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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.

This paper delineates the contemporary landscape, challenges, and prospective developments in human-centred artificial intelligence (AI) within the ambit of smart farming, a pivotal element of the emergent Agriculture 5.0, supplanting Agriculture 4.0. Analogous to Industry 4.0, agriculture has witnessed a trend towards comprehensive automation, often marginalizing human involvement. However, this approach has encountered limitations in agricultural contexts for various reasons. While AI’s capacity to assume human tasks is acknowledged, the inclusion of human expertise and experiential knowledge (human-in-the-loop) often proves indispensable, corroborated by the Moravec’s Paradox: tasks simple for humans are complex for AI. Furthermore, social, ethical, and legal imperatives necessitate human oversight of AI, a stance strongly reflected in the European Union’s regulatory framework. Consequently, this paper explores the advancements in human-centred AI focusing on their application in agricultural processes. These technological strides aim to enhance crop yields, minimize labor and resource wastage, and optimize the farm-to-consumer supply chain. The potential of AI to augment human decision-making, thereby fostering a sustainable, efficient, and resilient agri-food sector, is a focal point of this discussion - motivated by the current worldwide extreme weather events. Finally, a framework for Agriculture 5.0 is presented, which balances technological prowess with the needs, capabilities, and contexts of human stakeholders. Such an approach, emphasizing accessible, intuitive AI systems that meaningfully complement human activities, is crucial for the successful realization of future Agriculture 5.0.

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.

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.

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.

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.

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.

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.

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.

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.

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.

AI-assisted decision-making that impacts individuals raises critical questions about transparency and fairness in artificial intelligence (AI). Much research has highlighted the reciprocal relationships between the transparency/explanation and fairness in AI-assisted decision-making. Thus, considering their impact on user trust or perceived fairness simultaneously benefits responsible use of socio-technical AI systems, but currently receives little attention. In this paper, we investigate the effects of AI explanations and fairness on human-AI trust and perceived fairness, respectively, in specific AI-based decision-making scenarios. A user study simulating AI-assisted decision-making in two health insurance and medical treatment decision-making scenarios provided important insights. Due to the global pandemic and restrictions thereof, the user studies were conducted as online surveys. From the participant’s trust perspective, fairness was found to affect user trust only under the condition of a low fairness level, with the low fairness level reducing user trust. However, adding explanations helped users increase their trust in AI-assisted decision-making. From the perspective of perceived fairness, our work found that low levels of introduced fairness decreased users’ perceptions of fairness, while high levels of introduced fairness increased users’ perceptions of fairness. The addition of explanations definitely increased the perception of fairness. Furthermore, we found that application scenarios influenced trust and perceptions of fairness. The results show that the use of AI explanations and fairness statements in AI applications is complex: we need to consider not only the type of explanations and the degree of fairness introduced, but also the scenarios in which AI-assisted decision-making is used.

Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon the availability of extensive, high-quality data, underscoring the pivotal role of sensor-based data acquisition in the digital transformation of forestry. However, the complexity and challenging conditions of forest environments often impede data collection efforts. Achieving the full potential of smart forestry necessitates a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data essential for AI applications. This paper highlights the symbiotic relationship between human expertise and the digital transformation in forestry, particularly under challenging conditions. We emphasize the human-in-the-loop approach, which allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. A critical aspect of this integration is the deployment of autonomous robotic systems in forests, functioning both as data collectors and processing hubs. These systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. We present our universal sensor platform, detailing our experiences and the critical importance of the initial phase in digital transformation—the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and our findings underscore its significance in advancing smart forestry.

Personas have successfully supported the development of classical user interfaces for more than two decades by mapping users’ mental models to specific contexts. The rapid proliferation of Artificial Intelligence (AI) applications makes it necessary to create new approaches for future human-AI interfaces. Human-AI interfaces differ from classical human-computer interfaces in many ways, such as gaining some degree of human-like cognitive, self-executing, and self-adaptive capabilities and autonomy, and generating unexpected outputs that require non-deterministic interactions. Moreover, the most successful AI approaches are so-called “black box” systems, where the technology and the machine learning process are opaque to the user and the AI output is far not intuitive. This work shows how the personas method can be adapted to support the development of human-centered AI applications, and we demonstrate this on the example of a medical context. This work is - to our knowledge - the first to provide personas for AI using an openly available Personas for AI toolbox. The toolbox contains guidelines and material supporting persona development for AI as well as templates and pictures for persona visualisation. It is ready to use and freely available to the international research and development community. Additionally, an example from medical AI is provided as a best practice use case. This work is intended to help foster the development of novel human-AI interfaces that will be urgently needed in the near future.

