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Atmospheric CO2 concentration is increasing, largely due to anthropogenic activities. Previous studies of individual free-air CO2 enrichment (FACE) experimental sites have shown significant impacts of elevated CO2 (eCO2) on soil microbial communities; however, no common microbial response patterns have yet emerged, challenging our ability to predict ecosystem functioning and sustainability in the future eCO2 environment. Here we analyzed 66 soil microbial communities from five FACE sites, and showed common microbial response patterns to eCO2, especially for key functional genes involved in carbon and nitrogen fixation (e.g., pcc/acc for carbon fixation, nifH for nitrogen fixation), carbon decomposition (e.g., amyA and pulA for labile carbon decomposition, mnp and lcc for recalcitrant carbon decomposition), and greenhouse gas emissions (e.g., mcrA for methane production, norB for nitrous oxide production) across five FACE sites. Also, the relative abundance of those key genes was generally increased and directionally associated with increased biomass, soil carbon decomposition, and soil moisture. In addition, a further literature survey of more disparate FACE experimental sites indicated increased biomass, soil carbon decay, nitrogen fixation, methane and nitrous oxide emissions, plant and soil carbon and nitrogen under eCO2. A conceptual framework was developed to link commonly responsive functional genes with ecosystem processes, such as pcc/acc vs. soil carbon storage, amyA/pulA/mnp/lcc vs. soil carbon decomposition, and nifH vs. nitrogen availability, suggesting that such common responses of microbial functional genes may have the potential to predict ecosystem functioning and sustainability in the future eCO2 environment.

Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming-induced biotic changes may influence biologically related parameters and the consequent projections in ESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5 years from a soil warming experiment at the Eight Mile Lake, Alaska, into the Terrestrial ECOsystem (TECO) model with a probabilistic inversion approach. The TECO model used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment-corrected) turnover rates of SOC in both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. The TECO model predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87 g/m2 , respectively, without or with changes in those parameters. Thus, warming-induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes in ESMs to improve the model performance in predicting C dynamics in permafrost regions.

Increases in carbon (C) inputs to soil can replenish soil organic C (SOC) through various mechanisms. However, recent studies have suggested that the increased C input can also stimulate the decomposition of old SOC via priming. Whether the loss of old SOC by priming can override C replenishment has not been rigorously examined. Here we show, through data-model synthesis, that the magnitude of replenishment is greater than that of priming, resulting in a net increase in SOC by a mean of 32% of the added new C. The magnitude of the net increase in SOC is positively correlated with the nitrogen-to-C ratio of the added substrates. Additionally, model evaluation indicates that a two-pool interactive model is a parsimonious model to represent the SOC decomposition with priming and replenishment. Our findings suggest that increasing C input to soils likely promote SOC accumulation despite the enhanced decomposition of old C via priming.

Terrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land-Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.

The enhanced vegetation growth by climate warming plays a pivotal role in amplifying the seasonal cycle of atmospheric CO 2 at northern lands (>50° N) since 1960s. However, the correlation between vegetation growth, temperature and seasonal amplitude of atmospheric CO 2 concentration have become elusive with the slowed increasing trend of vegetation growth and weakened temperature control on CO 2 uptake since late 1990s. Here, based on in situ atmospheric CO 2 concentration records from the Barrow observatory site, we found a slowdown in the increasing trend of the atmospheric CO 2 amplitude from 1990s to mid-2000s. This phenomenon was associated with the paused decrease in the minimum CO 2 concentration ([CO 2 ] min ), which was significantly correlated with the slowdown of vegetation greening and growing-season length extension. We then showed that both the vegetation greenness and growing-season length were positively correlated with spring but not autumn temperature over the northern lands. Furthermore, such asymmetric dependences of vegetation growth upon spring and autumn temperature cannot be captured by the state-of-art terrestrial biosphere models. These findings indicate that the responses of vegetation growth to spring and autumn warming are asymmetric, and highlight the need of improving autumn phenology in the models for predicting seasonal cycle of atmospheric CO 2 concentration.

