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Owing to its high nutritional content, raw milk contains a rich microbiota. Thus, to study microorganisms present in raw milk available in Xinjiang China, 142 raw milk samples from seven animal breeds (cow, sheep, goat, donkey, horse, camel, and yak) and four regions (Hami, Tarbagatay, Kashgar, and Ili) were analyzed by high-throughput DNA sequencing. These microorganisms were characterized by 10 dominant phyla. Proteobacteria (68.33%) was the major phylum, followed by Firmicutes (18.80%) and Thermi (3.16%). Horse milk contained more Bacteroidetes, sheep milk contained more Gammaproteobacteria, and donkey milk contained more unclassified sequences. Camel and donkey milk contained the highest and lowest bacterial diversity compared with that contained by the remaining milk samples, respectively. Additionally, spoilage microorganisms, including Chryseobacterium, Propionibacterium, and Flavobacterium, and pathogenic bacteria, including Ochrobactrum anthropi and Sphingomonas, were more prevalent in horse and yak milk, whereas probiotic lactic acid bacteria (LAB), such as Leuconostoc, Lactococcus, or Lactobacillus, were more prevalent in goat, donkey, and camel milk. Furthermore, Moraxella was abundantly present in goat, camel, and yak milk, Acinetobacter was more abundant in camel milk, and Pseudomonas was relatively abundant in sheep and donkey milk. Overall, specific harmful microorganisms and probiotic lactic acid bacteria were found in the raw milk samples obtained from different animals, which provided a basis for preventing and controlling the growth of harmful bacteria, as well as investigating probiotic resources in raw milk.

Little has been known about symbiotic relationships and host specificity for symbionts in the human gut microbiome so far. Bifidobacteria are a paragon of the symbiotic bacteria biota in the human gut. In this study, we characterized the population genetic structure of three bifidobacteria species from 58 healthy mother–infant pairs of three ethnic groups in China, geographically isolated, by Rep-PCR, multi-locus sequence analysis (MLSA), and in vitro carbohydrate utilization. One hundred strains tested were incorporated into 50 sequence types (STs), of which 29 STs, 17 STs, and 4 STs belong to B. longum subsp. longum, B. breve, and B. animalis subsp. lactis, respectively. The conspecific strains from the same mother–child pair were genetically very similar, supporting the vertical transmission of Bifidobacterium phylotypes from mother to offspring. In particular, results based on allele profiles and phylogeny showed that B. longum subsp. longum and B. breve exhibited considerable intraspecies genetic heterogeneity across three ethnic groups, and strains were clustered into ethnicity-specific lineages. Yet almost all strains of B. animalis subsp. lactis were incorporated into the same phylogenetic clade, regardless of ethnic origin. Our findings support the hypothesis of co-evolution between human gut symbionts and their respective populations, which is closely linked to the lifestyle of specific bacterial lineages. Hence, the natural and evolutionary history of Bifidobacterium species would be an additional consideration when selecting bifidobacterial strains for industrial and therapeutic applications.

Aroma in food plays an important role in food perception and acceptance, which depends on various mixtures of volatile organic compounds (VOCs). Moreover, VOCs are of great significance for aroma identification. In this study, headspace solid-phase microextraction (HS-SPME) combined with gas chromatography–mass spectrometry (GC-MS) technology was used to determine the VOCs in 10 pear syrups. A total of 127 VOCs were quantitatively determined, including 9 common VOCs and 46 characteristic VOCs of 10 pear syrups. The pear syrups were divided into three categories by cluster analysis, and thirty-eight differential VOCs were obtained using orthogonal partial least squares discrimination analysis (OPLS-DA) and fourteen key VOCs were selected by odor activity value (OAV). It was revealed that the key and common aroma components of pear syrups were butanoic acid, methyl ester, 2-methyl-, methyl ester and Hexanoic acid, and ethyl ester. The characteristic and differential VOCs were 10-Undecen-1-ol, Hexadecanal, n-Propylacetate, Cyclohexanol, 5-methyl-2-(1-methylethyl)-, (1S,2R,5S)-, Methional, Disulfide, dimethyl, 8-Nonenoic acid, ethyl ester, Naphthalene, 1,2-dihydro-1,1,6-trimethyl-, 3H-Purin-6-amine, N,N,3-trimethyl-, 2-Octanol,2,6-dimethyl-, Furyl hydroxymethyl ketone, Heptane, 2,2,4,6,6-pentamethyl-, and Butanoic acid,2-methyl-,methyl ester. The above results showed that different pear syrups had rich diversity in aroma compounds, with some components being shared among them while others are exclusive to specific syrups.

