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Peat is the main constituent of cultivation substrates and a precious non-renewable fossil material. Peatlands provide important ecosystem services and allow the absorption and storage of carbon. Protecting peatlands helps tackle climate change and contributes to biodiversity conservation. Due to its importance, it is necessary to implement strategies to reduce the use of peat, such as replacing it with biomass-based alternative growing media constituents, such as Sphagnum moss. In this study, we compared the metal release and binding properties at two different pH, antioxidant activity, and total phenolic content of peat and Sphagnum moss from the Tierra del Fuego (TdF) region of southern Patagonia. Levels of the elements were determined by inductively coupled plasma mass spectrometry (ICP-MS), while the types and amounts of functional groups were characterized and compared using Fourier transform infrared (FTIR) spectroscopy. The total phenol level and antioxidant capacity were assessed using the Folin-Ciocalteu method and 2,2-diphenyl-1-picrylhydrazyl test. There are generally higher concentrations of leachable elements in peat than in Sphagnum moss at pH = 2, except Cs, Rb, Ti, and Zr. In contrast, at pH = 5, levels of all leached elements are highest in Sphagnum moss. Sphagnum moss shows a higher metal adsorption capacity than peat, except for Be, Mn, Tl, and Zn. Finally, the results showed that both matrices contained similar total phenolic contents: 0.018 ± 0.011 mg gallic acid equivalent (GAE) per gram dry sample for peat and 0.020 ± 0.007 mg GAE g-1 for Sphagnum moss. Instead, Sphagnum moss extracts showed a significantly higher antioxidant activity [0.026 ± 0.028 mmol Trolox equivalents (TE) g-1] than that estimated in peat (0.009 ± 0.005 mmol TE g-1). Humic acids, carboxylic acids, and phenolic and lignin groups were identified as the functional groups that mainly determined the antioxidant activity of the Sphagnum moss compared to peat. The present study resulted in an advancement of knowledge of these materials for more thoughtful future use and possible replacements. [ABSTRACT FROM AUTHOR]

The predicted increase in demand for minor metals for modern technologies raises major concerns regarding potential environmental concentration increases. Among the minor metals, lithium (Li) is particularly noteworthy due to growing demand for battery production. Concerns have been raised about the impact on biota of increasing Li concentrations in the environment. To expand the knowledge of the effects of Li on plants, garden cress (Lepidium sativum L.), a model plant for ecotoxicity assay, was tested in a 72 h test in Petri plates. The results showed a stimulation effect of Li at the lowest concentration (Li chloride 10 mg L−1) on seed germination and primary root elongation. Conversely, higher Li concentrations (50 and 150 mg L−1) caused a progressive impairment in both parameters. A genotoxic effect of Li on root cells, evaluated through the alkaline comet assay, was observed at each concentration tested, particularly at 150 mg L−1 Li chloride. Elemental analysis showed that Li accumulated in the seedlings in a dose–concentration relationship, confirming its ability to be readily absorbed and accumulated in plants. Given the likely increase in Li levels in the environment, further research is required to clarify the toxicity mechanisms induced by Li on growth and nucleic acids. [ABSTRACT FROM AUTHOR]

We present a proof-of-concept edge illumination x-ray phase contrast system where the detector mask has been replaced by an indirect conversion detector in which sensitive and insensitive regions have been obtained by "patterning" the scintillator. This was achieved by creating a free-standing grid with period and aperture size matching that of a typical detector mask and filling the apertures with gadolinium oxysulfide. Images of various samples were collected with both the modified and the original edge illumination systems based on the use of two masks to characterize the performances of this detector design. We found that, despite the proof-of-concept nature of this attempt resulting in a structured detector with suboptimal performance, it allows effective separation of the attenuation and refraction channels through phase retrieval and the visualization of hard-to-detect features such as cartilage through the latter channel, thus demonstrating that the proposed approach holds the potential to lead to improved stability since it will use a single optical element facilitating the design of rotating phase contrast systems or the retrofitting of conventional x-ray systems. [ABSTRACT FROM AUTHOR]

Introduction: Synchrotron radiation micro-computed tomography (SRμCT) has been used as a non-invasive technique to examine the microstructure and tissue integration of biodegradable bone implants. To be able to characterize parameters regarding the disintegration and osseointegration of such materials quantitatively, the three-dimensional (3D) image data provided by SRμCT needs to be processed by means of semantic segmentation. However, accurate image segmentation is challenging using traditional automated techniques. This study investigates the effectiveness of deep learning approaches for semantic segmentation of SRμCT volumes of Mg-based implants in sheep bone ex vivo. Methodology: For this purpose different convolutional neural networks (CNNs), including U-Net, HR-Net, U[sup 2]-Net, from the TomoSeg framework, the Scaled U-Net framework, and 2D/3D U-Net from the nnU-Net framework were trained and validated. The image data used in this work was part of a previous study where biodegradable screws were surgically implanted in sheep tibiae and imaged using SRμCT after different healing periods. The comparative analysis of CNN models considers their performance in semantic segmentation and subsequent calculation of degradation and osseointegration parameters. The models' performance is evaluated using the intersection over union (loU) metric, and their generalization ability is tested on unseen datasets. Results and discussion: This work shows that the 2D nnU-Net achieves better generalization performance, with the degradation layer being the most challenging label to segment for all models. [ABSTRACT FROM AUTHOR]