Quantifying soil organic carbon (SOC) decomposition under warming is critical to predict carbon-climate feedbacks. According to the substrate regulating principle, SOC decomposition would decrease as labile SOC declines under field warming, but observations of SOC decomposition under warming do not always support this prediction. This discrepancy could result from varying changes in SOC components and soil microbial communities under warming. This study aimed to determine the decomposition of SOC components with different turnover times after subjected to long-term field warming and/or root exclusion to limit C input, and to test whether SOC decomposition is driven by substrate lability under warming. Taking advantage of a 12-year field warming experiment in a prairie, we assessed the decomposition of SOC components by incubating soils from control and warmed plots, with and without root exclusion for 3 years. We assayed SOC decomposition from these incubations by combining inverse modeling and microbial functional genes during decomposition with a metagenomic technique (GeoChip). The decomposition of SOC components with turnover times of years and decades, which contributed to 95% of total cumulative CO2 respiration, was greater in soils from warmed plots. But the decomposition of labile SOC was similar in warmed plots compared to the control. The diversity of C-degradation microbial genes generally declined with time during the incubation in all treatments, suggesting shifts of microbial functional groups as substrate composition was changing. Compared to the control, soils from warmed plots showed significant increase in the signal intensities of microbial genes involved in degrading complex organic compounds, implying enhanced potential abilities of microbial catabolism. These are likely responsible for accelerated decomposition of SOC components with slow turnover rates. Overall, the shifted microbial community induced by long-term warming accelerates the decomposition of SOC components with slow turnover rates and thus amplify the positive feedback to climate change.

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

Improving predictions of soil organic carbon (SOC) dynamics by multi-compartment models requires validation of turnover times of different SOC pools. Techniques such as laboratory incubation and isotope analysis have been adopted to estimate C turnover times, yet no studies have systematically compared these techniques and assessed the uncertainties associated with them. Here, we tested whether C turnover times of soil fractions were biased by methodology, and how this changed across soil particle sizes and ecosystems. We identified 52 studies that quantified C turnover times in different soil particles fractionated either according to aggregate size (e.g., macro- versus micro-aggregates) or according to soil texture (e.g., sand versus silt versus clay). C turnover times of these soil fractions were estimated by one of three methods: laboratory incubation (16 studies), δ13C shift due to C3–C4 vegetation change (25 studies), and 14C dating (19 studies). All methods showed that C turnover times of soil fractions generally increase with decreasing soil particle size. However, estimates of C turnover times within soil fractions differed significantly among methods, with incubation estimating the shortest turnover times and 14C the longest. The short C turnover times estimated by incubation are likely due to optimal environmental conditions for microbial decomposition existing in these studies, which is often a poor representation of field conditions. The 13C method can only be used when documenting a successive C3 versus C4 vegetation shift. C turnover times estimated by 14C were systematically higher than those estimated by 13C, especially for fine soil fractions (i.e., silt and clay). Overall, our findings highlight methodological uncertainties in estimating C turnover times of soil fractions, and correction factors should be explored to account for methodological bias when C turnover times estimated from different methods are used to parameterize soil C models.

Vasorin (VASN), a transmembrane protein heavily expressed in endothelial cells, has garnered recent interest due to its key role in vascular development and pathology. The oligomeric state of VASN is a crucial piece of knowledge given that receptor clustering is a frequent regulatory mechanism in downstream signaling activation and amplification. However, documentation of VASN oligomerization is currently absent. In this brief report, we describe the measurement of VASN oligomerization in its native membranous environment, leveraging a class of fluorescence fluctuation spectroscopy. Our investigation revealed that the majority of VASN resides in a monomeric state, while a minority of VASN forms homodimers in the cellular membrane. This result raises the intriguing possibility that ligand-independent clustering of VASN may play a role in transforming growth factor signaling. [ABSTRACT FROM AUTHOR]

Background: The gut microbiome is composed of various microorganisms such as bacteria, fungi, and protozoa, and constitutes an important part of the human gut. Its composition is closely related to human health and disease. Alzheimer's disease (AD) is a neurodegenerative disease whose underlying mechanism has not been fully elucidated. Recent research has shown that there are significant differences in the gut microbiota between AD patients and healthy individuals. Changes in the composition of gut microbiota may lead to the development of harmful factors associated with AD. In addition, the gut microbiota may play a role in the development and progression of AD through the gut–brain axis. However, the exact nature of this relationship has not been fully understood. Aims: This review will elucidate the types and functions of gut microbiota and their relationship with AD and explore in depth the potential mechanisms of gut microbiota in the occurrence of AD and the prospects for treatment strategies. Methods: Reviewed literature from PubMed and Web of Science using key terminologies related to AD and the gut microbiome. Results: Research indicates that the gut microbiota can directly or indirectly influence the occurrence and progression of AD through metabolites, endotoxins, and the vagus nerve. Discussion: This review discusses the future challenges and research directions regarding the gut microbiota in AD. Conclusion: While many unresolved issues remain regarding the gut microbiota and AD, the feasibility and immense potential of treating AD by modulating the gut microbiota are evident. [ABSTRACT FROM AUTHOR]

