Stimulation of soil respiration by elevated CO2 is enhanced under nitrogen limitation in a decade-long grassland study.

Whether and how CO2 and nitrogen (N) availability interact to influence carbon (C) cycling processes such as soil respiration remains a question of considerable uncertainty in projecting future C-climate feedbacks, which are strongly influenced by multiple global change drivers, including elevated atmospheric CO2 concentrations (eCO2) and increased N deposition. However, because decades of research on the responses of ecosystems to eCO2 and N enrichment have been done largely independently, their interactive effects on soil respiratory CO2 efflux remain unresolved. Here, we show that in a multifactor free-air CO2 enrichment experiment, BioCON (Biodiversity, CO2, and N deposition) in Minnesota, the positive response of soil respiration to eCO2 gradually strengthened at ambient (low) N supply but not enriched (high) N supply for the 12-y experimental period from 1998 to 2009. In contrast to earlier years, eCO2 stimulated soil respiration twice as much at low than at high N supply from 2006 to 2009. In parallel, microbial C degradation genes were significantly boosted by eCO2 at low but not high N supply. Incorporating those functional genes into a coupled C-N ecosystem model reduced model parameter uncertainty and improved the projections of the effects of different CO2 and N levels on soil respiration. If our observed results generalize to other ecosystems, they imply widely positive effects of eCO2 on soil respiration even in infertile systems.

Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling.

Soil carbon (C) and nitrogen (N) cycles and their complex responses to environmental changes have received increasing attention. However, large uncertainties in model predictions remain, partially due to the lack of explicit representation and parameterization of microbial processes. One great challenge is to effectively integrate rich microbial functional traits into ecosystem modeling for better predictions. Here, using soil enzymes as indicators of soil function, we developed a competitive dynamic enzyme allocation scheme and detailed enzyme-mediated soil inorganic N processes in the Microbial-ENzyme Decomposition (MEND) model. We conducted a rigorous calibration and validation of MEND with diverse soil C-N fluxes, microbial C:N ratios, and functional gene abundances from a 12-year CO2  × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO2 fluxes and multiple N variables, the model correctly predicted microbial C:N ratios and their negative response to enriched N supply. Model validation further showed that, compared to the changes in simulated enzyme concentrations and decomposition rates, the changes in simulated activities of eight C-N-associated enzymes were better explained by the measured gene abundances in responses to elevated atmospheric CO2 concentration. Our results demonstrated that using enzymes as indicators of soil function and validating model predictions with functional gene abundances in ecosystem modeling can provide a basis for testing hypotheses about microbially mediated biogeochemical processes in response to environmental changes. Further development and applications of the modeling framework presented here will enable microbial ecologists to address ecosystem-level questions beyond empirical observations, toward more predictive understanding, an ultimate goal of microbial ecology.

High carbon losses from oxygen-limited soils challenge biogeochemical theory and model assumptions.

Oxygen (O2 ) limitation contributes to persistence of large carbon (C) stocks in saturated soils. However, many soils experience spatiotemporal O2  fluctuations impacted by climate and land-use change, and O2 -mediated climate feedbacks from soil greenhouse gas emissions remain poorly constrained. Current theory and models posit that anoxia uniformly suppresses carbon (C) decomposition. Here we show that periodic anoxia may sustain or even stimulate decomposition over weeks to months in two disparate soils by increasing turnover and/or size of fast-cycling C pools relative to static oxic conditions, and by sustaining decomposition of reduced organic molecules. Cumulative C losses did not decrease consistently as cumulative O2 exposure decreased. After >1 year, soils anoxic for 75% of the time had similar C losses as the oxic control but nearly threefold greater climate impact on a CO2 -equivalent basis (20-year timescale) due to high methane (CH4 ) emission. A mechanistic model incorporating current theory closely reproduced oxic control results but systematically underestimated C losses under O2  fluctuations. Using a model-experiment integration (ModEx) approach, we found that models were improved by varying microbial maintenance respiration and the fraction of CH4 production in total C mineralization as a function of O2 availability. Consistent with thermodynamic expectations, the calibrated models predicted lower microbial C-use efficiency with increasing anoxic duration in one soil; in the other soil, dynamic organo-mineral interactions implied by our empirical data but not represented in the model may have obscured this relationship. In both soils, the updated model was better able to capture transient spikes in C mineralization that occurred following anoxic-oxic transitions, where decomposition from the fluctuating-O2 treatments greatly exceeded the control. Overall, our data-model comparison indicates that incorporating emergent biogeochemical properties of soil O2 variability will be critical for effectively modeling C-climate feedbacks in humid ecosystems.

