The surface‐enhanced Raman scattering spectra of 5 amphiphilic oligopeptides derived from EAK16 (AEAEAKAK)2, used as biomimetic coatings for medical devices, were obtained at 10−5–10−6 M in order to study the effects of systematic amino acid substitution along the peptide chain on the corresponding interaction with Ag colloidal nanoparticles. In addition, quantum‐mechanical data on 2 of the examined peptides were very useful for clarifying the assignment of bands, widely debated in the literature. In general, the peptide–nanoparticles interaction takes place through the COO− groups. The substitution of Ala by 2‐aminobutanoic acid in the sequence, corresponding to an increase of the hydrophobic chain length, is able to affect the peptide–Ag colloid interaction. It strengthens the interaction with NH3+ groups, mediated by the Cl− anions present in the colloidal solution, although the charge transfer interaction with the COO− ions remains the dominant interaction mechanism. When Tyr substitutes the hydrophobic Ala, the interaction mechanism is strongly affected because it takes mainly place through the Tyr residues, where the aromatic rings are predominantly perpendicular to the silver surface, partly as tyrosinate ion. Thus, the addition of the Arg‐Gly‐Asp sequence, useful to provide control on the bioactivity of the bone regeneration materials, does not change the interactions with the colloid, because the spacer amino acid substitution is the main factor affecting the peptide–nanoparticle interactions.

Cloud computing opens new perspectives for small-medium biotechnology laboratories that need to perform bioinformatics analysis in a flexible and effective way. This seems particularly true for hybrid clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones. A hybrid cloud broker, acting as an intermediary between users and public providers, can support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality. This paper analyses some economic and practical aspects of exploiting cloud computing in a real research scenario for the in silico drug discovery in terms of requirements, costs, and computational load based on the number of expected users. In particular, our work is aimed at supporting both the researchers and the cloud broker delivering an IaaS cloud infrastructure for biotechnology laboratories exposing different levels of nonfunctional requirements.

No abstract

No abstract

While the digital twins paradigm has attracted the interest of several research communities over the past twenty years, it has also gained ground recently in industrial environments, where mature technologies such as cloud, edge and IoT promise to enable the cost-effective implementation of digital twins. In the industrial manufacturing field, a digital model refers to a virtual representation of a physical product or process that integrates data taken from various sources, such as application program interface (API) data, historical data, embedded sensor data and open data, and that is capable of providing manufacturers with unprecedented insights into the product’s expected performance or the defects that may cause malfunctions. The EU-funded IoTwins project aims to build a solid platform that manufacturers can access to develop hybrid digital twins (DTs) of their assets, deploy them as close to the data origin as possible (on IoT gateway or on edge nodes) and take advantage of cloud-based resources to off-load intensive computational tasks such as, e.g., big data analytics and machine learning (ML) model training. In this paper, we present the main research goals of the IoTwins project and discuss its reference architecture, platform functionalities and building components. Finally, we discuss an industry-related use case that showcases how manufacturers can leverage the potential of the IoTwins platform to develop and execute distributed DTs for the the predictive-maintenance purpose.