Phylogenetic Diversity Indices from an Affine and Projective Viewpoint.

Phylogenetic diversity indices are commonly used to rank the elements in a collection of species or populations for conservation purposes. The derivation of these indices is typically based on some quantitative description of the evolutionary history of the species in question, which is often given in terms of a phylogenetic tree. Both rooted and unrooted phylogenetic trees can be employed, and there are close connections between the indices that are derived in these two different ways. In this paper, we introduce more general phylogenetic diversity indices that can be derived from collections of subsets (clusters) and collections of bipartitions (splits) of the given set of species. Such indices could be useful, for example, in case there is some uncertainty in the topology of the tree being used to derive a phylogenetic diversity index. As well as characterizing some of the indices that we introduce in terms of their special properties, we provide a link between cluster-based and split-based phylogenetic diversity indices that uses a discrete analogue of the classical link between affine and projective geometry. This provides a unified framework for many of the various phylogenetic diversity indices used in the literature based on rooted and unrooted phylogenetic trees, generalizations and new proofs for previous results concerning tree-based indices, and a way to define some new phylogenetic diversity indices that naturally arise as affine or projective variants of each other or as generalizations of tree-based indices.

Roadmap towards safe and sustainable advanced and innovative materials. (Outlook for 2024-2030).

The adoption of innovative advanced materials holds vast potential, contingent upon addressing safety and sustainability concerns. The European Commission advocates the integration of Safe and Sustainable by Design (SSbD) principles early in the innovation process to streamline market introduction and mitigate costs. Within this framework, encompassing ecological, social, and economic factors is paramount. The NanoSafety Cluster (NSC) delineates key safety and sustainability areas, pinpointing unresolved issues and research gaps to steer the development of safe(r) materials. Leveraging FAIR data management and integration, alongside the alignment of regulatory aspects, fosters informed decision-making and innovation. Integrating circularity and sustainability mandates clear guidance, ensuring responsible innovation at every stage. Collaboration among stakeholders, anticipation of regulatory demands, and a commitment to sustainability are pivotal for translating SSbD into tangible advancements. Harmonizing standards and test guidelines, along with regulatory preparedness through an exchange platform, is imperative for governance and market readiness. By adhering to these principles, the effective and sustainable deployment of innovative materials can be realized, propelling positive transformation and societal acceptance.

Bibliometric network mapping of farm accounting studies using a comprehensive dataset.

This data article aims to analyze the intellectual structure of farm accounting studies by examining bibliometric features. A dataset comprising 190 documents from the ISI Database within the farm accounting field was utilized. It delved into various aspects including the yearly publication and citation count concerning agricultural accounting, predominant research areas, keyword co-occurrence, bibliographic coupling among sources and documents, as well as co-citation patterns of referenced materials. Bibliometric network mapping techniques were employed for the analysis of the data. The analysis was conducted using VOSviewer, a scientific mapping analysis tool. The findings indicated a notable uptrend in publication and citation rates of agricultural accounting studies over the past decade. A significant portion of the dataset centered around agriculture and business economics. Key terms like ``biological assets,'' ``IAS 41,'' and ``fair value'' emerged as prominently used. The journal ``Custos e Agronegócio Online'' showed significant influence in terms of bibliographic coupling among sources.

Managing macromolecular crystallographic data with a laboratory information management system.

Protein crystallography is an established method to study the atomic structures of macromolecules and their complexes. A prerequisite for successful structure determination is diffraction-quality crystals, which may require extensive optimization of both the protein and the conditions, and hence projects can stretch over an extended period, with multiple users being involved. The workflow from crystallization and crystal treatment to deposition and publication is well defined, and therefore an electronic laboratory information management system (LIMS) is well suited to management of the data. Completion of the project requires key information on all the steps being available and this information should also be made available according to the FAIR principles. As crystallized samples are typically shipped between facilities, a key feature to be captured in the LIMS is the exchange of metadata between the crystallization facility of the home laboratory and, for example, synchrotron facilities. On completion, structures are deposited in the Protein Data Bank (PDB) and the LIMS can include the PDB code in its database, completing the chain of custody from crystallization to structure deposition and publication. A LIMS designed for macromolecular crystallography, IceBear, is available as a standalone installation and as a hosted service, and the implementation of key features for the capture of metadata in IceBear is discussed as an example.

Are Open Science instructions targeted to ecologists and evolutionary biologists sufficient? A literature review of guidelines and journal data policies.

Open science (OS) awareness and skills are increasingly becoming an essential part of everyday scientific work as e.g., many journals require authors to share data. However, following an OS workflow can seem challenging at first. Thus, instructions by journals and other guidelines are important. But how comprehensive are they in the field of ecology and evolutionary biology (Ecol Evol)? To find this out, we reviewed 20 published OS guideline articles aimed for ecologists or evolutionary biologists, together with the data policies of 17 Ecol Evol journals to chart the current landscape of OS guidelines in the field, find potential gaps, identify field-specific barriers for OS and discuss solutions to overcome these challenges. We found that many of the guideline articles covered similar topics, despite being written for a narrow field or specific target audience. Likewise, many of the guideline articles mentioned similar obstacles that could hinder or postpone a transition to open data sharing. Thus, there could be a need for a more widely known, general OS guideline for Ecol Evol. Following the same guideline could also enhance the uniformity of the OS practices carried on in the field. However, some topics, like long-term experiments and physical samples, were mentioned surprisingly seldom, although they are typical issues in Ecol Evol. Of the journals, 15 out of 17 expected or at least encouraged data sharing either for all articles or under specific conditions, e.g. for registered reports and 10 of those required data sharing at the submission phase. The coverage of journal data policies varied greatly between journals, from practically non-existing to very extensive. As journals can contribute greatly by leading the way and making open data useful, we recommend that the publishers and journals would invest in clear and comprehensive data policies and instructions for authors.

Data from three camera trapping pilots in the Amsterdam Water Supply Dunes of the Netherlands.

This paper presents the data (images, observations, metadata) of three different deployments of camera traps in the Amsterdam Water Supply Dunes, a Natura 2000 nature reserve in the coastal dunes of the Netherlands. The pilots were aimed at determining how different types of camera deployment (e.g. regular vs. wide lens, various heights, inside/outside exclosures) might influence species detections, and how to deploy autonomous wildlife monitoring networks. Two pilots were conducted in herbivore exclosures and mainly detected European rabbits (Oryctolagus cuniculus) and red fox (Vulpes vulpes). The third pilot was conducted outside exclosures, with the European fallow deer (Dama dama) being most prevalent. Across all three pilots, a total of 47,597 images were annotated using the Agouti platform. All annotations were verified and quality-checked by a human expert. A total of 2,779 observations of 20 different species (including humans) were observed using 11 wildlife cameras during 2021-2023. The raw image files (excluding humans), image metadata, deployment metadata and observations from each pilot are shared using the Camtrap DP open standard and the extended data publishing capabilities of GBIF to increase the findability, accessibility, interoperability, and reusability of this data. The data are freely available and can be used for developing artificial intelligence (AI) algorithms that automatically detect and identify species from wildlife camera images.

Multiple graphical views for automatically generating SQL for the MycoDiversity DB; making fungal biodiversity studies accessible.

Fungi is a highly diverse group of eukaryotic organisms that live under an extremely wide range of environmental conditions. Nowadays, there is a fundamental focus on observing how biodiversity varies on different spatial scales, in addition to understanding the environmental factors which drive fungal biodiversity. Metabarcoding is a high-throughput DNA sequencing technology that has positively contributed to observing fungal communities in environments. While the DNA sequencing data generated from metabarcoding studies are available in public archives, this valuable data resource is not directly usable for fungal biodiversity investigation. Additionally, due to its fragmented storage and distributed nature, it is not immediately accessible through a single user interface. We developed the MycoDiversity DataBase User Interface (https://mycodiversity.liacs.nl) to provide direct access and retrieval of fungal data that was previously inaccessible in the public domain. The user interface provides multiple graphical views of the data components used to reveal fungal biodiversity. These components include reliable geo-location terms, the reference taxonomic scientific names associated with fungal species and the standard features describing the environment where they occur. Direct observation of the public DNA sequencing data in association with fungi is accessible through SQL search queries created by interactively manipulating topological maps and dynamic hierarchical tree views. The search results are presented in configurable data table views that can be downloaded for further use. With the MycoDiversity DataBase User Interface, we make fungal biodiversity data accessible, assisting researchers and other stakeholders in using metabarcoding studies for assessing fungal biodiversity.

