Cancer immunotherapy as a new treatment option

Cancer can coaptate the immune control of the immune system (IS), evade immunity and its destruction. So, could we say openly that immunotherapy is a viable treatment option for patients with advanced cancer? Yes, immunotherapy would give us great advances in the war against cancer. Therefore, the development of a new generation of immune modulators (which have been analyzed in the following article) is necessary. In addition, these will be more effective if we use them in combination, taking advantage of their synergy.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Tripod-shaped penta (p-phenylene)s for the functionalization of silicon surfaces

In order to obtain nanostructured thin films to be used in biosensor devices, several chemical functionalization methods have been developed, such as Click chemistry or Suzuki carbon-carbon coupling reactions on surfaces.1 With the aim to control the orientation and spacing between grafted functional groups on a surface, tripodal oligo (p-phenylene)s have become the ideal anisotropic adsorbates due to their shape-persistent and self-standing characteristics.2 Here we report the synthesis and characterization of several tripod-shaped oligo(p-phenylene)s molecules with legs composed of five phenylene units, compounds 1, 2 and 3. In these structures, each leg is end-capped with an NH-Boc, NH2 and N3 group, respectively. The functional arm contains an acetylene group. The presented synthesis has as key step the Pd-catalyzed Suzuki cross-coupling reaction. In particular, a iodine derivative from the silicon core molecule reacts with the appropriate tetra(p-phenylene) boron derivative, thus generating the final tripod-shaped structure. The azide end-capped leg in 3 is specifically designed for its covalent incorporation on alkynyl terminated silicon surfaces by an easy and reproducible way. As a preliminary study, we present the alkynyl-functionalized silicon wafers nanostructuration with tripod 3 through the cooper catalyzed alkyne-azide cycloaddition (CuAAC) click reaction.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Learning sequences of rules using classifier systems with tags

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.The objective of this paper was to obtain an encoding structure that would allow the genetic evolution of rules in such a manner that the number of rules and relationship in a classifier system (CS) would be learnt in the evolution process. For this purpose, an area that allows the definition of rule groups has been entered into the condition and message part of the encoded rules. This area is called internal tag. This term was coined because the system has some similarities with natural processes that take place in certain animal species, where the existence of tags allows them to communicate and recognize each other. Such CS is called a tag classifier system (TCS). The TCS has been tested in the game of draughts and compared with the classical CS. The results show an improving of the CS performance

RTCS: a reactive with tags classifier system

In this work, a new Classifier System is proposed (CS). The system, a Reactive with Tags Classifier System (RTCS), is able to take into account environmental situations in intermediate decisions. CSs are special production systems, where conditions and actions are codified in order to learn new rules by means of Genetic Algorithms (GA). The RTCS has been designed to generate sequences of actions like the traditional classifier systems, but RTCS also has the capability of chaining rules among different time instants and reacting to new environmental situations, considering the last environmental situation to take a decision. In addition to the capability to react and generate sequences of actions, the design of a new rule codification allows the evolution of groups of specialized rules. This new codification is based on the inclusion of several bits, named tags, in conditions and actions, which evolve by means of GA. RTCS has been tested in robotic navigation. Results show the suitability of this approximation to the navigation problem and the coherence of tag values in rules classification.Publicad

A reactive approach to classifier systems

IEEE International Conference on Systems, Man, and Cybernetics. San Diego, CA, 11-14 Oct. 1998The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and/or sequences of actions. Classifier Systems (CS) have proven their ability of continuous learning, however they have some problems in reactive systems. A modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminating between rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations

Reactive with tags classifier system applied to real robot navigation

7th IEEE International Conference on Emerging Technologies and Factory Automation. Barcelona, 18-21 October 1999.A reactive with tags classifier system (RTCS) is a special classifier system. This system combines the execution capabilities of symbolic systems and the learning capabilities of genetic algorithms. A RTCS is able to learn symbolic rules that allow to generate sequence of actions, chaining rules among different time instants, and react to new environmental situations, considering the last environmental situation to take a decision. The capacity of RTCS to learn good rules has been prove in robotics navigation problem. Results show the suitability of this approximation to the navigation problem and the coherence of extracted rules

Applying classifier systems to learn the reactions in mobile robots

The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and sequences of actions. Classifier systems (CSs) have proven their ability of continuous learning, however, they have some problems in reactive systems. A modified CS, namely a reactive classifier system (RCS), is proposed to overcome those problems. Two special mechanisms are included in the RCS: the non-existence of internal cycles inside the CS (no internal cycles) and the fusion of environmental message with the messages posted to the message list in the previous instant (generation list through fusion). These mechanisms allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminate between rules and, second, the discovery of new rules to obtain a successful operation in dynamic environments. DiVerent experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations.Publicad

Knowledge acquisition including tags in a classifier system

Congress on Evolutionary Computation. Washington, DC, 6-9 July 1999.One of the major problems related to classifier systems is the loss of rules. This loss is caused by the genetic algorithm being applied on the entire population of rules jointly. Obviously, the genetic operators discriminate rules by the strength value, such that evolution favours the generation of the stronger rules. When the learning system works in an environment in which it is possible to generate a complete training set, the strength of the rules of the CS will reflect the relative relationship between rules satisfactorily and, therefore, the application of the genetic algorithm will produce the desired effects. However, when the learning process presents individual cases and allows the system to learn gradually from these cases, each learning interval with a set of individual cases can lead the strength to be distributed in favour of a given type of rules that would in turn be favoured by the genetic algorithm. Basically, the idea is to divide rules into groups such that they are forced to remain in the system. This contribution is a method of learning that allows similar knowledge to be grouped. A field in which knowledge-based systems researchers have done a lot of work is concept classification and the relationships that are established between these concepts in the stage of knowledge conceptualization for later formalization. This job of classifying and searching relationships is performed in the proposed classifier systems by means of a mechanism. Tags, that allows the classification and the relationships to be discovered without the need for expert knowledge

Robots autónomos : arquitecturas y control

Peer Reviewe

Fuzzy reasoning in K-means classification method

4 pages, no figures.-- Contributed to: 1999 EUSFLAT-ESTYLF Joint Conference (Palma de Mallorca, Spain, Sep 22-25, 1999).Domain analysis tries to reuse software in an effective way. New methodologies are starting to be able to automate the process, in different degrees, with the construction of a domain model for each problem. The general process is divided into several phases. One of the most diffcult tasks is the generation of the relationships which have to be defined between the components in the domain. In this paper the use of fuzzy logic and a statistical classification method in order to get the semantic relationships for each pair of components is presented.Publicad