Seismological research in Yunnan Province, China, and its tectonic implication between the Xianshuihe-Xiaojiang fault system and the Red River fault zone

The tectonic crosscutting relationship between the two most tectonic important Xianshuihe-Xiaojiang fault system (XFS) and the Red River fault zone (RRF) is a key basic problem but it is controversial now. These obscure further leads to a hot argument about the geodynamic model of the southeast margin of the Tibetan Plateau. In order to answer whether the XFS has cut across the RRF and extended southwardly, multiple seismological methods, including seismic relocation, b-value analysis, seismic energy, density study, focal mechanism inversion, and regional stress field research, are applied in the Yunnan Province area, China, where the XFS and the RRF intersects with each other. The results comprehensively demonstrate that the southern segment of the XFS has not been affected by the RRF, and it has continued for a length after crossing the RRF, but the Dian Bien Phu fault zone should not be an extension fault of the XFS. Along the SW direction, starting from the middle segment of the XFS, and cutting across the Qujiang fault, Shiping-Jianshui fault zone, RRF, Ailaoshan fault zone, Wuliangshan fault zone, and the southern section of the Daluo fault, the belt should be treated as the eastern boundary of the clockwise rotational in geodynamics model of the Tibetan Plateau in this study area. Based on these conclusion above and previous recognitions, a new geodynamic evolution model is proposed

Integrated Chemical and Transcriptomic Analyses Unveils Synthetic Characteristics of Different Medicinal Root Parts of Angelica Sinensis

Objective: Why are different medicinal parts including heads, bodies and tails of Angelicae Sinensis Radix (ASR) distinct in pharmaceutical activities? Here we explored their discrepancy in chemical constituents and transcriptome. Methods: ASR were separated into three medicinal parts: heads (rootstocks with petiole traces of ASR), bodies (taproots of ASR) and tails (lateral roots of ASR), and chemical and transcriptomic analyses were conducted simultaneously. Results: High performance liquid chromatography (HPLC) fingerprint results showed that five widely used active ingredients (ferulic acid, senkyunolide H, senkyunolide A, n-butylphathlide, and ligustilide) were distributed unevenly in the three ASR medicinal parts. Partial least squares-discriminant analysis (PLS-DA) demonstrated that the heads can be differentiated from the two other root parts due to different amounts of the main components. However, the content of ferulic acid (a main quality marker) was significantly higher in tails than in the heads and bodies. The transcriptome analysis found that 25,062, 10,148 and 29,504 unigenes were specifically expressed in the heads, bodies and tails, respectively. WGCNA analysis identified 17 co-expression modules, which were constructed from the 19,198 genes in the nine samples of ASR. Additionally, we identified 28 unigenes involved in two phenylpropanoid biosynthesis (PB) pathways about ferulic acid metabolism pathways, of which 17 unigenes (60.7%) in the PB pathway were highly expressed in the tails. The expression levels of PAL, C3H, and CQT transcripts were significantly higher in the tails than in other root parts. RT-qPCR analysis confirmed that PAL, C3H, and CQT genes were predominantly expressed in the tail parts, especially PAL, whose expression was more than doubled as compared with that in other root parts. Conclusion: Chemical and transcriptomic analyses revealed the distribution contents and pivotal transcripts of the ferulic acid biosynthesis-related pathways. The spatial gene expression pattern partially explained the discrepancy of integral medicinal activities of three medicinal root parts

Identifying and analyzing novel epilepsy-related genes using random walk with restart algorithm.

As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients' personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases

Literature Explorer: effective retrieval of scientific documents through nonparametric thematic topic detection

© 2020 The Authors. Published by Springer. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1007/s00371-019-01721-7Scientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through mining and interactive visualisation. We propose a novel topic mining method that is able to uncover “thematic topics” from a scientific corpus. These thematic topics have an explicit semantic association to the research themes that are commonly used by human researchers in scientific fields, and hence are human interpretable. They also contribute to effective document retrieval. The visual analytics suite consists of a set of visual components that are closely coupled with the underlying thematic topic detection to support interactive document retrieval. The visual components are adequately integrated under the design rationale and goals. Evaluation results are given in both objective measurements and subjective terms through expert assessments. Comparisons are also made against the outcomes from the traditional topic modelling methods.This research is supported by the European Commission with project Dr Inventor (No 611383), MyHealthAvatar (No 60929), and by the UK Engineering and Physical Sciences Research Council with project MyLifeHub (EP/L023830/1).Published onlin

Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request

Tissue Expression Difference between mRNAs and lncRNAs

Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon's entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups

Soil Respiration in Tibetan Alpine Grasslands: Belowground Biomass and Soil Moisture, but Not Soil Temperature, Best Explain the Large-Scale Patterns

The Tibetan Plateau is an essential area to study the potential feedback effects of soils to climate change due to the rapid rise in its air temperature in the past several decades and the large amounts of soil organic carbon (SOC) stocks, particularly in the permafrost. Yet it is one of the most under-investigated regions in soil respiration (Rs) studies. Here, Rs rates were measured at 42 sites in alpine grasslands (including alpine steppes and meadows) along a transect across the Tibetan Plateau during the peak growing season of 2006 and 2007 in order to test whether: (1) belowground biomass (BGB) is most closely related to spatial variation in Rs due to high root biomass density, and (2) soil temperature significantly influences spatial pattern of Rs owing to metabolic limitation from the low temperature in cold, high-altitude ecosystems. The average daily mean Rs of the alpine grasslands at peak growing season was 3.92 µmol CO2 m−2 s−1, ranging from 0.39 to 12.88 µmol CO2 m−2 s−1, with average daily mean Rs of 2.01 and 5.49 µmol CO2 m−2 s−1 for steppes and meadows, respectively. By regression tree analysis, BGB, aboveground biomass (AGB), SOC, soil moisture (SM), and vegetation type were selected out of 15 variables examined, as the factors influencing large-scale variation in Rs. With a structural equation modelling approach, we found only BGB and SM had direct effects on Rs, while other factors indirectly affecting Rs through BGB or SM. Most (80%) of the variation in Rs could be attributed to the difference in BGB among sites. BGB and SM together accounted for the majority (82%) of spatial patterns of Rs. Our results only support the first hypothesis, suggesting that models incorporating BGB and SM can improve Rs estimation at regional scale