Statin use and risk of Parkinson's disease among older adults in Japan: a nested case-control study using the Longevity Improvement and Fair Evidence study.

The association between statin use and the risk of Parkinson's disease remains inconclusive, particularly in Japan's super-ageing society. This study aimed to investigate the potential association between statin use and the risk of Parkinson's disease among Japanese participants aged ≥65 years. We used data from the Longevity Improvement and Fair Evidence Study, which included medical and long-term care claim data from April 2014 to December 2020 across 17 municipalities. Using a nested case-control design, we matched one case to five controls based on age, sex, municipality and cohort entry year. A conditional logistic regression model was used to estimate the odds ratios with 95% confidence intervals. Among the 56 186 participants (9397 cases and 46 789 controls), 53.6% were women. The inverse association between statin use and Parkinson's disease risk was significant after adjusting for multiple variables (odds ratio: 0.61; 95% confidence interval: 0.56-0.66). Compared with non-users, the dose analysis revealed varying odds ratios: 1.30 (1.12-1.52) for 1-30 total standard daily doses, 0.77 (0.64-0.92) for 31-90 total standard daily doses, 0.62 (0.52-0.75) for 91-180 total standard daily doses and 0.30 (0.25-0.35) for >180 total standard daily doses. Statin use among older Japanese adults was associated with a decreased risk of Parkinson's disease. Notably, lower cumulative statin doses were associated with an elevated risk of Parkinson's disease, whereas higher cumulative doses exhibited protective effects against Parkinson's disease development.

A Standardized Nomenclature Design for Systematic Referencing and Identification of Animal Cellular Material.

The documentation, preservation and rescue of biological diversity increasingly uses living biological samples. Persistent associations between species, biosamples, such as tissues and cell lines, and the accompanying data are indispensable for using, exchanging and benefiting from these valuable materials. Explicit authentication of such biosamples by assigning unique and robust identifiers is therefore required to allow for unambiguous referencing, avoid identification conflicts and maintain reproducibility in research. A predefined nomenclature based on uniform rules would facilitate this process. However, such a nomenclature is currently lacking for animal biological material. We here present a first, standardized, human-readable nomenclature design, which is sufficient to generate unique and stable identifying names for animal cellular material with a focus on wildlife species. A species-specific human- and machine-readable syntax is included in the proposed standard naming scheme, allowing for the traceability of donated material and cultured cells, as well as data FAIRification. Only when it is consistently applied in the public domain, as publications and inter-institutional samples and data are exchanged, distributed and stored centrally, can the risks of misidentification and loss of traceability be mitigated. This innovative globally applicable identification system provides a standard for a sustainable structure for the long-term storage of animal bio-samples in cryobanks and hence facilitates current as well as future species conservation and biomedical research.

A MATLAB Toolbox for Findable, Accessible, Interoperable, and Reusable Atom Probe Data Science.

Atom probe tomography (APT) data analytics have traditionally been based on manual analytics by researchers. As newer atom probes together with focused ion beam-based specimen preparation have opened APT to many more materials, yielding much more complex mass spectra, building up a systematic understanding of the pathway from raw data to final interpretation has increasingly become important. This demands a system in which the data and treatment can be traced, ideally by any interested party. Such an approach of findable, accessible, interoperable, and reusable (FAIR) data and analysis policies is becoming increasingly important, not just in APT. In this paper, we present a toolbox, written in MATLAB, which allows the user to store the raw and processed data in a standardized FAIR format (hierarchical data format 5) and process the data in a largely scriptable environment to minimize manual user input. This allows for the experiment data to be interchanged without owner explanations and the analysis to be reproduced. We have devised a metadata scheme that is extensible to other experiments in the materials science domain. With this toolbox, collective knowledge can be built up, and a large number of data sets can be analyzed in a fully automated fashion.

Introduction of the Capsules environment to support further growth of the SBGrid structural biology software collection.

The expansive scientific software ecosystem, characterized by millions of titles across various platforms and formats, poses significant challenges in maintaining reproducibility and provenance in scientific research. The diversity of independently developed applications, evolving versions and heterogeneous components highlights the need for rigorous methodologies to navigate these complexities. In response to these challenges, the SBGrid team builds, installs and configures over 530 specialized software applications for use in the on-premises and cloud-based computing environments of SBGrid Consortium members. To address the intricacies of supporting this diverse application collection, the team has developed the Capsule Software Execution Environment, generally referred to as Capsules. Capsules rely on a collection of programmatically generated bash scripts that work together to isolate the runtime environment of one application from all other applications, thereby providing a transparent cross-platform solution without requiring specialized tools or elevated account privileges for researchers. Capsules facilitate modular, secure software distribution while maintaining a centralized, conflict-free environment. The SBGrid platform, which combines Capsules with the SBGrid collection of structural biology applications, aligns with FAIR goals by enhancing the findability, accessibility, interoperability and reusability of scientific software, ensuring seamless functionality across diverse computing environments. Its adaptability enables application beyond structural biology into other scientific fields.

Koina: Democratizing machine learning for proteomics research.

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

Evaluating FAIR Digital Object and Linked Data as distributed object systems.

FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building an ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself. We compare the FDO approach with established Linked Data practices and the existing Web architecture, and provide a brief history of the Semantic Web while discussing why these technologies may have been difficult to adopt for FDO purposes. We conclude with recommendations for both Linked Data and FDO communities to further their adaptation and alignment.

Shiga toxin-producing Escherichia coli O157:H7 outbreak associated with school field trips at a farm animal exhibit-Tennessee, September-October 2023.

AIMS: In October 2023, the Tennessee Department of Health identified an outbreak of Shiga toxin-producing Escherichia coli (STEC) O157:H7 infections among elementary school students who attended school field trips to the same farm animal exhibit. Our aim was to determine STEC source and prevent additional illnesses by initiating epidemiologic, laboratory and environmental investigations. METHODS AND RESULTS: We identified cases using laboratory-based surveillance and by surveying caregivers of children who attended the exhibit. Probable cases were defined as illness with abdominal cramps or diarrhoea after attendance; confirmed cases were laboratory-confirmed STEC infection in an attendee or household contact. A site visit was conducted, and event organizers were interviewed. Human stool, animal faeces and environmental samples were tested for STEC O157:H7 by real-time polymerase chain reaction (PCR), culture and whole-genome sequencing (WGS). Approximately 2300 elementary school students attended the animal exhibit during 2 days. Field trip activities included contact with different farm animal species, drinking pasteurized milk outside animal enclosures and eating lunch in a separate building onsite. We received survey responses from 399 caregivers for 443 (19%) animal exhibit attendees. We identified 9 confirmed and 55 probable cases with illness onset dates during 26 September to 12 October. Seven children aged 1-7 years were hospitalized. Four children aged 1-6 years experienced haemolytic uraemic syndrome; none died. Laboratory testing identified STEC O157:H7 by culture from eight human stool samples with 0-1 allele difference by WGS. Three environmental samples had Shiga toxin (stx 2) genes detected by PCR, but no STEC isolates were recovered by culture. CONCLUSIONS: This is the largest reported STEC O157:H7 outbreak associated with an animal exhibit in Tennessee. We identified opportunities for educating school staff, event organizers and families about zoonotic disease risks associated with animal contact and published prevention measures.

Nuclear structure opportunities with GeV radioactive beams at FAIR.