Zygnematophyceae, a class of streptophyte green algae and sister group to land plants (Embryophytes) live in aquatic to semi-terrestrial habitats. The transition from aquatic to terrestrial environments requires adaptations in the physiology of vegetative cells and in the structural properties of their cell walls. Sexual reproduction occurs in Zygnematophyceae by conjugation and results in the formation of zygospores, possessing unique multi-layered cell walls, which might have been crucial in terrestrialization. We investigated the structure and chemical composition of field sampled Spirogyra sp. zygospore cell walls by multiple microscopical and spectral imaging techniques: light microscopy, confocal laser scanning microscopy, transmission electron microscopy following high pressure freeze fixation/freeze substitution, Raman spectroscopy and atomic force microscopy. This comprehensive analysis allowed the detection of the subcellular organization and showed three main layers of the zygospore wall, termed endo-, meso- and exospore. The endo- and exospore are composed of polysaccharides with different ultrastructural appearance, whereas the electron dense middle layer contains aromatic compounds as further characterized by Raman spectroscopy. The possible chemical composition remains elusive, but algaenan or a sporopollenin-like material is suggested. Similar compounds with a non-hydrolysable character can be found in moss spores and pollen of higher plants, suggesting a protective function against desiccation stress and high irradiation. While the tripartite differentiation of the zygospore wall is well established in Zygnematopyhceae, Spirogyra showed cellulose fibrils arranged in a helicoidal pattern in the endo- and exospore. Initial incorporation of lipid bodies during early zygospore wall formation was also observed, suggesting a key role of lipids in zygospore wall synthesis. Multimodal imaging revealed that the cell wall of the sexually formed zygospores possess a highly complex internal structure as well as aromatics, likely acting as protective compounds and leading to impregnation. Both, the newly discovered special three-dimensional arrangement of microfibrils and the integration of highly resistant components in the cell wall are not found in the vegetative state. The variety of methods gave a comprehensive view on the intricate zygospore cell wall and its potential key role in the terrestrial colonization and plant evolution is discussed.

A wide range of microorganisms inhabit biocrusts of arctic and sub-arctic regions. These taxa live and thrive under extreme conditions and, moreover, play important roles in biogeochemical cycling. Nevertheless, their diversity and abundance remain ambiguous. Here, we studied microbial community composition in biocrusts from Svalbard and Iceland using amplicon sequencing and epifluorescence microscopy. Sequencing of 16S rRNA gene revealed the dominance of Chloroflexi in the biocrusts from Iceland and Longyearbyen, and Acidobacteria in the biocrusts from Ny-Ålesund and South Svalbard. Within the 18S rRNA gene sequencing dataset, Chloroplastida prevailed in all the samples with dominance of Trebouxiophyceae in the biocrusts from Ny-Ålesund and Embryophyta in the biocrusts from the other localities. Furthermore, cyanobacterial number of cells and biovolume exceeded the microalgal in the biocrusts. Community compositions in the studied sites were correlated to the measured chemical parameters such as conductivity, pH, soil organic matter and mineral nitrogen contents. In addition, co-occurrence analysis showed the dominance of positive potential interactions and, bacterial and eukaryotic taxa co-occurred more frequently together.

In the current era of rapid technological advancement, machine learning (ML) is quickly becoming a dominant force in the development of smart environments [...]

Streptophyte green algae comprise the origin of land plants and therefore life on earth as we know it today. While terrestrialization opened new habitats, leaving the aquatic environment brought additional abiotic stresses. More-drastic temperature shifts and high light levels are major abiotic stresses in semi-terrestrial habitats, in addition to desiccation, which has been reviewed elsewhere. Zygnematophyceae, a species-rich class of streptophyte green algae, is considered a sister-group to embryophytes. They have developed a variety of avoidance and adaptation mechanisms to protect against temperature extremes and high radiation in the form of photosynthetically active and ultraviolet radiation (UV) radiation occurring on land. Recently, knowledge of transcriptomic and metabolomic changes as consequences of these stresses has become available. Land-plant stress-signaling pathways producing homologs of key enzymes have been described in Zygnematophyceae. An efficient adaptation strategy is their mat-like growth habit, which provides self-shading and protects lower layers from harmful radiation. Additionally, Zygnematophyceae possess phenolic compounds with UV-screening ability. Resting stages such as vegetative pre-akinetes tolerate freezing to a much higher extent than do young cells. Sexual reproduction occurs by conjugation without the formation of flagellated male gametes, which can be seen as an advantage in water-deficient habitats. The resulting zygospores possess a multilayer cell wall, contributing to their resistance to terrestrial conditions. Especially in the context of global change, understanding temperature and light tolerance is crucial.

Abstract Background Malignant brain tumor diseases exhibit differences within molecular features depending on the patient’s age. Methods In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers. Results Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification. Conclusions We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.

