A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter

In the literature, limited work has been conducted to develop sentiment resources for Saudi dialect. The lack of resources such as dialectical lexicons and corpora are some of the major bottlenecks to the successful development of Arabic sentiment analysis models. In this paper, a semi-supervised approach is presented to construct an annotated sentiment corpus for Saudi dialect using Twitter. The presented approach is primarily based on a list of lexicons built by using word embedding techniques such as word2vec. A huge corpus extracted from twitter is annotated and manually reviewed to exclude incorrect annotated tweets which is publicly available. For corpus validation, state-of-the-art classification algorithms (such as Logistic Regression, Support Vector Machine, and Naive Bayes) are applied and evaluated. Simulation results demonstrate that the Naive Bayes algorithm outperformed all other approaches and achieved accuracy up to 91%

Targeted siRNA Delivery Using Lipid Nanoparticles

Efficient intracellular delivery of small-interfering ribonucleic acid (siRNA) to the target organ or tissues in the body is assumed as the main hurdle for a widespread use of siRNAs in the clinics. Solid lipid-based nanoparticles (SLNs) and derivatives can potentially fit this purpose by enabling to overcome the extracellular and intracellular physiological barriers affecting the delivery. For that, rational formulations and rational process designs are needed. This chapter addresses a comprehensive description and critical appraisal of the main production methods of this particular type of lipid nanoparticles and the leading strategies to prompt a targeted delivery of siRNA