Getting your DUCs in a row - standardising the representation of Digital Use Conditions.

Improving patient care and advancing scientific discovery requires responsible sharing of research data, healthcare records, biosamples, and biomedical resources that must also respect applicable use conditions. Defining a standard to structure and manage these use conditions is a complex and challenging task. This is exemplified by a near unlimited range of asset types, a high variability of applicable conditions, and differing applications at the individual or collective level. Furthermore, the specifics and granularity required are likely to vary depending on the ultimate contexts of use. All these factors confound alignment of institutional missions, funding objectives, regulatory and technical requirements to facilitate effective sharing. The presented work highlights the complexity and diversity of the problem, reviews the current state of the art, and emphasises the need for a flexible and adaptable approach. We propose Digital Use Conditions (DUC) as a framework that addresses these needs by leveraging existing standards, striking a balance between expressiveness versus ambiguity, and considering the breadth of applicable information with their context of use.

Update of the Minimum Information About BIobank Data Sharing (MIABIS) Core Terminology to the 3rd Version.

Introduction: The Minimum Information About BIobank Data Sharing (MIABIS) is a biobank-specific terminology enabling the sharing of biobank-related data for different purposes across a wide range of database implementations. After 4 years in use and with the first version of the individual-level MIABIS component Sample, Sample donor, and Event, it was necessary to revise the terminology, especially to include biobanks that work more in the data domain than with samples. Materials & Methods: Nine use-cases representing different types of biobanks, studies, and networks participated in the development work. They represent types of data, specific sample types, or levels of organization that were not included earlier in MIABIS. To support our revision of the Biobank entity, we conducted a survey of European biobanks to chart the services they provide. An important stakeholder group for biobanks include researchers as the main users of biobanks. To be able to render MIABIS more researcher-friendly, we collected different sample/data requests to analyze the terminology adjustment needs in detail. During the update process, the Core terminology was iteratively reviewed by a large group of experts until a consensus was reached. Results: With this update, MIABIS was adjusted to encompass data-driven biobanks and to include data collections, while also describing the services and capabilities biobanks offer to their users, besides the retrospective samples. The terminology was also extended to accommodate sample and data collections of nonhuman origin. Additionally, a set of organizational attributes was compiled to describe networks. Discussion: The usability of MIABIS Core v3 was increased by extending it to cover more topics of the biobanking domain. Additionally, the focus was on a more general terminology and harmonization of attributes with the individual-level entities Sample, Sample donor, and Event to keep the overall terminology minimal. With this work, the internal semantics of the MIABIS terminology was improved.

Differential physiological and production responses of C3 and C4 crops to climate factor interactions.

This study examined the effect of the interactions of key factors associated with predicted climate change (increased temperature, and drought) and elevated CO2 concentration on C3 and C4 crop representatives, barley and sorghum. The effect of two levels of atmospheric CO2 concentration (400 and 800 ppm), three levels of temperature regime (21/7, 26/12 and 33/19°C) and two regimes of water availability (simulation of drought by gradual reduction of irrigation and well-watered control) in all combinations was investigated in a pot experiment within growth chambers for barley variety Bojos and sorghum variety Ruby. Due to differences in photosynthetic metabolism in C3 barley and C4 sorghum, leading to different responses to elevated CO2 concentration, we hypothesized mitigation of the negative drought impact in barley under elevated CO2 concentration and, conversely, improved performance of sorghum at high temperatures. The results demonstrate the decoupling of photosynthetic CO2 assimilation and production parameters in sorghum. High temperatures and elevated CO2 concentration resulted in a significant increase in sorghum above- and below-ground biomass under sufficient water availability despite the enhanced sensitivity of photosynthesis to high temperatures. However, the negative effect of drought is amplified by the effect of high temperature, similarly for biomass and photosynthetic rates. Sorghum also showed a mitigating effect of elevated CO2 concentration on the negative drought impact, particularly in reducing the decrease of relative water content in leaves. In barley, no significant factor interactions were observed, indicating the absence of mitigating the negative drought effects by elevated CO2 concentration. These complex interactions imply that, unlike barley, sorghum can be predicted to have a much higher variability in response to climate change. However, under conditions combining elevated CO2 concentration, high temperature, and sufficient water availability, the outperforming of C4 crops can be expected. On the contrary, the C3 crops can be expected to perform even better under drought conditions when accompanied by lower temperatures.