Observed variation in soil properties can drive large variation in modelled forest functioning and composition during tropical forest secondary succession.

Censuses of tropical forest plots reveal large variation in biomass and plant composition. This paper evaluates whether such variation can emerge solely from realistic variation in a set of commonly measured soil chemical and physical properties. Controlled simulations were performed using a mechanistic model that includes forest dynamics, microbe-mediated biogeochemistry, and competition for nitrogen and phosphorus. Observations from 18 forest inventory plots in Guanacaste, Costa Rica were used to determine realistic variation in soil properties. In simulations of secondary succession, the across-plot range in plant biomass reached 30% of the mean and was attributable primarily to nutrient limitation and secondarily to soil texture differences that affected water availability. The contributions of different plant functional types to total biomass varied widely across plots and depended on soil nutrient status. In Central America, soil-induced variation in plant biomass increased with mean annual precipitation because of changes in nutrient limitation. In Central America, large variation in plant biomass and ecosystem composition arises mechanistically from realistic variation in soil properties. The degree of biomass and compositional variation is climate sensitive. In general, model predictions can be improved through better representation of soil nutrient processes, including their spatial variation.

Reduced carbon use efficiency and increased microbial turnover with soil warming.

Global soil carbon (C) stocks are expected to decline with warming, and changes in microbial processes are key to this projection. However, warming responses of critical microbial parameters such as carbon use efficiency (CUE) and biomass turnover (rB) are not well understood. Here, we determine these parameters using a probabilistic inversion approach that integrates a microbial-enzyme model with 22 years of carbon cycling measurements at Harvard Forest. We find that increasing temperature reduces CUE but increases rB, and that two decades of soil warming increases the temperature sensitivities of CUE and rB. These temperature sensitivities, which are derived from decades-long field observations, contrast with values obtained from short-term laboratory experiments. We also show that long-term soil C flux and pool changes in response to warming are more dependent on the temperature sensitivity of CUE than that of rB. Using the inversion-derived parameters, we project that chronic soil warming at Harvard Forest over six decades will result in soil C gain of <1.0% on average (1st and 3rd quartiles: 3.0% loss and 10.5% gain) in the surface mineral horizon. Our results demonstrate that estimates of temperature sensitivity of microbial CUE and rB can be obtained and evaluated rigorously by integrating multidecadal datasets. This approach can potentially be applied in broader spatiotemporal scales to improve long-term projections of soil C feedbacks to climate warming.

Water quality prediction based on sparse dataset using enhanced machine learning.

Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water quality data, less attention is given to areas that lack sufficient data. Therefore, it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction of water pollution using machine learning. We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated with the Load Estimator (LOADEST). The research was conducted at a river-lake confluence, an area with intricate hydrological patterns. We found that the Self-Attentive LSTM (SA-LSTM) model outperformed the other three machine learning models in predicting water quality, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.71 for CODMn and 0.57 for NH3N when utilizing LOADEST-augmented water quality data (referred to as the SA-LSTM-LOADEST model). The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error (RMSE) by 24.6% for CODMn and 21.3% for NH3N. Furthermore, the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly. Additionally, the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance. This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.

Rainfall effect on soil respiration depends on antecedent soil moisture.