The evaluation of water resource vulnerability to nonpoint source pollution in the presence of uncertainty remains a crucial concern. To enhance the accuracy of the assessment and pinpoint the key factors that affect watershed water quality, the integration of an artificial neural network (ANN) into the evaluation process is imperative. The research involved the collection of streams baseflow samples from thirty-eight sites between 1994 and 1999 in the Tomorrow-Waupaca River Watershed in central Wisconsin, USA, with a focus on examining the relationship between nitrate concentrations and a range of environmental and land use variables extracted from the watershed GIS database. This study utilized ANN methodology, combined with a bootstrap technique that employed a random resampling approach from a single input dataset, to simulate monthly stream baseflow nitrate concentrations. The effectiveness and predictive ability of the ANN model were assessed by comparing it to conventional multivariate regression methods. Through the use of ANN, more precise outcomes can be achieved while taking into account the uncertainty associated with the analysis. The findings demonstrated that the ANN outperformed both the multivariate linear regression and nonlinear quadratic response surface models in explaining the variance of stream nitrate and in external prediction consistency. This study also highlighted several key variables, such as the areal percentage of agricultural land and grassland, stream order, and the average slope of the groundwater flow path, that significantly impacted the stream baseflow nitrate concentrations in this watershed that was dominated by dairy farming and groundwater. Of these variables, the percentage of agricultural land emerged as the most significant factor.

Abstract 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.

The importance of forest floor plants (herbs and mosses) and understory communities on soil C dynamics has been grossly understudied in forest ecosystems; however, there is currently very little knowledge on the impact of forest floor vegetation composition on soil organic C (SOC) accumulation and the microbial metabolic processes. To bridge this gap of knowledge, a forest floor vegetation-removal experiment involving nonvascular mosses (Pleurozium schreberi (PS); Rhizomnium tuomikoskii (RT); and Hylocomiastrum pyrenaicum (HP)) and vascular sedges (Carex sp., CS) was conducted in a subalpine coniferous forest on the eastern edge of Tibetan Plateau, to investigate the associations of different forest floor vegetation communities with mineral soil C accumulation and microbial physiology (C use efficiency (CUE) and microbial biomass turnover). Soils beneath the forest floor vegetative communities differed in soil C and nitrogen (N) concentrations and had distinctively different microbial community structure and physiology. Compared to bare soils, sedge soils had significantly greater SOC and dissolved organic C (DOC) accumulation, greater microbial DNA, biomass C and phospholipid fatty acids (PLFAs) concentrations, and higher microbial CUE and shorter microbial biomass turnover time. While effects of mosses differed among species, P. schreberi had similar effects as sedges, but the effects of H. pyrenaicum and R. tuomikoskii were minimal. Relative to bare soil, P. schreberi and Carex sp. soils were 61.5% and 51.6% higher in microbial CUE and had an obviously shorter microbial biomass turnover time. Variations in the level of DOC and PLFAs (rather than their portion relative to SOC) were the most important regulators of microbial CUE and biomass turnover rate in soils with different forest floor vegetation covers. These results highlight how differences in soil organic matter quality that are directly related to the forest floor vegetation community influence the microbial CUE and biomass turnover and the long-term soil C dynamics.

Abstract 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.

Abstract 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.

