Plexin D1 mediates disturbed flow-induced M1 macrophage polarization in atherosclerosis.

Atherosclerosis preferentially develops at bifurcations exposed to disturbed flow. Plexin D1 (PLXND1) responds to mechanical forces and drives macrophage accumulation in atherosclerosis. Here, multiple strategies were used to identify the role of PLXND1 in site-specific atherosclerosis. Using computational fluid dynamics and three-dimensional light-sheet fluorescence-microscopy, the elevated PLXND1 in M1 macrophages was mainly distributed in disturbed flow area of ApoE-/- carotid bifurcation lesions, and visualization of atherosclerosis in vivo was achieved by targeting PLXND1. Subsequently, to simulate the microenvironment of bifurcation lesions in vitro, we co-cultured oxidized low-density lipoprotein (oxLDL)-treated THP-1-derived macrophages with shear-treated human umbilical vein endothelial cells (HUVECs). We found that oscillatory shear induced the increase of PLXND1 in M1 macrophages, and knocking down PLXND1 inhibited M1 polarization. Semaphorin 3E, the ligand of PLXND1 which was highly expressed in plaques, strongly enhanced M1 macrophage polarization via PLXND1 in vitro. Our findings provide insights into pathogenesis in site-specific atherosclerosis that PLXND1 mediates disturbed flow-induced M1 macrophage polarization.

SGLT2 inhibitor dapagliflozin alleviates intramyocardial hemorrhage and adverse ventricular remodeling via suppressing hepcidin in myocardial ischemia-reperfusion injury.

Intramyocardial hemorrhage (IMH), a reperfusion therapy-associated complication, is the extravasation of red blood cells caused by severe microvascular injury. IMH is an independent predictor of adverse ventricular remodeling (AVR) after acute myocardial infarction (AMI). Hepcidin, a major regulator of iron uptake and systemic distribution, is a key factor affecting AVR. However, the role of cardiac hepcidin in the development of IMH has not been completely elucidated. This study aimed to explore if sodium-dependent glucose co-transporter 2 inhibitor (SGLT2i) exerts therapeutic effects on IMH and AVR by suppressing hepcidin and to elucidate the underlying mechanisms. SGLT2i alleviated IMH and AVR in the ischemia-reperfusion injury (IRI) mouse model. Additionally, SGLT2i downregulated the cardiac levels of hepcidin in IRI mice, suppressed M1-type macrophage polarization, and promoted M2-type macrophage polarization. The effects of hepcidin knockdown on macrophage polarization were similar to those of SGLT2i in RAW264.7 cells. SGLT2i treatment or hepcidin knockdown inhibited the expression of MMP9, an inducer of IMH and AVR, in RAW264.7 cells. Regulation of macrophage polarization and reduction of MMP9 expression by SGLT2i and hepcidin knockdown is achieved through activation of pSTAT3. In conclusion, this study demonstrated that SGLT2i alleviated IMH and AVR by regulating macrophage polarization. The potential mechanism through which SGLT2i exerted its therapeutic effect seems to involve the downregulation of MMP9 via the hepcidin-STAT3 pathway.

The novel Nsp9-interacting host factor H2BE promotes PEDV replication by inhibiting endoplasmic reticulum stress-mediated apoptosis.

Porcine epidemic diarrhoea (PED) caused by porcine epidemic diarrhoea virus (PEDV) has led to significant economic losses in the swine industry worldwide. Histone Cluster 2, H2BE (HIST2H2BE), the main protein component in chromatin, has been proposed to play a key role in apoptosis. However, the relationship between H2BE and PEDV remains unclear. In this study, H2BE was shown to bind and interact with PEDV nonstructural protein 9 (Nsp9) via immunoprecipitation-mass spectrometry (IP-MS). Next, we verified the interaction of Nsp9 with H2BE by immunoprecipitation and immunofluorescence. H2BE colocalized with Nsp9 in the cytoplasm and nuclei. PEDV Nsp9 upregulated the expression of H2BE by inhibiting the expression of IRX1. We demonstrated that overexpression of H2BE significantly promoted PEDV replication, whereas knockdown of H2BE by small interfering RNA (siRNA) inhibited PEDV replication. Overexpression of H2BE led to significantly inhibited GRP78 expression, phosphorylated PERK (p-PERK), phosphorylated eIF2 (p-eIF2), phosphorylated IRE1 (p-IRE1), and phosphorylated JNK (p-JNK); negatively regulated CHOP and Bax expression and caspase-9 and caspase-3 cleavage; and promoted Bcl-2 production. Knocking down H2BE exerted the opposite effects. Furthermore, we found that after deletion of amino acids 1-28, H2BE did not promote PEDV replication. In conclusion, these studies revealed the mechanism by which H2BE is associated with ER stress-mediated apoptosis to regulate PEDV replication. Nsp9 upregulates H2BE. H2BE plays a role in inhibiting apoptosis and thus facilitating viral replication, which depends on the N-terminal region of H2BE (amino acids 1-28). These findings provide a reference for host-PEDV interactions and offer the possibility for developing strategies for PEDV decontamination and prevention.

