Zygospore development of Spirogyra (Charophyta) investigated by serial block-face scanning electron microscopy and 3D reconstructions.

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.

Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

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.

CLARUS: An interactive explainable AI platform for manual counterfactuals in graph neural networks.

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/.

Sensors for Digital Transformation in Smart Forestry.

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.

The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations.

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.

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.

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).

Lipid degradation and photosynthetic traits after prolonged darkness in four Antarctic benthic diatoms, including the newly described species Planothidium wetzelii sp. nov.

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.

3D-reconstructions of zygospores in Zygnema vaginatum (Charophyta) reveal details of cell wall formation, suggesting adaptations to extreme habitats.

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 µm3 in young cells; 0.68 ± 0.74 µm3 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.

Toward human-level concept learning: Pattern benchmarking for AI algorithms.

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.

Improved Methods for Acetocarmine and Haematoxylin Staining to Visualize Chromosomes in the Filamentous Green Alga Zygnema (Charophyta).

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.

Three dimensional computed tomography texture analysis of pulmonary lesions: Does radiomics allow differentiation between carcinoma, neuroendocrine tumor and organizing pneumonia?

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.

The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.

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.

Automatic ECG-based detection of left ventricular hypertrophy and its predictive value in haemodialysis patients.

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.

Terrestrial Trentepohlia sp. (Ulvophyceae) from alpine and coastal collection sites show strong desiccation tolerance and broad light and temperature adaptation.

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, DIOC6, 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 Pmax 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.

Explainability and causability in digital pathology.

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.

CT texture analysis reliability in pulmonary lesions: the influence of 3D vs. 2D lesion segmentation and volume definition by a Hounsfield-unit threshold.

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.

AI for life: Trends in artificial intelligence for biotechnology.

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

Chromosome-level genomes of multicellular algal sisters to land plants illuminate signaling network evolution.

The filamentous and unicellular algae of the class Zygnematophyceae are the closest algal relatives of land plants. Inferring the properties of the last common ancestor shared by these algae and land plants allows us to identify decisive traits that enabled the conquest of land by plants. We sequenced four genomes of filamentous Zygnematophyceae (three strains of Zygnema circumcarinatum and one strain of Z. cylindricum) and generated chromosome-scale assemblies for all strains of the emerging model system Z. circumcarinatum. Comparative genomic analyses reveal expanded genes for signaling cascades, environmental response, and intracellular trafficking that we associate with multicellularity. Gene family analyses suggest that Zygnematophyceae share all the major enzymes with land plants for cell wall polysaccharide synthesis, degradation, and modifications; most of the enzymes for cell wall innovations, especially for polysaccharide backbone synthesis, were gained more than 700 million years ago. In Zygnematophyceae, these enzyme families expanded, forming co-expressed modules. Transcriptomic profiling of over 19 growth conditions combined with co-expression network analyses uncover cohorts of genes 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.

Novel SYN1 Variant in Two Brothers with Focal Epilepsy and Their Prompt Response to Valproate.

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.

Frozen mountain pine needles: The endodermis discriminates between the ice-containing central tissue and the ice-free fully functional mesophyll.

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.