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Biological nitrogen fixation (BNF), the conversion of N 2 into bioavailable nitrogen (N), is the main process for replenishing N loss in the biosphere. However, BNF in groundwater systems remains poorly understood. In this study, we examined the activity, abundance, and community composition of diazotrophs in groundwater in the Hetao Plain of Inner Mongolia using 15 N tracing methods, reverse transcription qPCR (RT-qPCR), and metagenomic/metatranscriptomic analyses. 15 N 2 tracing incubation of near in situ groundwater (9.5-585.4 nmol N L -1 h -1 ) and N 2 -fixer enrichment and isolates (13.2-1728.4 nmol N g -1 h -1 , as directly verified by single-cell resonance Raman spectroscopy), suggested that BNF is a non-negligible source of N in groundwater in this region. The expression of nifH genes ranged from 3.4 × 10 3 to 1.2 × 10 6 copies L -1 and was tightly correlated with dissolved oxygen (DO), Fe(II), and NH 4 + . Diazotrophs in groundwater were chiefly aerobes or facultative anaerobes, dominated by Stutzerimonas, Pseudomonas, Paraburkholderia, Klebsiella, Rhodopseudomonas, Azoarcus, and additional uncultured populations. Active diazotrophs, which prefer reducing conditions, were more metabolically diverse and potentially associated with nitrification, sulfur/arsenic mobilization, Fe(II) transport, and CH 4 oxidation. Our results highlight the importance of diazotrophs in subsurface geochemical cycles., (© 2023. The Author(s), under exclusive licence to International Society for Microbial Ecology.)

Dissimilatory arsenate reduction from arsenic (As)-bearing minerals into highly mobile arsenite is one of the key mechanisms of As release into groundwater. To detect the microbial diversity and As-metabolizing gene clusters of indigenous arsenate-reducing bacteria in high As groundwater in the Hetao Plain of Inner Mongolia, China, three anaerobic arsenate-reducing bacteria were isolated and arrA and arsC gene-based clone libraries of four in situ groundwater samples were constructed. The strains IMARCUG-11(G-11), IMARCUG-C1(G-C1) and IMARCUG-12(G-12) were phylogenetically belonged to genera Paraclostridium, Citrobacter and Klebsiella, respectively. They could reduce >99% of 1 mM arsenate under anoxic conditions with lactate as a carbon source in 60 h, 72 h and 84 h, respectively. As far as we know, this was the first report of arsenate reduction by genus Paraclostridium. Compared with strain G-11 (arsC) and G-C1 (arsRBC), strain G-12 contained two incomplete ars operons (operon1: arsABC, operon2: arsBC), indicating that these strains might present different strategies to resist As toxicity. Phylogenetic analysis illuminating by the arrA genes showed that in situ arsenate-reducing bacterial communities were diverse and mainly composed of Desulfobacterales (53%, dominated by Geobacter), Betaproteobacteria (12%), and unidentified groups (35%). Based on the arsC gene analysis, the indigenous arsenate-reducing bacterial communities were mainly affiliated with Omnitrophica (88%) and Deltaproteobacteria (11%, dominated by Geobacter and Syntrophobacterales). Results of this study expanded our understanding of indigenous arsenic-reducing bacteria in high As groundwater aquifers., (© 2020. Springer Science+Business Media, LLC, part of Springer Nature.)

