Tiago Martins

Selective A2A receptor antagonist prevents microglia-mediated neuroinflammation and protects retinal ganglion cells from high intraocular pressure–induced transient ischemic injury

Glaucoma is a leading cause of vision loss and blindness worldwide, characterized by chronic and progressive neuronal loss. Reactive microglial cells have been recognized as a neuropathologic feature, contributing to local inflammation and retinal neurodegeneration. In a recent in vitro work (organotypic cultures), we demonstrated that blockade of adenosine A2A receptor (A2AR) prevents the neuroinflammatory response and affords protection to retinal ganglion cells (RGCs) against exposure to elevated hydrostatic pressure (EHP), to mimic elevated intraocular pressure (IOP), the main risk factor for glaucoma development. Herein, we investigated whether a selective A2AR antagonist (SCH 58261) could modulate retinal microglia reactivity and their inflammatory response. Furthermore, we took advantage of the high IOP-induced transient ischemia (ischemia-reperfusion, I-R) animal model to evaluate the protective role of A2AR blockade in the control of retinal neuroinflammation and neurodegeneration. Primary microglial cell cultures were challenged either with lipopolysaccharide or with EHP, in the presence or absence of A2AR antagonist SCH 58261 (50 nM). In addition, I-R injury was induced in adult Wistar rats after intravitreal administration of SCH 58261 (100 nM, 5 μL). Our results showed that SCH 58261 attenuated microglia reactivity and the increased expression and release of proinflammatory cytokines. Moreover, intravitreal administration of SCH 58261 prevented I-R-induced cell death and RGC loss, by controlling microglial-mediated neuroinflammatory response. These results prompt the proposal that A2AR blockade may have great potential in the management of retinal neurodegenerative diseases characterized by microglia reactivity and RGC death, such as glaucoma and ischemic diseases.

Evotype: Evolutionary Type Design

An evolutionary generative system for type design, Evotype, is described. The system uses a Genetic Algorithm to evolve a set of individuals composed of line segments, each encoding the shape of a specific character, i.e. a glyph. To simultaneously evolve glyphs for the entire alphabet, an island model is adopted. To assign fitness we resort to a scheme based on Optical Character Recognition. We study the evolvability of the proposed approach as well as the impact of the migration in the evolutionary process. The migration mechanism is explored through three experimental setups: fitness guided migration, random migration, and no migration. We analyse the experimental results in terms of fitness, migration paths, and appearance of the glyphs. The results show the ability of the system to find suitable glyphs and the impact of the migration strategy in the evolutionary process.

An Interface for Fitness Function Design

Fitness assignment is one of the biggest challenges in evolutionary art. Interactive evolutionary computation approaches put a significant burden on the user, leading to human fatigue. On the other hand, autonomous evolutionary art systems usually fail to give the users the opportunity to express and convey their artistic goals and preferences. Our approach empowers the users by allowing them to express their intentions through the design of fitness functions. We present a novel responsive interface for designing fitness function in the scope of evolutionary ant paintings. Once the evolutionary runs are concluded, further control is given to the users by allowing them to specify the rendering details of selected pieces. The analysis of the experimental results highlights how fitness function design influences the outcomes of the evolutionary runs, conveying the intentions of the user and enabling the evolution of a wide variety of images.

Evotype: From Shapes to Glyphs

Typography plays a key communication role in the contemporary information-dense culture. Type design is a central, complex, and time consuming task. In this work we develop the generative system to type design based on an evolutionary algorithm. The key novel contributions are twofold. First, in terms of representation it relies on the use of assemblages of shapes to form glyphs. There are no limitations to the types of shapes that can be used. Second, we explore a compromise between legibility and expressiveness, testing different automatic fitness assignment schemes. The attained results show that we are able to evolve a wide variety of alternative glyphs, making the proposed system a viable alternative for real-world applications in the field of type design.

Evotype: Towards the Evolution of Type Stencils

Typefaces are an essential resource employed by graphic designers. The increasing demand for innovative type design work increases the need for good technological means to assist the designer in the creation of a typeface. We present an evolutionary computation approach for the generation of type stencils to draw coherent glyphs for different characters. The proposed system employs a Genetic Algorithm to evolve populations of type stencils. The evaluation of each candidate stencil uses a hill climbing algorithm to search the best configurations to draw the target glyphs. We study the interplay between legibility, coherence and expressiveness, and show how our framework can be used in practice.

Beyond Interactive Evolution: Expressing Intentions through Fitness Functions

ABSTRACT Photogrowth is a creativity support tool for the creation of nonphotorealistic renderings of images. The authors discuss its evolution from a generative art application to an interactive evolutionary art tool and finally into a meta-level interactive art system in which users express their artistic intentions through the design of a fitness function. The authors explore the impact of these changes on the sense of authorship, highlighting the range of imagery that can be produced by the system.