Climate change has a discernible influence on rainfall patterns, thus potentially affecting the intricate dynamics of soil respiration (Rs) and soil carbon storage. However, we still lack a profound understanding of the determinants of Rs response to rainfall events. Here, utilizing a comprehensive 10-year dataset (2004-2013), we explored the direction and magnitude of Rs response to rainfall events and the underlying determinants in a temperate forest. Based on the identified 368 rainfall events over the study period, we demonstrate that rainfall suppresses Rs when the soil moisture is optimal and moist in the growing season, whereas its effect on Rs during the non-growing season is minimal. Notably, antecedent soil moisture, rather than rainfall amount, shows a substantial impact on Rs during the growing season (coefficient of determination (R2) = 0.37 for antecedent soil moisture, and R2 < 0.01 for rainfall amount). Incorporating antecedent soil moisture significantly enhances the explanatory power (R2) from 0.09 to 0.45 regarding the relative changes in Rs following rainfall events. Our results highlight the environmental dependency of Rs response to rainfall events and suggest that incorporating the role of antecedent soil moisture could enhance predictability and reduce uncertainty in ecosystem modeling.

Effect of Cinnamaldehyde on Systemic Candida albicans Infection in Mice.

OBJECTIVE: To investigate the therapeutic efficacy of cinnamaldehyde (CA) on systemic Candida albicans infection in mice and to provide supportive data for the development of novel antifungal drugs. METHODS: Ninety BALB/c mice were randomly divided into 3 groups according to a random number table: CA treatment group, fluconazole (positive control) group, and Tween saline (negative control) group, with 30 mice in each group. Initially, all groups of mice received consecutive intraperitoneal injections of cyclophosphamide at 200 mg/kg for 2 days, followed by intraperitoneal injection of 0.25 mL C. albicans fungal suspension (concentration of 1.0 × 107 CFU/mL) on the 4th day, to establish an immunosuppressed systemic Candida albicans infection animal model. Subsequently, the mice were orally administered CA, fluconazole and Tween saline, at 240, 240 mg/kg and 0.25 mL/kg respectively for 14 days. After a 48-h discontinuation of treatment, the liver, small intestine, and kidney tissues of mice were collected for fungal direct microscopic examination, culture, and histopathological examination. Additionally, renal tissues from each group of mice were collected for (1,3)- ß -D-glucan detection. The survival status of mice in all groups was monitored for 14 days of drug administration. RESULTS: The CA group exhibited a fungal clearance rate of C. albicans above 86.7% (26/30), significantly higher than the fluconazole group (60.0%, 18/30, P<0.01) and the Tween saline group (30.0%, 9/30, P<0.01). Furthermore, histopathological examination in the CA group revealed the disappearance of inflammatory cells and near-normal restoration of tissue structure. The (1,3)-ß-D-glucan detection value in the CA group (860.55 ± 126.73 pg/mL) was significantly lower than that in the fluconazole group (1985.13 ± 203.56 pg/mL, P<0.01) and the Tween saline group (5910.20 ± 320.56 pg/mL, P<0.01). The mouse survival rate reached 90.0% (27/30), higher than the fluconazole group (60.0%, 18/30) and the Tween saline group (30.0%, 9/30), with a significant difference between the two groups (both P<0.01). CONCLUSIONS: CA treatment exhibited significant therapeutic efficacy in mice with systemic C. albicans infection. Therefore, CA holds potential as a novel antifungal agent for targeted treatment of C. albicans infection.

Experimental warming accelerates positive soil priming in a temperate grassland ecosystem.

Unravelling biosphere feedback mechanisms is crucial for predicting the impacts of global warming. Soil priming, an effect of fresh plant-derived carbon (C) on native soil organic carbon (SOC) decomposition, is a key feedback mechanism that could release large amounts of soil C into the atmosphere. However, the impacts of climate warming on soil priming remain elusive. Here, we show that experimental warming accelerates soil priming by 12.7% in a temperate grassland. Warming alters bacterial communities, with 38% of unique active phylotypes detected under warming. The functional genes essential for soil C decomposition are also stimulated, which could be linked to priming effects. We incorporate lab-derived information into an ecosystem model showing that model parameter uncertainty can be reduced by 32-37%. Model simulations from 2010 to 2016 indicate an increase in soil C decomposition under warming, with a 9.1% rise in priming-induced CO2 emissions. If our findings can be generalized to other ecosystems over an extended period of time, soil priming could play an important role in terrestrial C cycle feedbacks and climate change.

Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses.

