Search

Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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., (© 2024. The Author(s).)

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., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Raman spectroscopy was used to study the complex interactions and morphogenesis of the green seaweed Ulva (Chlorophyta) and its associated bacteria under controlled conditions in a reductionist model system. Integrating multiple imaging techniques contributes to a more comprehensive understanding of these biological processes. Therefore, Raman spectroscopy was introduced as a non-invasive, label-free tool for examining chemical information of the tripartite community Ulva mutabilis-Roseovarius sp.-Maribacter sp. The study explored cell differentiation, cell wall protrusion, and bacterial-macroalgae interactions of intact algal thalli. Using Raman spectroscopy, the analysis of the CH x -stretching wavenumber region distinguished spatial regions in Ulva germination and cellular malformations under axenic conditions and upon inoculation with a specific bacterium in bipartite communities. The spectral information was used to guide in-depth analyses within the fingerprint region and to identify substance classes such as proteins, lipids, and polysaccharides, including evidence for ulvan found in cell wall protrusions., (© 2024 The Authors. ChemPhysChem published by Wiley-VCH GmbH.)

Recent research on a special type of sexual reproduction and zygospore formation in Zygnematophyceae, the sister group of land plants, is summarized. Within this group, gamete fusion occurs by conjugation. Zygospore development in Mougeotia, Spirogyra and Zygnema is highlighted, which has recently been studied using Raman spectroscopy, allowing chemical imaging and detection of changes in starch and lipid accumulation. Three-dimensional reconstructions after serial block-face scanning electron microscopy (SBF-SEM) or focused ion beam SEM (FIB-SEM) made it possible to visualize and quantify cell wall and organelle changes during zygospore development. The zygospore walls undergo strong modifications starting from uniform thin cell walls to a multilayered structure. The mature cell wall is composed of a cellulosic endospore and exospore and a central mesospore built up by aromatic compounds. In Spirogyra , the exospore and endospore consist of thick layers of helicoidally arranged cellulose fibrils, which are otherwise only known from stone cells of land plants. While starch is degraded during maturation, providing building blocks for cell wall formation, lipid droplets accumulate and fill large parts of the ripe zygospores, similar to spores and seeds of land plants. Overall, data show similarities between streptophyte algae and embryophytes, suggesting that the genetic toolkit for many land plant traits already existed in their shared algal ancestor. This article is part of the theme issue 'The evolution of plant metabolism'.

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Antimicrobial photodynamic therapy (aPDT) is an evolving treatment strategy against human pathogenic microbes such as the Candida species, including the emerging pathogen C. auris . Using a modified EUCAST protocol, the light-enhanced antifungal activity of the natural compound parietin was explored. The photoactivity was evaluated against three separate strains of five yeasts, and its molecular mode of action was analysed via several techniques, i.e., cellular uptake, reactive electrophilic species (RES), and singlet oxygen yield. Under experimental conditions ( λ  = 428 nm, H = 30 J/cm 2 , PI = 30 min), microbial growth was inhibited by more than 90% at parietin concentrations as low as c = 0.156 mg/L (0.55 µM) for C. tropicalis and Cryptococcus neoformans , c = 0.313 mg/L (1.10 µM) for C. auris , c = 0.625 mg/L (2.20 µM) for C. glabrata , and c = 1.250 mg/L (4.40 µM) for C. albicans . Mode-of-action analysis demonstrated fungicidal activity. Parietin targets the cell membrane and induces cell death via ROS-mediated lipid peroxidation after light irradiation. In summary, parietin exhibits light-enhanced fungicidal activity against all Candida species tested (including C. auris ) and Cryptococcus neoformans , covering three of the four critical threats on the WHO's most recent fungal priority list., Competing Interests: The authors declare that they have no conflict of interest., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Zygnematophyceae are the algal sisters of land plants. Here we sequenced four genomes of filamentous Zygnematophyceae, including chromosome-scale assemblies for three strains of Zygnema circumcarinatum. We inferred traits in the ancestor of Zygnematophyceae and land plants that might have ushered in the conquest of land by plants: expanded genes for signaling cascades, environmental response, and multicellular growth. Zygnematophyceae and land plants share all the major enzymes for cell wall synthesis and remodifications, and gene gains shaped this toolkit. Co-expression network analyses uncover gene cohorts that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution., (© 2024. The Author(s).)

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., Competing Interests: Authors LJK and MS-Z are employed by the company Saatzucht Edelhof GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Chang-Brahim, Koppensteiner, Beltrame, Bodner, Saranti, Salzinger, Fanta-Jende, Sulzbachner, Bruckmüller, Trognitz, Samad-Zamini, Zechner, Holzinger and Molin.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Antreich, Permann, Xiao, Tiloca and Holzinger.)

