Designing cnc knit for hybrid membrane and bending active structures

Recent advances in computation allow for the integration of design and simulation of highly interrelated systems, such as hybrids of structural membranes and bending active elements. The engaged complexities of forces and logistics can be mediated through the development of materials with project specific properties and detailing. CNC knitting with high tenacity yarn enables this practice and offers an alternative to current woven membranes. The design and fabrication of an 8m high fabric tower through an interdisciplinary team of architects, structural and textile engineers, allowed to investigate means to design, specify, make and test CNC knit as material for hybrid structures in architectural scale. This paper shares the developed process, identifies challenges, potentials and future work.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Peptaibolin analogues by incorporation of α,α-dialkylglycines: synthesis and study of their membrane permeating ability

Analogues of Peptaibolin, a peptaibol with antibiotic activity, incorporating α,α-dialkylglycines (Deg, Dpg, and Ac6c) at selected positions were synthesised by MW-SPPS and fully characterized. A control analogue incorporating L-alanine was also prepared. The native peptide and the analogues were studied by fluorescence spectroscopy for their membrane permeating activity. Small unilamellar vesicles (SUVs) of egg phosphatidylcholine/ cholesterol (70:30) containing an encapsulated fluorescence probe (6-carboxyfluorescein) were used as membrane models. The assays of carboxyfluorescein release from SUVs upon peptide addition showed that Peptaibolin-Dpg and Peptaibolin-Ac6c are the most active peptides. These results indicate that the structure of the α,α-dialkylglycines is crucial for the membrane permeating ability of these Peptaibolin analogues.

Lipids under stress - a lipidomic approach for the study of mood disorders

The emerging field of lipidomics has identified lipids as key players in disease physiology. Their physicochemical diversity allows precise control of cell structure and signaling events through modulation of membrane prop- erties and trafficking of proteins. As such, lipids are important regulators of brain function and have been implicated in neurodegenerative and mood disorders. Importantly, environmental chronic stress has been associated with anxiety and depression and its exposure in rodents has been extensively used as a model to study these diseases. With the accessibility to modern mass- spectrometry lipidomic platforms, it is now possible to snapshot the extensively interconnected lipid network. Here, we review the fundamentals of lipid biology and outline a framework for the interpretation of lipidomic studies as a new approach to study brain pathophysiology. Thus, lipid profiling provides an exciting avenue for the identification of disease signatures with important implications for diagnosis and treatment of mood disorders.