We developed a microbial-enzyme-mediated decomposition (MEND) model, based on the Michaelis-Menten kinetics, that describes the dynamics of physically defined pools of soil organic matter (SOC). These include particulate, mineral-associated, dissolved organic matter (POC, MOC, and DOC, respectively), microbial biomass, and associated exoenzymes. The ranges and/or distributions of parameters were determined by both analytical steady-state and dynamic analyses with SOC data from the literature. We used an improved multi-objective parameter sensitivity analysis (MOPSA) to identify the most important parameters for the full model: maintenance of microbial biomass, turnover and synthesis of enzymes, and carbon use efficiency (CUE). The model predicted that an increase of 2 degrees C (baseline temperature 12 degrees C) caused the pools of POC-cellulose, MOC, and total SOC to increase with dynamic CUE and decrease with constant CUE, as indicated by the 50% confidence intervals. Regardless of dynamic or constant CUE, the changes in pool size of POC, MOC, and total SOC varied from -8% to 8% under +2 degrees C. The scenario analysis using a single parameter set indicates that higher temperature with dynamic CUE might result in greater net increases in both POC-cellulose and MOC pools. Different dynamics of various SOC pools reflected the catalytic functions of specific enzymes targeting specific substrates and the interactions between microbes, enzymes, and SOC. With the feasible parameter values estimated in this study, models incorporating fundamental principles of microbial-enzyme dynamics can lead to simulation results qualitatively different from traditional models with fast/slow/passive pools.

Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments.

Knowledge about global patterns of the decomposition kinetics of distinct soil organic matter (SOM) pools is crucial to robust estimates of land-atmosphere carbon fluxes under climate change. However, the current Earth system models often adopt globally-consistent reference SOM decomposition rates (kref), ignoring effects from edaphic-climate heterogeneity. Here, we compile a comprehensive set of edaphic-climatic and SOM decomposition data from published incubation experiments and employ machine-learning techniques to develop models capable of predicting the expected sizes and kref of multiple SOM pools (fast, slow, and passive). We show that soil texture dominates the turnover of the fast pools, whereas pH predominantly regulates passive SOM decomposition. This suggests that pH-sensitive bacterial decomposers might have larger effects on stable SOM decomposition than previously believed. Using these predictive models, we provide a 1-km resolution global-scale dataset of the sizes and kref of these SOM pools, which may improve global biogeochemical model parameterization and predictions.

Exploring the competitive dynamic enzyme allocation scheme through enzyme cost minimization.

Enzyme allocation (or synthesis) is a crucial microbial trait that mediates soil biogeochemical cycles and their responses to climate change. However, few microbial ecological models address this trait, particularly concerning multiple enzyme functional groups that regulate complex biogeochemical processes. Here, we aim to fill this gap by developing a COmpetitive Dynamic Enzyme ALlocation (CODEAL) scheme for six enzyme groups that act as indicators of inorganic nitrogen (N) transformations in the Microbial-ENzyme Decomposition (MEND) model. This allocation scheme employs time-variant allocation coefficients for each enzyme group, fostering mutual competition among the multiple groups. We show that the principle of enzyme cost minimization is achieved by using the substrate's saturation level as the factor for enzyme allocation, resulting in an enzyme-efficient pathway with minimal enzyme cost per unit metabolic flux. It suggests that the relative substrate availability affects the trade-off between enzyme production and metabolic flux. Our research has the potential to give insights into the nuanced dynamics of the N cycle and inspire the evolving landscape of enzyme-mediated biogeochemical processes in microbial ecological modeling, which is gaining increasing attention.

Soil temperature, microbial biomass and enzyme activity are the critical factors affecting soil respiration in different soil layers in Ziwuling Mountains, China.