Background: Lack of trust in artificial intelligence (AI) models in medicine is still the key blockage for the use of AI in clinical decision support systems (CDSS). Although AI models are already performing excellently in systems medicine, their black-box nature entails that patient-specific decisions are incomprehensible for the physician. Explainable AI (XAI) algorithms aim to "explain" to a human domain expert, which input features influenced a specific recommendation. However, in the clinical domain, these explanations must lead to some degree of causal understanding by a clinician., Results: We developed the CLARUS platform, aiming to promote human understanding of graph neural network (GNN) predictions. CLARUS enables the visualisation of patient-specific networks, as well as, relevance values for genes and interactions, computed by XAI methods, such as GNNExplainer. This enables domain experts to gain deeper insights into the network and more importantly, the expert can interactively alter the patient-specific network based on the acquired understanding and initiate re-prediction or retraining. This interactivity allows us to ask manual counterfactual questions and analyse the effects on the GNN prediction., Conclusion: We present the first interactive XAI platform prototype, CLARUS, that allows not only the evaluation of specific human counterfactual questions based on user-defined alterations of patient networks and a re-prediction of the clinical outcome but also a retraining of the entire GNN after changing the underlying graph structures. The platform is currently hosted by the GWDG on https://rshiny.gwdg.de/apps/clarus/., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

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.

Purpose: This contribution explores the underuse of artificial intelligence (AI) in the health sector, what this means for practice, and how much the underuse can cost. Attention is drawn to the relevance of an issue that the European Parliament has outlined as a "major threat" in 2020. At its heart is the risk that research and development on trusted AI systems for medicine and digital health will pile up in lab centers without generating further practical relevance. Our analysis highlights why researchers, practitioners and especially policymakers, should pay attention to this phenomenon., Methods: The paper examines the ways in which governments and public agencies are addressing the underuse of AI. As governments and international organizations often acknowledge the limitations of their own initiatives, the contribution explores the causes of the current issues and suggests ways to improve initiatives for digital health., Results: Recommendations address the development of standards, models of regulatory governance, assessment of the opportunity costs of underuse of technology, and the urgency of the problem., Conclusions: The exponential pace of AI advances and innovations makes the risks of underuse of AI increasingly threatening., Competing Interests: Conflict of interestThe authors declare no conflict of interest., (© The Author(s) 2023.)

This article introduces a new basis for optimising cable corridor layouts in timber extraction on steep terrain by using a digital twin of a forest. Traditional approaches for generating cable corridor layouts rely on less accurate contour maps, which can lead to layouts which rely on infeasible supports, undermining confidence in the generated layouts. We present a detailed simulational approach which uses high-resolution tree maps and digital terrain models to compute realistic representations of all possible cable corridors in a given terrain. We applied established methods in forestry to compute feasible cable corridors in a designated area, including rope deflection, determining sufficient tree anchors and placing intermediate supports where necessary. The proposed individual cable corridor trajectories form the foundation for an optimised overall layout that enables a reduction of installation and operation costs and promotes sustainable timber extraction practices on steep terrain. As a next step we aim to mathematically optimise the layout of feasible cable corridors based on multiple criteria (cost, ergonomic aspects, ecological aspects), and integrate the results into an user-friendly workflow., Competing Interests: Conflict of interestThe authors declare that there are no conflicts of interest., (© The Author(s) 2024.)

For the present study, we collected the Ulvophyceae species Trentepohlia aurea from limestone rock near Berchtesgaden, Germany, and the closely related taxa T. umbrina from Tilia cordata tree bark and T. jolithus from concrete wall both in Rostock, Germany. Freshly sampled material stained with Auramine O, DIOC 6 , and FM 1-43 showed an intact physiological status. Cell walls were depicted with calcofluor white and Carbotrace. When subjected to three repeated and controlled cycles of desiccation over silica gel (~ 10% relative humidity) followed by rehydration, T. aurea recovered about 50% of the initial photosynthetic yield of photosystem II (YII). In contrast, T. umbrina and T. jolithus recovered to 100% of the initial YII. HPLC and GC analysis of compatible solutes found highest proportions of erythritol in T. umbrina and mannitol/arabitol in T. jolithus. The lowest total compatible solute concentrations were detected in T. aurea, while the C/N ratio was highest in this species, indicative of nitrogen limitation. The prominent orange to red coloration of all Trentepohlia was due to extremely high carotenoid to Chl a ratio (15.9 in T. jolithus, 7.8 in T. aurea, and 6.6. in T. umbrina). Photosynthetic oxygen production was positive up to ~ 1500 µmol photons m -2  s -1 with the highest P max and alpha values in T. aurea. All strains showed a broad temperature tolerance with optima for gross photosynthesis between 20 and 35 °C. The presented data suggest that all investigated Trentepohlia species are well adapted to their terrestrial lifestyle on exposed to sunlight on a vertical substrate with little water holding capacity. Nevertheless, the three Trentepohlia species differed concerning their desiccation tolerance and compatible solute concentrations. The lower compatible solute contents in T. aurea explain the incomplete recovery of YII after rehydration., (© 2023. The Author(s).)