Heterogeneous Nuclear Protein U Degraded the m6A Methylated TRAF3 Transcript by YTHDF2 To Promote Porcine Epidemic Diarrhea Virus Replication.

Porcine epidemic diarrhea virus (PEDV) belongs to the genus Alphacoronavirus of the Coronaviridae family and can cause fatal watery diarrhea in piglets, causing significant economic losses. Heterogeneous nuclear protein U (HNRNPU) is a novel RNA sensor involved in sensing viral RNA in the nucleus and mediating antiviral immunity. However, it remains elusive whether and how cytoplasmic PEDV can be sensed by the RNA sensor HNRNPU. In this study we determined that HNRNPU was the binding partner of Nsp13 by immunoprecipitation-liquid chromatography-tandem mass spectrometry (IP/LC-MS/MS) analysis. The interaction between Nsp13 and HNRNPU was demonstrated by using coimmunoprecipitation and confocal immunofluorescence. Next, we identified that HNRNPU expression is significantly increased during PEDV infection, whereas the transcription factor hepatocyte nuclear factor 1α (HNF1A) could negatively regulate HNRNPU expression. HNRNPU was retained in the cytoplasm by interaction with PEDV Nsp13. We found that HNRNPU overexpression effectively facilitated PEDV replication, while knockdown of HNRNPU impaired viral replication, suggesting a promoting function of HNRNPU to PEDV infection. Additionally, HNRNPU was found to promote PEDV replication by affecting TRAF3 degradation at the transcriptional level to inhibit PEDV-induced beta interferon (IFN-ß) production. Mechanistically, HNRNPU downregulates TRAF3 mRNA levels via the METTL3-METTL14/YTHDF2 axis and regulates immune responses through YTHDF2-dependent mRNA decay. Together, our findings reveal that HNRNPU serves as a negative regulator of innate immunity by degrading TRAF3 mRNA in a YTHDF2-dependent manner and consequently facilitating PEDV propagation. Our findings provide new insights into the immune escape of PEDV. IMPORTANCE PEDV, a highly infectious enteric coronavirus, has spread rapidly worldwide and caused severe economic losses. During virus infection, the host regulates innate immunity to inhibit virus infection. However, PEDV has evolved a variety of different strategies to suppress host IFN-mediated antiviral responses. Here, we identified that HNRNPU interacted with viral protein Nsp13. HNRNPU protein expression was upregulated, and the transcription factor HNF1A could negatively regulate HNRNPU expression during PEDV infection. HNRNPU also downregulated TRAF3 mRNA through the METTL3-METTL14/YTHDF2 axis to inhibit the production of IFN-ß and downstream antiviral genes in PEDV-infected cells, thereby promoting viral replication. Our findings reveal a new mechanism with which PEDV suppresses the host antiviral response.

TS-Net: Two-stage deformable medical image registration network based on new smooth constraints.

Medical image registration can establish the spatial consistency of the corresponding anatomical structures between different medical images, which is important in medical image analysis. In recent years, with the rapid development of deep learning, the image registration methods based on deep learning greatly improve the speed, accuracy, and robustness of registration. Regrettably, these methods typically do not work well for large deformations and complex deformations in the image, and neglect to preserve the topological properties of the image during deformation. Aiming at these problems, we propose a new network TS-Net that learns deformation from coarse to fine and transmits information of different scales in the two stages. Two-stage network learning deformation from coarse to fine can gradually learn the large and complex deformations in images. In the second stage, the feature maps downsampled in the first stage for skip connection can expand the local receptive field and obtain more local information. The smooth constraints function used in the past is to impose the same restriction on the global, which is not targeted. In this paper, we propose a new smooth constraints function for each voxel deformation, which can better ensure the smoothness of the transformation and maintain the topological properties of the image. The experiments on brain datasets with complex deformations and heart datasets with large deformations show that our proposed method achieves better results while maintaining the topological properties of deformations compared to existing deep learning-based registration methods.

