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The publicly available document encapsulates the first version of the Catalogue of Services of the future EC4Safenano Centre (CoS 2019).
The CoS 2019 is structured in 12 Service Categories and 27 Service Topics, for each of the 12 categories considered. This architecture configures a 12 x 27 matrix that allows ordering the potential EC4Safenano offer in 324 types of services/groups of services.
Each type of service/group of services is described, in a simple and friendly way, by means of a specific service sheet: the EC4Safenano - Service Data Sheet (EC4-SDS). These EC4-SDSs allow structuring and summarizing the information of each service, providing the customer with a concise view of characteristics of the service and also the contact details with the service provider.
The CoS 2019 deploys a map of services consisting of a set of 100 EC4-SDSs, covering 7 of the 12 Service Categories and 17 of the 27 Service Topics.
The harmonization of services is visualized as a future necessary step in EC4Safenano, in order to strengthen the offer and provide added value to customers with a growing offer of harmonized services in future versions of the CoS.
The information contained in this document is structured in 3 main sections, as follows:
• Catalogue structure. This section describes in short the main characteristics of the CoS 2019.
• Catalogue content. This section represents the core part of the document and encapsulates the set of 100 SDSs displaying the offer proposed by the CoS 2019.
• Online Catalogue. This section describes the resources implemented by EC4Safenano to facilitate the on-line consultation of the CoS 2019 by customers and other interested parties.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
Honeybee symbionts, predominantly bacteria, play important roles in honeybee health, nutrition, and pathogen protection, thereby supporting colony health. On the other hand, fungi are often considered indicators of poor bee health, and honeybee microbiome studies generally exclude fungi and yeasts. We hypothesized that yeasts may be an important aspect of early honeybee biology, and if yeasts provide a mutual benefit to their hosts, then honeybees could provide a refuge during metamorphosis to ensure the presence of yeasts at emergence. We surveyed for yeast and fungi during pupal development and metamorphosis in worker bees using fungal-specific quantitative polymerase chain reaction (qPCR), next-generation sequencing, and standard microbiological culturing. On the basis of yeast presence in three distinct apiaries and multiple developmental stages, we conclude that yeasts can survive through metamorphosis and in naïve worker bees, albeit at relatively low levels. In comparison, known bacterial mutualists, like Gilliamella and Snodgrassella, were generally not found in pre-eclosed adult bees. Whether yeasts are actively retained as an important part of the bee microbiota or are passively propagating in the colony remains unknown. Our demonstration of the constancy of yeasts throughout development provides a framework to further understand the honeybee microbiota.
There are viral, fungal, bacterial and trypanosomal pathogens that negatively impact the individual and superorganismal health of the western honey bee. One fungal pathogen, Ascosphaera apis , affects larvae and causes the disease chalkbrood. A previous genome analysis of As. apis revealed that its genome encodes for RNA interference genes, similar to other fungi and eukaryotes. Here, we examined whether As. apis -targeting double-stranded RNA species could disrupt the germination of As. apis.
We observed that when spores were co-incubated with As. apis -targeting dsRNA, fewer spores were activated for germination, suggesting an uptake of exogenous genetic material at the very onset of germination and consequent damage to essential transcripts needed for germination. Overall, these results indicate that the causative agent of chalkbrood disease, As. apis , can be successfully targeted using an RNAi-based strategy.
The coming years are expected to bring rapid changes in the nanotechnology regulatory landscape, with the establishment of a new framework for nano-risk governance, in silico approaches for characterisation and Risk assessment of nanomaterials, and novel procedures for the early identification and management of nanomaterial risks. In this context, Safe(r)-by-Design (SbD) emerges as a powerful preventive approach to support the development of safe and sustainable (SSbD) nanotechnology-based products and processes throughout the life cycle. This paper summarises the work undertaken to develop a blueprint for the deployment and operation of a permanent European Centre of collaborating laboratories and research organisations supporting safe Innovation in nanotechnologies. The proposed entity, referred to as “the Centre”, will establish a ‘one-stop shop’ for nanosafety-related services and a central contact point for addressing stakeholder questions about nanosafety. Its operation will rely on significant business, legal and market knowledge, as well as other tools developed and acquired through the EU-funded EC4SafeNano project and subsequent ongoing activities. The proposed blueprint adopts a demand-driven service update scheme to allow the necessary vigilance and flexibility to identify opportunities and adjust its activities and services in the rapidly evolving regulatory and nano risk governance landscape.
The proposed Centre will play a major role as a conduit to transfer scientific knowledge between the Research and commercial laboratories or consultants able to provide high quality nanosafety services, and the end-users of such services (e.g., industry, SMEs, consultancy firms, and regulatory authorities). The Centre will harmonise service provision, and bring novel risk assessment and management approaches, e.g. in silico methodologies, closer to practice, notably through SbD/SSbD, and decisively support safe and sustainable innovation of industrial production in the nanotechnology industry according to the European Chemicals Strategy for Sustainability.
Concomitant species that appear at the same or very similar times in a mass-spectral analysis can clutter a spectrum because of the coexistence of many analyte-related ions (e.g., molecular ions, adducts, fragments). One method to extract ions stemming from the same origin is to exploit the chemical information encoded in the time domain, where the individual temporal appearances inside the complex structures of chronograms or chromatograms differ with respect to analytes. By grouping ions with very similar or identical time-domain structures, single-component mass spectra can be reconstructed, which are much easier to interpret and are library-searchable. While many other approaches address similar objectives through the Pearson’s correlation coefficient, we explore an alternative method based on a modified cross-correlation algorithm to compute a metric that describes the degree of similarity between features inside any two ion chronograms. Furthermore, an automatic workflow was devised to be capable of categorizing thousands of mass-spectral peaks into different groups within a few seconds. This approach was tested with direct mass-spectrometric analyses as well as with a simple, fast, and poorly resolved LC–MS analysis. Single-component mass spectra were extracted in both cases and were identified based on accurate mass and a mass-spectral library search.