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Hepatocellular carcinoma (HCC) is a complex disease with a multi-step carcinogenic process from preneoplastic lesions, including cirrhosis, low-grade dysplastic nodules (LGDNs), and high-grade dysplastic nodules (HGDNs) to HCC. There is only an elemental understanding of its molecular pathogenesis, for which a key problem is to identify when and how the critical transition happens during the HCC initiation period at a molecular level. In this work, for the first time, we revealed that LGDNs is the tipping point (i.e., pre-HCC state rather than HCC state) of hepatocarcinogenesis based on a series of gene expression profiles by a new mathematical model termed dynamic network biomarkers (DNB)—a group of dominant genes or molecules for the transition. Different from the conventional biomarkers based on the differential expressions of the observed genes (or molecules) for diagnosing a disease state, the DNB model exploits collective fluctuations and correlations of the observed genes, thereby predicting the imminent disease state or diagnosing the critical state. Our results show that DNB composed of 59 genes signals the tipping point of HCC (i.e., LGDNs). On the other hand, there are a large number of differentially expressed genes between cirrhosis and HGDNs, which highlighted the stark differences or drastic changes before and after the tipping point or LGDNs, implying the 59 DNB members serving as the early-warning signals of the upcoming drastic deterioration for HCC. We further identified the biological pathways responsible for this transition, such as the type I interferon signaling pathway, Janus kinase–signal transducers and activators of transcription (JAK–STAT) signaling pathway, transforming growth factor (TGF)-β signaling pathway, retinoic acid-inducible gene I (RIG-I)-like receptor signaling pathway, cell adhesion molecules, and cell cycle. In particular, pathways related to immune system reactions and cell adhesion were downregulated, and pathways related to cell growth and death were upregulated. Furthermore, DNB was validated as an effective predictor of prognosis for HCV-induced HCC patients by survival analysis on independent data, suggesting a potential clinical application of DNB. This work provides biological insights into the dynamic regulations of the critical transitions during multistep hepatocarcinogenesis.

Jankowski's Bunting Emberiza jankowskii is endemic to China, Russia and Northern Korea, and was listed as a ‘Vulnerable’ species. The population in Dagang Forestry of western Jilin is one of the small remaining discrete breeding populations in the species' range. Very little information on the nesting biology and population dynamics has previously been published. We studied the nesting biology from 1999 to 2002 and population dynamics of the bunting from 1999 to 2006 (except 2003). A total of 74 nesting attempts were monitored. Jankowski's Bunting breeding season began in late April and usually ended in late July. Both sexes participated in nest-building, feeding young and defending the nest. Mean full clutch size for three years combined was 5.26 ± 0.76 eggs, and ranged from four to seven. Clutch size decreased with nest-initiation date. Mean hatching rate was 41.2%. Overall probability of Mayfield nest success to fledging was low for the three years, averaging 0.218 ± 0.007. The factors leading to low nest success include nest parasitization, nest predation, human activities and nest abandonment. Low survival of Jankowski's Bunting nests may be a factor in declining populations and the slow recovery of populations because of low recruitment at the population and the individual level. The population of Jankowski's Bunting in the Dagang Forestry grassland was small and declined dramatically from 1999 to 2006. The main threat is habitat loss and fragmentation due to agriculture, tree planting and housing following human colonization of the region. The habitat has been reduced in extent by c. 70% since the 1960s. In addition, grazing by domestic livestock dramatically destroyed their preferred vegetation. Furthermore, the restriction to several small, discrete sites makes the bunting inherently vulnerable to catastrophic and stochastic events that can eliminate subpopulations. Jankowski's bunting is one of the most threatened species in China and faces an unpredictable future. Maintaining the structure and general composition of remaining Jankowski's Bunting nesting habitat is important to ensure continued presence of this species in western Jilin and worldwide.

A new species of a new genus, Atopderma ellipta gen. et sp. nov. is described from the Middle Jurassic Jiulongshan Formation of Daohugou, Inner Mongolia, China. Because of poor preservation, the placement of these specimens is contentious, either Dermaptera or Coleoptera. However, we are inclined to attribute them to Dermaptera based on differences of five key characters. Detailed description and illustration of the specimens are given. Possible reasons for lacking good and complete abdomen preservation are also discussed.

BackgroundThe flowering transition which is controlled by a complex and intricate gene regulatory network plays an important role in the reproduction for offspring of plants. It is a challenge to identify the critical transition state as well as the genes that control the transition of flower development. With the emergence of massively parallel sequencing, a great number of time-course transcriptome data greatly facilitate the exploration of the developmental phase transition in plants. Although some network-based bioinformatics analyses attempted to identify the genes that control the phase transition, they generally overlooked the dynamics of regulation and resulted in unreliable results. In addition, the results of these methods cannot be self-explained.ResultsIn this work, to reveal a critical transition state and identify the transition-specific genes of flower development, we implemented a genome-wide dynamic network analysis on temporal gene expression data in Arabidopsis by dynamic network biomarker (DNB) method. In the analysis, DNB model which can exploit collective fluctuations and correlations of different metabolites at a network level was used to detect the imminent critical transition state or the tipping point. The genes that control the phase transition can be identified by the difference of weighted correlations between the genes interested and the other genes in the global network. To construct the gene regulatory network controlling the flowering transition, we applied NARROMI algorithm which can reduce the noisy, redundant and indirect regulations on the expression data of the transition-specific genes. In the results, the critical transition state detected during the formation of flowers corresponded to the development of flowering on the 7th to 9th day in Arabidopsis. Among of 233 genes identified to be highly fluctuated at the transition state, a high percentage of genes with maximum expression in pollen was detected, and 24 genes were validated to participate in stress reaction process, as well as other floral-related pathways. Composed of three major subnetworks, a gene regulatory network with 150 nodes and 225 edges was found to be highly correlated with flowering transition. The gene ontology (GO) annotation of pathway enrichment analysis revealed that the identified genes are enriched in the catalytic activity, metabolic process and cellular process.ConclusionsThis study provides a novel insight to identify the real causality of the phase transition with genome-wide dynamic network analysis.Electronic supplementary materialThe online version of this article (10.1186/s12870-018-1589-6) contains supplementary material, which is available to authorized users.