Toward a common standard for data and specimen provenance in life sciences.

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

"Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling.

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.

Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability.

Diagnostic histopathology faces increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure. The learning models for histopathology are inherently complex and opaque from the user's perspective. Hence different methods have been developed to interpret their behavior. However, relatively limited attention has been devoted to the connection between interpretation methods and the knowledge of experienced pathologists. The main contribution of this paper is a method for comparing morphological patterns used by expert pathologists to detect cancer with the patterns identified as important for inference of learning models. Given the patch-based nature of processing large-scale histopathological imaging, we have been able to show statistically that the VGG16 model could utilize all the structures that are observable by the pathologist, given the patch size and scan resolution. The results show that the neural network approach to recognizing prostatic cancer is similar to that of a pathologist at medium optical resolution. The saliency maps identified several prevailing histomorphological features characterizing carcinoma, e.g., single-layered epithelium, small lumina, and hyperchromatic nuclei with halo. A convincing finding was the recognition of their mimickers in non-neoplastic tissue. The method can also identify differences, i.e., standard patterns not used by the learning models and new patterns not yet used by pathologists. Saliency maps provide added value for automated digital pathology to analyze and fine-tune deep learning systems and improve trust in computer-based decisions.

Provenance of specimen and data - A prerequisite for AI development in computational pathology.

AI development in biotechnology relies on high-quality data to train and validate algorithms. The FAIR principles (Findable, Accessible, Interoperable, and Reusable) and regulatory frameworks such as the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR) specify requirements on specimen and data provenance to ensure the quality and traceability of data used in AI development. In this paper, a framework is presented for recording and publishing provenance information to meet these requirements. The framework is based on the use of standardized models and protocols, such as the W3C PROV model and the ISO 23494 series, to capture and record provenance information at various stages of the data generation and analysis process. The framework and use case illustrate the role of provenance information in supporting the development of high-quality AI algorithms in biotechnology. Finally, the principles of the framework are illustrated in a simple computational pathology use case, showing how specimen and data provenance can be used in the development and documentation of an AI algorithm. The use case demonstrates the importance of managing and integrating distributed provenance information and highlights the complex task of considering factors such as semantic interoperability, confidentiality, and the verification of authenticity and integrity.

BIBBOX, a FAIR toolbox and App Store for life science research.

Data quality has recently become a critical topic for the research community. European guidelines recommend that scientific data should be made FAIR: findable, accessible, interoperable and reusable. However, as FAIR guidelines do not specify how the stated principles should be implemented, it might not be straightforward for researchers to know how actually to make their data FAIR. This can prevent life-science researchers from sharing their datasets and pipelines, ultimately hindering the progress of research. To address this difficulty, we developed the BIBBOX, which is a platform that supports researchers publishing their datasets and the associated software in a FAIR manner.

Growth, physiology, and stomatal parameters of plant polyploids grown under ice age, present-day, and future CO2 concentrations.

Polyploidy plays an important role in plant evolution, but knowledge of its eco-physiological consequences, such as of the putatively enlarged stomata of polyploid plants, remains limited. Enlarged stomata should disadvantage polyploids at low CO2 concentrations (namely during the Quaternary glacial periods) because larger stomata are viewed as less effective at CO2 uptake. We observed the growth, physiology, and epidermal cell features of 15 diploids and their polyploid relatives cultivated under glacial, present-day, and potential future atmospheric CO2 concentrations (200, 400, and 800 ppm respectively). We demonstrated some well-known polyploidy effects, such as faster growth and larger leaves, seeds, stomata, and other epidermal cells. The stomata of polyploids, however, tended to be more elongated than those of diploids, and contrary to common belief, they had no negative effect on the CO2 uptake capacity of polyploids. Moreover, polyploids grew comparatively better than diploids even at low, glacial CO2 concentrations. Higher polyploids with large genomes also showed increased operational stomatal conductance and consequently, a lower water-use efficiency. Our results point to a possible decrease in growth superiority of polyploids over diploids in a current and future high CO2 climatic scenarios, as well as the possible water and/or nutrient dependency of higher polyploids.

Privacy risks of whole-slide image sharing in digital pathology.