Zhaoyuan is located in Jiaodong uplift zone. Proterozoic altered granites are widely distributed. The trace elements are enriched in geothermal fields. In order to ascertain the occurrence conditions of the trace components, the granite geothermal reservoir and geothermal resources. We use hydrochemical analysis, reservoir temperature analysis and effective energy conversion methods, establish Gibbs model, PHREEQC model, reservoir estimation, and obtain the occurrence conditions and reservoir environment of trace components of thermal water. The results show that (1) Zhaoyuan geothermal water, similar to seawater, is Cl—Na type. The total dissolved solids (TDS) of geothermal water ranges from 1359.7 mg/L to 5302.0 mg/L, and the mass concentrations of strontium, bromine and metasilicic acid are 26.20, 7.50 and 88.00 mg/L, respectively, which exceed the national medical thermal mineral water quality standards. (2) The Linglong granite in the northeast of the geothermal field has high strontium content, ranging from 334 mg/kg to 1 805 mg/kg, which is an important source of strontium. (3) The reservoir temperature of geothermal field is between 107 °C and 215 °C, and the mixing proportion of cold water is between 33.6% and 58.9% calculated from silicon enthalpy diagram. The calculation results indicate that available energy is 19.73 TJ/a, 5479.57 MW·h and 471.16 toe between 40 and 60 °C. The total available energy is 301.57 TJ/a, 83771.53 MW·h, and 7203.06 toe over 60 °C. The trace components come from lixiviation between strontium-rich granite surrounding rock and groundwater. The enrichment process of trace elements is affected by geothermal reservoir. This study may provide a better understanding of the characteristics of geothermal water in the granite thermal reservoirs, and enrich and promote the theory of geothermal water-rock interactions.

Microencapsulation of essential oils can provide sustained release properties of their products. The reasonable selection of microcapsule wall materials is considered to be the key to effectively controlling the action time of essential oils and improving the stability of the products. From the classification of proteins and polysaccharides, the research status of the wall materials of sustained-release microcapsule of essential oils are summarized, and their development trend and prospects are introduced. It is expected to provide a useful basis for the research and industrial production of essential oil microcapsules in food industry and other related fields.

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.

Abstract Symptom phenotypes have continuously been an important clinical entity for clinical diagnosis and management. However, non-specificity of symptom phenotypes for clinical diagnosis is one of the major challenges that need be addressed to advance symptom science and precision health. Network medicine has delivered a successful approach for understanding the underlying mechanisms of complex disease phenotypes, which will also be a useful tool for symptom science. Here, we extracted symptom co-occurrences from clinical textbooks to construct phenotype network of symptoms with clinical co-occurrence and incorporated high-quality symptom-gene associations and protein–protein interactions to explore the molecular network patterns of symptom phenotypes. Furthermore, we adopted established network diversity measure in network medicine to quantify both the phenotypic diversity (i.e., non-specificity) and molecular diversity of symptom phenotypes. The results showed that the clinical diversity of symptom phenotypes could partially be explained by their underlying molecular network diversity (PCC = 0.49, P-value = 2.14E-08). For example, non-specific symptoms, such as chill, vomiting, and amnesia, have both high phenotypic and molecular network diversities. Moreover, we further validated and confirmed the approach of symptom clusters to reduce the non-specificity of symptom phenotypes. Network diversity proposes a useful approach to evaluate the non-specificity of symptom phenotypes and would help elucidate the underlying molecular network mechanisms of symptom phenotypes and thus promotes the advance of symptom science for precision health.

BackgroundScrub 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.ObjectiveThis 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.MethodMonthly 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.ResultsA 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 (PConclusionsThese 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.

Soil is the largest carbon (C) pool in terrestrial ecosystems. A small change of soil organic carbon (SOC) storage may have a substantial effect on the CO2 concentration in the atmosphere, potentially leading to global climate change. Forest stand density has been reported to influence SOC storage, yet the effects are often inconsistent. In order to reveal the mechanisms of effect of stand density on SOC storage, larch plantations with three different stand densities (which were 2000, 3300 and 4400 trees per hectare) were chosen. Soil properties were measured in three soil layers which are: 0–20 cm, 20–40 cm and 40–60 cm. An incubation experiment with 14C-labeled cellulose addition was subsequently conducted to study the decomposition of SOC and cellulose, as well as the enzymes activity involved in C and nutrients cycle. The results showed that SOC storage increased with increasing stand density in larch plantations, which was due to the higher C stored in heavy fraction instead of light fraction in higher density. The decomposition of added cellulose decreased with increasing stand density in each soil layer, as well as the cumulative soil derived CO2 emission rate. The activity of enzymes involved in C-cycle and C- and nitrogen (N)-cycle remained unaffected by stand density in the 0–20 cm and 20–40 cm layers. The enzyme activity involved in the phosphorus (P)-cycle did not change corresponding to the stand density in each soil layer. Enzymes involved in the N-cycle showed the highest activity in the middle stand density in 0–20 cm, but no difference was observed among different densities in the subsurface layer except for tyr in the 40–60 cm layer, which showed the lowest activity in high stand density. Cellulose addition stimulated the extracellular enzymes activity involved in the C-cycle and P-cycle in the 0–20 cm layer, and the stimulation declined with increasing stand density. However, significant stimulation of cellulose addition to C-cycle involved enzymes activity was not found in the subsurface layer. We aim to reveal the mechanism of effects of stand density of larch plantations on SOC storage by focusing on the cellulose and SOC decomposition and the corresponding extracellular enzymes activity. In the plots of higher stand density, larch plantations may lead to a weaker C output and stronger C input, which leads to the higher SOC storage.