Abstract Background Many methodological approaches have focused so far on physiological and molecular responses of plant tissues to freezing but only little knowledge is available on the consequences of extracellular ice-formation on cellular ultrastructure that underlies physiological reactions. In this context, the preservation of a defined frozen state during the entire fixation procedure is an essential prerequisite. However, current techniques are not able to fix frozen plant tissues for transmission electron microscopy (TEM) without interrupting the cold chain. Chemical fixation by glutaraldehyde and osmium tetroxide is not possible at sub-zero temperatures. Cryo-fixation methods, such as high pressure freeze fixation (HPF) representing the state-of-the-art technique for best structural preservation, are not equipped for freezing frozen samples. In order to overcome this obstacle, a novel technical approach for maintaining the cold chain of already frozen plant samples prior and during HPF is presented. Results Different algae (Micrasterias denticulata, Klebsormidium crenulatum) and higher plant tissues (Lemna sp., Ranunculus glacialis, Pinus mugo) were successfully frozen and prepared for HPF at freezing temperatures (− 2 °C, − 5 °C, − 6 °C) within a newly developed automatic freezing unit (AFU), that we manufactured from a standard laboratory freezer. Preceding tests on photosynthetic electron transport and ability to plasmolyse show that the temperatures applied did not impair electron transport in PSII nor cell vitality. The transfer of the frozen specimen from the AFU into the HPF-device and subsequently cryo-fixation were performed without intermediate thawing. After cryo-substitution and further processing, the resulting TEM-micrographs showed excellent ultrastructure preservation of the different organisms when compared to specimens fixed at ambient temperature. Conclusions The method presented allows preserving the ultrastructure of plant cells in the frozen state during cryo-fixation. The resulting high quality TEM-images represent an important step towards a better understanding of the consequences of extracellular ice formation on cellular ultrastructure. It has the potential to provide new insights into changes of organelle structure, identification of intracellular injuries during ice formation and may help to understand freezing and thawing processes in plant tissues. It may be combined with analytical TEM such as electron energy loss spectroscopy (EELS), X-ray analyses (EDX) and various other electron microscopic techniques.

In response to socioeconomic development, the number of machine learning applications has increased, along with the calls for algorithmic transparency and further sustainability in terms of energy efficient technologies. Modern computer algorithms that process large amounts of information, particularly artificial intelligence methods and their workhorse machine learning, can be used to promote and support sustainability; however, they consume a lot of energy themselves. This work focuses and interconnects two key aspects of artificial intelligence regarding the transparency and sustainability of model development. We identify frameworks for measuring carbon emissions from Python algorithms and evaluate energy consumption during model development. Additionally, we test the impact of explainability on algorithmic energy consumption during model optimization, particularly for applications in health and, to expand the scope and achieve a widespread use, civil engineering and computer vision. Specifically, we present three different models of classification, regression and object-based detection for the scenarios of cancer classification, building energy, and image detection, each integrated with explainable artificial intelligence (XAI) or feature reduction. This work can serve as a guide for selecting a tool to measure and scrutinize algorithmic energy consumption and raise awareness of emission-based model optimization by highlighting the sustainability of XAI.

Abstract Background In classification and diagnostic testing, the receiver-operator characteristic (ROC) plot and the area under the ROC curve (AUC) describe how an adjustable threshold causes changes in two types of error: false positives and false negatives. Only part of the ROC curve and AUC are informative however when they are used with imbalanced data. Hence, alternatives to the AUC have been proposed, such as the partial AUC and the area under the precision-recall curve. However, these alternatives cannot be as fully interpreted as the AUC, in part because they ignore some information about actual negatives. Methods We derive and propose a new concordant partial AUC and a new partial c statistic for ROC data—as foundational measures and methods to help understand and explain parts of the ROC plot and AUC. Our partial measures are continuous and discrete versions of the same measure, are derived from the AUC and c statistic respectively, are validated as equal to each other, and validated as equal in summation to whole measures where expected. Our partial measures are tested for validity on a classic ROC example from Fawcett, a variation thereof, and two real-life benchmark data sets in breast cancer: the Wisconsin and Ljubljana data sets. Interpretation of an example is then provided. Results Results show the expected equalities between our new partial measures and the existing whole measures. The example interpretation illustrates the need for our newly derived partial measures. Conclusions The concordant partial area under the ROC curve was proposed and unlike previous partial measure alternatives, it maintains the characteristics of the AUC. The first partial c statistic for ROC plots was also proposed as an unbiased interpretation for part of an ROC curve. The expected equalities among and between our newly derived partial measures and their existing full measure counterparts are confirmed. These measures may be used with any data set but this paper focuses on imbalanced data with low prevalence. Future work Future work with our proposed measures may: demonstrate their value for imbalanced data with high prevalence, compare them to other measures not based on areas; and combine them with other ROC measures and techniques.

