key issue in cobalt-based catalysts/PMS systems, which results in the decline of catalytic ability and serious secondary pollution. Hence, a nitrogen-rich carbon framework with cobalt node (Co-NC-920) with low cobalt leaching was synthesized based on zeolite imidazole framework (ZIF) and g-C3N4 to activate peroxymonosulfate (PMS) for the degradation of carbamazepine (CBZ). With the restriction of nitrogen-rich carbon framework, cobalt can disperse better and form stable cobalt-nitrogen bonds, thus only 0.09 mg/L cobalt ions were leached in the solution. More than 99.9% of CBZ can be removed within 30 min of PMS addition. Further investigation revealed that 1O2, SO4•- and high-valent cobalt species were primarily responsible for CBZ degradation in the Co-NC-920/PMS system and different reactive oxygen species (ROS) were distinguished and quantified, finding 1O2 was predominant. The degradation process was realized by the coexistence of free radicals and non-free radicals. Moreover, CBZ degradation capacity of the catalyst was evaluated under the influence of common anions and in actual waterbody. Finally, the possible degradation pathways of CBZ were proposed and the toxicity of the intermediates was analyzed. This work provides a new approach for the synthesis of cobalt-based nitrogen-rich carbon catalysts with low leaching and high efficient.

Response of OC, TN, and TP deposition mediated by aquatic photosynthetic community structures in shallow karst surface waters under different land uses.

Land use change alters the hydrochemical features, nutrient outputs, and community structure of aquatic photosynthetic organisms in watersheds and has an important impact on C, N, and P biogeochemical processes. In shallow water environments, sediments are the most important burial sites for C, N, and P; however, the factors underlying the control of their deposition by land use changes remain unclear. In this study, the relationship among hydrochemical features, aquatic photosynthetic organism community structure, and C, N, and P deposition in surface waters associated with different land uses was studied at the Shawan Karst Water-Carbon Cycle Test Site, Puding, SW China, by combining field monitoring and laboratory experiments performed over a complete hydrological year from September 2018 to August 2019. The results indicate that (1) OC and TN deposition showed small differences among ponds associated with five land uses, while TP was significantly higher in ponds associated with shrubland and grassland than in ponds of cultivated land, bare soil, and bare rock. (2) Cultivated land increased OC and TN deposition by increasing N and P output and planktonic algae biomass in surface waters, while grassland and shrubland ponds mainly by increasing DIC output and macrophyte biomass. (3) Compared with cultivated land, grassland and shrubland significantly enhanced TP deposition by promoting the deposition of calcium-bound P and biogenic P from macrophytes and their epiphytic algae in surface waters. In conclusion, the shift of cultivated land and bare soil to grassland and shrubland may be conducive to the formation of benign aquatic ecosystems and stabilization of C, N, and P sinks in karst shallow surface waters.

Using ecological niche modeling to predict the potential distribution of scrub typhus in Fujian Province, China.

BACKGROUND: Despite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated. METHODS: A negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk. RESULTS: The final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus. CONCLUSIONS: These findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.

The association between air pollutants, meteorological factors and tuberculosis cases in Beijing, China: A seven-year time series study.