Access to large volumes of so-called whole-slide images-high-resolution scans of complete pathological slides-has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of pathologists, and research. Nevertheless, a methodology based on risk analysis for evaluating the privacy risks associated with sharing such imaging data and applying the principle "as open as possible and as closed as necessary" is still lacking. In this article, we develop a model for privacy risk analysis for whole-slide images which focuses primarily on identity disclosure attacks, as these are the most important from a regulatory perspective. We introduce a taxonomy of whole-slide images with respect to privacy risks and mathematical model for risk assessment and design . Based on this risk assessment model and the taxonomy, we conduct a series of experiments to demonstrate the risks using real-world imaging data. Finally, we develop guidelines for risk assessment and recommendations for low-risk sharing of whole-slide image data.

The effect of elevated CO2 on photosynthesis is modulated by nitrogen supply and reduced water availability in Picea abies.

It is assumed that the stimulatory effects of elevated CO2 concentration ([CO2]) on photosynthesis and growth may be substantially reduced by co-occurring environmental factors and the length of CO2 treatment. Here, we present the study exploring the interactive effects of three manipulated factors ([CO2], nitrogen supply and water availability) on physiological (gas-exchange and chlorophyll fluorescence), morphological and stoichiometric traits of Norway spruce (Picea abies) saplings after 2 and 3 years of the treatment under natural field conditions. Such multifactorial studies, going beyond two-way interactions, have received only limited attention until now. Our findings imply a significant reduction of [CO2]-enhanced rate of CO2 assimilation under reduced water availability which deepens with the severity of water depletion. Similarly, insufficient nitrogen availability leads to a down-regulation of photosynthesis under elevated [CO2] being particularly associated with reduced carboxylation efficiency of the Rubisco enzyme. Such adjustments in the photosynthesis machinery result in the stimulation of water-use efficiency under elevated [CO2] only when it is combined with a high nitrogen supply and reduced water availability. These findings indicate limited effects of elevated [CO2] on carbon uptake in temperate coniferous forests when combined with naturally low nitrogen availability and intensifying droughts during the summer periods. Such interactions have to be incorporated into the mechanistic models predicting changes in terrestrial carbon sequestration and forest growth in the future.

AI for life: Trends in artificial intelligence for biotechnology.

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper.

Interactive effects of UV radiation and water deficit on production characteristics in upland grassland and their estimation by proximity sensing.

An increase in extreme weather and changes in other conditions associated with ongoing climate change are exposing ecosystems to a very wide range of environmental drivers that interact in ways which are not sufficiently understood. Such uncertainties in how ecosystems respond to multifactorial change make it difficult to predict the impacts of environmental change on ecosystems and their functions. Since water deficit (WD) and ultraviolet radiation (UV) trigger similar protective mechanisms in plants, we tested the hypothesis that UV modulates grassland acclimation to WD, mainly through changes in the root/shoot (R/S) ratio, and thus enhances the ability of grassland to acquire water from the soil and hence maintain its productivity. We also tested the potential of spectral reflectance and thermal imaging for monitoring the impacts of WD and UV on grassland production parameters. The experimental plots were manipulated by lamellar shelters allowing precipitation to pass through or to be excluded. The lamellas were either transmitting or blocking the UV. The results show that WD resulted in a significant decrease in aboveground biomass (AB). In contrast, belowground biomass (BB), R/S ratio, and total biomass (TB) increased significantly in response to WD, especially in UV exclusion treatment. UV exposure had a significant effect on AB and BB, but only in the last year of the experiment. The differences in the effect of WD between years show that the effect of precipitation removal is largely influenced by the potential evapotranspiration (PET) in a given year and hence mainly by air temperatures, while the resulting effect on production parameters is best correlated with the water balance given by the difference between precipitation and PET. Canopy temperature and selected spectral reflectance indices showed a significant response to WD and also significant relationships with morphological (AB, R/S) and biochemical (C/N ratio) parameters. In particular, the vegetation indices NDVI and RDVI provided the best correlations of biomass changes caused by WD and thus the highest potential to remotely sense drought effects on terrestrial vegetation.

UV radiation and drought interact differently in grass and forb species of a mountain grassland.

