Diverse Bacterial PKS Sequences Derived From Okadaic Acid-Producing Dinoflagellates

Okadaic acid (OA) and the related dinophysistoxins are isolated from dinoflagellates of the genus Prorocentrum and Dinophysis. Bacteria of the Roseobacter group have been associated with okadaic acid producing dinoflagellates and have been previously implicated in OA production. Analysis of 16S rRNA libraries reveals that Roseobacter are the most abundant bacteria associated with OA producing dinoflagellates of the genus Prorocentrum and are not found in association with non-toxic dinoflagellates. While some polyketide synthase (PKS) genes form a highly supported Prorocentrum clade, most appear to be bacterial, but unrelated to Roseobacter or Alpha-Proteobacterial PKSs or those derived from other Alveolates Karenia brevis or Crytosporidium parvum

Cyclopiazonic Acid Biosynthesis of Aspergillus flavus and Aspergillus oryzae

Cyclopiazonic acid (CPA) is an indole-tetramic acid neurotoxin produced by some of the same strains of A. flavus that produce aflatoxins and by some Aspergillus oryzae strains. Despite its discovery 40 years ago, few reviews of its toxicity and biosynthesis have been reported. This review examines what is currently known about the toxicity of CPA to animals and humans, both by itself or in combination with other mycotoxins. The review also discusses CPA biosynthesis and the genetic diversity of CPA production in A. flavus/oryzae populations

Click-Through Rate Prediction of Online Banners Featuring Multimodal Analysis

As the online advertisement industry continues to grow, it is predicted that online advertisement will account for about 45% of global advertisement spending by 2020.a Thus, predicting the click-through rates (CTRs) of advertisements is increasingly crucial for the advertisement industry. Many studies have already addressed the CTR prediction. However, most studies tried to solve the problem using only metadata such as user id, URL of the landing page, business category, device type, etc., and did not include multimedia contents such as images or texts. Using these multimedia features with deep learning techniques, we propose a method to effectively predict CTRs for online banners, a popular form of online advertisements. We show that multimedia features of advertisements are useful for the task at hand. In our previous work [ 1 ], we proposed a CTR prediction model, which outperformed the state-of-the-art method that uses the three features mentioned above, and also we introduced an attention network for visualizing how much each feature affected the prediction result. In this work, we introduce another text analysis technique and more detailed metadata. As a result, we have achieved much better performance as compared to our previous work. Besides, for better analyzing of our model, we introduce another visualization technique to show regions in an image that make its CTR better or worse. Our prediction model gives us useful suggestions for improving design of advertisements to acquire higher CTRs. </jats:p