A ViT-AMC Network With Adaptive Model Fusion and Multiobjective Optimization for Interpretable Laryngeal Tumor Grading From Histopathological Images.

The tumor grading of laryngeal cancer pathological images needs to be accurate and interpretable. The deep learning model based on the attention mechanism-integrated convolution (AMC) block has good inductive bias capability but poor interpretability, whereas the deep learning model based on the vision transformer (ViT) block has good interpretability but weak inductive bias ability. Therefore, we propose an end-to-end ViT-AMC network (ViT-AMCNet) with adaptive model fusion and multiobjective optimization that integrates and fuses the ViT and AMC blocks. However, existing model fusion methods often have negative fusion: 1). There is no guarantee that the ViT and AMC blocks will simultaneously have good feature representation capability. 2). The difference in feature representations learning between the ViT and AMC blocks is not obvious, so there is much redundant information in the two feature representations. Accordingly, we first prove the feasibility of fusing the ViT and AMC blocks based on Hoeffding's inequality. Then, we propose a multiobjective optimization method to solve the problem that ViT and AMC blocks cannot simultaneously have good feature representation. Finally, an adaptive model fusion method integrating the metrics block and the fusion block is proposed to increase the differences between feature representations and improve the deredundancy capability. Our methods improve the fusion ability of ViT-AMCNet, and experimental results demonstrate that ViT-AMCNet significantly outperforms state-of-the-art methods. Importantly, the visualized interpretive maps are closer to the region of interest of concern by pathologists, and the generalization ability is also excellent. Our code is publicly available at https://github.com/Baron-Huang/ViT-AMCNet.

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE-MRI Radiomics Nomogram.

Objective: To investigate the value of preoperative prediction of breast cancer axillary lymph node metastasis based on intratumoral and peritumoral dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) radiomics nomogram. Material and Methods. In this study, a radiomics model was developed based on a training cohort involving 250 patients with breast cancer (BC) who had undergone axillary lymph node (ALN) dissection between June 2019 and January 2021. The intratumoral and peritumoral radiomics features were extracted from the second postcontrast images of DCE-MRI. Based on filtered radiomics features, the radiomics signature was built by using the least absolute shrinkage and selection operator method. The Support Vector Machines (SVM) learning algorithm was used to construct intratumoral, periatumoral, and intratumoral combined periatumoral models for predicting axillary lymph node metastasis (ALNM) in BC. Nomogram performance was determined by its discrimination, calibration, and clinical value. Multivariable logistic regression was adopted to establish a radiomics nomogram. Results: The intratumoral combined peritumoral radiomics signature, which was composed of fifteen ALN status-related features, showed the best predictive performance and was associated with ALNM in both the training and validation cohorts (P < 0.001). The prediction efficiency of the intratumoral combined peritumoral radiomics model was higher than that of the intratumoral radiomics model and the peritumoral radiomics model. The AUCs of the training and verification cohorts were 0.867 and 0.785, respectively. The radiomics nomogram, which incorporated the radiomics signature, MR-reported ALN status, and MR-reported maximum diameter of the lesion, showed good calibration and discrimination in the training (AUC = 0.872) and validation cohorts (AUC = 0.863). Conclusion: The intratumoral combined peritumoral radiomics model derived from DCE-MRI showed great predictive value for ALNM and may help to improve clinical decision-making for BC.

Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer.

Breast cancer is a malignant tumor with the highest incidence rate in women. Current diagnostic methods are time-consuming, costly, and dependent on physician experience. In this study, we used serum Raman spectroscopy combined with multiple classification algorithms to implement an auxiliary diagnosis method for breast cancer, which will help in the early diagnosis of breast cancer patients. We analyzed the serum Raman spectra of 171 invasive ductal carcinoma (IDC) and 100 healthy volunteers; The analysis showed differences in nucleic acids, carotenoids, amino acids, and lipid concentrations in their blood. These differences provide a theoretical basis for this experiment. First, we used adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky-Golay (SG) for baseline correction and smoothing denoising to remove the effect of noise on the experiment. Then, the Principal component analysis (PCA) algorithm was used to extract features. Finally, we built four classification models: support vector machine (SVM), decision tree (DT), linear discriminant analysis (LDA), and Neural Network Language Model (NNLM). The LDA, SVM, and NNLM achieve 100% accuracy. As supplementary, we added the classification experiment of the raw data. By comparing the experimental results of the two groups, We concluded that the NNLM was the best model. The results show the reliability of the combination of serum Raman spectroscopy and classification models under large sample conditions.

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism.

PURPOSE: Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. METHODS: Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. RESULTS: Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. CONCLUSIONS: This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

Influence of the Cavity Structure in the Excavation Roadway on the Gas Explosion Characteristics.

Gas explosion accidents are one of the most severe coal mine disasters. Usually, they can cause considerable property losses and casualties, which seriously restrict the development of the coal mining industry. This study used Ansys/Fluent software to simulate gas explosions in excavation roadways with different cavity structures, and 11 models with different cavity structures were established. The study results show that the propagation law of gas explosion in an excavation roadway with different cavity structures was affected by the cavity shapes, the oval cavity of the long axis/short axis ratio (LA/SA), and the cavity numbers. The overpressure, impulse, and flame speed decreased when a cavity existed, compared to the values in a tube without a cavity. The values of overpressure, impulse, and flame speed were smallest in a rectangular cavity. Furthermore, with increasing the LA/SA, the strength of the gas explosion was reduced significantly. The more the cavities were, the better the intensity of gas explosions was controlled. The research results can provide theoretical support and an experimental basis for preventing and controlling gas explosion accidents.

The complete mitochondrial genome of Metidiocerus impressifrons (Kirschbaum, 1868) (Hemiptera: Cicadellidae: Idiocerinae).

We determined the complete mitochondrial genome (mitogenome) of the leafhopper Metidiocerus impressifrons by next-generation sequencing. The mitogenome sequence was 16,426 bp in length and consists of 13 protein-coding genes, 22 transfer RNA (tRNA) genes, 2 ribosomal RNA (rRNA) genes, and a control region. Moreover, the nucleotide composition biases toward A and T, which together made up 78.2% of the entirety. The complete mitochondrial genomes of Metidiocerus impressifrons and other 27 species were used for phylogenetic analysis using the Bayesian method. The above results would facilitate our understanding of the evolution of Idiocerinae mitochondrial genome.

CVDF DYNAMIC-A Dynamic Fuzzy Testing Sample Generation Framework Based on BI-LSTM and Genetic Algorithm.

As one of the most effective methods of vulnerability mining, fuzzy testing has scalability and complex path detection ability. Fuzzy testing sample generation is the key step of fuzzy testing, and the quality of sample directly determines the vulnerability mining ability of fuzzy tester. At present, the known sample generation methods focus on code coverage or seed mutation under a critical execution path, so it is difficult to take both into account. Therefore, based on the idea of ensemble learning in artificial intelligence, we propose a fuzzy testing sample generation framework named CVDF DYNAMIC, which is based on genetic algorithm and BI-LSTM neural network. The main purpose of CVDF DYNAMIC is to generate fuzzy testing samples with both code coverage and path depth detection ability. CVDF DYNAMIC generates its own test case sets through BI-LSTM neural network and genetic algorithm. Then, we integrate the two sample sets through the idea of ensemble learning to obtain a sample set with both code coverage and vulnerability mining ability for a critical execution path of the program. In order to improve the efficiency of fuzzy testing, we use heuristic genetic algorithm to simplify the integrated sample set. We also innovatively put forward the evaluation index of path depth detection ability (pdda), which can effectively measure the vulnerability mining ability of the generated test case set under the critical execution path of the program. Finally, we compare CVDF DYNAMIC with some existing fuzzy testing tools and scientific research results and further propose the future improvement ideas of CVDF DYNAMIC.

The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma.