Soil microorganisms are critical biological indicators for evaluating soil health and play a vital role in carbon (C)-climate feedback. In recent years, the accuracy of models in terms of predicting soil C pools has been improved by considering the involvement of microbes in the decomposition process in ecosystem models, but the parameter values of these models have been assumed by researchers without combining observed data with the models and without calibrating the microbial decomposition models. Here, we conducted an observational experiment from April 2021 to July 2022 in the Ziwuling Mountains, Loess Plateau, China, to explore the main influencing factors of soil respiration (RS) and determine which parameters can be incorporated into microbial decomposition models. The results showed that the RS rate is significantly correlated with soil temperature (TS) and moisture (MS), indicating that TS increases soil C loss. We attributed the non-significant correlation between RS and soil microbial biomass carbon (MBC) to variations in microbial use efficiency, which mitigated ecosystem C loss by reducing the ability of microorganisms to decompose organic resources at high temperatures. The structural equation modeling (SEM) results demonstrated that TS, microbial biomass, and enzyme activity are crucial factors affecting soil microbial activity. Our study revealed the relations between TS, microbial biomass, enzyme activity, and RS, which had important scientific implications for constructing microbial decomposition models that predict soil microbial activity under climate change in the future. To better understand the relationship between soil dynamics and C emissions, it will be necessary to incorporate climate data as well as RS and microbial parameters into microbial decomposition models, which will be important for soil conservation and reducing soil C loss in the Loess Plateau.

Ecosystem services from partially harvested riparian buffers can offset biomass production costs.

There is a broad consensus that riparian buffers provide environmental benefits and increase resilience to climate change. In this study, we examined the potential benefits of multi-zone riparian buffers with outer layers planted in perennial crops (i.e., partially harvested buffers). This was accomplished by developing a simplified regional modeling tool, BioVEST, which was applied in the Mid-Atlantic region of the USA. Our analysis revealed that a substantial portion of variable costs to produce biomass for energy can potentially be offset by values provided by ecosystem services from partially harvested riparian buffers. Ecosystem services were monetized and found to represent a substantial fraction (median = ~42%) of variable crop production cost. Simulated water-quality improvements and carbon benefits generally occurred where buffer area was available, but hotspots occurred in different watersheds, suggesting potential trade-offs in decisions about buffer locations. A portion of buffers could be eligible for ecosystem service payments under US government incentive programs. Partially harvested buffers could represent a sustainable and climate-resilient part of multi-functional agricultural landscapes, and one that could become economically viable if farmers are able to reap the value of providing ecosystem services and if logistical challenges are resolved. Our results suggest that payments for ecosystem services can close the gap between what biorefineries are willing to pay and what landowners are willing to accept to grow and harvest perennials along streams.

Mechanisms, clinically curative effects, and antifungal activities of cinnamon oil and pogostemon oil complex against three species of Candida.

The anti-fungus mechanisms and curative effects of cinnamon oil and pogostemon oil complexes towards intestinal Candida infections were investigated. We measured the minimal inhibitory concentration (MIC) values of the complexes against Candida using proportionally-diluted test-tube medium, and examined the evolution of the morphology and structures of Candida albicans using scanning electronic microscopy (SEM) and transmission electronic microscopy (TEM). We found that the average MIC values of the complexes against the fungi were 0.064 mg/mL (cinnamon oil), 0.032 mg/mL (pogostemon oil) for Candida albicans, 0.129 mg/ mL (cinnamon oil), 0.064 mg/mL (pogostemon oil) for Candida tropicalis, and 0.129 mg/mL (cinnamon oil), 0.064 mg/mL (pogostemon oil), for Candida krusei. SEM examination over a 24-48 h period showed that the morphology of Candida albicans cells changed significantly. Irregular hollows appeared on the surfaces, inside organelles were destroyed and the cells burst after treatment. TEM examination over a 48 - 72 h period indicated that the cell walls were damaged, organelles were destroyed and most cytoplasms became empty bubbles. Sixty intestinal Candida-infected patients were treated with a capsule containing cinnamon and pogostemon oil. The curative ratio was 71.67% (43/60), and the improvement ratio was 28.33% (17/ 60), giving a total ratio of 100%. Thus, the cinnamon oil and pogostemon oil complexes had strong anti-fungus effects against Candida albicans, Candida tropicalis, and Candida krusei. They impacted the morphology and sub-micro structures of the fungus within 48 - 72 h, and eventually denatured and killed the cells. The complexes have also shown considerable curative effects to intestinal Candida infections.

Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China.

Quantifying the climatic and anthropogenic effects on hydrological processes has received considerable attention. However, diverse conclusions could be drawn when different models and forcing datasets are used. This is particularly uncertain and challenging in poorly gauged arid regions. Here we aim to tackle this issue in the poorly gauged Xiangride River Basin within the Qaidam Basin, one of the three prominent inland basins in China. We applied two distinct models (Budyko Mezentsev-Choudhurdy-Yang and process-based SWAT) to a poorly-gauged inland basin in West China. The model simulations were driven by four precipitation products including Tropical Rainfall Measuring Mission (TRMM) 3B42 V7, Global Precipitation Measurement (GPM) IMERG V6, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and China Meteorological Assimilation Driving Datasets (CMADS). Our results indicate that MSWEP performed best (NSE = 0.64 vs. 0.36-0.59 for other datasets) in the baseline period (2009-2012), whereas CMADS was more accurate during the impacted period (2013-2016); CMADS and GPM might underestimate the precipitation in the baseline and impacted period, respectively. Hydrological processes during the impacted period are presumed to be influenced by climate variation and/or human activities, compared to the relatively natural status in the baseline period. We conclude that runoff decline between the two periods was mainly affected by human activities (-66 to 94%), whereas the contribution of climate variation was more likely positive. A literature survey reveals that major anthropogenic effects in the study area includes reservoir, road construction and cropland expansion that could lead to runoff decrease. We recommend the use of process-based model (e.g., SWAT) in studies like this, as process-based models driven by high-quality remote-sensed or reanalysis climate datasets, better represents the spatiotemporal hydrological change under altered conditions, whereas the steady-state assumption of soil water for the Budyko model may not be fully satisfied during a short period.

Effect of Cinnamaldehyde on C. albicans cell wall and (1,3)- ß - D-glucans in vivo.

BACKGROUND: The incidence rate of invasive candidiasis is high, its treatment is difficult, and the prognosis is poor. In this study, an immunosuppressive mouse model of invasive Candida albicans (C. albicans) infection was constructed to observe the effects of cinnamaldehyde (CA) on the C. albicans cell wall structure and cell wall (1,3)-ß-D-glucan contents. This study provides a theoretical basis for CA treatment to target invasive C. albicans infection. METHODS: Immunosuppressed mice with invasive C. albicans infection were given an oral dosage of CA (240 mg.kg- 1.d- 1) for 14 days. Then, mouse lung tissue samples were collected for detection of the levels of (1,3)-ß-D-glucan and transmission electron microscopy observations, using fluconazole as a positive control and 2% Tween 80 saline as a negative control. RESULTS: The immunosuppressive mouse model of invasive C. albicans infection was successfully established. The levels of (1,3)-ß-D-glucan in the CA treatment group, fluconazole positive control group, invasive C. albicans infection immunosuppressive mouse model group, and 2% Tween 80 normal saline control group were 86.55 ± 126.73 pg/ml, 1985.13 ± 203.56 pg/ml, 5930.57 ± 398.67 pg/ml and 83.36 ± 26.35 pg/ml, respectively. Statistically, the CA treatment group, fluconazole positive control group and invasive C. albicans infection immunosuppressive mouse model group were compared with each other (P < 0.01) and compared with the 2% Tween 80 saline group (P < 0.01), showing that the differences were very significant. Comparison of the CA treatment group with the fluconazole positive control group (P < 0.05) displayed a difference as well. Electron microscopy showed that CA destroyed the cell wall of C. albicans, where the outer layer of the cell wall fell off and became thinner and the nuclei and organelles dissolved, but the cell membrane remained clear and intact. CONCLUSION: CA destroys the cell wall structure of C. albicans by interfering with the synthesis of (1,3)-ß-D-glucan to kill C. albicans. However, CA does not affect the cell membrane. This study provides a theoretical basis for CA treatment to target invasive C. albicans infection.

Conversion of marginal land into switchgrass conditionally accrues soil carbon but reduces methane consumption.

Switchgrass is a deep-rooted perennial native to the US prairies and an attractive feedstock for bioenergy production; when cultivated on marginal soils it can provide a potential mechanism to sequester and accumulate soil carbon (C). However, the impacts of switchgrass establishment on soil biotic/abiotic properties are poorly understood. Additionally, few studies have reported the effects of switchgrass cultivation on marginal lands that have low soil nutrient quality (N/P) or in areas that have experienced high rates of soil erosion. Here, we report a comparative analyses of soil greenhouse gases (GHG), soil chemistry, and microbial communities in two contrasting soil types (with or without switchgrass) over 17 months (1428 soil samples). These soils are highly eroded, 'Dust Bowl' remnant field sites in southern Oklahoma, USA. Our results revealed that soil C significantly increased at the sandy-loam (SL) site, but not at the clay-loam (CL) site. Significantly higher CO2 flux was observed from the CL switchgrass site, along with reduced microbial diversity (both alpha and beta). Strikingly, methane (CH4) consumption was significantly reduced by an estimated 39 and 47% at the SL and CL switchgrass sites, respectively. Together, our results suggest that soil C stocks and GHG fluxes are distinctly different at highly degraded sites when switchgrass has been cultivated, implying that carbon balance considerations should be accounted for to fully evaluate the sustainability of deep-rooted perennial grass cultivation in marginal lands.

Effect and Safety of Cinnamaldehyde on Immunosuppressed Mice with Invasive Pulmonary Candidiasis.

OBJECTIVE: To evaluate the effect and safety of cinnamaldehyde on immunosuppressed mice with invasive pulmonary candidiasis. METHODS: An immunosuppressed BALB/c mouse model was established by intraperitoneal administration of cyclophosphamide (200 mg/kg) once daily for 2 days. The immunosuppressed mouse with invasive pulmonary candidiasis model was further established by nasal perfusion of Candida albicans suspension. In the cinnamaldehyde treatment group, immunosuppressed mice with invasive pulmonary candidiasis were orally given cinnamaldehyde 240 mg/(kg·d) for 14 consecutive days. Fluconazole and 0.9% saline were used as the positive and negative controls, respectively. The mice in the cinnamaldehyde safety evaluation group were orally administered cinnamaldehyde 480 mg/(kg·d) for 42 days to observe the safety of the drug. Microscopic identification, fungal culture, histopathological examination, and (1,3)-beta-D-glucans detection were conducted to analyze the effect of cinnamaldehyde on C. albicans. RESULTS: The fungal clearance rate in the cinnamaldehyde treatment group was higher than that in the fluconazole control group (80.00% vs. 56.67%, P<0.05). The level of (1,3)-ß-D-glucan in the cinnamaldehyde treatment group was lower than that in the fluconazole positive control group (1160.62 ±89.65 pg/mL vs. 4285.87 ± 215.62 pg/mL, P<0.05). The survival rate of mice in the cinnamaldehyde safety evaluation group was 100%, and no significant pathological changes of kidney, lung and liver were observed. CONCLUSIONS: Cinnamaldehyde was effective and safe in treating immunosuppressed BALB/c mice with invasive pulmonary candidiasis. It would be a potentially novel drug for anti-candidiasis infection.

Multi-year incubation experiments boost confidence in model projections of long-term soil carbon dynamics.

Global soil organic carbon (SOC) stocks may decline with a warmer climate. However, model projections of changes in SOC due to climate warming depend on microbially-driven processes that are usually parameterized based on laboratory incubations. To assess how lab-scale incubation datasets inform model projections over decades, we optimized five microbially-relevant parameters in the Microbial-ENzyme Decomposition (MEND) model using 16 short-term glucose (6-day), 16 short-term cellulose (30-day) and 16 long-term cellulose (729-day) incubation datasets with soils from forests and grasslands across contrasting soil types. Our analysis identified consistently higher parameter estimates given the short-term versus long-term datasets. Implementing the short-term and long-term parameters, respectively, resulted in SOC loss (-8.2 ± 5.1% or -3.9 ± 2.8%), and minor SOC gain (1.8 ± 1.0%) in response to 5 °C warming, while only the latter is consistent with a meta-analysis of 149 field warming observations (1.6 ± 4.0%). Comparing multiple subsets of cellulose incubations (i.e., 6, 30, 90, 180, 360, 480 and 729-day) revealed comparable projections to the observed long-term SOC changes under warming only on 480- and 729-day. Integrating multi-year datasets of soil incubations (e.g., > 1.5 years) with microbial models can thus achieve more reasonable parameterization of key microbial processes and subsequently boost the accuracy and confidence of long-term SOC projections.