A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media.

In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression.

Organic cattle products: Authenticating production origin by analysis of serum mineral content.

An authentication procedure for differentiating between organic and non-organic cattle production on the basis of analysis of serum samples has been developed. For this purpose, the concentrations of fourteen mineral elements (As, Cd, Co, Cr, Cu, Fe, Hg, I, Mn, Mo, Ni, Pb, Se and Zn) in 522 serum samples from cows (341 from organic farms and 181 from non-organic farms), determined by inductively coupled plasma spectrometry, were used. The chemical information provided by serum analysis was employed to construct different pattern recognition classification models that predict the origin of each sample: organic or non-organic class. Among all classification procedures considered, the best results were obtained with the decision tree C5.0, Random Forest and AdaBoost neural networks, with hit levels close to 90% for both production types. The proposed method, involving analysis of serum samples, provided rapid, accurate in vivo classification of cattle according to organic and non-organic production type.