Sistemas de diseño procedimental Implementando el bioaprendizaje

Traditional architectural design and architectural design education, which are strongly based on rational-functionalist design ideals, are categorically segregated into modelling, analysis and prototyping. En el diseño arquitectónico tradicional y su educación, que se basan fuertemente en los ideales de diseño racional-funcionalista, se segregan categóricamente en modelado, análisis y creación de prototipos. O design arquitectónico tradicional e a educação em design arquitectónico, que se baseiam fortemente em ideais de design racional-funcionalista, estão categoricamente segregados em modelação, análise e prototipagem

Biological pattern based on reaction-diffusion mechanism employed as fabrication strategy for a shell structure

This paper examines how generative architectural design processes aim to apply the principles of biological morphogenesis to the design and building of mechanical or architectural structures. Despite the revolution in computation aided design and interdisciplinary upgrades of digital fabrication technologies, design processes fail to acknowledge materials, tools and construction logic in an early design stage, as manifested in nature. The objective of this paper is to introduce a design workflow, based on the knowledge of the tool, material properties, design intuition and aesthetic criteria, to translate biological skin patterns to fabrication processes, incorporating three materials and procedures in a single parametric workflow. Mesh relaxation processes and weighted mesh graph representations are examined as design potentials for stripe organization in fabrication in analogy to numerical simulations of a reaction-diffusion (RD) mechanism. A thin shell and landscape emerge as a self-organizing system in equilibrium. The paper argues that skin patterns in fabrication open a new field for interdisciplinary investigation.</p

Employing mesh segmentation algorithms as fabrication strategies. Pattern generation based on reaction- diffusion mechanism

This paper examines how the evolution of architectural generative design processes aim to apply similar physical and geometrical principles of biological processes taking place during development and to translate them to fabrication processes. In analogy to the reaction-diffusion mechanism for biological pattern prediction, the logic of stripe is used as construction system and examined for its structural behaviour. Both, mesh relaxation processes and weighted mesh graphs representations are employed as design tools for the construction of a minimal thin shell structural skin with branching topologies. Eventually the design workflow is extended to engage also collaborative fabrication processes and to steer the design based on intuition, knowledge of the fabrication tools, properties of the materials, manufacturing simulations and logic of assemble. This approach could lead to the optimization of material usage and machine time and facilitate the assembly process of a physical object which integrates the whole process into its form. The outcomes have been used to fabricate a prototype, using three different materials and digital fabrication methods, to examine the stability and the mechanical connectivity by taking in count the tolerances. The paper argues that biological skin patterns and segmentation in fabrication open a new field of interdisciplinary investigation and architectural applications.</p

Angiopoietin expression in ovine corpora lutea during the luteal phase: Effects of nutrition, arginine and follicle stimulating hormone

The aim of this study was to evaluate angiopoietin (ANGPT) 1 and 2, and tyrosine-protein kinase receptor 2 (TIE2) expression in the corpora lutea (CL) of FSH-treated, or non-treated sheep administered arginine (Arg) or vehicle (saline, Sal), and fed a control (C), excess (O) or restricted (U) diet. Ewes from each dietary group were treated with Arg or Sal (experiment 1), and with FSH (experiment 2). Luteal tissues were collected at the early-, mid- and/or late-luteal phases of the estrous cycle. Protein and mRNA expression was determined using immunohistochemistry followed by image analysis, and quantitative RT-PCR, respectively. The results demonstrated that ANGPT1 and TIE2 proteins were localized to luteal capillaries and endothelial cells of larger blood vessels, and ANGPT2 was localized to tunica media of larger blood vessels. TIE2 protein was also present in luteal cells. In experiment 1, ANGPT1 protein expression was greater in O than C during early- and mid-luteal phases, and was greatest during late-luteal phase, less at the mid- and least at the early-luteal phase; 2) TIE2 protein expression was greatest at the mid-, less at the early- and least at the late-luteal phase; 3) ANGPT1 and 2 mRNA expression was greater at the mid- and late- than the early-luteal phase, and TIE2 mRNA expression was greatest at the late-, less at the mid- and least at the early-luteal phase. The ANGPT1/2 ratio was less at the early- than mid- or late-luteal phases. In experiment 2, ANGPT1 protein expression was greater in O during the mid-luteal phase than in other groups, and was greater at the mid- than early-luteal phase. TIE2 protein expression was highest at the mid-, less at the early- and least during the late-luteal phase. ANGPT1 and 2, and TIE2 mRNA expression was higher at the mid- than the early-luteal phase. During mid-luteal phase, ANGPT1 mRNA expression was greater in C than O and U, ANGPT2 was greatest in C, less in O and least in U, and TIE2 mRNA expression was greater in C than O and U. The ANGPT1/2 ratio was higher in U than in any other group. Comparison of FSH vs. Sal treatment effects (experiment 2 vs. experiment 1) demonstrated that FSH affected ANGPT1 and/or -2, and TIE2 protein and mRNA expression depending on luteal phase and/or diet. Thus, expression of ANGPTs and TIE2 in the CL changes during the luteal lifespan, indicating their involvement in luteal vascular formation, stabilization and degradation. Moreover, this study has demonstrated that plane of nutrition and/or FSH treatment affect the ANGPT system, and may alter luteal vascularity and luteal function in sheep

Antibody neutralization activity after challenge.

(A) Micro-neutralization assays performed in the presence of 350 TCID50 per well of virus and sera collected 5 DPI. In all cases, the left panel represents neutralization activity against USA-WA1/2020 (clear: unvaccinated; solid: vaccinated) and the right panel neutralization activity against the variant of infection (clear: unvaccinated; solid: vaccinated). Statistical analysis: n = 5/group; Mann-Whitney U test. (B) Antigenic map constructed with ID50 values of unvaccinated animals. Each square in the grid represents one antigenic distance unit (C) Antigenic map constructed with ID50 values of BNT162b2 primed animals. Each square in the grid represents one antigenic distance unit.</p

Lung pathology.

(A) Radar charts representing mean pathology scores, scaled 1 to 5. In all cases, scores for uninfected unvaccinated (black) and vaccinated (red) individuals are included for comparison. Mean pathology scores of challenged unvaccinated individuals are shown in blue and challenged BNT162b2 primed individuals in orange. From left to right and top to bottom: Mock vs WA1/2020. Mock vs Alpha. Mock vs Beta. Mock vs Delta. Mock vs Mu. Histological parameters: 0 = none, 1 = minimal, 2 = mild, 3 = moderate, 4 = marked, 5 = severe. Overall lesion scores are scaled to 0–5 for representation. (B) Section slides with Hematoxylin and Eosin staining of lungs from one animal in each experimental group.</p

Lung RNA-seq.

(A) PCA plot showing the relationship between vaccinated and unvaccinated groups challenged with different variants. For each group, centroids are calculated. (B) Pairwise comparisons of the number of up and down-regulated genes between unvaccinated and vaccinated animals challenged with each variant (adj. p(C) Heatmap showing gene expression of the top 20 genes involved in the defense response to virus (GO:0051607). (D) Heatmap of the 1,000 most significant genes. A common legend for A, C and D panels is included. Hierarchical clustering was performed through Ward’s clustering criterion. Relevant genes from cluster 1 and 2 are highlighted on the right.</p