Feature extraction and selection for Arabic tweets authorship authentication

© 2017, Springer-Verlag Berlin Heidelberg. In tweet authentication, we are concerned with correctly attributing a tweet to its true author based on its textual content. The more general problem of authenticating long documents has been studied before and the most common approach relies on the intuitive idea that each author has a unique style that can be captured using stylometric features (SF). Inspired by the success of modern automatic document classification problem, some researchers followed the Bag-Of-Words (BOW) approach for authenticating long documents. In this work, we consider both approaches and their application on authenticating tweets, which represent additional challenges due to the limitation in their sizes. We focus on the Arabic language due to its importance and the scarcity of works related on it. We create different sets of features from both approaches and compare the performance of different classifiers using them. We experiment with various feature selection techniques in order to extract the most discriminating features. To the best of our knowledge, this is the first study of its kind to combine these different sets of features for authorship analysis of Arabic tweets. The results show that combining all the feature sets we compute yields the best results

Metabolic phenotyping of opioid and psychostimulant addiction: A novel approach for biomarker discovery and biochemical understanding of the disorder.

Despite the progress in characterising the pharmacological profile of drugs of abuse, their precise biochemical impact remains unclear. The metabolome reflects the multifaceted biochemical processes occurring within a biological system. This includes those encoded in the genome but also those arising from environmental/exogenous exposures and interactions between the two. Using metabolomics, the biochemical derangements associated with substance abuse can be determined as the individual transitions from recreational drug to chronic use (dependence). By understanding the biomolecular perturbations along this time course and how they vary across individuals, metabolomics can elucidate biochemical mechanisms of the addiction cycle (dependence/withdrawal/relapse) and predict prognosis (recovery/relapse). In this review, we summarise human and animal metabolomic studies in the field of opioid and psychostimulant addiction. We highlight the importance of metabolomics as a powerful approach for biomarker discovery and its potential to guide personalised pharmacotherapeutic strategies for addiction targeted towards the individual's metabolome

Separating T Cell Targeting Components onto Magnetically Clustered Nanoparticles Boosts Activation

T cell activation requires the coordination of a variety of signaling molecules including T cell receptor-specific signals and costimulatory signals. Altering the composition and distribution of costimulatory molecules during stimulation greatly affects T cell functionality for applications such as adoptive cell therapy (ACT), but the large diversity in these molecules complicates these studies. Here, we develop and validate a reductionist T cell activation platform that enables streamlined customization of stimulatory conditions. This platform is useful for the optimization of ACT protocols as well as the more general study of immune T cell activation. Rather than decorating particles with both signal 1 antigen and signal 2 costimulus, we use distinct, monospecific, paramagnetic nanoparticles, which are then clustered on the cell surface by a magnetic field. This allows for rapid synthesis and characterization of a small number of single-signal nanoparticles which can be systematically combined to explore and optimize T cell activation. By increasing cognate T cell enrichment and incorporating additional costimulatory molecules using this platform, we find significantly higher frequencies and numbers of cognate T cells stimulated from an endogenous population. The magnetic field-induced association of separate particles thus provides a tool for optimizing T cell activation for adoptive immunotherapy and other immunological studies