Abstract: In this study, corona virus disease 2019 (COVID-19) transmission networks were built to analyze the epidemic situation of COVID-19 in Liaoning and Jilin provinces in early 2020. We explore the characteristics of the spread of COVID-19, and put forward effective recommendations for epidemic prevention and control. We collected demographic characteristics, exposure history, and course of action of COVID-19 cases. We described the demographic and case characteristics of these cases to show the basic characteristics of COVID-19 cases in both provinces. Combined with the spatial analysis of confirmed cases, the distribution law of the number of confirmed cases in different regions was analyzed. We exhibit the relationship among COVID-19 cases with a transmission network. The transmission characteristics of COVID-19 were analyzed through the transmission network. Mainly cases in Liaoning and Jilin provinces were imported cases from other provinces and the vast majority of these cases were related to Hubei province. The number of confirmed cases in different regions was positively correlated with their GDP and population. The main clinical symptoms of the cases were fever. Judge from the transmission network relationship between the 2 provinces, the transmission chain in Liaoning province contains fewer cases than that in Jilin province. The main transmission routes of the local cases in the 2 provinces were the family members, and the infection of the imported cases were mainly occurred in public places. It was estimated that the unidentified asymptomatic infected cases in the 2 provinces account for approximately 7.3% of the total number of infected cases. The length of the transmission chain suggests that the spread of COVID-19 can be effectively controlled with effective prevention measures., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: The tooth morphology course is an important basic dental course. However, it is difficult to fully reflect the three-dimensional (3D) morphological characteristics of tooth structure in two-dimensional pictures in traditional textbooks. The aim of this study was to assess the effect of 3D-printed plastic model teeth in the teaching of tooth morphology., Methods: Twenty-two undergraduate students who matriculated at the School of Stomatology, the Fourth Medical University, in 2014 and 23 who matriculated in 2016 participated in the study. Each student who matriculated in 2016 was given a full set of fourteen standard 3D-printed plastic model teeth for use during the learning process, and an anonymous questionnaire was used to evaluate the usefulness of the 3D-printed plastic model teeth from the perspective of the students., Results: There was no significant difference between the two groups in the scores of the theoretical examination or the total score. However, for the score of the sculpted gypsum teeth, the students who used the 3D-printed plastic model teeth in their studies scored significantly higher (P = 0.002). More than 90% of the students thought that the 3D-printed plastic model teeth were of great help or were very helpful for mastering the anatomy of teeth and for carving the gypsum teeth., Conclusion: Standard 3D-printed plastic teeth can effectively assist students in learning tooth morphology by transforming two-dimensional pictures and descriptions in the textbook into a 3D conformation, effectively promoting students' learning and mastery of tooth morphology and structure. Additionally, the results suggest that 3D-printed plastic model teeth are of great help to the students in mastering and improving their carving skills.

As a country with the highest CO 2 emissions and at the turning point of socio-economic transition, China's effort to reduce CO 2 emissions will be crucial for climate change mitigation. Yet, due to geospatial variations of CO 2 emissions in different cities, it is important to develop city-specific policies and tools to help control and reduce CO 2 emissions. The key question is how to identify and quantify these variations so as to provide reference for the formulation of the corresponding mitigation policies. This paper attempts to answer this question through a case study of 26 cities in the Yangtze River Delta. The CO 2 emissions pattern of each city is measured by two statistics: Gini coefficient to describe its quantitative pattern and Global Moran's I index to capture its spatial pattern. It is found that Gini coefficients in all these cities are all greater than 0.94, implying a highly polarized pattern in terms of quantity; and the maximum value for Global Moran's I index is 0.071 with a standard deviation of 0.021, indicating a weak spatial clustering trend but strong difference among these cities. So, it would be more efficient for these cities at current stage to reduce CO 2 emissions by focusing on the large emission sources at certain small localities, particularly the very built-up areas rather than covering all the emission sources on every plot of the urban prefectures. And by a combination of these two metrics, the 26 cities are regrouped into nine types with most of them are subject to type HL and ML. These reclassification results then can serve as reference for customizing mitigation policies accordingly and positioning these policies in a more accurate way in each city., (Copyright © 2019. Published by Elsevier Ltd.)

Arsenic methylation is regarded as an effective way of arsenic detoxification. Current knowledge about arsenic biomethylation in high arsenic groundwater remains limited. In the present study, 16 high arsenic groundwater samples from deep wells of the Hetao Plain were investigated using clone library and quantitative polymerase chain reaction (qPCR) analyses of arsM genes as well as geochemical analysis. The concentrations of methylated arsenic (including monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA)) varied from 2.40 to 16.85 μg/L. Both bacterial and archaeal arsenic methylating populations were detected in the high arsenic aquifer. They were dominated by Proteobacteria, Firmicutes, Gemmatimonadetes, Nitrospirae, Methanomicrobia and a large unidentified group. The abundances of predominant populations were correlated positively to either total organic carbon or total arsenic and arsenite concentrations. The arsM gene abundances in high arsenic groundwater ranged from below detection to 5.71 × 10 6 copies/L and accounted for 0-3.32‰ of total bacterial and archaeal 16S rRNA genes. The arsM gene copies in high arsenic groundwater showed closely positive correlations with methylated arsenic concentrations. The overall results implied that arsenic methylating microorganisms were abundant and diverse in high arsenic groundwater. This was the first study of arsenic methylating microbial communities in high arsenic groundwater aquifers and might provide useful information for arsenic bioremediation in groundwater systems.