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A major challenge for breath research is the lack of standardization in sampling and analysis. To address this, a test that utilizes a standardized intervention and a defined study protocol has been proposed to explore disparities in breath research across different analytical platforms and to provide benchmark values for comparison. Specifically, the Peppermint Experiment involves the targeted analysis in exhaled breath of volatile constituents of peppermint oil after ingestion of the encapsulated oil. Data from the Peppermint Experiment performed by proton transfer reaction mass spectrometry (PTR-MS) and selected ion flow tube mass spectrometry (SIFT-MS) are presented and discussed herein, including the product ions associated with the key peppermint volatiles, namely limonene, α- and β-pinene, 1,8-cineole, menthol, menthone and menthofuran. The breath washout profiles of these compounds from 65 individuals were collected, comprising datasets from five PTR-MS and two SIFT-MS instruments. The washout profiles of these volatiles were evaluated by comparing the log-fold change over time of the product ion intensities associated with each volatile. Benchmark values were calculated from the lower 95% confidence interval of the linear time-to-washout regression analysis for all datasets combined. Benchmark washout values from PTR-MS analysis were 353 min for the sum of monoterpenes and 1,8-cineole (identical product ions), 173 min for menthol, 330 min for menthofuran, and 218 min for menthone; from SIFT-MS analysis values were 228 min for the sum of monoterpenes, 281 min for the sum of monoterpenes and 1,8-cineole, and 370 min for menthone plus 1,8-cineole. Large inter- and intra-dataset variations were observed, whereby the latter suggests that biological variability plays a key role in how the compounds are absorbed, metabolized and excreted from the body via breath. This variability seems large compared to the influence of sampling and analytical procedures, but further investigations are recommended to clarify the effects of these factors.
We compare results of simulations of solar facular-like conditions performed using the numerical codes MURaM and STAGGER. Both simulation sets have a similar setup, including the initial condition of ≈200 G vertical magnetic flux. After interpolating the output physical quantities to constant optical depth, we compare them and test them against inversion results from solar observations. From the snapshots, we compute the monochromatic continuum in the visible and infrared, and the full Stokes vector of the Fe i spectral line pair around 6301–6302 Å. We compare the predicted spectral lines (at the simulation resolution and after smearing to the HINODE SP/SOT resolution) in terms of their main parameters for the Stokes I line profiles, and of their area and amplitude asymmetry for the Stokes V profiles. The codes produce magnetoconvection with similar appearance and distribution in temperature and velocity. The results also closely match the values from recent relevant solar observations. Although the overall distribution of the magnetic field is similar in both radiation-magnetohydrodynamic (RMHD) simulation sets, a detailed analysis reveals substantial disagreement in the field orientation, which we attribute to the differing boundary conditions. The resulting differences in the synthetic spectra disappear after spatial smearing to the resolution of the observations. We conclude that the two sets of simulations provide robust models of solar faculae. Nevertheless, we also find differences that call for caution when using results from RMHD simulations to interpret solar observational data.
Rheumatoid arthritis is characterised by a progressive, intermittent inflammation at the synovial membrane, which ultimately leads to the destruction of the synovial joint. The synovial membrane as the joint capsule's inner layer is lined with fibroblast-like synoviocytes that are the key player supporting persistent arthritis leading to bone erosion and cartilage destruction. While microfluidic models that model molecular aspects of bone erosion between bone-derived cells and synoviocytes have been established, RA's synovial-chondral axis has not yet been realised using a microfluidic 3D model based on human patient in vitro cultures. Consequently, we established a chip-based three-dimensional tissue coculture model that simulates the reciprocal cross talk between individual synovial and chondral organoids. When co-cultivated with synovial organoids, we could demonstrate that chondral organoids induce a higher degree of cartilage physiology and architecture and show differential cytokine response compared to their respective monocultures highlighting the importance of reciprocal tissue-level cross talk in the modelling of arthritic diseases.
Coculture systems employing adipose tissue-derived mesenchymal stromal/stem cells (ASC) and endothelial cells (EC) represent a widely used technique to model vascularization. Within this system, cell-cell communication is crucial for the achievement of functional vascular network formation. Extracellular vesicles (EVs) have recently emerged as key players in cell communication by transferring bioactive molecules between cells. In this study we aimed to address the role of EVs in ASC/EC cocultures by discriminating between cells, which have received functional EV cargo from cells that have not. Therefore, we employed the Cre-loxP system, which is based on donor cells expressing the Cre recombinase, whose mRNA was previously shown to be packaged into EVs and reporter cells containing a construct of floxed dsRed upstream of the eGFP coding sequence. The evaluation of Cre induced color switch in the reporter system via EVs indicated that there is no EV-mediated RNA transmission either between EC themselves or EC and ASC. However, since Cre mRNA was not found present in EVs, it remains unclear if Cre mRNA is generally not packaged into EVs or if EVs are not taken up by the utilized cell types. Our data indicate that this technique may not be applicable to evaluate EV-mediated cell-to-cell communication in an in vitro setting using EC and ASC. Further investigations will require a functional system showing efficient and specific loading of Cre mRNA or protein into EVs.
Recent progress in machine learning and deep learning has enabled the implementation of plant and crop detection using systematic inspection of the leaf shapes and other morphological characters for identification systems for precision farming. However, the models used for this approach tend to become black-box models, in the sense that it is difficult to trace characters that are the base for the classification. The interpretability is therefore limited and the explanatory factors may not be based on reasonable visible characters. We investigate the explanatory factors of recent machine learning and deep learning models for plant classification tasks. Based on a Daucus carota and a Beta vulgaris image data set, we implement plant classification models and compare those models by their predictive performance as well as explainability. For comparison we implemented a feed forward convolutional neuronal network as a default model. To evaluate the performance, we trained an unsupervised Bayesian Gaussian process latent variable model as well as a convolutional autoencoder for feature extraction and rely on a support vector machine for classification. The explanatory factors of all models were extracted and analyzed. The experiments show, that feed forward convolutional neuronal networks (98.24%
and 96.10% mean accuracy) outperforms the Bayesian Gaussian process latent variable pipeline (92.08% and 94.31% mean accuracy) as well as the convolutional autoenceoder pipeline (92.38% and 93.28%
mean accuracy) based approaches in terms of classification accuracy, even though not significant for Beta vulgaris images. Additionally, we found that the neuronal network used biological uninterpretable image regions for the plant classification task. In contrast to that, the unsupervised learning models rely on explainable visual characters. We conclude that supervised convolutional neuronal networks must be used carefully to ensure biological interpretability. We recommend unsupervised machine learning, careful feature investigation, and statistical feature analysis for biological applications. View Full-Text
Keywords: deep learning; machine learning; plant leaf morphometrics; explainable AI