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.
As
a guiding backbone of origami structural folding, the long single-stranded
scaffold is the key enabler of all kinds of complex DNA nanostructures.
However, the folding of most origami nanostructures depends heavily
on very limited species of scaffolds. It is desirable to have a wider
choice of scaffolds for the extended design space. Here we develop
a new method based on rolling circle amplification to prepare scaffolds
of custom lengths and sequences. We demonstrate that amplicons templated
by viral genome and plasmid can serve as scaffolds to fold DNA origami
nanostructures.
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