Microbial carbon use efficiency promotes global soil carbon storage.

Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5-7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8-11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

Convergence in simulating global soil organic carbon by structurally different models after data assimilation.

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein.

INTRODUCTION AND OBJECTIVES: The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning (ML) technology with a demographic, medical history, and noninvasive biomarker data. These models can enhance the decision-making capabilities of physicians for HCC in hepatitis B virus (HBV)-related cirrhosis patients with low serum alpha-fetoprotein (AFP) levels. PATIENTS AND METHODS: A total of 6,980 patients treated between January 2012 and December 2018 were included. Pre-treatment laboratory tests and clinical data were obtained. The significant risk factors for HCC were identified, and the relative risk of each variable affecting its diagnosis was calculated using ML and univariate regression analysis. The data set was then randomly partitioned into validation (20 %) and training sets (80 %) to develop the ML models. RESULTS: Twelve independent risk factors for HCC were identified using Gaussian naïve Bayes, extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and selection operation regression models. Multivariate analysis revealed that male sex, age >60 years, alkaline phosphate >150 U/L, AFP >25 ng/mL, carcinoembryonic antigen >5 ng/mL, and fibrinogen >4 g/L were the risk factors, whereas hypertension, calcium <2.25 mmol/L, potassium ≤3.5 mmol/L, direct bilirubin >6.8 µmol/L, hemoglobin <110 g/L, and glutamic-pyruvic transaminase >40 U/L were the protective factors in HCC patients. Based on these factors, a nomogram was constructed, showing an area under the curve (AUC) of 0.746 (sensitivity = 0.710, specificity=0.646), which was significantly higher than AFP AUC of 0.658 (sensitivity = 0.462, specificity=0.766). Compared with several ML algorithms, the XGBoost model had an AUC of 0.832 (sensitivity = 0.745, specificity=0.766) and an independent validation AUC of 0.829 (sensitivity = 0.766, specificity = 0.737), making it the top-performing model in both sets. The external validation results have proven the accuracy of the XGBoost model. CONCLUSIONS: The proposed XGBoost demonstrated a promising ability for individualized prediction of HCC in HBV-related cirrhosis patients with low-level AFP.

Unraveling the Contribution of MulSOS2 in Conferring Salinity Tolerance in Mulberry (Morus atropurpurea Roxb).

Salinity is one of the most serious threats to sustainable agriculture. The Salt Overly Sensitive (SOS) signaling pathway plays an important role in salinity tolerance in plants, and the SOS2 gene plays a critical role in this pathway. Mulberry not only has important economic value but also is an important ecological tree species; however, the roles of the SOS2 gene associated with salt stress have not been reported in mulberry. To gain insight into the response of mulberry to salt stress, SOS2 (designated MulSOS2) was cloned from mulberry (Morus atropurpurea Roxb), and sequence analysis of the amino acids of MulSOS2 showed that it shares some conserved domains with its homologs from other plant species. Our data showed that the MulSOS2 gene was expressed at different levels in different tissues of mulberry, and its expression was induced substantially not only by NaCl but also by ABA. In addition, MulSOS2 was exogenously expressed in Arabidopsis, and the results showed that under salt stress, transgenic MulSOS2 plants accumulated more proline and less malondialdehyde than the wild-type plants and exhibited increased tolerance to salt stress. Moreover, the MulSOS2 gene was transiently overexpressed in mulberry leaves and stably overexpressed in the hairy roots, and similar results were obtained for resistance to salt stress in transgenic mulberry plants. Taken together, the results of this study are helpful to further explore the function of the MulSOS2 gene, which provides a valuable gene for the genetic breeding of salt tolerance in mulberry.

[Identification and expression pattern analysis of Trihelix transcription factor family genes of ginseng].

The function of the Trihelix transcription factor is that it plays an important role in many abiotic stresses, especially in the signaling pathway of low temperature, drought, flood, saline, abscisic acid, methyl jasmonate, and other abiotic stresses. However, there are few studies on the Trihelix gene family of ginseng. In this study, 41 Trihelix gene family members were identified and screened from the ginseng genome database, and their physicochemical properties, cis-acting elements, subcellular localization, chromosomal assignment, and abiotic stress-induced expression patterns were analyzed by bioinformatics methods. The results showed that 85% of Trihelix family members of ginseng were located in the nucleus, and the main secondary structure of Trihelix protein was random coil and α helix. In the promoter region of Trihelix, cis-acting regulatory elements related to various abiotic stresses such as low temperature, hormone response, and growth and development were identified. Through the collinearity analysis of interspecific Trihelix transcription factors of model plants Arabidopsis thaliana and ginseng, 19 collinear gene pairs were found between A. thaliana and ginseng, and no collinear gene pairs existed on chromosomes 3, 6, and 12 only. qRT-PCR analysis showed that the expression of GWHGBEIJ010320.1 was significantly up-regulated under low temperature stress, a significant response to low temperature stress. This study lays a foundation for further research on the role of the Trihelix transcription factor of ginseng in abiotic stress, as well as the growth and development of ginseng.