Summary: Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA)., Availability and Implementation: The source code is available at https://github.com/pievos101/Ensemble-GNN, and the data at Zenodo (DOI: 10.5281/zenodo.8305122)., (© The Author(s) 2023. Published by Oxford University Press.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Juchem, Schimani, Holzinger, Permann, Abarca, Skibbe, Zimmermann, Graeve and Karsten.)

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., (©Copyright : © 2023 The Authors; This is an open access article under the CC BY license.)

Purpose: To investigate whether CT texture analysis allows differentiation between adenocarcinomas, squamous cell carcinomas, carcinoids, small cell lung cancers and organizing pneumonia and between carcinomas and neuroendocrine tumors., Method: This retrospective study included patients 133 patients (30 patients with organizing pneumonia, 30 patients with adenocarcinoma, 30 patients with squamous cell carcinoma, 23 patients with small cell lung cancer, 20 patients with carcinoid), who underwent CT-guided biopsy of the lung and had a corresponding histopathologic diagnosis. Pulmonary lesions were segmented in consensus by two radiologists with and without a threshold of -50HU in three dimensions. Groupwise comparisons were performed to assess for differences between all five above-listed entities and between carcinomas and neuroendocrine tumors., Results: Pairwise comparisons of the five entities revealed 53 statistically significant texture features when using no HU-threshold and 6 statistically significant features with a threshold of -50HU. The largest AUC (0.818 [95%CI 0.706-0.930]) was found for the feature wavelet-HHH_glszm_SmallAreaEmphasis for discrimination of carcinoid from the other entities when using no HU-threshold. In differentiating neuroendocrine tumors from carcinomas, 173 parameters proved statistically significant when using no HU threshold versus 52 parameters when using a -50HU-threshold. The largest AUC (0.810 [95%CI 0.728-0,893]) was found for the parameter original_glcm_Correlation for discrimination of neuroendocrine tumors from carcinomas when using no HU-threshold., Conclusions: CT texture analysis revealed features that differed significantly between malignant pulmonary lesions and organizing pneumonia and between carcinomas and neuroendocrine tumors of the lung. Applying a HU-threshold for segmentation substantially influenced the results of texture analysis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Machine 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., Objective: Various 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., Methods: The 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., Results: FeatureCloud 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., Conclusions: FeatureCloud 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., (©Julian Matschinske, Julian Späth, Mohammad Bakhtiari, Niklas Probul, Mohammad Mahdi Kazemi Majdabadi, Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Anne Hartebrodt, Balazs-Attila Orban, Sándor-József Fejér, Olga Zolotareva, Supratim Das, Linda Baumbach, Josch K Pauling, Olivera Tomašević, Béla Bihari, Marcus Bloice, Nina C Donner, Walid Fdhila, Tobias Frisch, Anne-Christin Hauschild, Dominik Heider, Andreas Holzinger, Walter Hötzendorfer, Jan Hospes, Tim Kacprowski, Markus Kastelitz, Markus List, Rudolf Mayer, Mónika Moga, Heimo Müller, Anastasia Pustozerova, Richard Röttger, Christina C Saak, Anna Saranti, Harald H H W Schmidt, Christof Tschohl, Nina K Wenke, Jan Baumbach. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.07.2023.)

Objective. Left ventricular hypertrophy (LVH) is one of the most severe risk factors in patients with end-stage kidney disease (ESKD) regarding all-cause and cardiovascular mortality. It contributes to the risk of sudden cardiac death which accounts for approximately 25% of deaths in ESKD patients. Electrocardiography (ECG) is the least expensive way to assess whether a patient has LVH, but manual annotation is cumbersome. Thus, an automated approach has been developed to derive ECG-based LVH parameters. The aim of the current study is to compare automatic to manual measurements and to investigate their predictive value for cardiovascular and all-cause mortality. Approach. From the 12-lead 24 h ECG measurements of 301 ESKD patients undergoing haemodialysis, three different LVH parameters were calculated. Peguero-Lo Presti voltage, Cornell voltage, and Sokolow-Lyon voltage were automatically derived and compared to the manual annotations. To determine the agreement between manual and automatic measurements and their predictive value, Bland-Altman plots were created and Cox regression analysis for cardiovascular and all-cause mortality was performed. Main results. The median values for the automatic assessment were: Peguero-Lo Presti voltage 1.76 mV (IQR 1.29-2.55), Cornell voltage 1.14 mV (IQR 0.721-1.66), and Sokolow-Lyon voltage 1.66 mV (IQR 1.08-2.23). The mean differences when compared to the manual measurements were -0.027 mV (0.21 SD), 0.027 mV (0.13 SD) and -0.025 mV (0.24 SD) for Peguero-Lo Presti, Cornell, and Sokolow-Lyon voltage, respectively. The categorial LVH detection based on pre-defined thresholds differed in only 13 cases for all indices between manual and automatic assessment. Proportional hazard ratios only differed slightly in categorial LVH detection between manually and automatically determined LVH parameters; no differences could be found for continuous parameters. Significance. This study provides evidence that automatic algorithms can be as reliable in LVH parameter assessment and risk prediction as manual measurements in ESKD patients undergoing haemodialysis., (Creative Commons Attribution license.)