Abstract Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an ever-increasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for “Big data”. Analysis of neuron and/or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI- and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.

Functional and structural adjustments of plants in response to environmental factors, including those occurring in alpine habitats, can result in transient acclimation, plastic phenotypic adjustments and/or heritable adaptation. To unravel repeatedly selected traits with potential adaptive advantage, we studied parallel (ecotypic) and non-parallel (regional) differentiation in leaf traits in alpine and foothill ecotypes of Arabidopsis arenosa. Leaves of plants from eight alpine and eight foothill populations, representing three independent alpine colonization events in different mountain ranges, were investigated by microscopy techniques after reciprocal transplantation. Most traits clearly differed between the foothill and the alpine ecotype, with plastic adjustments to the local environment. In alpine populations, leaves were thicker, with altered proportions of palisade and spongy parenchyma, and had fewer trichomes, and chloroplasts contained large starch grains with less stacked grana thylakoids compared to foothill populations. Geographical origin had no impact on most traits except for trichome and stomatal density on abaxial leaf surfaces. The strong parallel, heritable ecotypic differentiation in various leaf traits and the absence of regional effects suggests that most of the observed leaf traits are adaptive. These trait shifts may reflect general trends in the adaptation of leaf anatomy associated with the colonization of alpine habitats.

The taxonomy of coccoid cyanobacteria, such as Chroococcidiopsis, Pleurocapsa, Chroococcus, Gloeothece, Gloeocapsa, Gloeocapsopsis, and the related recent genera Sinocapsa and Aliterella, can easily be intermixed when solely compared on a morphological basis. There is still little support on the taxonomic position of some of the addressed genera, as genetic information is available only for a fraction of species that have been described solely on morphology. Modern polyphasic approaches that combine classic morphological investigations with DNA-based molecular analyses and the evaluation of ecological properties can disentangle these easily confusable unicellular genera. By using such an approach, we present here the formal description of two novel unicellular cyanobacterial species that inhabit the Coastal Range of the Atacama Desert, Gloeocapsopsis dulcis (first reported as Gloeocapsopsis AAB1) and Gloeocapsopsis diffluens. Both species could be clearly separated from previously reported species by 16S rRNA and 16S–23S ITS gene sequencing, the resulting secondary structures, p-distance analyses of the 16S–23S ITS, and morphology. For avoiding further confusions emendation of the genus Gloeocapsopsis as well as epitypification of the type species Gloeocapsopsis crepidinum based on the strain LEGE06123 were conducted.

Zygnematophyceae green algae (ZGA) have been shown to be the closest relatives of land plants. Three nuclear genomes (Spirogloea muscicola, Mesotaenium endlicherianum, and Penium margaritaceum) of ZGA have been recently published, and more genomes are underway. Here we analyzed two Zygnema circumcarinatum strains SAG 698-1a (mating +) and SAG 698-1b (mating −) and found distinct cell sizes and other morphological differences. The molecular identities of the two strains were further investigated by sequencing their 18S rRNA, psaA and rbcL genes. These marker genes of SAG 698-1a were surprisingly much more similar to Z. cylindricum (SAG 698-2) than to SAG 698-1b. Phylogenies of these marker genes also showed that SAG 698-1a and SAG 698-1b were well separated into two different Zygnema clades, where SAG 698-1a was clustered with Z. cylindricum, while SAG 698-1b was clustered with Z. tunetanum. Additionally, physiological parameters like ETRmax values differed between SAG 698-1a and SAG 698-1b after 2 months of cultivation. The de-epoxidation state (DEPS) of the xanthophyll cycle pigments also showed significant differences. Surprisingly, the two strains could not conjugate, and significantly differed in the thickness of the mucilage layer. Additionally, ZGA cell walls are highly enriched with sticky and acidic polysaccharides, and therefore the widely used plant nuclear extraction protocols do not work well in ZGA. Here, we also report a fast and simple method, by mechanical chopping, for efficient nuclear extraction in the two SAG strains. More importantly, the extracted nuclei were further used for nuclear genome size estimation of the two SAG strains by flow cytometry (FC). To confirm the FC result, we have also used other experimental methods for nuclear genome size estimation of the two strains. Interestingly, the two strains were found to have very distinct nuclear genome sizes (313.2 ± 2.0 Mb in SAG 698-1a vs. 63.5 ± 0.5 Mb in SAG 698-1b). Our multiple lines of evidence strongly indicate that SAG 698-1a possibly had been confused with SAG 698-2 prior to 2005, and most likely represents Z. cylindricum or a closely related species.