Among abiotic stressors, drought and enhanced ultraviolet radiation (UV) received a lot of attention, because of their potential to impair plant growth. Since drought and UV induce partially similar protective mechanisms, we tested the hypothesis that UV ameliorates the effect of reduced water availability (WA) in selected grass (Holcus mollis and Agrostis capillaris) and forb species (Hypericum maculatum and Rumex acetosa). During 2011-2014, an outdoor manipulation experiment was conducted on a mountain grassland ecosystem (Beskydy Mts; Czech Republic). Lamellar shelters were used to pass (WAamb) or exclude (WA-) incident precipitation in order to simulate reduced water availability (WA). In addition, the lamellas were made from acrylics either transmitting (UVamb) or blocking (UV-) incident UV. Generally, both UV exposure and reduced WA enhanced epidermal UV-screening, while exposure to both factors resulted in less than additive interactions. Although UV radiation increased epidermal UV-screening rather in the grass (up to 29 % in A. capillaris) than forb (up to 12 % in H. maculatum) species and rather in well-watered than reduced WA plants, such acclimation response did not result in significant alleviation of reduced WA effects on gas exchange and morphological parameters. The study contributes to a better understanding of plant responses to complex environmental conditions and will help for successful modelling forecasts of future climate change impacts.

Lightweight Distributed Provenance Model for Complex Real-world Environments.

Provenance is information describing the lineage of an object, such as a dataset or biological material. Since these objects can be passed between organizations, each organization can document only parts of the objects life cycle. As a result, interconnection of distributed provenance parts forms distributed provenance chains. Dependant on the actual provenance content, complete provenance chains can provide traceability and contribute to reproducibility and FAIRness of research objects. In this paper, we define a lightweight provenance model based on W3C PROV that enables generation of distributed provenance chains in complex, multi-organizational environments. The application of the model is demonstrated with a use case spanning several steps of a real-world research pipeline - starting with the acquisition of a specimen, its processing and storage, histological examination, and the generation/collection of associated data (images, annotations, clinical data), ending with training an AI model for the detection of tumor in the images. The proposed model has become an open conceptual foundation of the currently developed ISO 23494 standard on provenance for biotechnology domain.

The Common Provenance Model: Capturing Distributed Provenance in Life Sciences Processes.

The distributed nature of modern research emphasizes the importance of collecting and sharing the history of digital and physical material, to improve the reproducibility of experiments and the quality and reusability of results. Yet, the application of the current methodologies to record provenance information is largely scattered, leading to silos of provenance information at different granularities. To tackle this fragmentation, we developed the Common Provenance Model, a set of guidelines for the generation of interoperable provenance information, and to allow the reconstruction and the navigation of a continuous provenance chain. This work presents the first version of the model, available online, based on the W3C PROV Data Model and the Provenance Composition pattern.

Field experiments underestimate aboveground biomass response to drought.

Researchers use both experiments and observations to study the impacts of climate change on ecosystems, but results from these contrasting approaches have not been systematically compared for droughts. Using a meta-analysis and accounting for potential confounding factors, we demonstrate that aboveground biomass responded only about half as much to experimentally imposed drought events as to natural droughts. Our findings indicate that experimental results may underestimate climate change impacts and highlight the need to integrate results across approaches.

Automated annotations of epithelial cells and stroma in hematoxylin-eosin-stained whole-slide images using cytokeratin re-staining.

The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable size and shape, together with stromal cells, extracellular matrix, and blood vessels. Distinguishing stroma from epithelium is a critical component of artificial intelligence (AI) methods developed to detect and analyze carcinomas. In this paper, we propose a novel automated workflow that enables large-scale guidance of AI methods to identify the epithelial component. The workflow is based on re-staining existing hematoxylin and eosin (H&E) formalin-fixed paraffin-embedded sections by immunohistochemistry for cytokeratins, cytoskeletal components specific to epithelial cells. Compared to existing methods, clinically available H&E sections are reused and no additional material, such as consecutive slides, is needed. We developed a simple and reliable method for automatic alignment to generate masks denoting cytokeratin-rich regions, using cell nuclei positions that are visible in both the original and the re-stained slide. The registration method has been compared to state-of-the-art methods for alignment of consecutive slides and shows that, despite being simpler, it provides similar accuracy and is more robust. We also demonstrate how the automatically generated masks can be used to train modern AI image segmentation based on U-Net, resulting in reliable detection of epithelial regions in previously unseen H&E slides. Through training on real-world material available in clinical laboratories, this approach therefore has widespread applications toward achieving AI-assisted tumor assessment directly from scanned H&E sections. In addition, the re-staining method will facilitate additional automated quantitative studies of tumor cell and stromal cell phenotypes.

The Data Use Ontology to streamline responsible access to human biomedical datasets.

Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset's allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers' discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide.