Depression is a major contributor to the overall global burden of diseases. Traditionally, doctors diagnose depressed people face to face via referring to clinical depression criteria. However, more than 70% of the patients would not consult doctors at early stages of depression, which leads to further deterioration of their conditions. Meanwhile, people are increasingly relying on social media to disclose emotions and sharing their daily lives, thus social media have successfully been leveraged for helping detect physical and mental diseases. Inspired by these, our work aims to make timely depression detection via harvesting social media data. We construct well-labeled depression and non-depression dataset on Twitter, and extract six depression-related feature groups covering not only the clinical depression criteria, but also online behaviors on social media. With these feature groups, we propose a multimodal depressive dictionary learning model to detect the depressed users on Twitter. A series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines. Finally, we analyze a large-scale dataset on Twitter to reveal the underlying online behaviors between depressed and non-depressed users.

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Aims With the global atmospheric nitrogen (N) deposition increasing, the effect of N deposition on terrestrial plant diversity has been widely studied. Some studies have reviewed the effects of N deposition on plant species diversity; however, all studies addressed the effects of N deposition on plant community focused on species richness in specific ecosystem. There is a need for a systematic meta-analysis covering multiple dimensions of plant diversity in multiple climate zones and ecosystems types. Our goal was to quantify changes in species richness, evenness and uncertainty in plant communities in response to N addition across different environmental and experimental contexts. Methods We performed a meta-analysis of 623 experimental records published in English and Chinese journals to evaluate the response of terrestrial plant diversity to the experimental N addition in China. Three metrics were used to quantify the change in plant diversity: species richness (SR), evenness (Pielou index) uncertainty (Shannon index). Important Findings Results showed that (i) N addition negatively affected SR in temperate, Plateau zones and subtropical zone, but had no significant effect on Shannon index in subtropical zones; (ii) N addition decreased SR, Shannon index and Pielou index in grassland, and the negative effect of N addition on SR was stronger in forest than in grassland; (iii) N addition negatively affected plant diversity (SR, Shannon index and Pielou index) in the long term, whereas it did not affect plant diversity in the short term. Furthermore, the increase in N addition levels strengthened the negative effect of N deposition on plant diversity with long experiment duration; and (iv) the negative effect of ammonium nitrate (NH4NO3) addition on SR was stronger than that of urea (CO(NH2)2) addition, but the negative effect of NH4NO3 addition on Pielou index was weaker than that of CO(NH2)2 addition. Our results indicated that the effects of N addition on plant diversity varied depending on climate zones, ecosystem types, N addition levels, N type and experiment duration. This underlines the importance of integrating multiple dimensions of plant diversity and multiple factors into assessments of plant diversity to global environmental change.

The complete mitochondrial genome of the leafhopper Parathailocyba orla (Dworakowska, 1977) was sequenced and annotated in this study. The mitochondrial genome is 15,382 bp long, and nucleotide composition of the whole mitogenome is highly A þ T biased (A: 45.8%, T: 33.3%, G: 8.6%, C: 12.3%). 12 PCGs have ATN as the start codon, except for atp8 gene uses TTG. All protein-coding genes used TAA as stop codon, except for nad5, cox2 and cox3, which use incomplete T-as stop codon. Phylogenetic analysis using 13 PCGs showed that P. orla was clustered with three Typhlocybinae species, which was consistent with the conventional classification.