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This study focused on the impact in 3T3 fibroblasts of several types of empty and curcumin- and resveratrol-loaded solid lipid nanoparticles (SLN) on cell viability and lipid metabolism in relation to their lipid content and encapsulated drug. SLN, prepared by hot homogenization/ultrasonication, were characterized with respect to size, polydispersity index, and zeta potential. Compritol® 888 ATO at different concentrations (4%, 5%, and 6% wt/wt) was chosen as lipid matrix while Poloxamer 188 (from 2.2% to 3.3% wt/wt) and Transcutol (TRC; 2% or 4%) were added as nanoparticle excipients. Prepared SLN were able to encapsulate high drug amount (encapsulation efficiency percentage of about 97-99%). All empty SLN did not show cytotoxicity (by MTT assay, at 24 h of incubation) in 3T3 cells independently of the lipid and TRC amount, while a viability reduction in the range 5-11% and 12-27% was observed in 3T3 cells treated with curcumin-loaded and resveratrol-loaded SLN, respectively. SLN without TRC did not affect cell lipid metabolism, independently from the lipid content. Empty and loaded SLN formulated with 4% of Compritol and 4% of TRC significantly affected, after 24 h of incubation at the dose of 5 μl/ml, cell polar lipids (phospholipids and free cholesterol) and fatty acid profile, with respect to control cells. Loaded compounds significantly modulated the impact of the corresponding empty formulation on cell lipids. Therefore, the combined impact on lipid metabolism of SLN and loaded drug should be taken in consideration in the evaluation of the toxicity, potential application, and therapeutic effects of new formulations.

Workflow Run RO-Crate (https://w3id.org/ro/wfrun/), is a set of profiles of RO-Crate (https://doi.org/10.3233/DS-210053) that capture workflow provenance in a lightweight FAIR data package, in order to support traceability, reproducibility and interoperable description of diverse computational analysis. We implemented the profile in multiple workflow systems, including Galaxy, COMPSs, StreamFlow, WfExS, Sapporo, Autosubmit. The command line tool runcrate (https://pypi.org/project/runcrate/) can convert from the precursor CWLProv (https://doi.org/10.1093/gigascience/giz095), and display or validate crates according to the profiles, with (prototype) repeat of a previous execution. The profiles are organised by increasing levels of details, allowing gradual adaptation, ranging from arbitrary sets of computational processes (implied user-driven workflows), through a WorkflowHub-compatible crate with workflow definition, to a full provenance trace for each step, their input and output values. This use of RO-Crate allows the contextualization of a computational workflow and its execution, e.g. relating to people, organisations, projects, funding, data sources and wider research questions and studies. For instance, in the TRE-FX project (https://trefx.uk/) such crates are used as a lingua franca across federated Trusted Research Environments, as it can also address the security and review aspects. Workflow Run working group collaborates across ELIXIR nodes and EU-wide projects (BY-COVID, EOSC-Life, EJP-RD, EuroHPC, eFlows4HP, EuroScienceGateway, BioExcel-2) as well as national projects. After this first stable release of the profiles we are now expanding with more workflow systems, and tracking computational resources such as containers and memory usage.

Focusing on the relationship between two important scientists in the development of ecological thought during the first half of the twentieth century, this paper argues that Yale limnologist G. E. Hutchinson's adoption of the biogeochemical approach in the late 1930s builds on the 1920s work of the Russian scientist V. I. Vernadsky. An analysis of Hutchinson’s scientific publications shows that he first referred to Vernadsky in 1940, on two different occasions. This article analyzes the dynamics of Hutchinson’s formulation of the biogeochemical approach, providing historical context and linking its early application to the existing limnological tradition.

Just as data is expected to be FAIR (Findable, Accessible, Interoperable, Reusable), so are workflows, specialised forms of software that encode processing pipelines and analytical know-how. Sharing workflows opens them to review and reuse, supports their citability with due author credit and attribution, and provides steps towards reproducibility of the data results, with accurately documented method and provenance lineage of derived data. Reusing workflows, including adapting them for new data, parameters, or steps, improves the sharing of processing know-how, and improves productivity and reliability, saving labour. Using workflows as an access point to the tools and computational resources of EOSC democratises the use of computational platforms and the availability of computational methods that are beyond the skills of a large body of researchers. The production of workflows is labour-intensive and a highly skilled task, and as such they are important assets to be exchanged in EOSC. Across the BMS RIs, workflows are scattered in private or platform-specific repositories and hidden in containers, data repositories or supplementary materials. The aim of this deliverable is to overcome this fragmentation without losing autonomy of the workflow developers and users by creating WorkflowHub[1], a unifying workflow registry with rich metadata capture for findability and to support value-added services for workflow testing using the LifeMonitorservice. WorkflowHub is workflow manager agnostic, supporting workflows in their native repositories and instances and links to registries such as bio.tools, to support discovery over the fragmented EOSC ecosystem. To date 319 workflows have been registered from 14 different workflow managers: the most popular are Galaxy, Nextflow, Snakemake, Python Scripts and Jupyter Notebooks. 229 workflows are linked to an external URL, 71 of which are linked to a git repository. However, it also supports the manual uploading and storage of files, and as such also acts as a repository: currently, 90 workflows are uploaded as files. The registry supports a common API to simplify access for tool developers and uses the GA4GH TRS API for supporting the direct execution of workflows. It also supports workflow snapshot preservation, DOI publishing, citation and monitoring, partnering with DataCite in the scholarly communication landscape. The Hub’s emphasis is on machine- and human-readable metadata to drive FAIR capability, requiring a new workflow metadata framework to be developed and adopted. Standardised workflow identifiers and metadata descriptions support workflow discovery, reuse, preservation, interoperability and monitoring and metadata harvesting using standard protocols. As workflows are multi-component objects (requiring links to test data, example runs, explanatory documentation, etc.) we use the RO-Crate specification for packaging workflows, which is an implementation mechanism for FAIR Digital Objects as envisioned by the EOSC Interoperability Framework. These digital objects are used to exchange workflows, their metadata and their companion objects (e.g., workflow tests) between the Hub, Workflow Managers and workflow services such as LifeMonitor, and to deposit workflows in EOSC long-term preservation archives such as Zenodo.&nbsp

The original scope of task 8.3 is to develop a framework to assess the impact on reproducibility of the availability of life-science open data and workflows in the cloud.Such a framework should include the understanding of the tools and services produced and made available, the gaps they are filling up and their expected impact.A great part of the activities within EOSC-Life is actually related to reproducibility and provides in several ways tools that will have an impact. We describe here such activities and explain why and how they will have an impact on reproducibility in life sciences. In addition, for each action, we provide an indication of how we could measure its impact in practical ways, using adequate proxies. For these reasons, we changed the title of the deliverable, from “framework to assess ...” to “framework to enhance reproducibility”.First of all, we reasoned on what can be the contribution of open science to improve the reproducibility of research. Publicly sharing data, protocols, tools and computational workflows makes it possible to compare or combine the data and outcomes from different studies within a discipline as well as integrate data across scientific domains. It allows conclusions to be validated and possibly corrected as well as being reinforced by meta-analyses. Replication data and test/training data can also be used in many applications to contribute to reproducible research. Moreover, new hypotheses, different from the original aims of the study, can be explored. Datasets can be re-used to develop and test new methods, to conduct scientific and technical benchmarking activities and to support training activities. Therefore, in addition to generating more value from research investments, data sharing has the potential to increase confidence in research outcomes and increase knowledge dissemination. These benefits of open sharing have long been recognized in some fields such as bioinformatics, which has a long history of publicly sharing data with, for example, public repositories for nucleotide sequences going back 30 years and the Protein Data Bank (PDB), a repository of information about the 3D structures of proteins, nucleic acids, and complex assemblies, that celebrates its 50th birthday this year.In this notion, any improvement in sharing of data, tools and workflows among scientists and across disciplines, which is the aim of EOSC-Life and the wider EOSC, will contribute to reproducible science. In addition to this general scope, several specific actions to frame transparency in the reporting of experimental protocols, data and analytical workflows warrant the reproducibility of every single object (experimental results, data, or workflows) that is made available on the cloud.We describe here the initiatives in EOSC-Life to implement existing tools for reproducibility as well as to develop new tools for its enhancement. As the final goal of EOSC-Life is to make data resources available to the wider community of life scientists, although necessarily technical in several points, this document aims at a general readership, including experimental in addition to data scientists.

PDF file, 64K, Effect of RKI-1447 and RKI-1313 on anchorage dependent proliferation/survival of MDA-MB-231 cells. Cells were seeded in a 96-well plate and treated with increasing concentrations of RKI-1447 or RKI-1313. The effects of the compounds on cell viability under anchorage-dependent conditions were determined by MTT assay as described below. MTT Assay Method: MTT assay was performed exactly as described by us (see reference 41 in manuscript) to determine the effects of RKI-1447 on anchorage-dependent cell proliferation. Briefly, cells were plated in a 96 well tissue culture plate (1200 cells per well) and incubated for 24 hours. After incubation the cells were treated with vehicle or increasing concentrations of RKI-1447 or RKI-1313 for 72 hours. After incubation, freshly prepared MTT (2mg/ml) was added to each well and incubated for 3 hours. After incubation the plates were read at 540 nm.

PDF file - 6958K, Figure S2: STAT3-C inhibits the ability of S3I-1757 to suppress migration and invasion. MDA-MB-468 cells were transiently transfected with vector or STAT3C, plated either for migration (A) or invasion (B) and then treated with S3I-1757 as described in Methods. The cells were then processed for migration (A) and invasion (B) assays as described under Methods.

PDF file, 58K, Percent tumor volume change after mouse treatment with vehicle. ErbB2 mammary tumor transgenic mice were treated with Vehicle (20% HPCD) as described under Materials and Methods. Percent change in tumor volume was calculated for each tumor using the following formula; (Vf-V0)/V0*100; where V0= Initial volume (at the beginning of treatment) and Vf= Final volume (at the end of treatment).

PDF file, 56K, Percent mouse weight change after vehicle treatment. ErbB2 mammary tumor transgenic mice were treated with Vehicle (20% HPCD) as described under Materials and Methods. Final percent change in weight was calculated for each mouse using the following formula; (Wf-W0)/W0*100; where W0= Initial weight (at the beginning of treatment) and Wf= Final weight (at the end of treatment).

PDF file, 59K, Percent mouse weight change after RKI-1447 treatment. ErbB2 mammary tumor transgenic mice were treated with RKI-1447 as described under Materials and Methods. Final percent change in weight was calculated for each mouse using the following formula; (Wf-W0)/W0*100; where W0= Initial weight (at the beginning of treatment) and Wf= Final weight (at the end of treatment).

PDF file, 131K, Molecular mode of action of RKI-1447. Interaction of the ATP site of Rock1 kinase in complex with RKI-1447 determined by X-ray crystallography at 2.9 � resolution. The hinge region is indicated in orange, the DFG motif in cyan, and RKI-1447 in yellow. Displayed in red is the Fo-Fc electron density map contoured at 2.5σ around the RKI-1447 inhibitor which was omitted during refinement. Potential hydrogen bonding and van der Waal interactions are shown as black and green dotted lines, respectively.