Humankind depends on the sustainability of soils for its survival and wellbeing. Threatened by a rapidly changing world, our soils suffer from degradation and biodiversity loss, making it increasingly important to understand the role of soil biodiversity in soil aggregation, a key parameter for soil sustainability. We here provide evidence of the contribution of soil biota to soil aggregation on macro- and microaggregate scales, and evaluate how specific traits, soil biota groups and species interactions contribute to this. We conducted a global meta-analysis comprising 279 soil biota species. Our study shows a clear positive effect of soil biota on soil aggregation, with bacteria and fungi generally appearing more important for soil aggregation than soil animals. Bacteria contribute strongly to both macro- and microaggregates while fungi strongly affect macro-aggregation. Motility, body size and population density were important traits modulating effect sizes. Investigating species interactions across major taxonomic groups revealed their beneficial impact on soil aggregation. At the broadest level our results highlight the need to consider biodiversity as a causal factor in soil aggregation. This will require a shift from the current management and physicochemical perspective to an approach that fully embraces the significance of soil organisms, their diversity and interactions.

Increasing grain yields of food cereal crops is a major goal in future sustainable agriculture. We quantitatively analyzed the potential role of arbuscular mycorrhizal (AM) fungi in enhancing grain yields of seven cereal crops with exceptional importance for human nutrition across the globe: corn, wheat, rice, barley, sorghum, millet and oat.We conducted a meta-analysis for three datasets including both English and Chinese language publications: the 'whole' dataset including both laboratory and field studies (168 articles); the 'field' dataset comprising only field studies (97 studies); and the 'field-inoculation' dataset including only AM fungal inoculation studies conducted in field conditions (70 articles).We found that the AM fungal effect on grain yield was less pronounced in field and noninoculation studies. AM fungal inoculation in field led to a 16% increase (overall effect) based on the 'field-inoculation' dataset; this effect was variable (77% trials had positive values), crop-specific, lower for new cultivars released after 1950 and further modulated by soil pH.Although there are neutral and negative effects of AM fungi on grain yields, we emphasize the importance of integrating AM fungi in sustainable agriculture to increase grain yields of cereal crops.

Soil structure, the complex arrangement of soil into aggregates and pore spaces, is a key feature of soils and soil biota. Among them, filamentous saprobic fungi have welldocumented effects on soil aggregation. However, it is unclear what properties, or traits, determine the overall positive effect of fungi on soil aggregation. To achieve progress, it would be helpful to systematically investigate a broad suite of fungal species for their trait expression and the relation of these traits to soil aggregation. Here, we apply a trait-based approach to a set of 15 traits measured under standardized conditions on 31 fungal strains including Ascomycota, Basidiomycota, and Mucoromycota, all isolated from the same soil. We find large differences among these fungi in their ability to aggregate soil, including neutral to positive effects, and we document large differences in trait expression among strains. We identify biomass density, i.e., the density with which a mycelium grows (positive effects), leucine aminopeptidase activity (negative effects) and phylogeny as important factors explaining differences in soil aggregate formation (SAF) among fungal strains; importantly, growth rate was not among the important traits. Our results point to a typical suite of traits characterizing fungi that are good soil aggregators, and our findings illustrate the power of employing a trait-based approach to unravel biological mechanisms underpinning soil aggregation. Such an approach could now be extended also to other soil biota groups. In an applied context of restoration and agriculture, such trait information can inform management, for example to prioritize practices that favor the expression of more desirable fungal traits.

Since many graph data are often noisy and incomplete in real applications, it has become increasingly important to retrieve graphs g in the graph database D that approximately match the query graph q, rather than exact graph matching. In this paper, we study the problem of graph similarity search, which retrieves graphs that are similar to a given query graph under the constraint of graph edit distance. We propose a systematic method for edit-distance based similarity search problem. Specifically, we derive two lower bounds, i.e., partition-based and branch-based bounds, from different perspectives. More importantly, a hybrid lower bound incorporating both ideas of the two lower bounds is proposed, which is theoretically proved to have higher (at least not lower) pruning power than using the two lower bounds together. We also present a uniform index structure, namely u-tree, to facilitate effective pruning and efficient query processing. Extensive experiments confirm that our proposed approach outperforms the existing approaches significantly, in terms of both the pruning power and query response time.

RDF knowledge graphs have attracted increasing attentions these years. However, due to the schema-free nature of RDF data, it is very difficult for users to have full knowledge of the underlying schema. Furthermore, the same kind of information can be represented in diverse graph fragments. Hence, it is a huge challenge to formulate complex SPARQL expressions by taking the union of all possible structures. In this paper, we propose an effective framework to access the RDF repository even if users have no full knowledge of the underlying schema. Specifically, given a SPARQL query, the system could return as more answers that match the query based on the semantic similarity as possible. Interestingly, we propose a systematic method to mine diverse semantically equivalent structure patterns. More importantly, incorporating both structural and semantic similarities we are the first to propose a novel similarity measure, semantic graph edit distance . In order to improve the efficiency performance, we apply the semantic summary graph to summarize the knowledge graph, which supports both high-level pruning and drill-down pruning. We also devise an effective lower bound based on the TA-style access to each of the candidate sets. Extensive experiments over real datasets confirm the effectiveness and efficiency of our approach.

Cas9/CRISPR has been reported to efficiently induce targeted gene disruption and homologous recombination in both prokaryotic and eukaryotic cells. Thus, we developed a Guide RNA Sequence Design Platform for the Cas9/CRISPR silencing system for model organisms. The platform is easy to use for gRNA design with input query sequences. It finds potential targets by PAM and ranks them according to factors including uniqueness, SNP, RNA secondary structure, and AT content. The platform allows users to upload and share their experimental results. In addition, most guide RNA sequences from published papers have been put into our database.

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Graphs have been widely used to model complex data in many real-world applications. Answering vertex join queries over large graphs is meaningful and interesting, which can benefit friend recommendation in social networks and link prediction, etc. In this paper, we adopt "SimRank" to evaluate the similarity of two vertices in a large graph because of its generality. Note that "SimRank" is purely structure dependent and it does not rely on the domain knowledge. Specifically, we define a SimRank-based join (SRJ) query to find all the vertex pairs satisfying the threshold in a data graph G. In order to reduce the search space, we propose an estimated shortest-path distance based upper bound for SimRank scores to prune unpromising vertex pairs. In the verification, we propose a novel index, called h-go cover, to efficiently compute the SimRank score of a single vertex pair. Given a graph G, we only materialize the SimRank scores of a small proportion of vertex pairs (called h-go covers), based on which, the SimRank score of any vertex pair can be computed easily. In order to handle large graphs, we extend our technique to the partition-based framework. Thorough theoretical analysis and extensive experiments over both real and synthetic datasets confirm the efficiency and effectiveness of our solution.