Artificial intelligence (AI) today is very successful at standard pattern-recognition tasks due to the availability of large amounts of data and advances in statistical data-driven machine learning. However, there is still a large gap between AI pattern recognition and human-level concept learning. Humans can learn amazingly well even under uncertainty from just a few examples and are capable of generalizing these concepts to solve new conceptual problems. The growing interest in explainable machine intelligence requires experimental environments and diagnostic/benchmark datasets to analyze existing approaches and drive progress in pattern analysis and machine intelligence. In this paper, we provide an overview of current AI solutions for benchmarking concept learning, reasoning, and generalization; discuss the state-of-the-art of existing diagnostic/benchmark datasets (such as CLEVR, CLEVRER, CLOSURE, CURI, Bongard-LOGO, V-PROM, RAVEN, Kandinsky Patterns, CLEVR-Humans, CLEVRER-Humans, and their extension containing human language); and provide an outlook of some future research directions in this exciting research domain., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

The streptophyte green algal class Zygnematophyceae is the immediate sister lineage to land plants. Their special form of sexual reproduction via conjugation might have played a key role during terrestrialization. Thus, studying Zygnematophyceae and conjugation is crucial for understanding the conquest of land. Moreover, sexual reproduction features are important for species determination. We present a phylogenetic analysis of a field-sampled Zygnema strain and analyze its conjugation process and zygospore morphology, both at the micro- and nanoscale, including 3D-reconstructions of the zygospore architecture. Vegetative filament size (26.18 ± 1.07 μm) and reproductive features allowed morphological determination of Zygnema vaginatum, which was combined with molecular analyses based on rbcL sequencing. Transmission electron microscopy (TEM) depicted a thin cell wall in young zygospores, while mature cells exhibited a tripartite wall, including a massive and sculptured mesospore. During development, cytological reorganizations were visualized by focused ion beam scanning electron microscopy (FIB-SEM). Pyrenoids were reorganized, and the gyroid cubic central thylakoid membranes, as well as the surrounding starch granules, degraded (starch granule volume: 3.58 ± 2.35 μm 3 in young cells; 0.68 ± 0.74 μm 3 at an intermediate stage of zygospore maturation). Additionally, lipid droplets (LDs) changed drastically in shape and abundance during zygospore maturation (LD/cell volume: 11.77% in young cells; 8.79% in intermediate cells, 19.45% in old cells). In summary, we provide the first TEM images and 3D-reconstructions of Zygnema zygospores, giving insights into the physiological processes involved in their maturation. These observations help to understand mechanisms that facilitated the transition from water to land in Zygnematophyceae., (© 2023 The Authors. Physiologia Plantarum published by John Wiley & Sons Ltd on behalf of Scandinavian Plant Physiology Society.)

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., (© 2023 The Authors. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

Synapsins are neuron-specific phosphoproteins that modulate neurotransmitter release, synaptic plasticity, and molecular processes shaping higher brain functions. Pathogenic synapsin-1 ( SYN1 ) variants are associated with epilepsy, intellectual disabilities, and behavioral problems. We detected a novel SYN1 variant [c.477_479delTGG (p.Gly160del)] in brothers with focal epilepsy with secondary generalization. The deleted amino acid was found to be highly conserved among mammalian species. In electroencephalography, the older brother showed a bioelectrical status epilepticus and was also diagnosed with attention deficit hyperactivity disorder. Behavioral abnormalities were seen before or after the seizures. Both patients responded quickly to treatment with valproate. Our case reports are consistent with the clinical heterogeneity of the pathogenic SYN1 variants described in the literature., Competing Interests: None declared., (Thieme. All rights reserved.)

