Changes in plant species richness distribution in Tibetan alpine grasslands under different precipitation scenarios

Species richness is the core of biodiversity-ecosystem functioning (BEF) research. Nevertheless, it is difficult to accurately predict changes in plant species richness under different climate scenarios, especially in alpine biomes. In this study, we surveyed plant species richness from 2009 to 2017 in 75 alpine meadows (AM), 199 alpine steppes (AS), and 71 desert steppes (DS) in the Tibetan Autonomous Region, China. Along with 20 environmental factors relevant to species settlement, development, and survival, we first simulated the spatial pattern of plant species richness under current climate conditions using random forest modelling. Our results showed that simulated species richness matched well with observed values in the field, showing an evident decrease from meadows to steppes and then to deserts. Summer precipitation, which ranked first among the 20 environmental factors, was further confirmed to be the most critical driver of species richness distribution. Next, we simulated and compared species richness patterns under four different precipitation scenarios, increasing and decreasing summer precipitation by 20% and 10%, relative to the current species richness pattern. Our findings showed that species richness in response to altered precipitation was grassland-type specific, with meadows being sensitive to decreasing precipitation, steppes being sensitive to increasing precipitation, and deserts remaining resistant. In addition, species richness at low elevations was more sensitive to decreasing precipitation than to increasing precipitation, implying that droughts might have stronger influences than wetting on species composition. In contrast, species richness at high elevations (also in deserts) changed slightly under different precipitation scenarios, likely due to harsh physical conditions and small species pools for plant recruitment and survival. Finally, we suggest that policymakers and herdsmen pay more attention to alpine grasslands in central Tibet and at low elevations where species richness is sensitive to precipitation changes

A new remote data integrity checking scheme for cloud storage

Cloud storage services enable user to enjoy high-capacity and high-quality storage with less overhead, but it also brings many potential threats, for example, data integrality, data availability and so on. In this paper, we propose a new remote integrality and availability checking scheme for cloud storage. This new scheme can check mass file\u27s integrality and availability with less storage, computation and communication resource. The new scheme also supports data dynamic update, public verifiability and privacy preserving

Structural Attack to Anonymous Graph of Social Networks

With the rapid development of social networks and its applications, the demand of publishing and sharing social network data for the purpose of commercial or research is increasing. However, the disclosure risks of sensitive information of social network users are also arising. The paper proposes an effective structural attack to deanonymize social graph data. The attack uses the cumulative degree of n-hop neighbors of a node as the regional feature and combines it with the simulated annealing-based graph matching method to explore the nodes reidentification in anonymous social graphs. The simulation results on two social network datasets show that the attack is feasible in the nodes reidentification in anonymous graphs including the simply anonymous graph, randomized graph and k-isomorphism graph

Multiobjective deep clustering and its applications in single-cell RNA-seq data

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Single-cell RNA sequencing is a transformative technology that enables us to study the heterogeneity of the tissue at the cellular level. Clustering is used as the key computational approach to group cells under the transcriptome profiles from single-cell RNA-seq data. However, accurate identification of distinct cell types is facing the challenge of high dimensionality, and it could cause uninformative clusters when clustering is directly applied on the original transcriptome. To address such challenge, an evolutionary multiobjective deep clustering (EMDC) algorithm is proposed to identify single-cell RNA-seq data in this study. First, EMDC removes redundant and irrelevant genes by applying the differential gene expression analysis to identify differentially expressed genes across biological conditions. After that, a deep autoencoder is proposed to project the high-dimensional data into different low-dimensional nonlinear embedding subspaces under different bottleneck layers. Then, the basic clustering algorithm is applied in those nonlinear embedding subspaces to generate some basic clustering results to produce the cluster ensemble. To lessen the unnecessary cost produced by those clusterings in the ensemble, the multiobjective evolutionary optimization is designed to prune the basic clustering results in the ensemble, unleashing its cell type discovery performance under three objective functions. Multiple experiments have been conducted on 30 synthetic single-cell RNA-seq datasets and six real single-cell RNA-seq datasets, which reveal that EMDC outperforms eight other clustering methods and three multiobjective optimization algorithms in cell type identification. In addition, we have conducted extensive comparisons to effectively demonstrate the impact of each component in our proposed EMDC

