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Current commercial H9 avian influenza viruses (AIVs) vaccines cannot provide satisfactory antibody titers and protective immunity against AIVs in duck. Toll like receptors (TLR) ligand as AIVs adjuvants can activate dendritic cells to improve immune responses in multiple animals, while the studies were absent in duck. Therefore, we investigated TLR ligands pam2CSK4, poly (I:C) and/or imiquimod enhance immune responses to inactivated H9N2 avian influenza antigen (H9N2 IAIV) in peripheral blood monocyte-derived dendritic cells (MoDCs) and duck. In vitro, we observed that transcription factor NF-κB, Th1/Th2 type cytokines (IFN-γ, IL-2 and IL-6) and the ability of catching H9N2 IAIV antigen were significantly up-regulated when H9N2 IAIV along with TLR ligands (pam2CSK4, poly (I:C) and imiquimod, alone or combination) in duck MoDCs. Also, the best enhancement effects were showed in combination of pam2CSK4, poly (I:C) and imiquimod group, whereas IFN-α showed no significant enhancement in all experimental groups. In vivo, the results demonstrated that the percentages of CD4+/ CD8+ T lymphocytes, the levels of Th1/Th2 type cytokines and H9N2 HI titers were significant enhanced in combination of pam2CSK4, poly (I:C) and imiquimod group. However, pam2CSK4 alone or combining with imiquimod showed no enhancement or additive effects on Th1 cytokines (IFN-γ and IL-2), Th2 cytokines (IL-6) and HI titers in Muscovy duck, respectively. Taken together, our results concluded that not all TLR ligands showed enhancement of immune responses to H9N2 IAIV in duck. The combination of poly (I:C), imiquimod and pam2CSK4 that can be an effectively adjuvant candidate for H9N2 AIVs inactivated vaccine in duck, which provide novel insights in explore waterfowl vaccine.

Current commercial H9 avian influenza viruses (AIVs) vaccines cannot provide satisfactory antibody titers and protective immunity against AIVs in duck. Toll like receptors (TLR) ligand as AIVs adjuvants can activate dendritic cells to improve immune responses in multiple animals, while the studies were absent in duck. Therefore, we investigated TLR ligands pam2CSK4, poly (I:C) and/or imiquimod enhance immune responses to inactivated H9N2 avian influenza antigen (H9N2 IAIV) in peripheral blood monocyte-derived dendritic cells (MoDCs) and duck. In vitro, we observed that transcription factor NF-κB, Th1/Th2 type cytokines (IFN-γ, IL-2 and IL-6) and the ability of catching H9N2 IAIV antigen were significantly up-regulated when H9N2 IAIV along with TLR ligands (pam2CSK4, poly (I:C) and imiquimod, alone or combination) in duck MoDCs. Also, the best enhancement effects were showed in combination of pam2CSK4, poly (I:C) and imiquimod group, whereas IFN-α showed no significant enhancement in all experimental groups. In vivo, the results demonstrated that the percentages of CD4+/ CD8+ T lymphocytes, the levels of Th1/Th2 type cytokines and H9N2 HI titers were significant enhanced in combination of pam2CSK4, poly (I:C) and imiquimod group. However, pam2CSK4 alone or combining with imiquimod showed no enhancement or additive effects on Th1 cytokines (IFN-γ and IL-2), Th2 cytokines (IL-6) and HI titers in Muscovy duck, respectively. Taken together, our results concluded that not all TLR ligands showed enhancement of immune responses to H9N2 IAIV in duck. The combination of poly (I:C), imiquimod and pam2CSK4 that can be an effectively adjuvant candidate for H9N2 AIVs inactivated vaccine in duck, which provide novel insights in explore waterfowl vaccine.

ObjectiveTo estimate the prevalence of disability and anxiety in Covid-19 survivors at discharge from hospital and analyze relative risk by exposures.DesignMulti-center retrospective cohort study.SettingTwenty-eight hospitals located in eight provinces of China.MethodsA total of 432 survivors with laboratory-confirmed SARS CoV-2 infection participated in this study. At discharge, we assessed instrumental activities of daily living (IADL) with Lawton's IADL scale, dependence in activities of daily living (ADL) with the Barthel Index, and anxiety with Zung's self-reported anxiety scale. Exposures included comorbidity, smoking, setting (Hubei vs. others), disease severity, symptoms, and length of hospital stay. Other risk factors considered were age, gender, and ethnicity (Han vs. Tibetan).ResultsPrevalence of at least one IADL problem was 36.81% (95% CI: 32.39-41.46). ADL dependence was present in 16.44% (95% CI: 13.23-20.23) and 28.70% (95% CI: 24.63-33.15) were screened positive for clinical anxiety. Adjusted risk ratio (RR) of IADL limitations (RR 2.48, 95% CI: 1.80-3.40), ADL dependence (RR 2.07, 95% CI 1.15-3.76), and probable clinical anxiety (RR 2.53, 95% CI 1.69-3.79) were consistently elevated in survivors with severe Covid-19. Age was an additional independent risk factor for IADL limitations and ADL dependence; and setting (Hubei) for IADL limitations and anxiety. Tibetan ethnicity was a protective factor for anxiety but a risk factor for IADL limitations.ConclusionA significant proportion of Covid-19 survivors had disability and anxiety at discharge from hospital. Health systems need to be prepared for an additional burden resulting from rehabilitation needs of Covid-19 survivors.

Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40 °C with 5 °C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25 °C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15 °C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy.

Sinonasal squamous cell carcinoma (SSCC) and nasal inverted papilloma (NIP) represent the predominant type of malignant and benign tumors in sinonasal tract, respectively. CD4+ CD25+ Foxp3+ natural regulatory T (Treg) cells might play critical role(s) in the suppression of anti-tumor immune response and thus shed light on tumor progression from benign to malignant.This study aimed to evaluate the frequency and suppressive capacity of Treg cells in SSCC compared to NIP and further to explore the underlying mechanisms.Frequencies of Treg, Th1 and Th2 cells were evaluated by flow cytometry in tissue homogenate and peripheral blood from 31 SSCC patients, 32 NIP patients and 35 normal controls. Treg cells were tested for regulatory function by co-culture with effector T cells. CCR4 and its ligands, CCL22 and CCL17, were analyzed by flow cytometry and Luminex, respectively. The chemoattractant properties of CCR4/CCL22 and CCR4/CCL17 for Treg cells were assessed using the Boyden chamber technique, to elucidate the potential mechanisms of Treg recruitment in tumor microenvironment. Treg cells induction via TGF-β was assessed with transwells after local CD4+ Foxp3+ T cells were assessed by immunohistochemistry and TGF-β concentration was measured by Luminex.Tumor-infiltrating Treg cells increased significantly from normal to NIP to SSCC (P ≤ 0.001 for normal vs. NIP and P = 0.004 for NIP vs. SSCC). Significantly elevated frequency and enhanced suppression capacity of circulating Treg cells in SSCC were detected compared to NIP and healthy controls, concomitant with Th1 decrease and Th2 increase. Apparently increased CCL22 attracted CCR4-expressing Treg cells to tumor microenvironment in SSCC, compared to NIP. SSCC produced significantly more TGF-β than NIP and thus possessed greater potential for Treg cell induction.Frequency and suppressive capacity of Treg cells enhanced with progression of malignancy from NIP to SSCC. Circulating Treg cells were recruited to tumor tissue via CCR4/CCL22 signalling, whereas tumor-synthesised TGF-β contributed to induction of peripheral Treg cells.

The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.

A commonly used strategy to improve search accuracy is through feedback techniques. Most existing work on feedback relies on positive information, and has been extensively studied in information retrieval. However, when a query topic is difficult and the results from the first-pass retrieval are very poor, it is impossible to extract enough useful terms from a few positive documents. Therefore, the positive feedback strategy is incapable to improve retrieval in this situation. Contrarily, there is a relatively large number of negative documents in the top of the result list, and it has been confirmed that negative feedback strategy is an important and useful way for adapting this scenario by several recent studies. In this paper, we consider a scenario when the search results are so poor that there are at most three relevant documents in the top twenty documents. Then, we conduct a novel study of multiple strategies for relevance feedback using both positive and negative examples from the first-pass retrieval to improve retrieval accuracy for such difficult queries. Experimental results on these TREC collections show that the proposed language model based multiple model feedback method which is generally more effective than both the baseline method and the methods using only positive or negative model.

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

Temperature is one of the most significant environmental factors that affects germination of grass seeds. Reliable prediction of the optimal temperature for seed germination is crucial for determining the suitable regions and favorable sowing timing for turf grass cultivation. In this study, a back-propagation-artificial-neural-network-aided dual quintic equation (BP-ANN-QE) model was developed to improve the prediction of the optimal temperature for seed germination. This BP-ANN-QE model was used to determine optimal sowing times and suitable regions for three Cynodon dactylon cultivars (C. dactylon, 'Savannah' and 'Princess VII'). Prediction of the optimal temperature for these seeds was based on comprehensive germination tests using 36 day/night (high/low) temperature regimes (both ranging from 5/5 to 40/40°C with 5°C increments). Seed germination data from these temperature regimes were used to construct temperature-germination correlation models for estimating germination percentage with confidence intervals. Our tests revealed that the optimal high/low temperature regimes required for all the three bermudagrass cultivars are 30/5, 30/10, 35/5, 35/10, 35/15, 35/20, 40/15 and 40/20°C; constant temperatures ranging from 5 to 40°C inhibited the germination of all three cultivars. While comparing different simulating methods, including DQEM, Bisquare ANN-QE, and BP-ANN-QE in establishing temperature based germination percentage rules, we found that the R(2) values of germination prediction function could be significantly improved from about 0.6940-0.8177 (DQEM approach) to 0.9439-0.9813 (BP-ANN-QE). These results indicated that our BP-ANN-QE model has better performance than the rests of the compared models. Furthermore, data of the national temperature grids generated from monthly-average temperature for 25 years were fit into these functions and we were able to map the germination percentage of these C. dactylon cultivars in the national scale of China, and suggested the optimum sowing regions and times for them.

