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Abstract The authors introduce a novel framework that integrates federated learning with Graph Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop methodologies. This advanced framework innovatively employs collaborative voting mechanisms on subgraphs within a Protein-Protein Interaction (PPI) network, situated in a federated ensemble-based deep learning context. This methodological approach marks a significant stride in the development of explainable and privacy-aware Artificial Intelligence, significantly contributing to the progression of personalized digital medicine in a responsible and transparent manner.

In this paper we present NiaAML, an AutoML framework that we have developed for creating machine learning pipelines and hyperparameter tuning. The composition of machine learning pipelines is presented as an optimization problem that can be solved using various stochastic, population-based, nature-inspired algorithms. Nature-inspired algorithms are powerful tools for solving real-world optimization problems, especially those that are highly complex, nonlinear, and involve large search spaces where traditional algorithms may struggle. They are applied widely in various fields, including robotics, operations research, and bioinformatics. This paper provides a comprehensive overview of the software architecture, and describes the main tasks of NiaAML, including the automatic composition of classification and regression pipelines. The overview is supported by an practical illustrative example.

Objective Data sharing promotes the scientific progress. However, not all data can be shared freely due to privacy issues. This work is intended to foster FAIR sharing of sensitive data exemplary in the biomedical domain, via an integrated computational approach for utilizing and enriching individual datasets by scientists without coding experience. Methods We present an in silico pipeline for openly sharing controlled materials by generating synthetic data. Additionally, it addresses the issue of inexperience to computational methods in a non-IT-affine domain by making use of a cyberinfrastructure that runs and enables sharing of computational notebooks without the need of local software installation. The use of a digital twin based on cancer datasets serves as exemplary use case for making biomedical data openly available. Quantitative and qualitative validation of model output as well as a study on user experience are conducted. Results The metadata approach describes generalizable descriptors for computational models, and outlines how to profit from existing data resources for validating computational models. The use of a virtual lab book cooperatively developed using a cloud-based data management and analysis system functions as showcase enabling easy interaction between users. Qualitative testing revealed a necessity for comprehensive guidelines furthering acceptance by various users. Conclusion The introduced framework presents an integrated approach for data generation and interpolating incomplete data, promoting Open Science through reproducibility of results and methods. The system can be expanded from the biomedical to any other domain while future studies integrating an enhanced graphical user interface could increase interdisciplinary applicability.

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

Abstract The current move towards digital pathology enables pathologists to use artificial intelligence (AI)‐based computer programmes for the advanced analysis of whole slide images. However, currently, the best‐performing AI algorithms for image analysis are deemed black boxes since it remains – even to their developers – often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black‐box machine‐learning systems more transparent. These XAI methods are a first step towards making black‐box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive ‘what‐if’‐questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human‐in‐the‐loop and bringing medical experts' experience and conceptual knowledge to AI processes.

Sexual reproduction of Zygnematophyceae by conjugation is a less investigated topic due to the difficulties of the induction of this process and zygospore ripening under laboratory conditions. For this study, we collected field sampled zygospores of Spirogyra mirabilis and three additional Spirogyra strains in Austria and Greece. Serial block-face scanning electron microscopy was performed on high pressure frozen and freeze substituted zygospores and 3D reconstructions were generated, allowing a comprehensive insight into the process of zygospore maturation, involving storage compound and organelle rearrangements. Chloroplasts are drastically changed, while young stages contain both parental chloroplasts, the male chloroplasts are aborted and reorganised as ‘secondary vacuoles’ which initially contain plastoglobules and remnants of thylakoid membranes. The originally large pyrenoids and the volume of starch granules is significantly reduced during maturation (young: 8 ± 5 µm³, mature: 0.2 ± 0.2 µm³). In contrast, lipid droplets (LDs) increase significantly in number upon zygospore maturation, while simultaneously getting smaller (young: 21 ± 18 µm³, mature: 0.1 ± 0.2 and 0.5 ± 0.9 µm³). Only in S. mirabilis the LD volume increases (34 ± 29 µm³), occupying ~50% of the zygospore volume. Mature zygospores contain barite crystals as confirmed by Raman spectroscopy with a size of 0.02 - 0.05 µm³. The initially thin zygospore cell wall (~0.5 µm endospore, ~0.8 µm exospore) increases in thickness and develops a distinct, electron dense mesospore, which has a reticulate appearance (~1.4 µm) in Spirogyra sp. from Greece. The exo- and endospore show cellulose microfibrils in a helicoidal pattern. In the denser endospore, pitch angles of the microfibril layers were calculated: ~18 ± 3° in S. mirabilis, ~20 ± 3° in Spirogyra sp. from Austria and ~38 ± 8° in Spirogyra sp. from Greece. Overall this study gives new insights into Spirogyra sp. zygospore development, crucial for survival during dry periods and dispersal of this genus.

Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers’ Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain.