Deep learning classification of uveal melanoma based on histopathological images and identification of a novel indicator for prognosis of patients.

BACKGROUND: Deep learning has been extensively used in digital histopathology. The purpose of this study was to test deep learning (DL) algorithms for predicting the vital status of whole-slide image (WSI) of uveal melanoma (UM). METHODS: We developed a deep learning model (Google-net) to predict the vital status of UM patients from histopathological images in TCGA-UVM cohort and validated it in an internal cohort. The histopathological DL features extracted from the model and then were applied to classify UM patients into two subtypes. The differences between two subtypes in clinical outcomes, tumor mutation, and microenvironment, and probability of drug therapeutic response were investigated further. RESULTS: We observed that the developed DL model can achieve a high accuracy of > = 90% for patches and WSIs prediction. Using 14 histopathological DL features, we successfully classified UM patients into Cluster1 and Cluster2 subtypes. Compared to Cluster2, patients in the Cluster1 subtype have a poor survival outcome, increased expression levels of immune-checkpoint genes, higher immune-infiltration of CD8 + T cell and CD4 + T cells, and more sensitivity to anti-PD-1 therapy. Besides, we established and verified prognostic histopathological DL-signature and gene-signature which outperformed the traditional clinical features. Finally, a well-performed nomogram combining the DL-signature and gene-signature was constructed to predict the mortality of UM patients. CONCLUSIONS: Our findings suggest that DL model can accurately predict vital status in UM patents just using histopathological images. We found out two subgroups based on histopathological DL features, which may in favor of immunotherapy and chemotherapy. Finally, a well-performing nomogram that combines DL-signature and gene-signature was constructed to give a more straightforward and reliable prognosis for UM patients in treatment and management.

Voltage-dependent anion channel proteins associate with dynamic Bamboo mosaic virus-induced complexes.

Infection cycles of viruses are highly dependent on membrane-associated host factors. To uncover the infection cycle of Bamboo mosaic virus (BaMV) in detail, we purified the membrane-associated viral complexes from infected Nicotiana benthamiana plants and analyzed the involved host factors. Four isoforms of voltage-dependent anion channel (VDAC) proteins on the outer membrane of mitochondria were identified due to their upregulated expression in the BaMV complex-enriched membranous fraction. Results from loss- and gain-of-function experiments indicated that NbVDAC2, -3, and -4 are essential for efficient BaMV accumulation. During BaMV infection, all NbVDACs concentrated into larger aggregates, which overlapped and trafficked with BaMV virions to the structure designated as the "dynamic BaMV-induced complex." Besides the endoplasmic reticulum and mitochondria, BaMV replicase and double-stranded RNAs were also found in this complex, suggesting the dynamic BaMV-induced complex is a replication complex. Yeast two-hybrid and pull-down assays confirmed that BaMV triple gene block protein 1 (TGBp1) could interact with NbVDACs. Confocal microscopy revealed that TGBp1 is sufficient to induce NbVDAC aggregates, which suggests that TGBp1 may play a pivotal role in the NbVDAC-virion complex. Collectively, these findings indicate that NbVDACs may associate with the dynamic BaMV-induced complex via TGBp1 and NbVDAC2, -3, or -4 and can promote BaMV accumulation. This study reveals the involvement of mitochondrial proteins in a viral complex and virus infection.

Significant effects of precipitation frequency on soil respiration and its components-A global synthesis.

Global warming intensifies the hydrological cycle, which results in changes in precipitation regime (frequency and amount), and will likely have significant impacts on soil respiration (Rs ). Although the responses of Rs to changes in precipitation amount have been extensively studied, there is little consensus on how Rs will be affected by changes in precipitation frequency (PF) across the globe. Here, we synthesized the field observations from 296 published papers to quantify the effects of PF on Rs and its components using meta-analysis. Our results indicated that the effects of PF on Rs decreased with an increase in background mean annual precipitation. When the data were grouped by climate conditions, increased PF showed positive effects on Rs under the arid condition but not under the semi-humid or humid conditions, whereas decreased PF suppressed Rs across all the climate conditions. The positive effects of increased PF mainly resulted from the positive response of heterotrophic respiration under the arid condition while the negative effects of decreased PF were mainly attributed to the reductions in root biomass and respiration. Overall, our global synthesis provided for the first time a comprehensive analysis of the divergent effects of PF on Rs and its components across climate regions. This study also provided a framework for understanding and modeling responses of ecosystem carbon cycling to global precipitation change.

