Soil C, N and P stoichiometry in different forest stand types in the middle and lower reaches of the Beijiang River, China.

We examined the vertical distribution characteristics of soil organic carbon (C), total nitrogen (N), total phosphorus (P) and their ecological stoichiometric ratios in 0-80 cm soil profile under three forest stand types in the middle and lower reaches of the Beijiang River, including broad-leaved forest, coniferous forest, and mixed coniferous and broad-leaved forest. The results showed that soil C, N and P contents of the three forest stand types were 12.17-14.25, 1.14-1.31, and 0.27-0.30 g·kg-1, respectively. The contents of C and N decreased with the increases of soil depth. The content of C and N in each soil layer showed that coniferous and broad-leaved mixed forest > coniferous forest > broad-leaved forest. There was no significant difference in P content among the three stand types, and there was no obvious variation in the vertical profile. The soil C/N, C/P, and N/P of the three forest types were 11.2-11.3, 49.0-60.3, and 4.5-5.7, respectively. There was no significant difference in soil C/N among the three stand types. The highest soil C/P and N/P were found in the mixed forest. There was no interaction between soil depth and stand type in affecting soil C, N, P contents and their stoichiometric ratios. There was significant positive correlation between C and N, and between N and C/P in each stand type and soil layer. Soil C/P and N/P had stronger ecological indicating effects on stand types. The coniferous and broad-leaved mixed forest was strongly limited by P availability.

GraphDPA: Predicting drug-pathway associations by graph convolutional networks.

Pathway-based drug discovery is a promising strategy for the discovery of drugs with low toxicity and side effects. However, identifying the associations between drug and targeting pathways is challenging for this method. The formation of various biomolecular interaction databases and the development of neural network technology provide new ways for the large-scale prediction of drug-pathway associations. This article proposes a new model called GraphDPA, which represents the drug and pathway-gene association as a graph. We use graph convolutional networks (GCN) to learn the features of the drug and pathway and predict the drug-pathway association. The results show that GraphDPA can predict drug-pathway associations with high accuracy, which verify the potential of the GCN in drug discovery.

Effective use of sequence information to predict CRISPR-Cas9 off-target.

The CRISPR/Cas9 gene-editing system is the third-generation gene-editing technology that has been widely used in biomedical applications. However, off-target effects occurring CRISPR/Cas9 system has been a challenging problem it faces in practical applications. Although many predictive models have been developed to predict off-target activities, current models do not effectively use sequence pair information. There is still room for improved accuracy. This study aims to effectively use sequence pair information to improve the model's performance for predicting off-target activities. We propose a new coding scheme for coding sequence pairs and design a new model called CRISPR-IP for predicting off-target activity. Our coding scheme distinguishes regions with different functions in the sequence pairs through the function channel. Moreover, it distinguishes between bases and base pairs using type channels, effectively representing the sequence pair information. The CRISPR-IP model is based on CNN, BiLSTM, and the attention layer to learn features of sequence pairs. We performed performance verification on two data sets and found that our coding scheme can represent sequence pair information effectively, and the CRISPR-IP model performance is better than others. Data and source codes are available at https://github.com/BioinfoVirgo/CRISPR-IP.

[Relationships between leaf functional traits of Machilus pauhoi understory seedlings from different provenances and geographical environmental factors.] / ä¸åŒç§æºåˆ¨èŠ±æ¥ æž—ä¸‹å¹¼è‹—å¶åŠŸèƒ½æ€§çŠ¶ä¸Žåœ°ç†çŽ¯å¢ƒçš„å ³ç³».

To understand the adaptation mechanism of Machilus pauhoi seedlings to environmental changes, leaf functional traits of M. pauhoi seedlings from nine provenances, as well as relationships between leaf traits and environmental factors were analyzed. The results showed that leaf functional traits of M. pauhoi seedlings from different provenances showed significant intraspecific variations, with the coefficient of variation ranging from 8.8% to 28.2%. Specific leaf area, leaf relative water content, leaf tissue density and leaf thickness differed significantly among seedlings from different provenances, implying high leaf morphological plasticity. Moreover, leaf tissue density was positively correlated to leaf dry matter content and leaf relative water content, but negatively correlated to specific leaf area and leaf thickness. Specific leaf area showed significantly negative correlations with leaf dry matter content and leaf area. The covariation among those traits examined here indicated that M. pauhoi seedlings could adjust and balance the combination of leaf traits in response to environmental changes. Longitude, latitude, >10 ℃ annual accumulated temperature and mean annual temperature contributed largely to the variation of leaf functional traits of M. pauhoi seedlings. Leaf thickness decreased with the increase of longitude, while leaf dry matter content and leaf relative water content increased with the increase of longitude. Leaf tissue density was positively correlated with longitude and mean annual temperature, suggesting that longitude rather than mean annual temperature had greater effects on it. Leaf area was positively correlated with >10 ℃ annual accumulated temperature and longitude, indicating that annual accumulated temperature rather than longitude had greater effects on the variation of leaf area.

[Characteristics of leaf carbon, nitrogen and phosphorus stoichiometry in relation to plant size of Machilus pauhoi].

To explore the effects of stand age on variation patterns of leaf C, N, P stoichiometric characteristics of Machilus pauhoi, two stands, i.e., 9 and 13 years old, were selected. The relationships between leaf nutrient contents (C, N and P) and diameters at breast height (DBH) of individual plants were analyzed. The data revealed that the individual variations of seedlings in M. pauhoi stands were strengthened with the stand development. The stand age had significant effects on leaf C, N, P contents and C:N ratio but not on C:P and N: P ratios. Specifically, the mean values of leaf C, N, P contents and N:P ratio in the 9-year-old stand were lower than those in the 13-year-old stand, whereas, inverse pattern of C:N and C:P ratios were found in the two stands. Furthermore, leaf N and P stoichiometry varied significantly within the stand. Specifically, leaf N and P contents, as well as their stoichiometric ratios, linearly correlated with DBH in the 9-year-old stand. On the contrary, leaf N and P stoichiometry showed quadratic correlation in 13-year-old stand (except leaf C:N which linearly correlated with DBH). Lastly, nutrient transfer rates of leaf N and P in the 9-year-old stand were higher than that in 13-year-old stand, and the discrepancies of leaf nutrient transfer strategy between growing and non-growing seasons were caused by the different growth phases and environmental conditions.