Malaria Vaccine Adjuvants: Latest Update and Challenges in Preclinical and Clinical Research

There is no malaria vaccine currently available, and the most advanced candidate has recently reported a modest 30% efficacy against clinical malaria. Although many efforts have been dedicated to achieve this goal, the research was mainly directed to identify antigenic targets. Nevertheless, the latest progresses on understanding how immune system works and the data recovered from vaccination studies have conferred to the vaccine formulation its deserved relevance. Additionally to the antigen nature, the manner in which it is presented (delivery adjuvants) as well as the immunostimulatory effect of the formulation components (immunostimulants) modulates the immune response elicited. Protective immunity against malaria requires the induction of humoral, antibody-dependent cellular inhibition (ADCI) and effector and memory cell responses. This review summarizes the status of adjuvants that have been or are being employed in the malaria vaccine development, focusing on the pharmaceutical and immunological aspects, as well as on their immunization outcomings at clinical and preclinical stages.

An update of the J48Consolidated WEKA’s class: CTC algorithm enhanced with the notion of coverage

This document aims to describe an update of the implementation of the J48Consolidated class within WEKA platform. The J48Consolidated class implements the CTC algorithm [2][3] which builds a unique decision tree based on a set of samples. The J48Consolidated class extends WEKA’s J48 class which implements the well-known C4.5 algorithm. This implementation was described in the technical report "J48Consolidated: An implementation of CTC algorithm for WEKA". The main, but not only, change in this update is the integration of the notion of coverage in order to determine the number of samples to be generated to build a consolidated tree. We define coverage as the percentage of examples of the training sample present in –or covered by– the set of generated subsamples. So, depending on the type of samples that we use, we will need more or less samples in order to achieve a specific value of coverage.