Soil and vegetation characteristics during the formation of typical Ligularia virgaurea degraded grassland.

To understand the formation process of typical poisonous plant degraded grassland, we studied the cha-racteristics of vegetation and soil during the gradual expansion of Ligularia virgaurea into the native grassland of Qinghai-Tibet Plateau. The results showed that population density, plant height, coverage, and biomass of L. virgaurea increased during the formation of L. virgaurea degraded grassland. In comparison with native grassland, the degraded grassland had higher total aboveground biomass (113.9%), soil total nitrogen concentration (61.0%), NH4+-N (77.9%), organic carbon concentration (45.3%), available phosphorus concentration (78.8%) as well as soil microbial biomass carbon (42.1%) and nitrogen (47.4%), but lower NO3--N (40.1%) and species richness (28.5%) and aboveground biomass (45.7%) of other species beyond L. virgaurea. The extremely strong abilities of interspecific inhibition and morphological plasticity of L. virgaurea, as well as efficient nutrient accumulation and utilization were the keys to its successful expansion, which facilitated the formation of typical L. virgaurea degraded grassland.

Maternal di-(2-ethylhexyl) phthalate exposure elicits offspring IFN-λ upregulation: Insights from birth cohort, murine model, and in vitro mechanistic analysis.

Maternal exposure to di-(2-ethylhexyl)-phthalate (DEHP), an environmental endocrine disruptor, may lead to developmental immunotoxicity in offspring. The causal relationship and underlying mechanism require further study. A subset of Taiwan Maternal and Infant Cohort Study data (n = 283) was analyzed and found a significant association between urinary DEHP metabolite levels from the third trimester of pregnancy and plasma levels of IL-28A and IL-29, named IFNλs, in cord blood. A trans-maternal murine model mimicking human DEHP exposure way showed that bone marrow-derived dendritic cells from maternal DEHP-exposed F1 offspring secreted higher IL-28A levels than control cells, indicating a potential causal relationship. Human bronchial epithelial cell lines treated with DEHP or its primary metabolite, mono-(2-ethyl-5-hexyl) phthalate (MEHP), expressed significantly higher levels of IFNλs mRNA or protein than controls. MEHP's effect on IFNλs expression was blocked by peroxisome proliferator-activated receptor α (PPARα) and PPARγ antagonists, and inhibited by a histone acetyltransferase inhibitor or a histone methyltransferase inhibitor. Chromatin immunoprecipitation assay showed that MEHP treatment promoted histone modifications at H3 and H4 proteins at the promoter regions of Il28a and Il29 genes. These results suggest maternal DEHP exposure could result in high IFNλ expression in offspring, and the health risk of early-life exposure requires further investigation.

New polyketides from the basidiomycetous fungus Pholiota sp.

Two new polyketides, pholiotones B and C (1 and 2), and four known compounds, trichodermatide D (3), vermistatin (4), dehydroaltenuene A (5) and terpestacin (6) were isolated from the crude extract of Pholiota sp. Their structures were identified by NMR and MS spectroscopic data. The absolute configurations of compounds 1 and 2 were elucidated by modified Mosher's method, electronic circular dichroism (ECD) calculations and 13C NMR calculations as well as DP4+ probability analyses. All the compounds were evaluated for their antifungal and cytotoxicity.

Effect of sheep grazing on seed circulation on the Loess Plateau.

In grazing ecosystems, mature seeds fall directly to the soil to form the soil seed bank (SSB), or are ingested by grazing livestock to become part of the dung seed bank (DSB; i.e., seed circulation). Both the SSB and DSB form the basis for the natural regeneration of vegetation. However, little is known about the relationships between the SSB, DSB, and aboveground vegetation (AGV) community under different stocking rates (SRs). This study investigated the relationships between the SSB, seeds in Tan sheep (Ovis aries) dung, and AGV at different SRs (0, 2.7, 5.3, and 8.7 sheep ha-1) in a semiarid region of the Loess Plateau in China. We found that Tan sheep grazing increased the species richness heterogeneity of grassland vegetation, and negatively influenced the density of AGV. Under natural conditions, 17 species from soil-borne seeds and 10 species from Tan sheep dung germinated. There was low species similarity between the soil and DSBs and AGV. Sheep SR and the seed banks (soil and dung) were negatively correlated with AGV. Seeds are cycled from herbage to livestock to soil during cold season grazing; the seasonal nature of this seed dispersal is an adaptation to harsh, semiarid environments. Increased seed bank diversity under sheep grazing facilitates grassland regeneration on the Loess Plateau, similarly to other semiarid regions globally.