Understanding protein complexes is important for understanding the science of cellular organization and function. Many computational methods have been developed to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. However, interaction information obtained experimentally can be unreliable and incomplete. Reconstructing these PPI networks with PPI evidences from other sources can improve protein complex identification.We combined PPI information from 6 different sources and obtained a reconstructed PPI network for yeast through machine learning. Some popular protein complex identification methods were then applied to detect yeast protein complexes using the new PPI networks. Our evaluation indicates that protein complex identification algorithms using the reconstructed PPI network significantly outperform ones on experimentally verified PPI networks.We conclude that incorporating PPI information from other sources can improve the effectiveness of protein complex identification.

Drug-drug interaction (DDI) detection is particularly important for patient safety. However, the amount of biomedical literature regarding drug interactions is increasing rapidly. Therefore, there is a need to develop an effective approach for the automatic extraction of DDI information from the biomedical literature. In this paper, we present a Stacked Generalization-based approach for automatic DDI extraction. The approach combines the feature-based, graph and tree kernels and, therefore, reduces the risk of missing important features. In addition, it introduces some domain knowledge based features (the keyword, semantic type, and DrugBank features) into the feature-based kernel, which contribute to the performance improvement. More specifically, the approach applies Stacked generalization to automatically learn the weights from the training data and assign them to three individual kernels to achieve a much better performance than each individual kernel. The experimental results show that our approach can achieve a better performance of 69.24% in F-score compared with other systems in the DDI Extraction 2011 challenge task.

We investigated if elevated CO(2) could alleviate the negative effect of high temperature on fruit yield of strawberry (Fragaria × ananassa Duch. cv. Toyonoka) at different levels of nitrogen and also tested the combined effects of CO(2), temperature and nitrogen on fruit quality of plants cultivated in controlled growth chambers. Results show that elevated CO(2) and high temperature caused a further 12% and 35% decrease in fruit yield at low and high nitrogen, respectively. The fewer inflorescences and smaller umbel size during flower induction caused the reduction of fruit yield at elevated CO(2) and high temperature. Interestingly, nitrogen application has no beneficial effect on fruit yield, and this may be because of decreased sucrose export to the shoot apical meristem at floral transition. Moreover, elevated CO(2) increased the levels of dry matter-content, fructose, glucose, total sugar and sweetness index per dry matter, but decreased fruit nitrogen content, total antioxidant capacity and all antioxidant compounds per dry matter in strawberry fruit. The reduction of fruit nitrogen content and antioxidant activity was mainly caused by the dilution effect of accumulated non-structural carbohydrates sourced from the increased net photosynthetic rate at elevated CO(2). Thus, the quality of strawberry fruit would increase because of the increased sweetness and the similar amount of fruit nitrogen content, antioxidant activity per fresh matter at elevated CO(2). Overall, we found that elevated CO(2) improved the production of strawberry (including yield and quality) at low temperature, but decreased it at high temperature. The dramatic fluctuation in strawberry yield between low and high temperature at elevated CO(2) implies that more attention should be paid to the process of flower induction under climate change, especially in fruits that require winter chilling for reproductive growth.

The recognition and normalization of gene mentions in biomedical literature are crucial steps in biomedical text mining. We present a system for extracting gene names from biomedical literature and normalizing them to gene identifiers in databases. The system consists of four major components: gene name recognition, entity mapping, disambiguation and filtering. The first component is a gene name recognizer based on dictionary matching and semi-supervised learning, which utilizes the co-occurrence information of a large amount of unlabeled MEDLINE abstracts to enhance feature representation of gene named entities. In the stage of entity mapping, we combine the strategies of exact match and approximate match to establish linkage between gene names in the context and the EntrezGene database. For the gene names that map to more than one database identifiers, we develop a disambiguation method based on semantic similarity derived from the Gene Ontology and MEDLINE abstracts. To remove the noise produced in the previous steps, we design a filtering method based on the confidence scores in the dictionary used for NER. The system is able to adjust the trade-off between precision and recall based on the result of filtering. It achieves an F-measure of 83% (precision: 82.5% recall: 83.5%) on BioCreative II Gene Normalization (GN) dataset, which is comparable to the current state-of-the-art.

When one drug influences the level or activity of another drug this is known as a drug-drug interaction (DDI). Knowledge of such interactions is crucial for patient safety. However, the volume and content of published biomedical literature on drug interactions is expanding rapidly, making it increasingly difficult for DDIs database curators to detect and collate DDIs information manually. In this paper, we propose a single kernel-based approach to extract DDIs from biomedical literature. This novel kernel-based approach can effectively make full use of syntactic structural information of the dependency graph. In particular, our approach can efficiently represent both single subgraph topological information and the relation of two subgraphs in the dependency graph. Experimental evaluations showed that our single kernel-based approach can achieve state-of-the-art performance on the publicly available DDI corpus without exploiting multiple kernels or additional domain resources.

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species.

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.