Biodegradation-resistant multilayers coated with gold nanoparticles. Toward a tailor-made artificial extracellular matrix

Polymer multicomponent coatings such as multilayers mimic extracellular matrix (ECM) that attracts significant attention for their use as functional supports for advanced cell culture and tissue engineering. Herein, biodegradation and molecular transport in hyaluronan/polylysine multilayers coated with gold nanoparticles was described. Nanoparticle coating acts as semipermeable barrier that governs molecular transport into/from the multilayers and makes them biodegradation resistant. Model protein lysozyme (mimics of ECM soluble signals) diffuses in the multilayers as fast and slow diffusing populations existing in an equilibrium. Such composite system may have high potential to be exploited as degradation-resistant drug delivery platforms suitable for cell-based applications. The extracellular matrix (ECM) provides not only a structural support for cell-based applications

Binding mechanism of the model charged dye carboxyfluorescein to hyaluronan/polylysine multilayers

Biopolymer-based multilayers become more and more attractive due to the vast span of biological application they can be used for, e.g., implant coatings, cell culture supports, scaffolds. Multilayers have demonstrated superior capability to store enormous amounts of small charged molecules, such as drugs, and release them in a controlled manner; however, the binding mechanism for drug loading into the multilayers is still poorly understood. Here we focus on this mechanism using model hyaluronan/polylysine (HA/PLL) multilayers and a model charged dye, carboxyfluorescein (CF). We found that CF reaches a concentration of 13 mM in the multilayers that by far exceeds its solubility in water. The high loading is not related to the aggregation of CF in the multilayers. In the multilayers, CF molecules bind to free amino groups of PLL; however, intermolecular CF–CF interactions also play a role and (i) endow the binding with a cooperative nature and (ii) result in polyadsorption of CF molecules, as proven by fitting of the adsorption isotherm using the BET model. Analysis of CF mobility in the multilayers by fluorescence recovery after photobleaching has revealed that CF diffusion in the multilayers is likely a result of both jumping of CF molecules from one amino group to another and movement, together with a PLL chain being bound to it. We believe that this study may help in the design of tailor-made multilayers that act as advanced drug delivery platfor

EMI Security Architecture

This document describes the various architectures of the three middlewares that comprise the EMI software stack. It also outlines the common efforts in the security area that allow interoperability between these middlewares. The assessment of the EMI Security presented in this document was performed internally by members of the Security Area of the EMI project

A Cloud-Based Framework for Machine Learning Workloads and Applications

[EN] In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.This work was supported by the project DEEP-Hybrid-DataCloud ``Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud'' that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant 777435Lopez Garcia, A.; Marco De Lucas, J.; Antonacci, M.; Zu Castell, W.; David, M.; Hardt, M.; Lloret Iglesias, L.... (2020). A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access. 8:18681-18692. https://doi.org/10.1109/ACCESS.2020.2964386S1868118692

INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures

[EN] This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. 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