Identification of Cancer Driver Modules Based on Graph Clustering from Multiomics Data.

A major challenge in cancer genomics is to identify cancer driver genes and modules. Most existing methods to identify cancer driver modules (iCDM) identify groups of genes whose somatic mutational patterns exhibit either mutual exclusivity or high coverage of patient samples, without considering other biological information from multiomics data sets. Here we integrate mutual exclusivity, coverage, and protein-protein interaction information to construct an edge-weighted network, and present a graph clustering approach based on symmetric non-negative matrix factorization to iCDM. iCDM was tested on pan-cancer data and the results were compared with those from several advanced computational methods. Our approach outperformed other methods in recovering known cancer driver modules, and the identified driver modules showed high accuracy in classifying normal and tumor samples.

Burrow characteristics and ecological significance of Marmota himalayana in the northeastern Qinghai-Tibetan Plateau.

Burrows provide burrowing animals with a place to hibernate, reproduce, and avoid predators and harsh weather conditions and thus have a vital impact on their survival. However, the general physical characteristics and ecological functions of Marmota himalayana burrows as well as whether there are differences in burrow traits under different terrains (e.g., sunny slopes, shady slopes, and flat areas) are not well understood. From July to August 2019 (warm season), we used unmanned aerial vehicles to fly at low altitudes and slow speeds to locate 131 M. himalayana burrows (45 on shaded slopes, 51 on sunny slopes, and 35 on flat areas) in the northeastern Qinghai-Tibetan Plateau region. We then measured the physical characteristics (burrow density, entrance size, first tunnel length, volume, orientation, and plant characteristics near the burrow entrance) of these burrows on site. We found that terrain had a substantial influence on burrow density, orientation, and entrance size and on the angle of the burrow entrance; species richness had a substantial impact on path density and tunnel volume. The physical parameters of the M. himalayana burrows showed that they function to protect the marmots from natural enemies and bad weather, provide good drainage, and maintain a stable microclimate around the entrance. We discuss the ability of burrowing animals (e.g., M. himalayana) to adapt to the external environment based on their burrow characteristics.

DMFMDA: Prediction of Microbe-Disease Associations Based on Deep Matrix Factorization Using Bayesian Personalized Ranking.

Identifying the microbe-disease associations is conducive to understanding the pathogenesis of disease from the perspective of microbe. In this paper, we propose a deep matrix factorization prediction model (DMFMDA) based on deep neural network. First, the disease one-hot encoding is fed into neural network, which is transformed into a low-dimensional dense vector in implicit semantic space via embedding layer, and so is microbe. Then, matrix factorization is realized by neural network with embedding layer. Furthermore, our model synthesizes the non-linear modeling advantages of multi-layer perceptron based on the linear modeling advantages of matrix factorization. Finally, different from other methods using square error loss function, Bayesian Personalized Ranking optimizes the model from a ranking perspective to obtain the optimal model parameters, which makes full use of the unobserved data. Experiments show that DMFMDA reaches average AUCs of 0.9091 and 0.9103 in the framework of 5-fold cross validation and Leave-one-out cross validation, which is superior to three the-state-of-art methods. In case studies, 10, 9 and 9 out of top-10 candidate microbes are verified by recently published literature for asthma, inflammatory bowel disease and colon cancer, respectively. In conclusion, DMFMDA is successful application of deep learning in the prediction of microbe-disease association.

Exploring the potential microRNA sponge interactions of breast cancer based on some known interactions.

MicroRNA (miRNA) sponges' regulatory mechanisms play an important role in developing human cancer. Herein, we develop a new method to explore potential miRNA sponge interactions (EPMSIs) for breast cancer. Based on some known interactions, and a matching gene expression profile, EPMSIs explored other potential miRNA sponge interactions for breast cancer. Every interaction is inferred with a value representing interaction intensity. Then, we apply a clustering algorithm called BCPlaid to potential interactions. Ten modules are identified; nine of them are closely associated with biological enrichments. When we employ a classification algorithm to separate normal and tumor samples in each module, each module demonstrates powerful classification performance. Furthermore, EPMSI illustrates a new method to explore the miRNA sponge regulatory network for breast cancer by applying its superior performance.

A Novel Method for Identifying the Potential Cancer Driver Genes Based on Molecular Data Integration.

The identification of the cancer driver genes is essential for personalized therapy. The mutation frequency of most driver genes is in the middle (2-20%) or even lower range, which makes it difficult to find the driver genes with low-frequency mutations. Other forms of genomic aberrations, such as copy number variations (CNVs) and epigenetic changes, may also reflect cancer progression. In this work, a method for identifying the potential cancer driver genes (iPDG) based on molecular data integration is proposed. DNA copy number variation, somatic mutation, and gene expression data of matched cancer samples are integrated. In combination with the method of iKEEG, the "key genes" of cancer are identified, and the change in their expression levels is used for auxiliary evaluation of whether the mutated genes are potential drivers. For a mutated gene, the concept of mutational effect is defined, which takes into account the effects of copy number variation, mutation gene itself, and its neighbor genes. The method mainly includes two steps: the first step is data preprocessing. First, DNA copy number variation and somatic mutation data are integrated. Then, the integrated data are mapped to a given interaction network, and the diffusion kernel is used to form the mutation effect matrix. The second step is to obtain the key genes by using the iKGGE method, and construct the connection matrix by means of the gene expression data of the key genes and mutation impact matrix of the mutated genes. Experiments on TCGA breast cancer and Glioblastoma multiforme datasets demonstrate that iPDG is effective not only to identify the known cancer driver genes but also to discover the rare potential driver genes. When measured by functional enrichment analysis, we find that these genes are clearly associated with these two types of cancers.

Induction of osteoclast-like cell formation by leptin-induced soluble intercellular adhesion molecule secreted from cancer cells.

BACKGROUND: Leptin is considered a tumorigenic adipokine, suggested to promote tumorigenesis and progression in many cancers. On the other hand, intercellular adhesion molecule-1 (ICAM-1) shows altered expression in a variety of benign and malignant diseases. Histologically, ICAM-1 expression is reported as proportional to cancer stage and considered as a potential diagnosis biomarker. The altered expressions of ICAM-1 and its soluble form in malignant diseases have gained interests in recent years. MATERIAL AND METHODS: The expression of ICAM-1 and its regulatory signaling were examined by Western blot or flow cytometry. The effect of soluble ICAM-1 on osteoclast formation was investigated by tartrate-resistance acid phosphatase staining of RAW cells and tumor-induced osteolysis in vivo. RESULTS: In our study, we found that leptin enhanced soluble ICAM-1 production but not surface ICAM-1 expression in lung and breast cancer cells, and this effect was regulated through leptin receptor (ObR), while silencing ObR abrogated leptin-induced soluble ICAM-1 expression. In addition, we revealed that leptin administration provoked the JAK1/2, STAT3, FAK, ERK, and GSK3αß signaling cascade, leading to the elevation of ICAM-1 expression. Moreover, soluble ICAM-1 secreted by leptin-stimulated cancer cells synergize with the receptor activator of nuclear factor kappa-B ligand (RANKL) in inducing osteoclast formation. Soluble ICAM also enhanced tumor-induced osteolysis in vivo. CONCLUSION: These findings suggest that soluble ICAM-1 produced under leptin treatment enhances osteoclast formation and is involved in tumor-induced osteolysis.Leptin plays an important role in physiology in health and diseases. Leptin affects immune responses that may induce inflammation and carcinogenesis. Leptin is also considered as a tumorigenic adipokine suggested to promote tumorigenesis and progression in many cancers. On the other hand, intercellular adhesion molecule-1 (ICAM-1) shows altered expression in a variety of benign and malignant diseases. Histologically, ICAM-1 expression is reported to be proportional to cancer stage and considered as a potential diagnosis biomarker. It has been reported that soluble ICAM-1 allows tumor cells to escape from immune recognition and stimulates angiogenesis and tumor growth. The altered expressions of ICAM-1 and its soluble form in malignant diseases have gained interests in recent years. In our study, we found that leptin enhanced soluble ICAM-1 production but not surface ICAM-1 expression in lung and breast cancer cells, and this effect was regulated through leptin receptor (ObR), while silencing ObR abrogated leptin-induced soluble ICAM-1 expression. In addition, we revealed that leptin administration provoked the JAK1/2, STAT3, FAK, ERK, and GSK3αß signaling cascade, leading to the elevation of ICAM-1 expression. Moreover, soluble ICAM-1 secreted by leptin-stimulated cancer cells synergize with receptor activator of nuclear factor-kappa B ligand in inducing osteoclast formation. Soluble ICAM also enhanced tumor-induced osteolysis in vivo. These findings suggest that soluble ICAM-1 produced under leptin treatment is possibly involved in lung and breast cancer bone metastasis.

Prediction of Potential Associations Between MicroRNA and Disease Based on Bayesian Probabilistic Matrix Factorization Model.

The association between microRNAs (miRNAs) and diseases is significant to understand the development and progression of many human diseases. Given the cost and complexity of biological experiments, the computational method for predicting the potential association between miRNAs and disease will be an effective complement. In this article, we have developed a model (microRNA and disease based on Bayesian probabilistic matrix factorization, MDBPMF) based on a fully Bayesian treatment of the probabilistic matrix factorization to find potential associations between miRNAs and diseases by using the HMDDv2.0 database, which contains proven miRNA-disease associations. We show that Bayesian probabilistic matrix factorization models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the HMDDv2.0 database. MDBPMF achieves reliable prediction with an average area under receiver operating characteristic curve of 0.8755 for eight complex diseases based on fivefold cross-validation, which indeed outperforms the state-of-the-art method. In addition, a case study of lung cancer further verifies the utility of our method.

Design and Usability Evaluation of Mobile Cloud Healthcare System for Diabetes Prevention.

In this study, a mobile cloud healthcare system was implemented to assist middle- and old-aged people with diabetes preventive healthcare. First of all, a prototype system was developed. It was a system relying on data mining computing technology and big data analytics. Besides, it was constructed under the environment architecture of VMware cloud computing. This mobile cloud healthcare system was developed via mobile devices. Its purpose was to set up a diabetes preventive healthcare service for users, and to further assess the usability of this mobile cloud care system.

An Integrated Framework for Identifying Mutated Driver Pathway and Cancer Progression.

Next-generation sequencing (NGS) technologies provide amount of somatic mutation data in a large number of patients. The identification of mutated driver pathway and cancer progression from these data is a challenging task because of the heterogeneity of interpatient. In addition, cancer progression at the pathway level has been proved to be more reasonable than at the gene level. In this paper, we introduce an integrated framework to identify mutated driver pathways and cancer progression (iMDPCP) at the pathway level from somatic mutation data. First, we use uncertainty coefficient to quantify mutual exclusivity on gene driver pathways and develop a computational framework to identify mutated driver pathways based on the adaptive discrete differential evolution algorithm. Then, we construct cancer progression model for driver pathways based on the Bayesian Network. Finally, we evaluate the performance of iMDPCP on real cancer somatic mutation datasets. The experimental results indicate that iMDPCP is more accurate than state-of-the-art methods according to the enrichment of KEGG pathways, and it also provides new insights on identifying cancer progression at the pathway level.

Prediction of Microbe-Disease Associations by Graph Regularized Non-Negative Matrix Factorization.

More and more evidence shows that microbes play crucial roles in human health and disease. The exploration of the relationship between microbes and diseases will help people to better understand the underlying pathogenesis and have important implications for disease diagnosis and prevention. However, the known associations between microbes and diseases are very less. We proposed a new method called non-negative matrix factorization microbe-disease associations (NMFMDA), which used Gaussian interaction profile kernel similarity measure, to calculate microbial similarity and disease similarity, and applied a logistic function to regulate disease similarity. And, based on the known microbe-disease associations, a graph-regularized non-negative matrix factorization model was utilized to simultaneously identify potential microbe-disease associations. Moreover, fivefold cross-validation was utilized to evaluate the performance of our method. It reached the reliable area under receiver operating characteristic curve (AUC) of 0.8891, higher than other state-of-the-art methods. Finally, the case studies on three complex human diseases (i.e., asthma, inflammatory bowel disease, and colon cancer) demonstrated the good performance of our method. In summary, our method can be considered as an effective computational model for predicting potential disease-microbe associations.

Fisetin inhibits cell migration via inducing HO-1 and reducing MMPs expression in breast cancer cell lines.

Metastasis is commonly seen in advanced stage of cancers, and matrix metalloproteinases (MMPs) are commonly up-regulated and have been identified as critical regulators. In this present study, a flavonoid, fisetin, which can be found in diverse foods, is investigated for its ability to inhibit cell motility, and the underlying mechanism is also studied in breast cancer cells (4T1 and JC cells). We have revealed that fisetin increased HO-1 mRNA and protein expressions. Besides, fisetin also elevated Nrf2 expression in nuclear fraction. By silencing Nrf2, fisetin-induced HO-1 expression was abrogated, suggested that HO-1 expression was mediated by up-regulation of the transcription factor Nrf2. In addition, we also found that fisetin decreased MMP-2 and MMP-9 enzyme activity and gene expression in both protein and mRNA levels. Moreover, by administration of HO-1 inhibitors, tin protoporphyrin and zinc protoporphyrin, fisetin-reduced MMP-2 and MMP-9 expressions were reversed. Furthermore, transfection of siRNA against HO-1 and Nrf2 also abolished MMP-2 and MMP-9 reduction exerted by fisetin. These findings suggest that fisetin-mediated MMP-2 and MMP-9 reduction is regulated by HO-1 through Nrf2. Therefore, fisetin may be useful as a potential therapeutic agent for the treatment of metastatic breast cancer.

An efficient strategy for identifying cancer-related key genes based on graph entropy.

Gene networks are beneficial to identify functional genes that are highly relevant to clinical outcomes. Most of the current methods require information about the interaction of genes or proteins to construct genetic network connection. However, the conclusion of these methods may be bias because of the current incompleteness of human interactome. In this paper, we propose an efficient strategy to use gene expression data and gene mutation data for identifying cancer-related key genes based on graph entropy (iKGGE). Firstly, we construct a gene network using only gene expression data based on the sparse inverse covariance matrix, then, cluster genes use the algorithm of parallel maximal cliques for quickly obtaining a series of subgraphs, and at last, we introduce a novel metric that combine graph entropy and the influence of upstream gene mutations information to measure the impact factors of genes. Testing of the three available cancer datasets shows that our strategy can effectively extract key genes that may play distinct roles in tumorigenesis, and the cancer patient risk groups are well predicted based on key genes.

Triterpenic Acids from Potentilla parvifolia and Their Protective Effects against Okadaic Acid Induced Neurotoxicity in Differentiated SH-SY5Y Cells.

Six triterpenic acids were separated and purified from the ethyl acetate extractive fraction of ethanol extracts of Potentilla parvifolia FISCH. using a variety of chromatographic methods. The neuroprotective effects of these triterpenoids were investigated in the present study, in which the okadaic acid induced neurotoxicity in human neuroblastoma SH-SY5Y cells were used as an Alzheimer's disease cell model in vitro. The cell model was established with all trans-retinoic acid (5 µmol/L, 4 d) and okadaic acid (40 nmol/L, 6 h) treatments to induce tau phosphorylation and synaptic atrophy. Subsequently, the neuroprotective effects of these triterpenic acids were evaluated in vitro by this cell model. Results from the Western blot and morphology analysis suggested that compounds 3-6 had the better neuroprotective effects. Furthermore, we tested the level of mitochondrial reactive oxygen species and mitochondrial membrane potential of these compounds in SH-SY5Y cells by flow cytometry technology to investigate the potential neuroprotective mechanism of these compounds. All of the results indicated that maybe the mechanism of compounds 5 and 6 is to protect the cell from mitochondrial oxidative stress injuries.

MIMPFC: Identifying miRNA-mRNA regulatory modules by combining phase-only correlation and improved rough-fuzzy clustering.

MicroRNAs (miRNAs) play a key role in gene expression and regulation in various organisms. They control a wide range of biological processes and are involved in several types of cancers by causing mRNA degradation or translational inhibition. However, the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With the accumulation of the expression data of miRNAs and mRNAs, many computational methods have been proposed to predict miRNA-mRNA regulatory relationship. However, most existing methods require the number of modules predefined that may be difficult to determine beforehand. Here, we propose a novel computational method to discover miRNA-mRNA regulatory modules by combining Phase-only correlation and improved rough-Fuzzy Clustering (MIMPFC). The proposed method is evaluated on three heterogeneous datasets, and the obtained results are further validated through relevant literatures, biological significance and functional enrichment analysis. The analysis results show that the identified modules are highly correlated with the biological conditions. A large part of the regulatory relationships found by MIMPFC has been confirmed in the experimentally verified databases. It demonstrates that the modules found by MIMPFC are biologically significant.

Inhibition of estrogen receptor reduces connexin 43 expression in breast cancers.

Connexins are widely supported as tumor suppressors due to their downregulation in cancers, nevertheless, more recent evidence suggests roles for connexins in facilitating tumor progression in later stages, including metastasis. One of the key factors regulating the expression, modification, stability, and localization of connexins is hormone receptors in hormone-dependent cancers. It is reasonable to consider that hormones/hormone receptors may modulate connexins expression and play critical roles in the cellular control of connexins during breast cancer progression. In estrogen receptor (ER)-positive breast cancers, tamoxifen and fulvestrant are widely used therapeutic agents and are considered to alter ER signaling. In this present study, we investigated the effects of fulvestrant and tamoxifen in Cx43 expression, and we also explored the role of Cx43 in ER-positive breast cancer migration and the relationship between Cx43 and ER. The involvement of estrogen/ER in Cx43 modulation was further verified by administering tyrosine kinase inhibitors and chemotherapeutic agents. We found that inhibition of ER promoted the binding of E3 ligase Nedd4 to Cx43, leading to Cx43 ubiquitination. Furthermore, inhibition of ER by fulvestrant and tamoxifen phosphorylated p38 MAPK, and inhibition of Rac, MKK3/6, and p38 reversed fulvestrant-reduced Cx43 expression. These findings suggest that Cx43 expression which may positively regulate cell migration is ER-dependent in ER-positive breast cancer cells.

GSMC: Combining Parallel Gibbs Sampling with Maximal Cliques for Hunting DNA Motif.

Regulatory elements are responsible for regulating gene transcription. Therefore, identification of these elements is a tremendous challenge in the field of gene expression. Transcription factors (TFs) play a key role in gene regulation by binding to target promoter sequences. A set of conserved sequence patterns with a highly similar structure that is bound by a TF is called a motif. Motif discovery has been a difficult problem over the past decades. Meanwhile, it is a foundation stone in meeting this challenge. Recent advances in obtaining genomic sequences and high-throughput gene expression analysis techniques have enabled the rapid development of computational methods for motif discovery. As a result, a large number of motif-finding algorithms aiming at various motif models have sprung up in the past few years. However, most of them are not suitable for analysis of the large data sets generated by next-generation sequencing. To better handle large-scale ChIP-Seq data and achieve better performance in computational time and motif detection accuracy, we propose an excellent motif-finding algorithm known as GSMC (Combining Parallel Gibbs Sampling with Maximal Cliques for hunting DNA Motif). The GSMC algorithm consists of two steps. First, we employ the commonly used Gibbs sampling to generating initial motifs. Second, we utilize maximal cliques to cluster motifs according to Similarity with Position Information Contents (SPIC). Consequently, we raise the detection accuracy in a great degree, in the meantime holding comparative computation efficiency. In addition, we can find much more credible cofactor interacting motifs.