BACKGROUND: Tuberculosis (TB) is a severe public health problem globally. Previous studies have revealed insufficient and inconsistent associations between air pollutants, meteorological factors and TB cases. Yet few studies have examined the associations between air pollutants, meteorological factors and TB cases in Beijing. OBJECTIVE: The purpose of this study was to explore the impact of air pollutants and meteorological factors on TB in Beijing, and to provide novel insights into public health managers to formulate control strategies of TB. METHODS: Data on the daily case of TB in Beijing during 2014-2020 were obtained from Chinese tuberculosis information management system. Concurrent data on the daily PM10, PM2.5, SO2, NO2, CO and O3, were obtained from the online publication platform of the Chinese National Environmental Monitoring Center. Daily average temperature, average wind speed, relative humidity, sunshine duration and total precipitation were collected from the China Meteorological Science Data Sharing Service System. A distributed lag non-linear model was fitted to identify the non-linear exposure-response relationship and the lag effects between air pollutions, meteorological factors and TB cases in Beijing. RESULTS: In the single-factor model, the excess risk (ER) of TB was significantly positively associated with every 10 µg/m3 increase in NO2 in lag 1 week (ER: 1.3%; 95% confidence interval [CI]: 0.4%, 2.3%) and every 0.1 m/s increase in average wind speed in lag 5 weeks (ER: 0.3%; 95% CI: 0.1%, 0.5%), and was negatively associated with every 10 µg/m3 increase in O3 in lag 1 week (ER: -1.2%; 95% CI: -1.8%, -0.5%), every 5 °C increase in average temperature (ER: -1.7%; 95% CI: -2.9%, -0.4%) and every 10% increase in average relative humidity (ER: -0.4%; 95% CI: -0.8%, -0.1%) in lag 10 weeks, respectively. In the multi-factor model, the lag effects between TB cases and air pollutants, meteorological factors were similar. The subgroup analysis suggests that the effects of NO2, O3, average wind speed and relative humidity on TB were greater in male or labor age subgroup, while the effect of CO was greater in the elderly. In addition, no significant associations were found between PM2.5, SO2, sunshine duration and TB cases. CONCLUSION: Our findings provide a better understanding of air pollutants and meteorological factors driving tuberculosis occurrence in Beijing, which enhances the capacity of public health manager to target early warning and disease control policy-making.

Spatial-temporal dynamics and time series prediction of HFRS in mainland China: A long-term retrospective study.

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China. The current study aims to characterize the spatial-temporal dynamics of HFRS in mainland China during a long-term period (1950-2018). A total of 1 665 431 cases of HFRS were reported with an average annual incidence of 54.22 cases/100 000 individuals during 1950-2018. The joint regression model was used to define the global trend of the HFRS cases with an increasing-decreasing-slightly increasing-decreasing-slightly increasing trend during the 68 years. Then spatial correlation analysis and wavelet cluster analysis were used to identify four types of clusters of HFRS cases located in central and northeastern China. Lastly, the prophet model outperforms auto-regressive integrated moving average model in the HFRS modeling. Our findings will help reduce the knowledge gap on the transmission dynamics and distribution patterns of the HFRS in mainland China and facilitate to take effective preventive and control measures for the high-risk epidemic area.

Lipid Biomarker Investigation of the Delivery and Preservation of Autochthonous Organic Carbon in the Pearl River and Its Contribution to the Carbon Sink: Evidence from the Water and Surface Sediment.

The molecular composition of the lipid biomarkers in the surface water, water column, and surface sediments collected along the Pearl River was investigated to identify the mechanisms of the delivery and preservation of autochthonous organic carbon (AOC) and to estimate its contribution to the carbon sink. The spatial distribution of these lipid biomarkers showed that samples collected at high-DIC-concentration sites (DIC: dissolved inorganic carbon) had prominent aquatic autochthonous signatures, while samples collected at low-DIC-concentration sites showed greater terrestrial contributions, which were described as the DIC fertilization effects. In the summer, typically, intense precipitation and flood erosion diluted the biogeochemical composition and carried terrestrial plant detritus. Therefore, the percentage of AOC (auto%) was higher in the winter than in the summer. According to the calculation of the lipid biomarkers, the values of the auto% were 65% (winter) and 54% (summer) in the surface water, 55.9% (winter) and 44.6% (summer) in the below-surface water, and 52.1% (winter) and 43.9% (summer) in the surface sediment, which demonstrated that AOC accounted for a major portion of the TOC. Vertical variability was mainly present in sites with intense flood erosion, which resulted in the mixing and deposition of resuspended sediments. There was a positive correlation of the clay content with the auto% value and the biogeochemical composition, showing that clay adsorbed the organic carbon in the water, vertically deposited it into the sediment, and was the dominant mechanism of the vertical delivery of organic carbon (OC). According to the new karst carbon sink model, based on coupled carbonate weathering and aquatic photosynthesis, the karst carbon sink flux (CSF) in the Pearl River was 2.69 × 106 t/a which was 1.7 times the original estimation (1.58 × 106 t/a), and this did not consider the formation of AOC. This indicated that previously, the contribution of the riverine system to the global karst carbon sink may have been highly underestimated.