Machine learning for accelerating process-based computation of land biogeochemical cycles.

Global change ecology nowadays embraces ever-growing large observational datasets (big-data) and complex mathematical models that track hundreds of ecological processes (big-model). The rapid advancement of the big-data-big-model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time-scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here, we introduce a machine-learning acceleration (MLA) tool to tackle this grand challenge. We focus on the most resource-consuming step in terrestrial biosphere models (TBMs): the equilibration of biogeochemical cycles (spin-up), a prerequisite that can take up to 98% of the computational time. Through three members of the ORCHIDEE TBM family part of the IPSL Earth System Model, including versions that describe the complex interactions between nitrogen, phosphorus and carbon that do not have any analytical solution for the spin-up, we show that an unoptimized MLA reduced the computation demand by 77%-80% for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels. Despite small biases in the MLA-derived equilibrium, the resulting impact on the predicted regional carbon balance over recent decades is minor. We expect a one-order of magnitude lower computation demand by optimizing the choices of machine learning algorithms, their settings, and balancing the trade-off between quality of MLA predictions and need for TBM simulations for training data generation and bias reduction. Our tool is agnostic to gridded models (beyond TBMs), compatible with existing spin-up acceleration procedures, and opens the door to a wide variety of future applications, with complex non-linear models benefit most from the computational efficiency.

Compensatory water effects link yearly global land CO2 sink changes to temperature.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales. Here we use empirical models based on eddy covariance data and process-based models to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance. Our study indicates that spatial climate covariation drives the global carbon cycle response.

[A rare case of neonatal-onset hepatic sinusoidal obstruction syndrome]. / æ–°ç”Ÿå„¿æœŸèµ·ç— çš„ç½•è§è‚çª¦é˜»å¡žç»¼åˆå¾1例.

A male infant, aged 1 month and 14 days, was admitted to the hospital due to abdominal distension lasting for 2 weeks and worsening for 3 days. The infant had a history of omphalitis. Physical examination revealed severe abdominal distension, prominent abdominal wall veins, hepatosplenomegaly, and massive ascites. There was a slight elevation in liver transaminase levels. Liver ultrasound and CT scans demonstrated the absence of visualization of the intrahepatic segment of the portal vein and the left, middle, and right veins of the liver, indicating occlusion of these vessels, along with surrounding fibrous hyperplasia. The clinical diagnosis was hepatic sinusoidal obstruction syndrome resulting from omphalitis. A large amount of bloody ascites developed after 12 days of hospitalization, resulting in hypovolemic shock and respiratory failure. The infant passed away following the family's decision to discontinue treatment. This article focuses on the diagnostic approach and multidisciplinary management of neonatal-onset hepatic sinusoidal obstruction syndrome, as well as provides insights into the differential diagnosis of hepatomegaly and ascites.

LncRNA ROR modulates myocardial ischemia-reperfusion injury mediated by the miR-185-5p/CDK6 axis.

LncRNAs and miRNAs are correlated with the pathogenesis of myocardial ischemia-reperfusion injury (MIRI). Whether lncRNA ROR or miR-185-5p plays a crucial role in MIRI is still unclear. In in-vitro, human cardiac myocytes (HCMs) were treated with hypoxia/reoxygenation (H/R). Wistar rats were used to set up an in-vitro I/R model by means of recanalization after ligation. Evaluation of the myocardial injury marker lactate dehydrogenase (LDH) in HCMs cells was performed. The expression of miR-185-5p and ROR, IL-1ß, and IL-18 were detected by qRT-PCR. ELISA was also performed to evaluate the secretion of IL-1ß and IL-18. Western blotting was carried out to determine CDK6, NLRP3, GSDMD-N, ASC, and cleaved-caspase1 protein expression. The relationship between miR-185-5p and CDK6 or ROR was confirmed by a dual-luciferase reporter assay. Our findings revealed that H/R treated HCMs showed a significantly decreased miR-185-5p expression and increased expression of CDK6 and ROR. ROR knockdown reduced H/R induced pyroptosis and inflammation, while knockdown of miR-185-5p accelerated the effect. Furthermore, miR-185-5p was negatively regulated and absorbed by ROR in HCMs. Overexpression of miR-185-5p reversed the H/R-induced cell pyroptosis and upregulation of LDH, IL-1ß, and IL-18. In HCMs, miR-185-5p was also negatively regulated and related to CDK6 expression. Moreover, overexpression of CDK6 significantly inhibited the effects of miR-185-5p mimics on the inflammatory response and pyroptosis of HCMs. Knockdown of ROR alleviated H/R-induced myocardial injury by elevating miR-185-5p and inhibiting CDK6 expression. Taken together, our results show that the ROR/miR-185-5p/CDK6 axis modulates cell pyroptosis induced by H/R and the inflammatory response of HCMs.

The gibberellic acid derived from the plastidial MEP pathway is involved in the accumulation of Bamboo mosaic virus.

A gene upregulated in Nicotiana benthamiana after Bamboo mosaic virus (BaMV) infection was revealed as 1-deoxy-d-xylulose-5-phosphate reductoisomerase (NbDXR). DXR is the key enzyme in the 2-C-methyl-d-erythritol-4-phosphate (MEP) pathway that catalyzes the conversion of 1-deoxy-d-xylulose 5-phosphate to 2-C-methyl-d-erythritol-4-phosphate. Knockdown and overexpression of NbDXR followed by BaMV inoculation revealed that NbDXR is involved in BaMV accumulation. Treating leaves with fosmidomycin, an inhibitor of DXR function, reduced BaMV accumulation. Subcellular localization confirmed that DXR is a chloroplast-localized protein by confocal microscopy. Furthermore, knockdown of 1-hydroxy-2-methyl-2-(E)-butenyl-4-diphosphate reductase, one of the enzymes in the MEP pathway, also reduced BaMV accumulation. The accumulation of BaMV increased significantly in protoplasts treated with isopentenyl pyrophosphate. Thus, the metabolites of the MEP pathway could be involved in BaMV infection. To identify the critical components involved in BaMV accumulation, we knocked down the crucial enzyme of isoprenoid synthesis, NbGGPPS11 or NbGGPPS2. Only NbGGPPS2 was involved in BaMV infection. The geranylgeranyl pyrophosphate (GGPP) synthesized by NbGGPPS2 is known for gibberellin synthesis. We confirmed this result by supplying gibberellic acid exogenously on leaves, which increased BaMV accumulation. The de novo synthesis of gibberellic acid could assist BaMV accumulation.

Nonlinear interactions of land carbon cycle feedbacks in Earth System Models.

Carbon cycle feedbacks were often quantified through the carbon-concentration and carbon-climate feedbacks with the assumption of no significant interaction between the two feedbacks in most previous studies. Here we calculated the strength of the interactions between the two responses using simulations of models participated in the phase 6 of the Coupled Model Intercomparison Project (CMIP6). We found that the nonlinear interaction contributed 11% of the land-atmosphere carbon exchange on average with large intermodel variation (from -20% to +162%). This nonlinear interaction is largely driven by the pattern of net primary production (NPP), with shifts in heterotrophic respiration that dampen the overall positive interactions from NPP. Photosynthetic rate per unit leaf area alone cannot adequately explain a wide variation of interactions in global NPP simulated by CMIP6 models. Plant respiration and processes that regulate leaf area are also important contributors to the interactions. Dominant factors that induce carbon-concentration and carbon-climate interactions are highly variable among models. One of those dominant factors is nutrient limitation. Using additional simulations of ACCESS-ESM1.5 that include both nitrogen and phosphorus limitation, we found that the estimated interactions by ACCESS-ESM1.5 with or without nutrient limitations covered the large intermodel variations among the CMIP6 models. It remains largely unknown how nutrient limitation complicates ecosystem's responses to simultaneously CO2 fertilization and warming at the global scale. Our modeling results point to a potential important role of nutrients, especially phosphorus on the nonlinear interactions. Yet, more studies are needed on ecosystem responses to concurrent changes in nutrient availability, atmospheric CO2 concentration, and warming.

Mycorrhizal fungi alleviate acidification-induced phosphorus limitation: Evidence from a decade-long field experiment of simulated acid deposition in a tropical forest in south China.