The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum spectra of glioma patients and healthy people and used feature engineering-based classification models for prediction. First, to reduce the dimensionality of the data, we used two feature extraction algorithms which are partial least squares (PLS) and principal component analysis (PCA). Then, the principal components were selected using the feature selection methods of four correlation indexes, namely, Relief-F (RF), the Pearson correlation coefficient (PCC), the F-score (FS) and term variance (TV). Finally, back-propagation neural network (BP), linear discriminant analysis (LDA) and support vector machine (SVM) classification models were established. To improve the reliability of the model, we used a fivefold cross validation to measure the prediction performance between different models. In this experiment, 33 classification models were established. Integrating 4 classification criteria, PLS-Relief-F-BP, PLS-F-Score-BP, PLS-LDA and PLS-Relief-F-SVM had better effects, and their accuracy rates reached 97.58%, 96.33%, 97.87% and 96.19%, respectively. The experimental results show that feature engineering can select more representative features, reduce computational time complexity and simplify the model. The classification model established in this experiment can not only increase the robustness of the model and shorten the discrimination time but also realize the rapid, stable and accurate diagnosis of glioma patients, which has high clinical application value.

Long-term variability in base cation, sulfur and nitrogen deposition and critical load exceedance of terrestrial ecosystems in China.

The rapid development of China's industrial economy and implementation of air pollution controls have led to great changes in sulfur (S), nitrogen (N) and base cation (BC) deposition in the past three decades. We estimated China's anthropogenic BC emissions and simulated BC deposition from 1985 to 2015 with a five-year interval using a multilayer Eulerian model. Deposition of S and N from 2000 to 2015 with a five-year interval was simulated with the EMEP MSC-W model and the Multi-resolution Emission Inventory of China (MEIC). The critical load (CL) and its exceedance were then calculated to evaluate the potential long-term acidification risks. From 1985 to 2005, the BC deposition in China was estimated to have increased by 16 % and then decreased by 33 % till 2015. S deposition was simulated to increase by 49 % from 2000 to 2005 and then decrease by 44 % in 2015, while N deposition increased by 32 % from 2000 to 2010 with a limited reduction afterward. The maximum CL of S was found to increase in 67 % of mainland China areas from 1985 to 2005 and to decline in 55 % of the areas from 2005 to 2015, attributed largely to the changed BC deposition. Consistent with the progress of national controls on SO2 and NOX emissions, the CL exceedance of S increased from 2.9 to 4.6 Mt during 2000-2005 and then decreased to 2.5 Mt in 2015, while that of N increased from 0.4 in 2000 to 1.2 Mt in 2010 and then decreased to 1.1 Mt in 2015. The reduced BC deposition due to particle emission controls partially offset the benefit of SO2 control on acidification risk reduction in the past decade. It demonstrates the need for a comprehensive strategy for multi-pollutant control against soil acidification.

Evaluating the effects of ground-level O3 on rice yield and economic losses in Southern China.

Ground-level ozone (O3) pollution and its impact on crop growth and yield have become one of the serious environmental problems in recent years, especially in economically active and densely populated areas. In this study, rice yield and the associated economic losses due to O3 were estimated by using observational O3 concentration ([O3]) data during growing seasons in Southern China. O3-induced yield losses were calculated by using O3 exposure metrics of AOT40 and M7. The spatial distribution of these two metrics is relatively consistent, the highest areas located in the Yangtze River Basin. Under the current O3 level, during double-early rice, double-late rice and single rice growing seasons, the relative yield losses estimated with AOT40 (M7) were 6.8% (1.2%), 10.2% (1.9%) and 10.4% (2.0%), respectively. O3-induced rice production loss for double-early rice, double-late rice and single rice totaled 2.4 million metric tons (0.4 million metric tons), 4.3 million metric tons (0.7 million metric tons) and 11.0 million metric tons (1.9 million metric tons) and associated economic losses were 108.1 million USD (18.3 million USD), 190.2 million USD (32.4 million USD) and 486.4 million USD (82.9 million USD) based on AOT40 (M7) metric. This study indicates that regional risks to rice from O3 exposure and provide quantitative evidence of O3-induced impacts on rice yields and economic losses across Southern China. Therefore, the establishment of scientific O3 risk assessment method is of great significance to prevent yield production and economic losses caused by O3 exposure. Policymakers should strengthen supervision of emissions of O3 precursors to mitigate the rise of O3 concentration, thereby reducing O3 damage to agricultural production.

Meteorological variations impeded the benefits of recent NOx mitigation in reducing atmospheric nitrate deposition in the Pearl River Delta region, Southeast China.

The trends and variability of atmospheric nitrogen deposition in the Pearl River Delta (PRD) region for the period 2008-2017 were investigated by integrating ground- and satellite-based observations and a chemical transport model, in order to gauge the effects of emission reductions and meteorological variability. We show that dry deposition observation of oxidized nitrogen decreased at the rate of 2.4% yr-1 for a moderate reduction in NOx emissions by 27% in the past decade, while reduced nitrogen presented an increase at the rate of 2.3% yr-1 despite no regulated interventions for NH3 emissions, which is likely related to changes in atmospheric gas-particle partitioning of NH3 as reductions in SO2 and NOx emissions. These results coincide with the trends in ground-level concentrations of oxidized and reduced nitrogen compounds in the atmosphere during 2008-2017. The changes in annual deposition fluxes of total oxidized and reduced nitrogen are not statistically significant trends and largely related with the inter-annual variability in their corresponding wet depositions, which reflects combined effects of variability in precipitation amount, and changes in atmospheric nitrogen compounds which dominates wet deposition of the oxidized and reduced forms. The meteorological conditions can mask 34% and 25% decrease in total oxidized and reduced nitrogen deposition on the decadal timescale, respectively. We conclude that meteorology-driven variability probably have masked the full response of oxidized nitrogen deposition to NOx emissions reduction. Our results also imply that persistent and integrated emission control strategies on NOx and NH3 are needed to effectively reduce total nitrogen deposition fluxes towards the critical limit in the PRD region.

Dry deposition of reactive nitrogen to different ecosystems across eastern China: A comparison of three community models.

The assessment of nitrogen ecosystem loads mostly use the method of sampling observation combined with numerical model to estimate the spatial distribution pattern of nitrogen dry deposition flux. The selection of models is important which directly affects the reliability of the deposition flux results. In this study, the performance of three widely used models (WRF-Chem, EMEP, CMAQ) are compared. The dry deposition fluxes of typical active nitrogen components over eastern China showed uncertainties by a factor of 0.5 ~ 2 between the oxidized nitrogen (OXN) results of the three models and the observation network while the reduced nitrogen (RDN) simulation results are underestimated by a quarter of the observation reports. These three models show different results on four typical ecosystems: simulation of EMEP got the highest for OXN dry deposition flux on each ecosystem (urban 14.94 ± 4.92kgN â‹… ha-1 â‹… yr-1, cropland/grassland 5.53 ± 5.11kgN â‹… ha-1 â‹… yr-1, forest 4.75 ± 4.32kgN â‹… ha-1 â‹… yr-1, water bodies 1.48 ± 1.53kgN â‹… ha-1 â‹… yr-1); WRF-Chem has the highest value of RDN on the urban (8.91 ± 6.44kgN â‹… ha-1 â‹… yr-1) and water bodies (1.01 ± 1.44kgN â‹… ha-1 â‹… yr-1) while EMEP is highest in cropland/grassland (3.42 ± 3.43kgN â‹… ha-1 â‹… yr-1) and forest (2.34 ± 1.94kgN â‹… ha-1 â‹… yr-1). CMAQ is in medium range for both OXN and RDN simulations on each ecosystem. Compare with the critical loads, CMAQ generates more exceeded critical load areas than WRF-Chem and EMEP on cropland/grassland and forests ecosystem. For water bodies, WRF-chem and CMAQ showed higher exceeding critical load areas than EMEP. In summary, EMEP generally underestimates while the CMAQ and WRF-Chem model would overestimate the impacts on the ecosystems. So, policy implementation needs special attention accounting the difference of simulation effect with different models.

[Characteristics of phytoplankton community changes in Dianshan Lake during peak period of algal blooms].

Based on the investigation data of phytoplankton in Dianshan Lake from May to October in 2009, the characteristics of phytoplankton community and the dominant species succession are studied. The results show that Cyanophyta and Chlorophta are the main taxa. Cyanophyta is dominant in cell abundance and Chlorophta is dominant in species variety. From the flat distribution, the species variety and density of Cyanophyta are higher in west and southwest. The peak of cell density reaches 23.40 x 10(7) cells x L(-1) in September due to the occurrence of cyanobacterial bloom, Cyanophyta account for 90.3 percents, with significant differences in each point (ANOVA, P < 0.05). An obvious succession of phytoplankton species is found, Microcystis of Cyanophyta become the dominant taxa and then conglutinated together to form water bloom. Temperature and pH are the main factors that affect the cyanobacterial bloom, and wind direction is an important reason for the horizontal distribution of the bloom-forming Microcystis. The phytoplankton diversity index is poor in central and western sites, diversity index decreases during cyanobacterial bloom and the community structures are simple.