Soil properties, such as clay content, are hypothesized to control decomposition of soil organic carbon (SOC). However, these hypotheses of soil property-C decomposition relationships have not been explicitly tested at large spatial scales. Here, we used a data-assimilation approach to evaluate the roles of soil properties and environmental factors in regulating decomposition of SOC. A three-pool (active, slow, and passive) C-cycling model was optimally fitted with 376 published laboratory incubation data from soils acquired from 73 sites with mean annual temperature ranging from -. 15 to 26. °C. Our results showed that soil physical and chemical properties regulated decomposition rates of the active and the slow C pools. Decomposition rates were lower for soils with high clay content, high field water holding capacity (WHC), and high C:N ratio. Multifactor regression and structural equation modeling (SEM) analyses showed that clay content was the most important variable in regulating decomposition of SOC. In contrast to the active and slow C pools, soil properties or environmental factors had little effect on the decomposition of the passive C pool. Our results show inverse soil property-C decomposition relationships and quantitatively evaluate the essential roles of soil texture (clay content) in controlling decomposition of SOC at a large spatial scale. The results may help model development and projection of changes in terrestrial C sequestration in the future.

Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

With the advent of modern, high-speed electrified rail systems, there has been increasing concern about electromagnetic safety in rail carriages. The aim of this study was to assess the electromagnetic safety of passengers on trains by utilizing advanced 3D electromagnetic simulation software. A comprehensive model of the electromagnetic environment experienced by passengers on a CR400AF train, specifically under the influence of catenary radiation, was constructed. We analyzed the magnetic field strength, electric field strength, and current density in the brains of 20 passengers in various positions in the train. The findings revealed that among the 20 passengers analyzed, the maximum and minimum magnetic induction intensity recorded in the brain were 8.41 and 0.01 μ T , respectively. The maximum and minimum induced electric field intensities were 1110 and 10 μ V / m , respectively. Lastly, the maximum and minimum induced current densities were 1200 and 10 μ A / m 2 , respectively. The results show that when people ride on the CR400AF train, the magnetic induction intensity, induced electric field strength, and induced current density in the brain are below the recommended basic limits of exposure to power frequency electromagnetic fields in the guidelines of the International Committee on Non-Ionizing Radiation Protection. The power frequency magnetic field generated by the catenary can be effectively shielded by the aluminum alloy car body. The final result of this study indicates that the electromagnetic exposure from the contact wire at the level 25 kV does not pose a threat to the health of passengers on the CR400AF train. [ABSTRACT FROM AUTHOR]

Odontogenic myxoma (OM) is a rare benign tumor in the jaws. It is considered locally aggressive but non-metastatic. Here, we report a case of Odontogenic myxoma observed at a relatively rare site, which occurs in the maxillary posterior interdental region and maxillary sinus, as well as involves the right nasal cavity, orbital floor, and skull base. A 20-year-old young female patient was referred to institution for right-sided nasal congestion and facial swelling. The right inferior nasal tract and common nasal tract of the patient were blocked with a reddish neoplasm. The clinical history and preoperative examination and iconography findings were indicative of an odontogenic cyst. The patient received Right-sided nasal sinus tumor resection via end nasal endoscopic anterior lacrimal saphenous fossa combined with Kollu's approach. The postoperative pathological findings were suggestive of an Odontogenic myxoma. Conservative surgical treatment is less surgically invasive, more acceptable to patients, and results in a better quality of survival. [ABSTRACT FROM AUTHOR]

The development of virtual prototype technology makes the vehicle multi-bod dynamics simulation and virtual fatigue durability calculation become the necessary means of engineering research,and obtaining reliable and effective road spectrum is the premise of the two means. This paper build road spectrum solution joint simulation platform based on FEMFAT LAB-ADAMS,and the response channel is established on the multi-body dynamics model according to the installation position of the actual vehicle sensor. The road spectrum is calculated based on the virtual iteration theory and the real road spectrum of the engine connection point and the shock absorber mounting point. The reliability of the road spectrum solution is verified by comparing the monitoring signal and the measured signal from three aspects of time domain characteristics,frequency domain characteristics and damage calculation. The results show that the reliable road spectrum can be used for vehicle multi-body dynamics simulation and virtual fatigue durability calculation.

A road simulation test is carried out with a passenger car body. Six signals are selected as the target iteration signals of the bench test from the multiple measuring points which meet the requirements of the iterative point selection. A screening method based on the correlation between different acceleration signals was proposed. Statistical correlation coefficient between the acceleration signals,and filter out the two sets of target iterative signals with the largest correlation and minimum correlation. The bench test shows that the greater the relevance of the target iteration signal group,the higher the accuracy of the road simulation test.