Co-effects of global climatic dynamics and local climatic factors on scrub typhus in mainland China based on a nine-year time-frequency analysis.

Background: Scrub Typhus (ST) is a rickettsial disease caused by Orientia tsutsugamushi. The number of ST cases has been increasing in China during the past decades, which attracts great concerns of the public health. Methods: We obtained monthly documented ST cases greater than 54 cases in 434 counties of China during 2012-2020. Spatiotemporal wavelet analysis was conducted to identify the ST clusters with similar pattern of the temporal variation and explore the association between ST variation and El Niño and La Niña events. Wavelet coherency analysis and partial wavelet coherency analysis was employed to further explore the co-effects of global and local climatic factors on ST. Results: Wavelet cluster analysis detected seven clusters in China, three of which are mainly distributed in Eastern China, while the other four clusters are located in the Southern China. Among the seven clusters, summer and autumn-winter peak of ST are the two main outbreak periods; while stable and fluctuated periodic feature of ST series was found at 12-month and 4-(or 6-) month according to the wavelet power spectra. Similarly, the three-character bands were also found in the associations between ST and El Niño and La Niña events, among which the 12-month period band showed weakest climate-ST association and the other two bands owned stronger association, indicating that the global climate dynamics may have short-term effects on the ST variations. Meanwhile, 12-month period band with strong association was found between the four local climatic factors (precipitation, pressure, relative humidity and temperature) and the ST variations. Further, partial wavelet coherency analysis suggested that global climatic dynamics dominate annual ST variations, while local climatic factors dominate the small periods. Conclusion: The ST variations are not directly attributable to the change in large-scale climate. The existence of these plausible climatic determinants stimulates the interests for more insights into the epidemiology of ST, which is important for devising prevention and early warning strategies.

Epidemiological characteristics and spatiotemporal patterns of scrub typhus in Fujian province during 2012-2020.

BACKGROUND: Scrub typhus has become a serious public health concern in the Asia-Pacific region including China. There were new natural foci continuously recognized and dramatically increased reported cases in mainland China. However, the epidemiological characteristics and spatiotemporal patterns of scrub typhus in Fujian province have yet to be investigated. OBJECTIVE: This study proposes to explore demographic characteristics and spatiotemporal dynamics of scrub typhus cases in Fujian province, and to detect high-risk regions between January 2012 and December 2020 at county/district scale and thereby help in devising public health strategies to improve scrub typhus prevention and control measures. METHOD: Monthly cases of scrub typhus reported at the county level in Fujian province during 2012-2020 were collected from the National Notifiable Disease Surveillance System. Time-series analyses, spatial autocorrelation analyses and space-time scan statistics were applied to identify and visualize the spatiotemporal patterns of scrub typhus cases in Fujian province. The demographic differences of scrub typhus cases from high-risk and low-risk counties in Fujian province were also compared. RESULTS: A total of 11,859 scrub typhus cases reported in 87 counties from Fujian province were analyzed and the incidence showed an increasing trend from 2012 (2.31 per 100,000) to 2020 (3.20 per 100,000) with a peak in 2018 (4.59 per 100,000). There existed two seasonal peaks in June-July and September-October every year in Fujian province. A significant positive spatial autocorrelation of scrub typhus incidence in Fujian province was observed with Moran's I values ranging from 0.258 to 0.471 (P<0.001). Several distinct spatiotemporal clusters mainly concentrated in north and southern parts of Fujian province. Compared to low-risk regions, a greater proportion of cases were female, farmer, and older residents in high-risk counties. CONCLUSIONS: These results demonstrate a clear spatiotemporal heterogeneity of scrub typhus cases in Fujian province, and provide the evidence in directing future researches on risk factors and effectively assist local health authorities in the refinement of public health interventions against scrub typhus transmission in the high risk regions.