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper., Competing Interests: Declaration of Competing Interest The Authors declare that there are no conflicts of interests., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Reproducibility problems are a known limitation of radiomics. The segmentation of the target lesion plays a critical role in texture analysis variability. This study's aim was to compare the interobserver reliability of manual 2D vs. 3D lung lesion segmentation with and without pre-definition of the volume using a threshold of - 50 HU., Methods: Seventy-five patients with histopathologically proven lung lesions (15 patients each with adenocarcinoma, squamous cell carcinoma, small cell lung cancer, carcinoid, and organizing pneumonia) who underwent an unenhanced CT scan of the chest were included. Three radiologists independently segmented each lesion manually in 3D and 2D with and without pre-segmentation volume definition by a HU threshold, and shape parameters and original, Laplacian of Gaussian-filtered, and wavelet-based texture features were derived. To assess interobserver reliability and identify the most robust texture features, intraclass correlation coefficients (ICCs) for different segmentation settings were calculated., Results: Shape parameters had high reliability (64-79% had excellent and good ICCs). Texture features had weak reliability levels, with the highest ICCs (38% excellent or good) found for original features in 3D segmentation without the use of a HU threshold. A small proportion (4.3-11.5%) of texture features had excellent or good ICC values at all segmentation settings., Conclusion: Interobserver reliability of texture features from CT scans of a heterogeneous collection of manually segmented lung lesions was low with a small proportion of features demonstrating high reliability independent of the segmentation settings. These results indicate a limited applicability of texture analysis and the need to define robust texture features in patients with lung lesions., Key Points: • Our study showed a low reproducibility of texture features when 3 radiologists independently segmented lung lesions in CT images, which highlights a serious limitation of texture analysis. • Interobserver reliability of texture features was low regardless of whether the lesion was segmented in 2D and 3D with or without a HU threshold. • In contrast to texture features, shape parameters showed a high interobserver reliability when lesions were segmented in 2D vs. 3D with and without a HU threshold of - 50., (© 2023. The Author(s).)

Introduction: Children's early development is affected by caregiving experiences, with lifelong health and well-being implications. Governments and civil societies need population-based measures to monitor children's early development and ensure that children receive the care needed to thrive. To this end, the WHO developed the Global Scales for Early Development (GSED) to measure children's early development up to 3 years of age. The GSED includes three measures for population and programmatic level measurement: (1) short form (SF) (caregiver report), (2) long form (LF) (direct administration) and (3) psychosocial form (PF) (caregiver report). The primary aim of this protocol is to validate the GSED SF and LF. Secondary aims are to create preliminary reference scores for the GSED SF and LF, validate an adaptive testing algorithm and assess the feasibility and preliminary validity of the GSED PF., Methods and Analysis: We will conduct the validation in seven countries (Bangladesh, Brazil, Côte d'Ivoire, Pakistan, The Netherlands, People's Republic of China, United Republic of Tanzania), varying in geography, language, culture and income through a 1-year prospective design, combining cross-sectional and longitudinal methods with 1248 children per site, stratified by age and sex. The GSED generates an innovative common metric (Developmental Score: D-score) using the Rasch model and a Development for Age Z-score (DAZ). We will evaluate six psychometric properties of the GSED SF and LF: concurrent validity, predictive validity at 6 months, convergent and discriminant validity, and test-retest and inter-rater reliability. We will evaluate measurement invariance by comparing differential item functioning and differential test functioning across sites., Ethics and Dissemination: This study has received ethical approval from the WHO (protocol GSED validation 004583 20.04.2020) and approval in each site. Study results will be disseminated through webinars and publications from WHO, international organisations, academic journals and conference proceedings., Registration Details: Open Science Framework https://osf.io/ on 19 November 2021 (DOI 10.17605/OSF.IO/KX5T7; identifier: osf-registrations-kx5t7-v1)., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Optimal performance is desired for decision-making in any field with binary classifiers and diagnostic tests, however common performance measures lack depth in information. The area under the receiver operating characteristic curve (AUC) and the area under the precision recall curve are too general because they evaluate all decision thresholds including unrealistic ones. Conversely, accuracy, sensitivity, specificity, positive predictive value and the F1 score are too specific-they are measured at a single threshold that is optimal for some instances, but not others, which is not equitable. In between both approaches, we propose deep ROC analysis to measure performance in multiple groups of predicted risk (like calibration), or groups of true positive rate or false positive rate. In each group, we measure the group AUC (properly), normalized group AUC, and averages of: sensitivity, specificity, positive and negative predictive value, and likelihood ratio positive and negative. The measurements can be compared between groups, to whole measures, to point measures and between models. We also provide a new interpretation of AUC in whole or part, as balanced average accuracy, relevant to individuals instead of pairs. We evaluate models in three case studies using our method and Python toolkit and confirm its utility.

Conifer (Pinaceae) needles are the most frost-hardy leaves. During needle freezing, the exceptional leaf anatomy, where an endodermis separates the mesophyll from the vascular tissue, could have consequences for ice management and photosynthesis. The eco-physiological importance of needle freezing behaviour was evaluated based on the measured natural freezing strain at the alpine treeline. Ice localisation and cellular responses to ice were investigated in mountain pine needles by cryo-microscopic techniques. Their consequences for photosynthetic activity were assessed by gas exchange measurements. The freezing response was related to the microchemistry of cell walls investigated by Raman microscopy. In frozen needles, ice was confined to the central vascular cylinder bordered by the endodermis. The endodermal cell walls were lignified. In the ice-free mesophyll, cells showed no freeze-dehydration and were found photosynthetically active. Mesophyll cells had lignified tangential cell walls, which adds rigidity. Ice barriers in mountain pine needles seem to be realised by a specific lignification patterning of cell walls. This, additionally, impedes freeze-dehydration of mesophyll cells and enables gas exchange of frozen needles. At the treeline, where freezing is a dominant environmental factor, the elaborate needle freezing pattern appears of ecological importance., (© 2023 The Authors. Physiologia Plantarum published by John Wiley & Sons Ltd on behalf of Scandinavian Plant Physiology Society.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Pushkareva, Elster, Holzinger, Niedzwiedz and Becker.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor AM declared a past collaboration with the author AH., (Copyright © 2022 Permann, Gierlinger and Holzinger.)

Purpose: To evaluate the utility of non-contrast-enhanced CT texture analysis (CTTA) for predicting the histopathological differentiation of pancreatic ductal adenocarcinomas (PDAC) and to compare non-contrast-enhanced CTTA texture features between primary PDAC and hepatic metastases of PDAC., Methods: This retrospective study included 120 patients with histopathologically confirmed PDAC. Sixty-five patients underwent CT-guided biopsy of primary PDAC, while 55 patients underwent CT-guided biopsy of hepatic PDAC metastasis. All lesions were segmented in non-contrast-enhanced CT scans for CTTA based on histogram analysis, co-occurrence matrix, and run-length matrix. Statistical analysis was conducted for 372 texture features using Mann-Whitney U test, Bonferroni-Holm correction, and receiver operating characteristic (ROC) analysis. A p value < 0.05 was considered statistically significant., Results: Three features were identified that differed significantly between histopathological G2 and G3 primary tumors. Of these, "low gray-level zone emphasis" yielded the largest AUC (0.87 ± 0.04), reaching a sensitivity and specificity of 0.76 and 0.83, respectively, when a cut-off value of 0.482 was applied. Fifty-four features differed significantly between primary and hepatic metastatic PDAC., Conclusion: Non-contrast-enhanced CTTA of PDAC identified differences in texture features between primary G2 and G3 tumors that could be used for non-invasive tumor assessment. Extensive differences between the features of primary and metastatic PDAC on CTTA suggest differences in tumor microenvironment., (© 2022. The Author(s).)

The process of finding a diagnosis in the medical domain relies on implicit knowledge and the experience of a human expert. In this article, we report on the observation of human decision making, shown by the example of pathology. By tracking the diagnostic steps, individual building blocks are identified, which not only contribute to a diagnostic finding, but can also be used in the future to train and develop artificial intelligence (AI) algorithms. This work also provides insights into the interaction of human experts regarding the observation time of so-called "hot spots," the magnification used for specific findings, and the overall observation and decision path followed. The documentation scheme yields a standardized examination procedure that shows the concept the pathologist is actually looking for as well as the possible features of findings that can be identified. This contribution indicates how important visualization is for human-centered AI, and specifically for enabling human oversight with respect to AI implementation in high-stake areas, such as medicine.

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (© 2022. The Author(s).)

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.

In medicine, particularly in radiology, there are great expectations in artificial intelligence (AI), which can "see" more than human radiologists in regard to, for example, tumor size, shape, morphology, texture and kinetics - thus enabling better care by earlier detection or more precise reports. Another point is that AI can handle large data sets in high-dimensional spaces. But it should not be forgotten that AI is only as good as the training samples available, which should ideally be numerous enough to cover all variants. On the other hand, the main feature of human intelligence is content knowledge and the ability to find near-optimal solutions. The purpose of this paper is to review the current complexity of radiology working places, to describe their advantages and shortcomings. Further, we give an AI overview of the different types and features as used so far. We also touch on the differences between AI and human intelligence in problem-solving. We present a new AI type, labeled "explainable AI," which should enable a balance/cooperation between AI and human intelligence - thus bringing both worlds in compliance with legal requirements. For support of (pediatric) radiologists, we propose the creation of an AI assistant that augments radiologists and keeps their brain free for generic tasks., (© 2021. The Author(s).)

Artificial Intelligence (AI) for the biomedical domain is gaining significant interest and holds considerable potential for the future of healthcare, particularly also in the context of in vitro diagnostics. The European In Vitro Diagnostic Medical Device Regulation (IVDR) explicitly includes software in its requirements. This poses major challenges for In Vitro Diagnostic devices (IVDs) that involve Machine Learning (ML) algorithms for data analysis and decision support. This can increase the difficulty of applying some of the most successful ML and Deep Learning (DL) methods to the biomedical domain, just by missing the required explanatory components from the manufacturers. In this context, trustworthy AI has to empower biomedical professionals to take responsibility for their decision-making, which clearly raises the need for explainable AI methods. Explainable AI, such as layer-wise relevance propagation, can help in highlighting the relevant parts of inputs to, and representations in, a neural network that caused a result and visualize these relevant parts. In the same way that usability encompasses measurements for the quality of use, the concept of causability encompasses measurements for the quality of explanations produced by explainable AI methods. This paper describes both concepts and gives examples of how explainability and causability are essential in order to demonstrate scientific validity as well as analytical and clinical performance for future AI-based IVDs., (Copyright © 2022. Published by Elsevier B.V.)

Motivation: The tremendous success of graphical neural networks (GNNs) already had a major impact on systems biology research. For example, GNNs are currently being used for drug target recognition in protein-drug interaction networks, as well as for cancer gene discovery and more. Important aspects whose practical relevance is often underestimated are comprehensibility, interpretability and explainability., Results: In this work, we present a novel graph-based deep learning framework for disease subnetwork detection via explainable GNNs. Each patient is represented by the topology of a protein-protein interaction (PPI) network, and the nodes are enriched with multi-omics features from gene expression and DNA methylation. In addition, we propose a modification of the GNNexplainer that provides model-wide explanations for improved disease subnetwork detection., Availability and Implementation: The proposed methods and tools are implemented in the GNN-SubNet Python package, which we have made available on our GitHub for the international research community (https://github.com/pievos101/GNN-SubNet)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Permann, Becker and Holzinger.)

Feature selection is a fundamental challenge in machine learning. For instance in bioinformatics, it is essential when one wishes to detect biomarkers. Tree-based methods are predominantly used for this purpose. In this paper, we study the stability of the feature selection methods BORUTA, VITA, and RRF (regularized random forest). In particular, we investigate the feature ranking instability of the associated stochastic algorithms. For stabilization of the feature ranks, we propose to compute consensus values from multiple feature selection runs, applying rank aggregation techniques. Our results show that these consolidated features are more accurate and robust, which helps to make practical machine learning applications more trustworthy.

Literature Review: Cystoscopy is the gold standard for initial macroscopic assessments of the human urinary bladder to rule out (or diagnose) bladder cancer (BCa). Despite having guidelines, cystoscopic findings are diverse and often challenging to classify. The extent of the false negatives and false positives in cystoscopic diagnosis is currently unknown. We suspect that there is a certain degree of under-diagnosis (like the failure to detect malignant tumours) and over-diagnosis (e.g. sending the patient for unnecessary transurethral resection of bladder tumors with anesthesia) that put the patient at risk., Conclusions: XAI robot-assisted cystoscopes would help to overcome the risks/flaws of conventional cystoscopy. Cystoscopy is considered a less life-threatening starting point for automation than open surgical procedures. Semi-autonomous cystoscopy requires standards and cystoscopy is a good procedure to establish a model that can then be exported/copied to other procedures of endoscopy and surgery. Standards also define the automation levels-an issue for medical product law. These cystoscopy skills do not give full autonomy to the machine, and represent a surgical parallel to 'Autonomous Driving' (where a standard requires a human supervisor to remain in the 'vehicle'). Here in robotic cystoscopy, a human supervisor remains bedside in the 'operating room' as a 'human-in-the-loop' in order to safeguard patients. The urologists will be able to delegate personal- and time-consuming cystoscopy to a specialised nurse. The result of automated diagnostic cystoscopy is a short video (with pre-processed photos from the video), which are then reviewed by the urologists at a more convenient time., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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.

Motivation: Limited data access has hindered the field of precision medicine from exploring its full potential, e.g. concerning machine learning and privacy and data protection rules.Our study evaluates the efficacy of federated Random Forests (FRF) models, focusing particularly on the heterogeneity within and between datasets. We addressed three common challenges: (i) number of parties, (ii) sizes of datasets and (iii) imbalanced phenotypes, evaluated on five biomedical datasets., Results: The FRF outperformed the average local models and performed comparably to the data-centralized models trained on the entire data. With an increasing number of models and decreasing dataset size, the performance of local models decreases drastically. The FRF, however, do not decrease significantly. When combining datasets of different sizes, the FRF vastly improve compared to the average local models. We demonstrate that the FRF remain more robust and outperform the local models by analyzing different class-imbalances.Our results support that FRF overcome boundaries of clinical research and enables collaborations across institutes without violating privacy or legal regulations. Clinicians benefit from a vast collection of unbiased data aggregated from different geographic locations, demographics and other varying factors. They can build more generalizable models to make better clinical decisions, which will have relevance, especially for patients in rural areas and rare or geographically uncommon diseases, enabling personalized treatment. In combination with secure multi-party computation, federated learning has the power to revolutionize clinical practice by increasing the accuracy and robustness of healthcare AI and thus paving the way for precision medicine., Availability and Implementation: The implementation of the federated random forests can be found at https://featurecloud.ai/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Timaviella Sciuto & Moro is a recently established cryptic genus of cyanobacteria separated from the morphologically close Leptolyngbya due to clear differences in the 16S rRNA gene sequence and the 16S-23S ITS region secondary structure. Conducting research on biological soil crusts in coastal ecotopes of Ukraine and Germany, we repeatedly observed thin filamentous cyanobacteria morphologically corresponding to the common terrestrial species Leptolyngbya edaphica (Elenkin) Anagnostidis & Komárek. Molecular data based on 16S rRNA gene sequence comparison of the original strains of the morphospecies indicated unambiguous assignment to the genus Timaviella . Based on this finding, we proposed the new nomenclatural combination Timaviella edaphica (Elenkin) O.M. Vynogr. & Mikhailyuk in our previous publication. Deeper molecular study of the four original strains which were morphologically identified as T . edaphica based on the 16S rRNA gene concatenated with the 16S-23S ITS region and 16S-23S ITS secondary structure analysis showed that they are not identical. Three of them (isolated from biocrusts of Black Sea coast and forest path near Kyiv, Ukraine) had high similarity both in 16S rRNA (99.7-100%) and 16S-23S ITS (99.8-100%) hence actually representing T . edaphica . The strain Us-6-3 isolated from biocrusts on sand dunes of Usedom Island in the Baltic Sea, Germany, differs both from original strains of T . edaphica and all published Timaviella species in 16S rRNA gene sequence identity, as well as in sequence and structure of the 16S-23S ITS region. Here we describe Timaviella dunensis sp. nov . and give an expanded description of T . edaphica based on morphological and molecular features. A tabular review of Timaviella species with data on their phenotypic and genotypic features, ecology and distribution is included.

Charophyte green algae (CGA) are assigned to be the closest relatives of land plants and therefore enlighten processes in the colonization of terrestrial habitats. For the transition from water to land, plants needed significant physiological and structural changes, as well as with regard to cell wall composition. Sequential extraction of cell walls of Nitellopsis obtusa (Charophyceae) and Spirogyra pratensis (Zygnematophyceae) offered a comparative overview on cell wall composition of late branching CGA. Because arabinogalactan-proteins (AGPs) are considered common for all land plant cell walls, we were interested in whether these special glycoproteins are present in CGA. Therefore, we investigated both species with regard to characteristic features of AGPs. In the cell wall of Nitellopsis, no hydroxyproline was present and no AGP was precipitable with the β-glucosyl Yariv's reagent (βGlcY). By contrast, βGlcY precipitation of the water-soluble cell wall fraction of Spirogyra yielded a glycoprotein fraction rich in hydroxyproline, indicating the presence of AGPs. Putative AGPs in the cell walls of non-conjugating Spirogyra filaments, especially in the area of transverse walls, were detected by staining with βGlcY. Labelling increased strongly in generative growth stages, especially during zygospore development. Investigations of the fine structure of the glycan part of βGlcY-precipitated molecules revealed that the galactan backbone resembled that of AGPs with 1,3- 1,6- and 1,3,6-linked Galp moieties. Araf was present only in small amounts and the terminating sugars consisted predominantly of pyranosidic terminal and 1,3-linked rhamnose residues. We introduce the term 'rhamnogalactan-protein' for this special AGP-modification present in S. pratensis., (© 2021 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)

Graph Neural Networks (GNN) show good performance in relational data classification. However, their contribution to concept learning and the validation of their output from an application domain's and user's perspective have not been thoroughly studied. We argue that combining symbolic learning methods, such as Inductive Logic Programming (ILP), with statistical machine learning methods, especially GNNs, is an essential forward-looking step to perform powerful and validatable relational concept learning. In this contribution, we introduce a benchmark for the conceptual validation of GNN classification outputs. It consists of the symbolic representations of symmetric and non-symmetric figures that are taken from a well-known Kandinsky Pattern data set. We further provide a novel validation framework that can be used to generate comprehensible explanations with ILP on top of the relevance output of GNN explainers and human-expected relevance for concepts learned by GNNs. Our experiments conducted on our benchmark data set demonstrate that it is possible to extract symbolic concepts from the most relevant explanations that are representative of what a GNN has learned. Our findings open up a variety of avenues for future research on validatable explanations for GNNs., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s) 2022, corrected publication 2022.)