Scrub typhus (ST) is expanding its geographical distribution in China and in many regions worldwide raising significant public health concerns. Accurate ST time-series modeling including uncovering the role of environmental determinants is of great importance to guide disease control purposes. This study evaluated the performance of three competing time-series modeling approaches at forecasting ST cases during 2012–2020 in eight high-risk counties in China. We evaluated the performance of a seasonal autoregressive-integrated moving average (SARIMA) model, a SARIMA model with exogenous variables (SARIMAX), and the long–short term memory (LSTM) model to depict temporal variations in ST cases. In our investigation, we considered eight environmental variables known to be associated with ST landscape epidemiology, including the normalized difference vegetation index (NDVI), temperature, precipitation, atmospheric pressure, sunshine duration, relative humidity, wind speed, and multivariate El Niño/Southern Oscillation index (MEI). The first 8-year data and the last year data were used to fit the models and forecast ST cases, respectively. Our results showed that the inclusion of exogenous variables in the SARIMAX model generally outperformed the SARIMA model. Our results also indicate that the role of exogenous variables with various temporal lags varies between counties, suggesting that ST cases are temporally non-stationary. In conclusion, our study demonstrates that the approach to forecast ST cases needed to take into consideration local conditions in that time-series model performance differed between high-risk areas under investigation. Furthermore, the introduction of time-series models, especially LSTM, has enriched the ability of local public health authorities in ST high-risk areas to anticipate and respond to ST outbreaks, such as setting up an early warning system and forecasting ST precisely.

This paper has taken a 25-horsepower tractor as the object and developed a distributed drive electric tractor (DDET), which may be applied to farms in paddy and upland fields. First, we designed the structure of the chassis drive system in the DDET, then analyzed the dynamic performances, built an evaluation index of dynamic performance for the DDET, and researched the influencing factors of dynamic performances. Moreover, we built the simulation model and studied the dynamic performance based on Advisor software in different working conditions. Finally, we developed the testing tractor of the DDET for test verification. Results show that the traction of DDET is about 5400 N, and the time from being static to being accelerated to a maximum speed of 28.15 km/h is about 5.5279 s. The theoretical analysis results, simulation results, and testing tractor verification results of the DDET designed in this paper are basically consistent, and the overall dynamic performance of the DDET is good.

This paper investigates the effect of supplementary cementitious materials (SCMs) on the fresh and mechanical properties of nano-silica modified cement-based materials (NSMCBM) based on the response surface method (RSM). Fly ash (FA), ground granulated blast-furnace slag (GGBFS), and silica fume (SF) were selected and the Box-Behnken design (BBD) method was used to design mix proportion. Besides, the quadratic term model was used to describe the relationship between independent variables and responses including fluidity, yield stress, plastic viscosity, thixotropy, and 3, 7, 28, and 56 d compressive strength. Based on the quadratic term model, the response surface of each response was drawn to understand the influence of SCMs. Results showed that FA had significant effect on fluidity and thixotropy while three kinds of SCMs had extremely significant effect on plastic viscosity. Response surface plot showed that NS could increase the plastic viscosity of NSMCBM to 1.445 Pa•s (M16). However, the addition of FA and GGBFS decreased the plastic viscosity to 0.9 Pa•s, which was comparable with the reference sample (M17). Such value was 37.7% lower than that of M16. Meanwhile, NS complemented the reduction of compressive strength caused by SCMs. Thus, the synergy effect of SCMs and NS could improve both fresh and mechanical properties. At last, multi-objective optimization was utilized to optimize the proportion of SCMs considering the interaction between SCMs to achieve desirable parameters.

As an integral part of source code files, code comments help improve program readability and comprehension. However, developers sometimes do not comment their program code adequately due to the incurred extra efforts, lack of relevant knowledge, unawareness of the importance of code commenting or some other factors. As a result, code comments can be inadequate, absent or even mismatched with source code, which affects the understanding, reusing and the maintenance of software. To solve these problems of code comments, researchers have been concerned with generating code comments automatically. In this work, we aim at conducting a survey of automatic code commenting researches. First, we generally analyze the challenges and research framework of automatic generation of program comments. Second, we present the classification of representative algorithms, the design principles, strengths and weaknesses of each category of algorithms. Meanwhile, we also provide an overview of the quality assessment of the generated comments. Finally, we summarize some future directions for advancing the techniques of automatic generation of code comments and the quality assessment of comments.

The eco-restoration was a very effective measure to solve the problem of environmental pollution caused by the exposed mine surface in the stone coal mine site. In this study, the dominant plant, Indigofera amblyantha Craib, was well adapted to the eco-restoration in stone coal mining area. The changes of nutrient elements, pH, heavy metals in substrate material, the biological concentration/transfer factor, and the distribution and diversity of bacteria and fungi in rhizosphere soil were investigated. The results show that the plant communities help slow down the loss of nutrient elements and the increase of the concentrations of heavy metals in the eco-restoration process. The Indigofera amblyantha Craib had the advantaged ability to enrich and transfer Cd, Cu, Mn, and its diversity index of microbial communities in rhizosphere soils was higher than that of other quadrats. These excellent properties found in this work help reveal the insight into the adaptability of Indigofera amblyantha Craib in the eco-restoration of stone coal mines. It is valuable to evaluate Indigofera amblyantha Craib for eco-restoration engineering of stone coal mine and extend the application in heavy metal contaminated sites.

The exposed slopes formed by stone mining had caused serious problems of environmental pollution. The ecological restoration was a very effective measure to solve it. In this study, the Indigofera amblyantha Craib was the dominant species in the ecorestoration during the first two years of ecological restoration, which was based on analyzing the importance value (IV) and diversity indexes of plant species. Meanwhile, the ecorestoration process helps slow down the increase of the concentrations of some heavy metals in the substrate material; however, the changing trends of these metals were not uniform. The root and leaf were the main parts of heavy metal distribution, respectively, corresponding to Pb, Cr, As, Sb, and Ni in root and Cu, Mn, and V in leaf. It confirmed that Indigofera amblyantha Craib had an advantage ability to enrich and transfer Cd, Cu, and Mn obtained from their biological concentration factor (BCF) and biological transfer factor (BTF). The Sb, Cd, and V were the main factors affecting the IV by the redundancy analysis. These fine characters of Indigofera amblyantha Craib help explain it well adapted to the ecorestoration of stone coal mines. The current results are valuable to evaluate and extend the application in ecorestoration engineering of mining areas and other heavy metal-contaminated sites.

Super absorbent polymer (SAP), known as a water retention agent, has a high capacity for water absorption, which can help enhance the soil structure. This paper studied the effects of SAP dosages on the root characteristics and anchorage of Amorpha fruticosa on rock slopes. The internal relationship between root growth effect and soil was discussed, and a specific reference was provided for the rational application of SAP on slopes. Using the pull-out and tensile resistance tests, we systematically studied the changes of soil properties, root distribution, root tensile strength, and root anchorage under six different SAP dosages. The results indicated that: (1) With the increase in SAP dosage, the natural soil water content and water content after 24 h of watering increased significantly, whereas the contents of TN, TP, and TK decreased dramatically. (2) With the increase in SAP dosage, the amount and length of first-order and secondary lateral roots decreased significantly, and there was no significant difference in diameter. The amount of downslope first-order and unembedded secondary lateral roots is greater than upslope. The amount of upslope embedded secondary lateral roots is greater than in downslope. (3) Tensile strength: embedded secondary root > non-embedded secondary root > first-order lateral root; upslope root > downslope root. (4) With the increase in SAP dosage, the plant anchorage drops noticeably. This study concluded that the significant addition of SAP could enhance the tensile strength of upslope embedded secondary lateral roots but would adversely affect soil nutrients, root distribution, and root anchorage. The addition of SAP in this test had no significant effect on improving slope stability. From the perspective of reinforcement capacity, we cannot blindly pursue the survival rate and other high dosage use of water retention agents to increase the risk of slope destabilization.

The present study focused on isolating an efficient enzyme production microorganism for ferulic acid (FA) production from wheat bran. A wild-type cellulase-, xylanase-, and feruloyl esterase-producing strain was isolated and identified as Penicillium oxalicum M1816. The genome was sequenced and assembled into 30.5 Mb containing 8301 predicted protein-coding genes. In total, 553 genes were associated with carbohydrate metabolism. Genomic CAZymes analysis indicated that P. oxalicum M1816, comprising 39 cellulolytic enzymes and 111 hemicellulases (including 5 feruloyl esterase genes), may play a vital role in wheat bran degradation and FA production. The crude enzyme of strain M1816 could release 1.85 ± 0.08 mg·g−1 FA from de-starched wheat bran (DSWB) at 12 h, which was significantly higher than other commercial enzymes. Meanwhile, when the strain M1816 was cultured in medium supplemented with DSWB, up to 92.89% of the total alkali-extractable FA was released. The process parameters of solid-state fermentation were optimized to enhance enzyme production. The optimized wheat bran Qu of P. oxalicum M1816 was applied to huangjiu fermentation, and the FA content was increased 12.4-fold compared to the control group. These results suggest that P. oxalicum M1816 is a good candidate for the development of fermented foods bio-fortified with FA.

A series of researches were conducted on the collection and organization of software engineering big data, software development knowledge representation and extraction, intelligent software development tools and service platforms. The purpose is to establish big data based intelligent software development technique systems, develop intelligent software development supporting tools, and form the next-generation intelligent software development environment and cloud-based platforms incorporating human, tools, and data. The outcome of the project includes a public service platform for the widespread innovation of the people and a series of intelligent software development environments for enterprises.

Mining big software data and utilizing the knowledge contained in it to explore intelligent methods for software development is an active research topic. However, existing researches on software developer and crowd collaboration have not yet formed systematic methods. Therefore, the key technologies for intelligent collaboration through in-depth analysis of developer behavior were studied. Besides, the corresponding support environment was also developed on the basis of the key technologies to improve the efficiency and quality of software development. Firstly, a large amount of data related to developers were collected and analyzed. Secondly, a systematic approach of analyzing developers and their collaboration which is called developer knowledge graph was proposed. Thirdly, supported by the developer knowledge graph, the collaborative development method based on intelligent recommendation was introduced thoroughly. Depending on the above technologies, the corresponding supporting tools were developed, and a system of intelligent collaborative development environment was provided. Finally, the future work was prospected.

The dioecious relic Eurycorymbus cavaleriei is a tertiary relict and only species of its genus within the family Sapindaceae. Using an Illumina platform, we sequenced its complete chloroplast (cp) genome. Our study reveals that E. cavaleriei has a typical cp genome of 158,537 bp in length, comprised of a large single-copy region (LSC) of 86,693 bp, a small single-copy region (SSC) of 18,012 bp, and two inverted repeat regions (IRs) of 26,916 bp, respectively. A total of 137 genes, 89 of which are protein-coding genes, 40 tRNA genes, 8 rRNA genes were identified. The overall GC content of the plastome is 38.0%. Phylogenetic analysis indicates that E. cavaleriei is closely related to the species of Dodonaea viscosa.

Japanese encephalitis (JE) is the most common cause of viral encephalitis and an important public health concern in the Asia-Pacific region, particularly in China where 50% of global cases are notified. To explore the association between environmental factors and human JE cases and identify the high risk areas for JE transmission in China, we used annual notified data on JE cases at the center of administrative township and environmental variables with a pixel resolution of 1 km × 1 km from 2005 to 2011 to construct models using ecological niche modeling (ENM) approaches based on maximum entropy. These models were then validated by overlaying reported human JE case localities from 2006 to 2012 onto each prediction map. ENMs had good discriminatory ability with the area under the curve (AUC) of the receiver operating curve (ROC) of 0.82–0.91, and low extrinsic omission rate of 5.44–7.42%. Resulting maps showed JE being presented extensively throughout southwestern and central China, with local spatial variations in probability influenced by minimum temperatures, human population density, mean temperatures, and elevation, with contribution of 17.94%–38.37%, 15.47%–21.82%, 3.86%–21.22%, and 12.05%–16.02%, respectively. Approximately 60% of JE cases occurred in predicted high risk areas, which covered less than 6% of areas in mainland China. Our findings will help inform optimal geographical allocation of the limited resources available for JE prevention and control in China, find hidden high-risk areas, and increase the effectiveness of public health interventions against JE transmission. Keywords: Japanese encephalitis, Ecological niche model, MaxEnt, China

To research the macroscopic deformation of rock microseismic damage, a high-precision microseismic monitoring system was established on the left bank slope of the Baihetan hydropower station in Southwestern China. Based on the microseismic monitoring and field deformation data, the seismic source radius was applied to characterize the rock fracture scale. Numerical simulations introduced the rock micro-fracture information into the three-dimensional numerical model of the left bank slope and established the damage constitutive model. The unloading deformation process of the left bank abutment rock mass is described by numerical calculations. The feedback analysis method considering the effect of microseismic damage is preliminary exploratory research, which provides a new idea for the stability analysis of similar high rock slopes.

The phenomenon of pistil abortion widely occurs in Japanese apricot, and imperfect flowers with pistil abortion seriously decrease the yield in production. Although transcriptome analyses have been extensively studied in the past, a systematic study of differential gene expression has not been performed in Japanese apricot. To investigate genes related to the pistil development of Japanese apricot, high-throughput sequencing technology (Illumina) was employed to survey gene expression profiles from perfect and imperfect Japanese apricot flower buds. 3,476,249 and 3,580,677 tags were sequenced from two libraries constructed from perfect and imperfect flower buds of Japanese apricot, respectively. There were 689 significant differentially-expressed genes between the two libraries. GO annotation revealed that highly ranked genes were those implicated in small molecule metabolism, cellular component organisation or biogenesis at the cellular level and fatty acid metabolism. According to the results, we assumed that late embryogenesis abundant protein (LEA), Dicer-like 3 (DCL3) Xyloglucan endotransglucosylase/hydrolase 2 (XTH2), Pectin lyase-like superfamily protein (PPME1), Lipid transfer protein 3 (LTP3), Fatty acid biosynthesis 1 (FAB1) and Fatty acid desaturase 5 (FAD5) might have relationships with the pistil abortion in Japanese apricot. The expression patterns of 36 differentially expressed genes were confirmed by real-time (RT)-PCR. This is the first report of the Illumina RNA-seq technique being used for the analysis of differentially-expressed gene profiles related to pistil abortion that both computationally and experimentally provides valuable information for the further functional characterisation of genes associated with pistil development in woody plants.

Automatic hydrochemical logging and in situ titration com­bined withlaboratory analysis were used to understand the spatial and temporal hydrochemical variations of the spring-fed, travertine-depositing stream in celebrated Huanglong Ravine, Sichuan, SW China. This is essential for protection of the Huanglong World Natural Heritage travertine land­scape. It was found that the deposition of travertine was due to very strong CO2 degassing from the water, leading to de­crease in pCO2 and specific conductivity (SpC), and increase in pH and SIc downstream from the Spring. However, regular downstream hydrochemical evolution was interrupted by di­lution withsnow-melt water and by renewed CO2 from some downstream springs. The chemistry of Huanglong Spring itself was stable at a diurnal scale thoughit was altered by the great Wenchuan earthquake of May 12 2008. However, in spring-fed pools downstream, pCO2 and SpC were lower, and pH and SIc were higher in daytime than at night, whichindicates that the deposition of travertine was faster during the daylight hours. This was due to the combined effects of higher water tempera­tures and higher aquatic algae photosynthesis. In addition, it was found that the phosphate concentration in the stream in­creased remarkably downstream in the tourist midseason, in­dicating water pollution by tourism activities. The increase of phosphate (an inhibitor of calcite precipitation) may be one of the reasons for the decrease in travertine deposition rates and accelerated propagation of discoloration by diatoms during the past decades, whichneeds to be given more comprehensive study and tackled in future for the protection of these world famous travertine deposits.