The multiple crises of unsustainability are provoking increasing stress and unpleasant emotions among students. If higher education is to fulfill its mission to support transformation processes toward sustainable development, it must adapt its pedagogical approaches to help students deepen their critical thinking and empower them to engage in these transformation processes. For this reason, emotions – which can also prevent critical thinking – should be carefully addressed within transformative learning journeys. However, these journeys are themselves challenging for learners and educators. They push them to abandon stable meaning perspectives, causing feelings of incoherence and tension. Learners need safe enough spaces to navigate these situations of uncertainty. The central questions of this manuscript are: What is meant by safe enough spaces? How can learners, educators, and higher education institutions create and hold such spaces? These questions are explored on three different levels: (1) the intrapersonal level, (2) the interpersonal level, and (3) the organizational and systemic level of discourses in higher education. For the intrapersonal level, perspectives inspired by neurobiology are used to discuss reaction patterns of our autonomous nervous system and present insights into stress development. Learners should feel bodily safe when engaging in transformative learning processes. This is supported by balancing the challenges learners face with the resources they have. For the interpersonal level, the manuscript argues that focusing solely on rational discourse is insufficient to support safe enough spaces for transformative learning. We call for a culture of edifying conversations supported by respectful relationships among learners, as they are more adequate for regaining self-direction. For the organizational and intertwined systemic level, the ambition is followed to make higher education institutions offer learning environments that feel safe enough for all involved. However, as these institutions are strongly influenced by dynamics of economization and competition, they do not necessarily empower learners to challenge and disrupt unsustainable and neoliberal discourses. The manuscript explores how learners and educators can cultivate engaged critique by acknowledging their own embeddedness in neoliberal dynamics and opening up so-called transformative spaces for institutional change. Finally, recommendations for educational practices in higher education for sustainable development are offered.

Lysosomes serve as dynamic regulators of cell and organismal physiology by integrating the degradation of macromolecules with receptor and nutrient signaling. Previous studies have established that activation of the transcription factor EB (TFEB) and transcription factor E3 (TFE3) induces the expression of lysosomal genes and proteins in signaling-inactive starved cells, that is, under conditions when activity of the master regulator of nutrient-sensing signaling mechanistic target of rapamycin complex 1 is repressed. How lysosome biogenesis is triggered in signaling-active cells is incompletely understood. Here, we identify a role for calcium release from the lumen of the endoplasmic reticulum in the control of lysosome biogenesis that is independent of mechanistic target of rapamycin complex 1. We show using functional imaging that calcium efflux from endoplasmic reticulum stores induced by inositol triphosphate accumulation upon depletion of inositol polyphosphate-5-phosphatase A, an inositol 5-phosphatase downregulated in cancer and defective in spinocerebellar ataxia, or receptor-mediated phospholipase C activation leads to the induction of lysosome biogenesis. This mechanism involves calcineurin and the nuclear translocation and elevated transcriptional activity of TFEB/TFE3. Our findings reveal a crucial function for inositol polyphosphate-5-phosphatase A–mediated triphosphate hydrolysis in the control of lysosome biogenesis via TFEB/TFE3, thereby contributing to our understanding how cells are able to maintain their lysosome content under conditions of active receptor and nutrient signaling.

German dairy farming has intensified markedly in recent years, and the demand for Veterinary Herd Health Management (VHHM) is rising. To protect farms from epidemics, ensure food safety, and prevent developing of antibiotic resistance, VHHM has been anchored in EU law since April 2021. Via an online survey, distributed by different farmers' organizations, dairy farmers were asked about the cooperation with their veterinarian. The aim was to evaluate farm performance as a function of participation in VHHM. From 216 analyzed questionnaires, 106 respondents participated in VHHM. Results showed that farmers who make use of VHHM and consult their veterinarian in decision-making frequently have the highest 305-day milk yield (305dMY), the lowest bulk tank somatic cell counts, and the lowest age at first calving (AFC). However, these farmers tended to have higher replacement rates and a higher mortality of cows in the period up to 60 days in milk (MORT60DIM). Furthermore, respondents who defined VHHM as “evaluation of herd data, strategic planning” had the highest 305dMY compared with those who defined VHHM through one of the different options given (“pregnancy checks and support in reproduction”/“problem solving”). In the multifactorial regression model, VHHM participating farms had a 660-kg higher 305dMY and 1 month less in AFC, compared with farms not participating in VHHM. However, within the VHHM participants, no association between VHHM practices and performance parameters was found. Further research is needed, to find out if tailored advice of the VHHM approach may show effect herein.

Vaginal cytology is an important examination method in the context of gynecological disorders and cycle staging in the bitch. While collection and preparation of samples are easy, the evaluation appears to be challenging. Inconsistent definitions of cell attributes such as size, cornification and the appearance of the nucleus have been published. The aim of the project was to develop a tutorial for vaginal cell determination. To get a deeper insight into the use of cytology in practice, an online survey was distributed to veterinarians interested in small animal reproduction. Participants were asked to define eight cells and answer questions. The agreement of the 16 participants, working in eight different countries, determining the cells was poor (κ = 0.412). Eleven respondents stated that vaginal cytology has a low reliability. Nevertheless, 13 participants use this tool regularly. The tutorial was developed as a flowchart based on the survey results, scientific literature and own measurements. It guides the user systematically through the evaluation of specific cell characteristics. An evaluation of the results of five raters with difference experience levels led to a high agreement (κ = 0.858). Vaginal cytology is a useful diagnostic tool, but it seems helpful to standardize the determination of cell types.

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease with an up to 80% mortality in case of rupture. Current biomarkers fail to account for size-independent risk of rupture. By combining the information of different molecular probes, multi-target molecular MRI holds the potential to enable individual characterization of AAA. In this experimental study, we aimed to examine the feasibility of simultaneous imaging of extracellular collagen and inflammation for size-independent prediction of risk of rupture in murine AAA. The study design consisted of: (1) A outcome-based longitudinal study with imaging performed once after one week with follow-up and death as the end-point for assessment of rupture risk. (2) A week-by-week study for the characterization of AAA development with imaging after 1, 2, 3 and 4 weeks. For both studies, the animals were administered a type 1 collagen-targeted gadolinium-based probe (surrogate marker for extracellular matrix (ECM) remodeling) and an iron oxide-based probe (surrogate marker for inflammatory activity), in one imaging session. In vivo measurements of collagen and iron oxide probes showed a significant correlation with ex vivo histology (p < 0.001) and also corresponded well to inductively-coupled plasma-mass spectrometry and laser-ablation inductively-coupled plasma mass spectrometry. Combined evaluation of collagen-related ECM remodeling and inflammatory activity was the most accurate predictor for AAA rupture (sensitivity 80%, specificity 100%, area under the curve 0.85), being superior to information from the individual probes alone. Our study supports the feasibility of a simultaneous assessment of collagen-related extracellular matrix remodeling and inflammatory activity in a murine model of AAA.

Radiofrequency ablation (RFA) is a curative treatment option for early stage hepatocellular carcinoma (HCC). Vascular inflow occlusion to the liver (Pringle manoeuvre) and multibipolar RFA (mbRFA) represent possibilities to generate large ablations. This study evaluated the impact of different interapplicator distances and a Pringle manoeuvre on ablation area and geometry of mbRFA. 24 mbRFA were planned in porcine livers in vivo. Test series with continuous blood flow had an interapplicator distance of 20 mm and 15 mm, respectively. For a Pringle manoeuvre, interapplicator distance was predefined at 20 mm. After liver dissection, ablation area and geometry were analysed macroscopically and histologically. Confluent and homogenous ablations could be achieved with a Pringle manoeuvre and an interapplicator distance of 15 mm with sustained hepatic blood flow. Ablation geometry was inhomogeneous with an applicator distance of 20 mm with physiological liver perfusion. A Pringle manoeuvre led to a fourfold increase in ablation area in comparison to sustained hepatic blood flow (p < 0.001). Interapplicator distance affects ablation geometry of mbRFA. Strict adherence to the planned applicator distance is advisable under continuous blood flow. The application of a Pringle manoeuvre should be considered when compliance with the interapplicator distance cannot be guaranteed.

The fate of melanin in the epidermis is of great interest due to its involvement in numerous physiological and pathological processes in the skin. Melanin localization can be assessed ex vivo and in vivo using its distinctive optical properties. Melanin exhibits a characteristic Raman spectrum band shape and discernible near-infrared excited (NIR) fluorescence. However, a detailed analysis of the capabilities of depth-resolved confocal Raman and fluorescence microspectroscopy in the evaluation of melanin distribution in the human skin is lacking. Here we demonstrate how the fraction of melanin at different depths in the human skin in vivo can be estimated from its Raman spectra (bands at 1,380 and 1,570 cm−1) using several procedures including a simple ratiometric approach, spectral decomposition and non-negative matrix factorization. The depth profiles of matrix factorization components specific to melanin, collagen and natural moisturizing factor provide information about their localization in the skin. The depth profile of the collagen-related matrix factorization component allows for precise determination of the dermal–epidermal junction, i.e. the epidermal thickness. Spectral features of fluorescence background originating from melanin were found to correlate with relative intensities of the melanin Raman bands. We also hypothesized that NIR fluorescence in the skin is not originated solely from melanin, and the possible impact of oxidized species should be taken into account. The ratio of melanin-related Raman bands at 1,380 and 1,570 cm−1 could be related to melanin molecular organization. The proposed combined analysis of the Raman scattering signal and NIR fluorescence could be a useful tool for rapid non-invasive in vivo diagnostics of melanin-related processes in the human skin.

Myeloid cells contribute to inflammation and demyelination in the early stages of multiple sclerosis (MS), but it is still unclear to what extent these cells are involved in active lesion formation in progressive MS (PMS). Here, we have harnessed the power of single-cell mass cytometry (CyTOF) to compare myeloid cell phenotypes in active lesions of PMS donors with those in normal-appearing white matter from the same donors and control white matter from non-MS donors. CyTOF measurements of a total of 74 targeted proteins revealed a decreased abundance of homeostatic and TNFhi microglia, and an increase in highly phagocytic and activated microglia states in active lesions of PMS donors. Interestingly, in contrast to results obtained from studies of the inflammatory early disease stages of MS, infiltrating monocyte-derived macrophages were scarce in active lesions of PMS, suggesting fundamental differences of myeloid cell composition in advanced stages of PMS.

The purpose of this study is to compare diagnostic performance of Prostate Imaging Reporting and Data System (PI-RADS) version (v) 2.1 and 2.0 for detection of Gleason Score (GS) ≥ 7 prostate cancer on MRI. Three experienced radiologists provided PI-RADS v2.0 scores and at least 12 months later v2.1 scores on lesions in 333 prostate MRI examinations acquired between 2012 and 2015. Diagnostic performance was assessed retrospectively by using MRI/transrectal ultrasound fusion biopsy and 10-core systematic biopsy as the reference. From a total of 359 lesions, GS ≥ 7 tumor was present in 135 lesions (37.60%). Area under the ROC curve (AUC) revealed slightly lower values for peripheral zone (PZ) and transition zone (TZ) scoring in v2.1, but these differences did not reach statistical significance. A significant number of score 2 lesions in the TZ were downgraded to score 1 in v2.1 showing 0% GS ≥ 7 tumor (0/11). The newly introduced diffusion-weighted imaging (DWI) upgrading rule in v2.1 was applied in 6 lesions from a total of 143 TZ lesions (4.2%). In summary, PI-RADS v2.1 showed no statistically significant differences in overall diagnostic performance of TZ and PZ scoring compared to v2.0. Downgraded BPH nodules showed favorable cancer frequencies. The new DWI upgrading rule for TZ lesions was applied in only few cases.

