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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
Diese Arbeit beschreibt eine Augmented Reality (AR) Applikation für den Einsatz in der Hochschullehre zum Thema Industrierobotik. Ziel ist es, sowohl das Lehren als auch das Lernen grundlegender Robotik-Inhalte durch die Bereitstellung einer interaktiven Methode zur Vermittlung der Lehrinhalte für Studierende zu verbessern. Die Studierenden sind in der Lage, direkt mit dem virtuellen Modell eines Industrieroboters zu interagieren und so selbstgesteuert die Lerninhalte zu vertiefen. Diese interaktive Methode verbindet die Studierenden direkt mit den Lehrinhalten und fördert das selbsterforschende Lernen. Eine weitere Anwendungsmöglichkeit sieht die Kombination einer Lektorenversion der AR Experience mit der Studierendenversion vor. Der Lektor hat die Möglichkeit, das Modell in AR zu steuern bzw. zu verändern und die Studierenden können auf Ihren Mobilgeräten die Änderungen live in AR mitverfolgen, um so auch im Distance Learning eine Verbindung Lektor – Studierende – Inhalt zusätzlich zu Videokonferenz-Tools herzustellen.
In the present work, using the recently introduced framework of local geometric deformations, special types of vector fields – so-called hidden Killing vector fields – are constructed, which solve the Killing equation not globally, but only locally, i.e. in local subregions of spacetime. Taking advantage of the fact that the vector fields coincide locally with Killing fields and therefore allow the consideration of integral laws that convert into exact physical conservation laws on local scales, balance laws in dynamical systems without global Killing symmetries are derived that mimic as closely as possible the conservation laws for energy and angular momentum of highly symmetric models. The utility of said balance laws is demonstrated by a concrete geometric example, namely a toy model for the binary merger of two extremal Reissner–Nordström black holes.
Wissensarbeiter:innen verbringen den überwiegenden Teil ihrer Arbeitszeit sitzend vor dem Computer. Die negativen Folgen von langem Sitzen für die Gesundheit sind bekannt: Zu langes Sitzen bedingt einen niedrigen Kalorienverbrauch, der Stoffwechsel und das Herz-Kreislaufsystem laufen auf Sparflamme. Entsprechend steigt das Risiko für Übergewicht, Diabetes, Bandscheibenvorfall und Herz-Kreislauf- Erkrankungen. Unter den Folgen von langem Sitzen leiden aber nicht nur die betroffenen Mitarbeiter:innen selbst, sondern auch deren Arbeitgeber:innen, weil Mitarbeiter:innen mit einem auf Bewegungsmangel zurückzuführenden reduzierten physischen und psychischen Wohlbefinden weniger produktiv und kreativ arbeiten bzw. aufgrund von Erkrankungen erst gar nicht arbeiten können. Der vorliegende Beitrag zeigt auf, wie Unternehmen durch das Setzen sanfter Bewegungszwänge, den Einsatz dynamischer Arbeitsstationen sowie die Integration niederschwelliger Fitnessmodule in die Bürolandschaft für mehr körperliche Aktivität im Arbeitsalltag sorgen können.
Hybrid courses with a focus on practice-orientated education and self-guided learning phases are on the rise on the higher education sector. Disciplines in Life Sciences implicate a high degree of practical laboratory expertise. The University of Applied Sciences (UAS) in Vienna, Austria, has thus been endeavoured offering students a high qualitative education integrating hybrid courses based on PBL principles, which consist of on-site (including the transmission of necessary background and practical laboratory training) and off-site (including self-study phases) sessions. As practical laboratory units are central in those courses, the restrictive measures, including the transition to a complete online teaching format due to the first Covid-19-pandemic lock-down, had severe effects on the implementation and the quality of the curriculum. According to surveys made specifically to address this problematic situation, it can be concluded that on-site practical units are fundamental for certain disciplines such as Life Sciences.
The repair of large bone defects remains challenging and often requires graft material due to limited availability of autologous bone. In clinical settings, collagen sponges loaded with excessive amounts of bone morphogenetic protein 2 (rhBMP-2) are occasionally used for the treatment of bone non-unions, increasing the risk of adverse events. Therefore, strategies to reduce rhBMP-2 dosage are desirable. Silk scaffolds show great promise due to their favorable biocompatibility and their utility for various biofabrication methods. For this study, we generated silk scaffolds with axially aligned pores, which were subsequently treated with 10× simulated body fluid (SBF) to generate an apatitic calcium phosphate coating. Using a rat femoral critical sized defect model (CSD) we evaluated if the resulting scaffold allows the reduction of BMP-2 dosage to promote efficient bone repair by providing appropriate guidance cues. Highly porous, anisotropic silk scaffolds were produced, demonstrating good cytocompatibility in vitro and treatment with 10× SBF resulted in efficient surface coating. In vivo, the coated silk scaffolds loaded with a low dose of rhBMP-2 demonstrated significantly improved bone regeneration when compared to the unmineralized scaffold. Overall, our findings show that this simple and cost-efficient technique yields scaffolds that enhance rhBMP-2 mediated bone healing.