The globally distributed green microalga Chlorella vulgaris (Chlorophyta) colonizes aquatic and terrestrial habitats, but the molecular mechanisms underpinning survival in these two contrasting environments are far from understood. Here, we compared the authentic strain of C. vulgaris from an aquatic habitat with a strain from a terrestrial high alpine habitat previously determined as Chlorella mirabilis. Molecular phylogeny of SSU rDNA (823 bp) showed that the two strains differed by one nucleotide only. Sequencing of the ITS2 region confirmed that both strains belong to the same species, but to distinct ribotypes. Therefore, the terrestrial strain was re-assessed as C. vulgaris. To study the response to environmental conditions experienced on land, we assessed the effects of irradiance and temperature on growth, of temperature on photosynthesis and respiration, and of desiccation and rehydration on photosynthetic performance. In contrast to the aquatic strain, the terrestrial strain tolerated higher temperatures and light conditions, had a higher photosynthesis-to-respiration ratio at 25°C, still grew at 30°C and was able to fully recover photosynthetic performance after desiccation at 84% relative humidity. The two strains differed most in their response to the dehydration/rehydration treatment, which was further investigated by untargeted GC–MS-based metabolite profiling to gain insights into metabolic traits differentiating the two strains. The two strains differed in their allocation of carbon and nitrogen into their primary metabolites. Overall, the terrestrial strain had higher contents of readily available nitrogen-based metabolites, especially amino acids and the polyamine putrescine. Dehydration and rehydration led to differential regulation of the amino acid metabolism, the tricarboxylic acid cycle and sucrose metabolism. The data are discussed with a view to differences in phenotypic plasticity of the two strains, and we suggest that the two genetically almost identical C. vulgaris strains are attractive models to study mechanisms that protect from abiotic stress factors, which are more frequent in terrestrial than aquatic habitats, such as desiccation and irradiation.

Adaptation strategies in freezing resistance were investigated in Klebsormidium crenulatum, an early branching streptophyte green alga related to higher plants. Klebsormidium grows naturally in unfavorable environments like alpine biological soil crusts, exposed to desiccation, high irradiation and cold stress. Here, chilling and freezing induced alterations of the ultrastructure were investigated. Control samples (kept at 20°C) were compared to chilled (4°C) as well as extracellularly frozen algae (−2 and −4°C). A software-controlled laboratory freezer (AFU, automatic freezing unit) was used for algal exposure to various temperatures and freezing was manually induced. Samples were then high pressure frozen and cryo-substituted for electron microscopy. Control cells had a similar appearance in size and ultrastructure as previously reported. While chilling stressed algae only showed minor ultrastructural alterations, such as small inward facing cell wall plugs and minor alterations of organelles, drastic changes of the cell wall and in organelle distribution were found in extracellularly frozen samples (−2°C and −4°C). In frozen samples, the cytoplasm was not retracted from the cell wall, but extensive three-dimensional cell wall layers were formed, most prominently in the corners of the cells, as determined by FIB-SEM and TEM tomography. Similar alterations/adaptations of the cell wall were not reported or visualized in Klebsormidium before, neither in controls, nor during other stress scenarios. This indicates that the cell wall is reinforced by these additional wall layers during freezing stress. Cells allowed to recover from freezing stress (−2°C) for 5 h at 20°C lost these additional cell wall layers, suggesting their dynamic formation. The composition of these cell wall reinforcement areas was investigated by immuno-TEM. In addition, alterations of structure and distribution of mitochondria, dictyosomes and a drastically increased endoplasmic reticulum were observed in frozen cells by TEM and TEM tomography. Measurements of the photosynthetic oxygen production showed an acclimation of Klebsormidium to chilling stress, which correlates with our findings on ultrastructural alterations of morphology and distribution of organelles. The cell wall reinforcement areas, together with the observed changes in organelle structure and distribution, are likely to contribute to maintenance of an undisturbed cell physiology and to adaptation to chilling and freezing stress.

Abstract Background Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important aspect for age related deterioration of the general patient condition is frailty. The frailty syndrome is associated with a high risk for falls, hospitalization, disability, and finally increased mortality. Using predictive data mining enables the discovery of potential risk factors and can be used as clinical decision support system, which provides the medical doctor with information on the probable clinical patient outcome. This enables the professional to react promptly and to avert likely adverse events in advance. Methods Medical data of 474 study participants containing 284 health related parameters, including questionnaire answers, blood parameters and vital parameters from the Toledo Study for Healthy Aging (TSHA) was used. Binary classification models were built in order to distinguish between frail and non-frail study subjects. Results Using the available TSHA data and the discovered potential predictors, it was possible to design, develop and evaluate a variety of different predictive models for the frailty syndrome. The best performing model was the support vector machine (SVM, 78.31%). Moreover, a methodology was developed, making it possible to explore and to use incomplete medical data and further identify potential predictors and enable interpretability. Conclusions This work demonstrates that it is feasible to use incomplete, imbalanced medical data for the development of a predictive model for the frailty syndrome. Moreover, potential predictive factors have been discovered, which were clinically approved by the clinicians. Future work will improve prediction accuracy, especially with regard to separating the group of frail patients into frail and pre-frail ones and analyze the differences among them.

Abstract Background A plethora of Web resources are available offering information on clinical, pre-clinical, genomic and theoretical aspects of cancer, including not only the comprehensive cancer projects as ICGC and TCGA, but also less-known and more specialized projects on pediatric diseases such as PCGP. However, in case of data on childhood cancer there is very little information openly available. Several web-based resources and tools offer general biomedical data which are not purpose-built, for neither pediatric nor cancer analysis. Additionally, many Web resources on cancer focus on incidence data and statistical social characteristics as well as self-regulating communities. Methods We summarize those resources which are open and are considered to support scientific fundamental research, while we address our comparison to 11 identified pediatric cancer-specific resources (5 tools, 6 databases). The evaluation consists of 5 use cases on the example of brain tumor research and covers user-defined search scenarios as well as data mining tasks, also examining interactive visual analysis features. Results Web resources differ in terms of information quantity and presentation. Pedican lists an abundance of entries with few selection features. PeCan and PedcBioPortal include visual analysis tools while the latter integrates published and new consortia-based data. UCSC Xena Browser offers an in-depth analysis of genomic data. ICGC data portal provides various features for data analysis and an option to submit own data. Its focus lies on adult Pan-Cancer projects. Pediatric Pan-Cancer datasets are being integrated into PeCan and PedcBioPortal. Comparing information on prominent mutations within glioma discloses well-known, unknown, possible, as well as inapplicable biomarkers. This summary further emphasizes the varying data allocation. Tested tools show advantages and disadvantages, depending on the respective use case scenario, providing inhomogeneous data quantity and information specifics. Conclusions Web resources on specific pediatric cancers are less abundant and less-known compared to those offering adult cancer research data. Meanwhile, current efforts of ongoing pediatric data collection and Pan-Cancer projects indicate future opportunities for childhood cancer research, that is greatly needed for both fundamental as well as clinical research.

The terrestrial green algal members of the genera Interfilum and Klebsormidium (Klebsormidiophyceae, Streptophyta) are found in biological soil crusts of extreme habitats around the world where they are regularly exposed, among other abiotic stress factors, to high levels of ultraviolet radiation (UVR). As a consequence those species synthesize and accumulate either one or two mycosporine-like amino acids (MAAs), but with a missing structural elucidation up to now. Therefore, in the present study both MAAs were chemically isolated and structurally elucidated. The two new compounds exhibit absorption maxima of 324 nm. MAA 1 has a molecular weight of 467 and MAA 2 of 305, and the latter (MAA 2) was identified as N-(4,5-dihydroxy-5-(hydroxymethyl)-2-methoxy-3-oxocyclohex-1-en-1-yl)-N-methylserine using one- and two-dimensional 1H and 13C-NMR spectroscopy. MAA 1 contains an additional sugar moiety. As trivial names for these two novel MAAs we suggest klebsormidin A and klebsormidin B. Different species from all previously described phylogenetic clades of Klebsormidiophyceae were chemically screened for their MAA composition in aqueous extracts using RP−HPLC and LC−MS. The novel klebsormidin A was present throughout all clades and hence could be suitable as a chemotaxonomic marker. Additionally, controlled UVR−exposure experiments with all investigated species showed that MAA biosynthesis and intracellular enrichment is strongly induced by short wavelengths, supporting the function of these compounds as natural UV−sunscreen as well as explaining the cosmopolitan distribution and ecological success of Interfilum and Klebsormidium in terrestrial habitats.

Trebouxiophyceae are microalgae occupying even extreme environments such as polar regions or deserts, terrestrial or aquatic, and can occur free-living or as lichen photobionts. Yet, it is poorly understood how environmental factors shape their metabolism. Here, we report on responses to light and temperature, and metabolic adjustments to desiccation in Diplosphaera epiphytica, isolated from a lichen, and Edaphochlorella mirabilis, isolated from Tundra soil, assessed via growth and photosynthetic performance parameters. Metabolite profiling was conducted by GC–MS. A meta-analysis together with data from a terrestrial and an aquatic Chlorella vulgaris strain reflected elements of phylogenetic relationship, lifestyle, and relative desiccation tolerance of the four algal strains. For example, compatible solutes associated with desiccation tolerance were up-accumulated in D. epiphytica, but also sugars and sugar alcohols typically produced by lichen photobionts. The aquatic C. vulgaris, the most desiccation-sensitive strain, showed the greatest variation in metabolite accumulation after desiccation and rehydration, whereas the most desiccation-tolerant strain, D. epiphytica, showed the least, suggesting that it has a more efficient constitutive protection from desiccation and/or that desiccation disturbed the metabolic steady-state less than in the other three strains. The authors hope that this study will stimulate more research into desiccation tolerance mechanisms in these under-investigated microorganisms.

The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art.

Abstract Background There is potential for medical research on the basis of routine data used from general practice electronic health records (GP eHRs), even in areas where there is no common GP research platform. We present a case study on menopausal women with hypertension and metabolic syndrome (MS). The aims were to explore the appropriateness of the standard definition of MS to apply to this specific, narrowly defined population group and to improve recognition of women at high CV risk. Methods We investigated the possible uses offered by available data from GP eHRs, completed with patients interview, in goal of the study, using a combination of methods. For the sample of 202 hypertensive women, 47–59 years old, a data set was performed, consisted of a total number of 62 parameters, 50 parameters used from GP eHRs. It was analysed by using a mixture of methods: analysis of differences, cutoff values, graphical presentations, logistic regression and decision trees. Results The age range found to best match the emergency of MS was 51–55 years. Deviations from the definition of MS were identified: a larger cut-off value of the waist circumference measure (89 vs 80 cm) and parameters BMI and total serum cholesterol perform better as components of MS than the standard parameters waist circumference and HDL-cholesterol. The threshold value of BMI at which it is expected that most of hypertensive menopausal women have MS, was found to be 25.5. The other best means for recognision of women with MS include triglycerides above the threshold of 1.7 mmol/L and information on statins use. Prevention of CVD should focus on women with a new onset diabetes and comorbidities of a long-term hypertension with anxiety/depression. Conclusions The added value of this study goes beyond the current paradigm on MS. Results indicate characteristics of MS in a narrowly defined, specific population group. A comprehensive view has been enabled by using heterogenoeus data and a smart combination of various methods for data analysis. The paper shows the feasibility of this research approach in routine practice, to make use of data which would otherwise not be used for research.

Abstract Background Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost. Main body We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell’s functional organization up to model building for predictive systems. Conclusion Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

The success of serious games usually depends on their capabilities to engage learners and to provide them with personalized gaming and learning experiences. Therefore, it is important to equip a game, as an autonomous computer system, with a certain level of understanding about individual learning trajectories and gaming processes. AI and machine learning technologies increasingly enter the field; these technologies often fail, however, since serious games either pose highly complex problems (combining gaming and learning process) or do not provide the extensive data bases that would be required. An interesting new direction is augmenting the strength of AI technologies with human intuition and human cognition. In the present paper, we investigated performance of the MAXMIN Ant System, a combinatorial optimization algorithm, with and without human interventions to the algorithmic procedure. As a testbed, we used a clone of the Travelling Salesman problem, the Travelling Snakesman game. We found some evidence that human interventions result in superior performance than the algorithm alone. The results are discussed regarding the applicability of this pathfinding algorithm in adaptive games, exemplified by Micro Learning Space adaptation systems.

Land plants inherited several traits from their green algal ancestors (Zygnematophyceae), including a polysaccharide-rich cell wall, which is a prerequisite for terrestrial survival. A major component of both land plant and Zygnematophyceaen cell walls is the pectin homogalacturonan (HG), and its high water holding capacity may have helped algae to colonize terrestrial habitats, characterized by water scarcity. To test this, HG was removed from the cell walls of Zygnema filaments by pectate lyase (PL), and their effective quantum yield of photosystem II (YII) as a proxy for photosynthetic performance was measured in response to desiccation stress by pulse amplitude modulation (PAM). Old filaments were found to contain more HG and are more resistant against desiccation stress but relatively lose more desiccation resistance after HG removal than young filaments. After rehydration, the photosynthetic performance recovered less efficiently in filaments with a HG content reduced by PL, independently of filament age. Immunolabeling showed that partial or un-methylesterified HG occurs throughout the longitudinal cell walls of both young and old filaments, while no labeling signal occurred when filaments were treated with PL prior labeling. This confirmed that most HG can be removed from the cell walls by PL. The initial labeling pattern was restored after ~3 days. A different form of methylesterified HG was restricted to cell poles and cross cell walls. In conclusion, it was shown that the accumulation of HG in Zygnema filaments increases their resistance against desiccation stress. This trait might have played an important role during the colonization of land by Zygnematophyceae, which founded the evolution of all land plants.

Charophytes represent the group of green algae whose ancestors invaded land and ultimately gave rise to land plants 450 million years ago. While Zygnematophyceae are believed to be the direct sister lineage to embryophytes, different members of this group (Penium, Spirogyra, Zygnema) and the advanced thallus forming Coleochaete as well as the sarcinoid basal streptophyte Chlorokybus were investigated concerning their vegetative extracellular matrix (ECM) properties. Many taxa exhibit adhesion phenomena that are critical for affixing to a substrate or keeping cells together in a thallus, however, there is a great variety in possible reactions to e.g., wounding. In this study an analysis of adhesion mechanisms revealed that arabinogalactan proteins (AGPs) are most likely key adhesion molecules. Through use of monoclonal antibodies (JIM13) or the Yariv reagent, AGPs were located in cell surface sheaths and cell walls that were parts of the adhesion focal zones on substrates including wound induced rhizoid formation. JIM5, detecting highly methyl-esterfied homoglacturonan and JIM8, an antibody detecting AGP glycan and LM6 detecting arabinans were also tested and a colocalization was found in several examples (e.g., Zygnema) suggesting an interplay between these components. AGPs have been described in this study to perform both, cell to cell adhesion in algae forming thalli and cell to surface adhesion in the filamentous forms. These findings enable a broader evolutionary understanding of the function of AGPs in charophyte green algae.

Extreme environments, such as alpine habitats at high elevation, are increasingly exposed to man-made climate change. Zygnematophyceae thriving in these regions possess a special means of sexual reproduction, termed conjugation, leading to the formation of resistant zygospores. A field sample of Spirogyra with numerous conjugating stages was isolated and characterized by molecular phylogeny. We successfully induced sexual reproduction under laboratory conditions by a transfer to artificial pond water and increasing the light intensity to 184 µmol photons m−2 s−1. This, however was only possible in early spring, suggesting that the isolated cultures had an internal rhythm. The reproductive morphology was characterized by light- and transmission electron microscopy, and the latter allowed the detection of distinctly oriented microfibrils in the exo- and endospore, and an electron-dense mesospore. Glycan microarray profiling showed that Spirogyra cell walls are rich in major pectic and hemicellulosic polysaccharides, and immuno-fluorescence allowed the detection of arabinogalactan proteins (AGPs) and xyloglucan in the zygospore cell walls. Confocal RAMAN spectroscopy detected complex aromatic compounds, similar in their spectral signature to that of Lycopodium spores. These data support the idea that sexual reproduction in Zygnematophyceae, the sister lineage to land plants, might have played an important role in the process of terrestrialization.

Ubiquitous computing and ambient assisted living environments offer promising solutions to meet the demographic change. An example are serious games for health care: Regular exercises mediated through games increase health, well-being, and autonomy of the residents whilst at the same time reducing the costs for caregiving. To understand which factors contribute to an increased acceptance of such exercise games in ambient assisted living environments, a prototypic game was evaluated with 32 younger and 32 older players. Game performance is influenced by age, need for achievement, and also gender. Acceptance and projected use are related to the believe in making the game a habit, current gaming frequency, and social influences. Notably, the game increased the perceived health of the subjects, which is an important issue. This article concludes with guidelines to successfully introduce serious exercise games into health care and future ideas to realize social inclusion in game design.

Conversational agents are gaining huge popularity in industrial applications such as digital assistants, chatbots, and particularly systems for natural language understanding (NLU). However, a major drawback is the unavailability of a common metric to evaluate the replies against human judgement for conversational agents. In this paper, we develop a benchmark dataset with human annotations and diverse replies that can be used to develop such metric for conversational agents. The paper introduces a high-quality human annotated movie dialogue dataset, HUMOD, that is developed from the Cornell movie dialogues dataset. This new dataset comprises 28,500 human responses from 9500 multi-turn dialogue history-reply pairs. Human responses include: (i) ratings of the dialogue reply in relevance to the dialogue history; and (ii) unique dialogue replies for each dialogue history from the users. Such unique dialogue replies enable researchers in evaluating their models against six unique human responses for each given history. Detailed analysis on how dialogues are structured and human perception on dialogue score in comparison with existing models are also presented.

We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted.

Plant including green algal cells are surrounded by a cell wall, which is a diverse composite of complex polysaccharides and crucial for their function and survival. Here we describe two simple protocols to visualize callose (1→3-β-D-glucose) and cellulose (1→4-β-D-glucose) and related polysaccharides in the cell walls of streptophyte green algae. Untreated or algal cells heated in NaOH are incubated in Calcofluor white (binding to β-glucans including cellulose) or Aniline blue (binding to callose), respectively. Both dyes can be visualized by epifluorescence microscopy.