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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.

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.

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.

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

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

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

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