South China has been experiencing very high rate of acid deposition and severe soil acidification in recent decades, which has been proposed to exacerbate the regional ecosystem phosphorus (P) limitation. We conducted a 10-year field experiment of simulated acid deposition to examine how acidification impacts seasonal changes of different soil P fractions in a tropical forest with highly acidic soils in south China. As expected, acid addition significantly increased occluded P pool but reduced the other more labile P pools in the dry season. In the wet season, however, acid addition did not change microbial P, soluble P and labile organic P pools. Acid addition significantly increased exchangeable Al3+ and Fe3+ and the activation of Fe oxides in both seasons. Different from the decline of microbial abundance in the dry season, acid addition increased ectomycorrhizal fungi and its ratio to arbuscular mycorrhiza fungi in the wet season, which significantly stimulated phosphomonoesterase activities and likely promoted the dissolution of occluded P. Our results suggest that, even in already highly acidic soils, the acidification-induced P limitation could be alleviated by stimulating ectomycorrhizal fungi and phosphomonoesterase activities. The differential responses and microbial controls of seasonal soil P transformation revealed here should be implemented into ecosystem biogeochemical model for predicting plant productivity under future acid deposition scenarios.

Tropical tall forests are more sensitive and vulnerable to drought than short forests.

Our limited understanding of the impacts of drought on tropical forests significantly impedes our ability in accurately predicting the impacts of climate change on this biome. Here, we investigated the impact of drought on the dynamics of forest canopies with different heights using time-series records of remotely sensed Ku-band vegetation optical depth (Ku-VOD), a proxy of top-canopy foliar mass and water content, and separated the signal of Ku-VOD changes into drought-induced reductions and subsequent non-drought gains. Both drought-induced reductions and non-drought increases in Ku-VOD varied significantly with canopy height. Taller tropical forests experienced greater relative Ku-VOD reductions during drought and larger non-drought increases than shorter forests, but the net effect of drought was more negative in the taller forests. Meta-analysis of in situ hydraulic traits supports the hypothesis that taller tropical forests are more vulnerable to drought stress due to smaller xylem-transport safety margins. Additionally, Ku-VOD of taller forests showed larger reductions due to increased atmospheric dryness, as assessed by vapor pressure deficit, and showed larger gains in response to enhanced water supply than shorter forests. Including the height-dependent variation of hydraulic transport in ecosystem models will improve the simulated response of tropical forests to drought.

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network.

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.

Toward a Global Model for Soil Inorganic Phosphorus Dynamics: Dependence of Exchange Kinetics and Soil Bioavailability on Soil Physicochemical Properties.

The representation of phosphorus (P) cycling in global land models remains quite simplistic, particularly on soil inorganic phosphorus. For example, sorption and desorption remain unresolved and their dependence on soil physical and chemical properties is ignored. Empirical parameter values are usually based on expert knowledge or data from few sites with debatable global representativeness in most global land models. To overcome these issues, we compiled from data of inorganic soil P fractions and calculated the fraction of added P remaining in soil solution over time of 147 soil samples to optimize three parameters in a model of soil inorganic P dynamics. The calibrated model performed well (r 2 > 0.7 for 122 soil samples). Model parameters vary by several orders of magnitude, and correlate with soil P fractions of different inorganic pools, soil organic carbon and oxalate extractable metal oxide concentrations among the soil samples. The modeled bioavailability of soil P depends on, not only, the desorption rates of labile and sorbed pool, inorganic phosphorus fractions, the slope of P sorbed against solution P concentration, but also on the ability of biological uptake to deplete solution P concentration and the time scale. The model together with the empirical relationships of model parameters on soil properties can be used to quantify bioavailability of soil inorganic P on various timescale especially when coupled within global land models.

Multiresidue screening of pesticides in Panax Ginseng C. A. Meyer by ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry.

In this study, an efficient screening method based on a modified quick, easy, cheap, effective, rugged, and safe extraction method combined with ultra-high-performance liquid chromatography coupled to tandem quadrupole time-of-flight mass spectrometry was established for the determination of 90 pesticides residues in Panax Ginseng. The accuracy of the method was then verified by analyzing the false positive rate and the screening detection limit in Ginseng. The results revealed that the screening detection limit of 33 of 90 pesticide residues were 0.01 mg·kg-1 , 22 species were 0.05 mg·kg-1 , 11 species were 0.10 mg·kg-1 , 8 species were 0.20 mg·kg-1 , and another 16 species were greater than 0.20 mg·kg-1 . A total of 73 pesticides were ultimately suitable to be practically applied for rapid analysis of pesticide residues in Ginseng. Finally, the established method was used to analyze the pesticide residues in 35 Ginseng samples available on the market. And the residual of dimethomorph, azoxystrobin, tebuconazole, and pyraclostrobin was relatively severe in Ginseng samples. This work expanded the range of pesticides detected and provided a rapid, effective method for pesticides screening in Ginseng.