Oxygen affinity to haemoglobin is indicated by the p50 value (pO2 at 50% O2Hb) and critically determines cellular oxygen availability. Although high Hb-O2 affinity can cause tissue hypoxia under conditions of well O2 saturated blood, individual differences in p50 are commonly not considered in clinical routine. Here, we investigated the diversity in Hb-O2 affinity in the context of physiological relevance. Oxyhaemoglobin dissociation curves (ODCs) of 60 volunteers (18–40 years, both sexes, either endurance trained or untrained) were measured at rest and after maximum exercise (VO2max) test. At rest, p50 values of all participants ranged over 7 mmHg. For comparison, right shift of ODC after VO2max test, representing the maximal physiological range to release oxygen to the tissue, indicated a p50 difference of up to 10 mmHg. P50 at rest differs significantly between women and men, with women showing lower Hb-O2 affinity that is determined by higher 2,3-BPG and BPGM levels. Regular endurance exercise did not alter baseline Hb-O2 affinity. Thus, p50 diversity is already high at baseline level and needs to be considered under conditions of impaired tissue oxygenation. For fast prediction of Hb-O2 affinity by blood gas analysis, only venous but not capillary blood samples can be recommended.

SARS-CoV-2, the cause of the COVID-19 pandemic, exploits host cell proteins for viral entry into human lung cells. One of them, the protease TMPRSS2, is required to activate the viral spike protein (S). Even though two inhibitors, camostat and nafamostat, are known to inhibit TMPRSS2 and block cell entry of SARS-CoV-2, finding further potent therapeutic options is still an important task. In this study, we report that a late-stage drug candidate, otamixaban, inhibits SARS-CoV-2 cell entry. We show that otamixaban suppresses TMPRSS2 activity and SARS-CoV-2 infection of a human lung cell line, although with lower potency than camostat or nafamostat. In contrast, otamixaban inhibits SARS-CoV-2 infection of precision cut lung slices with the same potency as camostat. Furthermore, we report that otamixaban's potency can be significantly enhanced by (sub-) nanomolar nafamostat or camostat supplementation. Dominant molecular TMPRSS2-otamixaban interactions are assessed by extensive 109 μs of atomistic molecular dynamics simulations. Our findings suggest that combinations of otamixaban with supplemental camostat or nafamostat are a promising option for the treatment of COVID-19.

In order to advance the mission of in silico cell biology, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) and Markov state models (MSMs) have enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes, the number of independent or weakly coupled subsystems increases, and the number of global system states increase exponentially, making the sampling of all distinct global states unfeasible. In this work, we present a technique called Independent Markov Decomposition (IMD) that leverages weak coupling between subsystems in order to compute a global kinetic model without requiring to sample all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology, we demonstrate that IMD can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.

Early postnatal life is characterized by a critical time period in which the developing neonatal immune system transitions from passive immunity, induced by protective maternal antibodies, to the competence of a fully functioning immune system. The inflammatory capability of both maternal and neonatal antibodies is governed by N-linked glycosylation of the Fc region, and though this has been examined extensively in adults, there is currently little information regarding antibody glycosylation patterns during early postnatal life. To characterize the murine IgG Fc glycosylation profile during early life, we used nano-LC-ESI-Qq-TOF mass spectrometry analysis to assess subclass specific Asn-297 glycosylation patterns in the serum of BALB/c mice from 5–60 days of age. From birth to adulthood, we observed a decline in proinflammatory Fc glycosylation in all IgG subclasses. This was shown by significantly reduced agalactosylated and monogalactosylated structures combined with increased sialylation after weaning at 45 and 60 days of age. This information indicates that the transition between neonatal life and adulthood in mice is accompanied by reduction of inflammatory IgG antibodies. Our study contributes to a growing body of literature indicating the importance of IgG Fc glycosylation and its association with inflammation during different life stages.

Mast cells (MCs) are multifunctional cells of the immune system and are found in skin and all major tissues of the body. They contribute to the pathology of several diseases including urticaria, psoriasis, atopic dermatitis and mastocytosis where they are increased at lesional sites. Histomorphometric analysis of skin biopsies serves as a routine method for the assessment of MC numbers and their activation status, which comes with major limitations. As of now, non-invasive techniques to study MCs in vivo are not available. Here, we describe a label-free imaging technique to visualize MCs and their activation status in the human papillary dermis in vivo. This technique uses two-photon excited fluorescence lifetime imaging (TPE-FLIM) signatures, which are different for MCs and other dermal components. TPE-FLIM allows for the visualization and quantification of dermal MCs in healthy subjects and patients with skin diseases. Moreover, TPE-FLIM can differentiate between two MC populations in the papillary dermis in vivo—resting and activated MCs with a sensitivity of 0.81 and 0.87 and a specificity of 0.85 and 0.84, respectively. Results obtained on healthy volunteers and allergy and mastocytosis patients indicate the existence of other MC subpopulations within known resting and activated MC populations. The developed method may become an important tool for non-invasive in vivo diagnostics and therapy control in dermatology and immunology, which will help to better understand pathomechanisms involving MC accumulation, activation and degranulation and to characterize the effects of therapies that target MCs.

Systemic and local posttraumatic responses are often monitored on mRNA expression level using quantitative real-time PCR (qRT-PCR), which requires normalisation to adjust for confounding sources of variability. Normalisation requests reference (housekeeping) genes stable throughout time and divergent experimental conditions in the tissue of interest, which are crucial for a reliable and reproducible gene expression analysis. Although previous animal studies analysed reference genes following isolated trauma, this multiple-trauma gene expression analysis provides a notable study analysing reference genes in primarily affected (i.e. bone/fracture callus and hypothalamus) and secondarily affected organs (i.e. white adipose tissue, liver, muscle and spleen), following experimental long bone fracture and traumatic brain injury. We considered tissue-specific and commonly used top-ranked reference candidates from different functional groups that were evaluated applying the established expression stability analysis tools NormFinder, GeNorm, BestKeeper and RefFinder. In conclusion, reference gene expression in primary organs is highly time point as well as tissue-specific, and therefore requires careful evaluation for qRT-PCR analysis. Furthermore, the general application of Ppia, particularly in combination with a second reference gene, is strongly recommended for the analysis of systemic effects in the case of indirect trauma affecting secondary organs through local and systemic pathophysiological responses.

In molecular simulations, the identification of suitable reaction coordinates is central to both the analysis and sampling of transitions between metastable states in complex systems. If sufficient simulation data are available, a number of methods have been developed to reduce the vast amount of high-dimensional data to a small number of essential degrees of freedom representing the reaction coordinate. Likewise, if the reaction coordinate is known, a variety of approaches have been proposed to enhance the sampling along the important degrees of freedom. Often, however, neither one nor the other is available. One of the key questions is therefore, how to construct reaction coordinates and evaluate their validity. Another challenges arises from the physical interpretation of reaction coordinates, which is often addressed by correlating physically meaningful parameters with conceptually well-defined but abstract reaction coordinates. Furthermore, machine learning based methods are becoming more and more applicable also to the reaction coordinate problem. This perspective highlights central aspects in the identification and evaluation of reaction coordinates and discusses recent ideas regarding automated computational frameworks to combine the optimization of reaction coordinates and enhanced sampling.

Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materials and molecules and to explore and visualize their chemical structures and compositions. Recently, it has become clear that many of the most effective representations share a fundamental formal connection. They can all be expressed as a discretization of n-body correlation functions of the local atom density, suggesting the opportunity of standardizing and, more importantly, optimizing their evaluation. We present an implementation, named librascal, whose modular design lends itself both to developing refinements to the density-based formalism and to rapid prototyping for new developments of rotationally equivariant atomistic representations. As an example, we discuss smooth overlap of atomic position (SOAP) features, perhaps the most widely used member of this family of representations, to show how the expansion of the local density can be optimized for any choice of radial basis sets. We discuss the representation in the context of a kernel ridge regression model, commonly used with SOAP features, and analyze how the computational effort scales for each of the individual steps of the calculation. By applying data reduction techniques in feature space, we show how to reduce the total computational cost by a factor of up to 4 without affecting the model’s symmetry properties and without significantly impacting its accuracy.

Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate and its zones. It has a crucial role for many diagnostic applications. Automatic segmentation such as that of the prostate and prostate zones from MR images facilitates many diagnostic and therapeutic applications. However, the lack of a clear prostate boundary, prostate tissue heterogeneity, and the wide interindividual variety of prostate shapes make this a very challenging task. To address this problem, we propose a new neural network to automatically segment the prostate and its zones. We term this algorithm Dense U-net as it is inspired by the two existing state-of-the-art tools—DenseNet and U-net. We trained the algorithm on 141 patient datasets and tested it on 47 patient datasets using axial T2-weighted images in a four-fold cross-validation fashion. The networks were trained and tested on weakly and accurately annotated masks separately to test the hypothesis that the network can learn even when the labels are not accurate. The network successfully detects the prostate region and segments the gland and its zones. Compared with U-net, the second version of our algorithm, Dense-2 U-net, achieved an average Dice score for the whole prostate of 92.1± 0.8% vs. 90.7 ± 2%, for the central zone of 89.5±2% vs. 89.1±2.2 %, and for the peripheral zone of 78.1± 2.5% vs. 75±3%. Our initial results show Dense-2 U-net to be more accurate than state-of-the-art U-net for automatic segmentation of the prostate and prostate zones.

Enzymes are fundamental to biological processes and involved in most pathologies. Here we demonstrate the concept of simultaneously mapping multiple enzyme activities (EA) by applying enzyme substrate libraries to tissue sections and analyzing their conversion by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). To that end, we spray-applied a solution of 20 naturally derived peptides that are known substrates for proteases, kinases, and phosphatases to zinc-fixed paraffin tissue sections of mouse kidneys. After enzyme conversion for 5 to 120 min at 37 °C and matrix application, the tissue sections were imaged by MALDI-IMS. We could image incubation time-dependently 16 of the applied substrates with differing signal intensities and 12 masses of expected products. Utilizing inherent enzyme amplification, EA-IMS can become a powerful tool to locally study multiple, potentially even lowly expressed, enzyme activities, networks, and their pharmaceutical modulation. Differences in the substrate detectability highlight the need for future optimizations.

The input of almost every machine learning algorithm targeting the properties of matter at the atomic scale involves a transformation of the list of Cartesian atomic coordinates into a more symmetric representation. Many of the most popular representations can be seen as an expansion of the symmetrized correlations of the atom density and differ mainly by the choice of basis. Considerable effort has been dedicated to the optimization of the basis set, typically driven by heuristic considerations on the behavior of the regression target. Here, we take a different, unsupervised viewpoint, aiming to determine the basis that encodes in the most compact way possible the structural information that is relevant for the dataset at hand. For each training dataset and number of basis functions, one can build a unique basis that is optimal in this sense and can be computed at no additional cost with respect to the primitive basis by approximating it with splines. We demonstrate that this construction yields representations that are accurate and computationally efficient, particularly when working with representations that correspond to high-body order correlations. We present examples that involve both molecular and condensed-phase machine-learning models.