Analyses of MicroRNA and mRNA Expression Profiles Reveal the Crucial Interaction Networks and Pathways for Regulation of Chicken Breast Muscle Development

There is a lack of understanding surrounding the molecular mechanisms involved in the development of chicken skeletal muscle in the late postnatal stage, especially in the regulation of breast muscle development related genes, pathways, miRNAs and other factors. In this study, 12 cDNA libraries and 4 small RNA libraries were constructed from Gushi chicken breast muscle samples from 6, 14, 22, and 30 weeks. A total of 15,508 known transcripts, 25,718 novel transcripts, 388 known miRNAs and 31 novel miRNAs were identified by RNA-seq in breast muscle at the four developmental stages. Through correlation analysis of miRNA and mRNA expression profiles, it was found that 417, 370, 240, 1,418, 496, and 363 negatively correlated miRNA–mRNA pairs of W14 vs. W6, W22 vs. W6, W22 vs. W14, W30 vs. W6, W30 vs. W14, and W30 vs. W22 comparisons, respectively. Based on the annotation analysis of these miRNA–mRNA pairs, we constructed the miRNA–mRNA interaction network related to biological processes, such as muscle cell differentiation, striated muscle tissue development and skeletal muscle cell differentiation. The interaction networks for signaling pathways related to five KEGG pathways (the focal adhesion, ECM-receptor interaction, FoxO signaling, cell cycle, and p53 signaling pathways) and PPI networks were also constructed. We found that ANKRD1, EYA2, JSC, AGT, MYBPC3, MYH11, ACTC1, FHL2, RCAN1, FOS, EGR1, and FOXO3, PTEN, AKT1, GADD45, PLK1, CCNB2, CCNB3 and other genes were the key core nodes of these networks, most of which are targets of miRNAs. The FoxO signaling pathway was in the center of the five pathway-related networks. In the PPI network, there was a clear interaction among PLK1 and CDK1, CCNB2, CDK1, and GADD45B, and CDC45, ORC1 and MCM3 genes. These results increase the understanding for the molecular mechanisms of chicken breast muscle development, and also provide a basis for studying the interactions between genes and miRNAs, as well as the functions of the pathways involved in postnatal developmental regulation of chicken breast muscle

Evolutionary multiobjective clustering algorithms with ensemble for patient stratification

The file attached to this record is the author's final peer reviewed version.Patient stratification has been studied widely to tackle subtype diagnosis problems for effective treatment. Due to the dimensionality curse and poor interpretability of data, there is always a long-lasting challenge in constructing a stratification model with high diagnostic ability and good generalization. To address these problems, this paper proposes two novel evolutionary multiobjective clustering algorithms with ensemble (NSGA-II-ECFE and MOEA/D-ECFE) with four cluster validity indices used as the objective functions. First, an effective ensemble construction method is developed to enrich the ensemble diversity. After that, an ensemble clustering fitness evaluation (ECFE) method is proposed to evaluate the ensembles by measuring the consensus clustering under those four objective functions. To generate the consensus clustering, ECFE exploits the hybrid co-association matrix from the ensembles and then dynamically selects the suitable clustering algorithm on that matrix. Multiple experiments have been conducted to demonstrate the effectiveness of the proposed algorithm in comparison with seven clustering algorithms, twelve ensemble clustering approaches, and two multiobjective clustering algorithms on 55 synthetic datasets and 35 real patient stratification datasets. The experimental results demonstrate the competitive edges of the proposed algorithms over those compared methods. Furthermore, the proposed algorithm is applied to extend its advantages by identifying cancer subtypes from five cancer-related single-cell RNA-seq datasets