In many domains of our daily life (e.g., agriculture, forestry, health, etc.), both laymen and experts need to classify entities into two binary classes (yes/no, good/bad, sufficient/insufficient, benign/malign, etc.). For many entities, this decision is difficult and we need another class called “maybe”, which contains a corresponding quantifiable tendency toward one of these two opposites. Human domain experts are often able to mark any entity, place it in a different class and adjust the position of the slope in the class. Moreover, they can often explain the classification space linguistically—depending on their individual domain experience and previous knowledge. We consider this human-in-the-loop extremely important and call our approach actionable explainable AI. Consequently, the parameters of the functions are adapted to these requirements and the solution is explained to the domain experts accordingly. Specifically, this paper contains three novelties going beyond the state-of-the-art: (1) A novel method for detecting the appropriate parameter range for the averaging function to treat the slope in the “maybe” class, along with a proposal for a better generalisation than the existing solution. (2) the insight that for a given problem, the family of t-norms and t-conorms covering the whole range of nilpotency is suitable because we need a clear “no” or “yes” not only for the borderline cases. Consequently, we adopted the Schweizer–Sklar family of t-norms or t-conorms in ordinal sums. (3) A new fuzzy quasi-dissimilarity function for classification into three classes: Main difference, irrelevant difference and partial difference. We conducted all of our experiments with real-world datasets.

Abstract Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific disciplines, such as systems biology, domain knowledge is available in the form of graphs or networks, and its use can improve model performance. We need network-based algorithms that are versatile and applicable in many research areas. In this work, we demonstrate subnetwork detection based on multi-modal node features using a novel Greedy Decision Forest (GDF) with inherent interpretability. The latter will be a crucial factor to retain experts and gain their trust in such algorithms. To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well. Systems biology is a good example of a field in which statistical data-driven machine learning enables the analysis of large amounts of multi-modal biomedical data. This is important to reach the future goal of precision medicine, where the complexity of patients is modeled on a system level to best tailor medical decisions, health practices and therapies to the individual patient. Our proposed explainable approach can help to uncover disease-causing network modules from multi-omics data to better understand complex diseases such as cancer.

Genome sizes of Zygnema spp. vary greatly, being unknown whether polyploidization occurred. The exact number of chromosomes in this genus is unknown since counting methods established for higher plants cannot be applied to green algae. The massive presence of pectins and arabinogalactan proteins in the cell wall interferes with the uptake of staining solutions; moreover, cell divisions in green algae are not restricted to meristems as in higher plants, which is another limiting factor. Cell divisions occur randomly in the thallus, due to the intercalary growth of algal filaments. Therefore, we increased the number of cell divisions via synchronization by changing the light cycle (10:14 h light/dark). The number of observed mitotic stages peaked at the beginning of the dark cycle. This protocol describes two methods for the visualization of chromosomes in the filamentous green alga Zygnema. Existing protocols were modified, leading to improved acetocarmine and haematoxylin staining methods as investigated by light microscopy. A freeze-shattering approach with liquid nitrogen was applied to increase the accessibility of the haematoxylin dye. These modified protocols allowed reliable chromosome counting in the genus Zygnema.Key features• Improved method for chromosome staining in filamentous green algae.• Optimized for the Zygnema strains SAG 698-1a (Z. cylindricum), SAG 698-1b (Z. circumcarinatum), and SAG 2419 (Zygnema ‘Saalach’).• This protocol builds upon the methods of chromosomal staining in green algae developed by Wittmann (1965), Staker (1971), and Fujii and Guerra (1998).• Cultivation and synchronization: 14 days; fixation and permeabilization: 24 h; staining: 1 h; image analysis and chromosome number quantification: up to 20 h.

In polar regions, the microphytobenthos has important ecological functions in shallow-water habitats, such as on top of coastal sediments. This community is dominated by benthic diatoms, which contribute significantly to primary production and biogeochemical cycling while also being an important component of polar food webs. Polar diatoms are able to cope with markedly changing light conditions and prolonged periods of darkness during the polar night in Antarctica. However, the underlying mechanisms are poorly understood. In this study, five strains of Antarctic benthic diatoms were isolated in the field, and the resulting unialgal cultures were identified as four distinct species, of which one is described as a new species, Planothidium wetzelii sp. nov. All four species were thoroughly examined using physiological, cell biological, and biochemical methods over a fully controlled dark period of 3 months. The results showed that the utilization of storage lipids is one of the key mechanisms in Antarctic benthic diatoms to survive the polar night, although different fatty acids were involved in the investigated taxa. In all tested species, the storage lipid content declined significantly, along with an ultrastructurally observable degradation of the chloroplasts. Surprisingly, photosynthetic performance did not change significantly despite chloroplasts decreasing in thylakoid membranes and an increased number of plastoglobules. Thus, a combination of biochemical and cell biological mechanisms allows Antarctic benthic diatoms to survive the polar night.

BackgroundMachine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures. ObjectiveVarious tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond. MethodsThe FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime. ResultsFeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites. ConclusionsFeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond.