Numerous studies have demonstrated that plant species diversity enhances ecosystem functioning in terrestrial ecosystems, including diversity effects on insect arthropods (herbivores, predators and parasitoids) and plants. Yet, the effects of increased plant diversity across trophic levels in different ecosystems and biomes have not yet been explored on a global scale. Through a global meta-analysis of 2914 observations from 351 studies, we found that increased plant species richness reduced herbivore abundance and damage but increased predator and parasitoid abundance, predation, parasitism, and overall plant performance. Moreover, increased predator/parasitoid performance was correlated with reduced herbivore abundance and enhanced plant performance. We

BackgroundmicroRNAs (miRNAs) are endogenous, noncoding, small RNAs that have essential regulatory functions in plant growth, development, and stress response processes. However, limited information is available about their functions in sexual reproduction of flowering plants. Pollen development is an important process in the life cycle of a flowering plant and is a major factor that affects the yield and quality of crop seeds.ResultsThis study aims to identify miRNAs involved in pollen development. Two independent small RNA libraries were constructed from the flower buds of the male sterile line (Bcajh97-01A) and male fertile line (Bcajh97-01B) of Brassica campestris ssp. chinensis. The libraries were subjected to high-throughput sequencing by using the Illumina Solexa system. Eight novel miRNAs on the other arm of known pre-miRNAs, 54 new conserved miRNAs, and 8 novel miRNA members were identified. Twenty-five pairs of novel miRNA/miRNA* were found. Among all the identified miRNAs, 18 differentially expressed miRNAs with over two-fold change between flower buds of male sterile line (Bcajh97-01A) and male fertile line (Bcajh97-01B) were identified. qRT-PCR analysis revealed that most of the differentially expressed miRNAs were preferentially expressed in flower buds of the male fertile line (Bcajh97-01B). Degradome analysis showed that a total of 15 genes were predicted to be the targets of seven miRNAs.ConclusionsOur findings provide an overview of potential miRNAs involved in pollen development and interactions between miRNAs and their corresponding targets, which may provide important clues on the function of miRNAs in pollen development.

In this study, the investigation of the expression of HIWI and its protein in hepatocellular carcinoma (HCC) was performed, and the relationships between HIWI expression and the location of HCC metastases were analyzed. Sets of fresh HCC and matched adjacent normal hepatic tissue and paraffin-embedded tissue slides were provided by the hospital hepatology and pathology departments. RT-PCR, Western blot, and immunohistochemistry were performed to detect HIWI mRNA and protein. Correlations between HIWI expression and patient's age, sex, type of tumor, and metastasis location were recorded. HIWI mRNA and protein levels were significantly higher in HCC tissues than in adjacent normal hepatic tissue (P < 0.05). Immunohistochemistry showed positive staining for HIWI in cell cytoplasm; however, the number of HIWI-positive cells in HCC tissue (65.2%; 60/92) was significantly higher than in adjacent normal hepatic tissue (27.2%; 25/92) (P < 0.05). HIWI expression was not correlated with patients' age, gender, tumors' size, and location but correlated with metastasis involving lymph nodes and other remote organs (P < 0.05). HIWI expression is significantly higher in HCC tissue than in adjacent normal hepatic tissue. The results of this study suggest that HIWI may have a crucial role in HCC carcinogenesis and could serve as a potential biomarker or treatment target for HCC.

Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database and excluded substandard samples. The study population was 835 and was divided into the training set (n = 668) and validation set (n = 167). Characteristics selected via survival analyses were used to construct the model. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to evaluate the prediction. The accuracy of the prediction model was excellent both in the training set (area under the ROC curve [AUC] = 0.977) and the validation set (AUC = 0.911). Decision curve analyses proved the model could be used to support clinical decisions. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Our prediction model had excellent accuracy and is therefore useful in clinical settings.