The Facility for Antiproton and Ion Research (FAIR) is in its final construction stage next to the campus of the Gesellschaft für Schwerionenforschung Helmholtzzentrum for heavy-ion research in Darmstadt, Germany. Once it starts its operation, it will be the main nuclear physics research facility in many basic sciences and their applications in Europe for the coming decades. Owing to the ability of the new fragment separator, Super-FRagment Separator, to produce high-intensity radioactive ion beams in the energy range up to about 2 GeV/nucleon, these can be used in various nuclear reactions. This opens a unique opportunity for various nuclear structure studies across a range of fields and scales: from low-energy physics via the investigation of multi-neutron systems and halos to high-density nuclear matter and the equation of state, following heavy-ion collisions, fission and study of short-range correlations in nuclei and hypernuclei. The newly developed reactions with relativistic radioactive beams (R3B) set up at FAIR would be the most suitable and versatile for such studies. An overview of highlighted physics cases foreseen at R3B is given, along with possible future opportunities, at FAIR. This article is part of the theme issue 'The liminal position of Nuclear Physics: from hadrons to neutron stars'.

Semantic units: organizing knowledge graphs into semantically meaningful units of representation.

BACKGROUND: In today's landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles-ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. RESULTS: We introduce "semantic units" as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource. CONCLUSIONS: Semantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.

What is a fair price for a medicine? Establishing the main elements of a fair price based on the current policy debate.

OBJECTIVE: Unfair medicines prices have been discussed widely as an obstacle for patient access. This article aims to structure the discussion about fair pricing of medicines, analyses the elements for a fair price, and assesses its practical implications. METHODS: A systematic literature research has been undertaken and complemented by gray literature. Definitions and elements of a fair price have been extracted from the sample, categorized via a thematic and a quantitative analysis, and mirrored against the traditional framework of 'iustum pretium' (fair price). RESULTS: The most often attributes of a fair price mentioned were affordability (n = 30), followed by value and research and development (R&D) investment (n = 20). Other frequently mentioned attributes are profitability (n = 19), transparency of R&D costs (n = 18), cost-effectiveness (n = 17), and manufacturing (n = 14). Nearly all definitions present fair price as a balance between different objectives. CONCLUSIONS: Most publications stipulate that medicines are a common good and should be affordable. At the same time, most publications also propose a pricing approach based on covering costs for R&D and/or on value. Consequently, most of the attempts to clarify fair price result in a value-affordability dilemma, which does not necessarily warrant patient access. Many social health systems implement pricing regardless of the debate. This systematic review offers a set of attributes for fair price and helps refining the existing pricing and reimbursement regulations. Once complemented by empirical datapoints, it provides the basis for developing a framework for fair pricing.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

Governance of advanced materials: Shaping a safe and sustainable future.

The past few decades of managing the uncertain risks associated with nanomaterials have provided valuable insights (knowledge gaps, tools, methods, etc.) that are equally important to promote safe and sustainable development and use of advanced materials. Based on these insights, the current paper proposes several actions to optimize the risk and sustainability governance of advanced materials. We emphasise the importance of establishing a European approach for risk and sustainability governance of advanced materials as soon as possible to keep up with the pace of innovation and to manage uncertainty among regulators, industry, SMEs and the public, regarding potential risks and impacts of advanced materials. Coordination of safe and sustainable advanced material research efforts, and data management according to the Findable, Accessible, Interoperable and Reusable (FAIR) principles will enhance the generation of regulatory-relevant knowledge. This knowledge is crucial to identify whether current regulatory standardised and harmonised test methods are adequate to assess advanced materials. At the same time, there is urgent need for responsible innovation beyond regulatory compliance which can be promoted through the Safe and Sustainable Innovation Approach. that combines the Safe and Sustainable by Design concept with Regulatory Preparedness, supported by a trusted environment. We further recommend consolidating all efforts and networks related to the risk and sustainability governance of advanced materials in a single, easy-to-use digital portal. Given the anticipated complexity and tremendous efforts required, we identified the need of establishing an organisational structure dedicated to aligning the fast technological developments in advanced materials with proper risk and sustainability governance. Involvement of multiple stakeholders in a trusted environment ensures a coordinated effort towards the safe and sustainable development, production, and use of advanced materials. The existing infrastructures and network of experts involved in the governance of nanomaterials would form a solid foundation for such an organisational structure.