During tissue invasion individual tumor cells exhibit two interconvertible migration modes, namely mesenchymal and amoeboid migration. The cellular microenvironment triggers the switch between both modes, thereby allowing adaptation to dynamic conditions. It is, however, unclear if this amoeboid-mesenchymal migration plasticity contributes to a more effective tumor invasion. We address this question with a mathematical model, where the amoeboid-mesenchymal migration plasticity is regulated in response to local extracellular matrix resistance. Our numerical analysis reveals that extracellular matrix structure and presence of a chemotactic gradient are key determinants of the model behavior. Only in complex microenvironments, if the extracellular matrix is highly heterogeneous and a chemotactic gradient directs migration, the amoeboid-mesenchymal migration plasticity allows a more widespread invasion compared to the non-switching amoeboid and mesenchymal modes. Importantly, these specific conditions are characteristic for in vivo tumor invasion. Thus, our study suggests that in vitro systems aiming at unraveling the underlying molecular mechanisms of tumor invasion should take into account the complexity of the microenvironment by considering the combined effects of structural heterogeneities and chemical gradients on cell migration.

The ability of humans to discriminate enantiomeric odour pairs is substance –specific. Current literature suggests that psychophysical discrimination of odour enantiomers mainly depends on the peripheral processing at the level of the olfactory sensory neurons (OSN). To study the influence of central processing in discrimination, we investigated differences in the electrophysiological responses to psychophysically indistinguishable (+)- and (−)- rose oxide enantiomers at peripheral and central-nervous levels in humans. We recorded the electro-olfactogram (EOG) from the olfactory epithelium and the EEG-derived olfactory event-related potentials (OERP). Results from a psychophysical three alternative forced choice test indicated indistinguishability of the two odour enantiomers. In a total of 19 young participants EOG could be recorded in 74 and OERP in 95% of subjects. Significantly different EOG amplitudes and latencies were recorded in response to the 2 stimuli. However, no such differences in amplitude or latency emerged for the OERP. In conclusion, although the pair of enantiomer could be discriminated at a peripheral level this did not lead to a central-nervous/cognitive differentiation of the two stimuli.

In this publication, the ultrasonic-assisted deep-drawing of fiber-based materials, whose implementation was presented in Löwe et al. (2016), was studied in detail. Methods were developed for measuring the properties of deep-drawn cups, including cup stability, shape deviation, and surface quality. The relationship between these properties and the process parameters were determined with a design of experiment, which allows the user to adjust the cup properties in order to optimize them.

Drug repositioning identifies new indications for known drugs. Here we report repositioning of the malaria drug amodiaquine as a potential anti-cancer agent. While most repositioning efforts emerge through serendipity, we have devised a computational approach, which exploits interaction patterns shared between compounds. As a test case, we took the anti-viral drug brivudine (BVDU), which also has anti-cancer activity, and defined ten interaction patterns using our tool PLIP. These patterns characterise BVDU’s interaction with its target s. Using PLIP we performed an in silico screen of all structural data currently available and identified the FDA approved malaria drug amodiaquine as a promising repositioning candidate. We validated our prediction by showing that amodiaquine suppresses chemoresistance in a multiple myeloma cancer cell line by inhibiting the chaperone function of the cancer target Hsp27. This work proves that PLIP interaction patterns are viable tools for computational repositioning and can provide search query information from a given drug and its target to identify structurally unrelated candidates, including drugs approved by the FDA, with a known safety and pharmacology profile. This approach has the potential to reduce costs and risks in drug development by predicting novel indications for known drugs and drug candidates.

Chitosan is a cationic natural polymer that is widely used as a topical dressing in wound management. Temporary coatings of removable denture bases with chitosan might be useful as supportive treatment in oral medicine. The aim of this study was to analyze the thickness, uniformity, and adhesive strength of chitosan coatings on simulated denture bases made from polymethyl methacrylate (PMMA). According to a standardized protocol, 20 PMMA cylinders (13 mm diameter, 5 mm in height) as well as 20 cubes (a = 25 mm) with intaglio U-shaped profiles were manufactured to simulate average sized alveolar ridges. Cylinders as well as cubes were divided into four test series with n = 5 each. After sandblasting with silica-modified alumina, one frontal surface of the PMMA cylinders and the intaglio surfaces of the U-shaped profiles was coated with chitosan acetate solution according to the following protocols: one layer of 2% chitosan acetate solution (test series I), one layer of 4% chitosan acetate solution (test series II), two layers of 2% chitosan acetate solution (test series III), and two layers of 4% chitosan acetate solution (test series IV). After drying and neutralization with NaOH, each cube was cut transversely and the coating thickness across the U-shaped profile assessed with a light microscope. Adhesive strength was evaluated by simulated tooth brushing and the loss of chitosan coating was evaluated qualitatively. Statistical analysis used Friedman ANOVA test for dependent samples and Kruskal-Wallis test for independent samples, post-hoc Dunn’s test (p < 0.05), and binomial test (p = 0.05). The mean chitosan coating thicknesses in the depth of the U-profiles were 71 µm (test series I), 77 µm (test series II), 121 µm (test series III), and 517 µm (test series VI). The thickness continuously decreased with rising angulation of the U-profile side walls. In test series I, the chitosan coating thickness significantly dropped above a 30° angulation of the U-profile side walls. In test series II to IV, the chitosan thickness drop was not statistically significant at angulations of 30° and 60°, but was at 90° angulation of the U-profile side walls. Adhesion strength was rated fair to good and did not differ significantly among the four test series. The coating technique described revealed chitosan layers with overall good adhesion strength but differing thicknesses. Coatings with one or two layers of 4% chitosan acetate solution allowed a relatively uniform chitosan thickness and thus might be usable in oral medicine.

Soil spectroscopy has experienced a tremendous increase in soil property characterisation, and can be used not only in the laboratory but also from the space (imaging spectroscopy). Partial least squares (PLS) regression is one of the most common approaches for the calibration of soil properties using soil spectra. Besides functioning as a calibration method, PLS can also be used as a dimension reduction tool, which has scarcely been studied in soil spectroscopy. PLS components retained from high-dimensional spectral data can further be explored with the gradient-boosted decision tree (GBDT) method. Three soil sample categories were extracted from the Land Use/Land Cover Area Frame Survey (LUCAS) soil library according to the type of land cover (woodland, grassland, and cropland). First, PLS regression and GBDT were separately applied to build the spectroscopic models for soil organic carbon (OC), total nitrogen content (N), and clay for each soil category. Then, PLS-derived components were used as input variables for the GBDT model. The results demonstrate that the combined PLS-GBDT approach has better performance than PLS or GBDT alone. The relative important variables for soil property estimation revealed by the proposed method demonstrated that the PLS method is a useful dimension reduction tool for soil spectra to retain target-related information.

This study tested whether the hormonal stress response to the DEX-CRH test may be predictive of the psychotherapy success for panic disorder (PD). Thirty-four patients diagnosed either with agoraphobia with PD or PD without agoraphobia were subjected to cognitive behavioural therapy (CBT). Patients (pre-therapy) and healthy volunteers were exposed to the DEX-CRH test. Blood samples were taken for cortisol and adrenocorticotropic hormone (ACTH) assessment. Established panic-specific questionnaires were handed out for the pre-therapy and post-therapy evaluation of disease severity (with reference to panic beliefs and agoraphobic cognitions, fear of bodily sensations, agoraphobic avoidance behaviour). Repeated measures ANCOVA were conducted for the analysis of the pre-therapy hormonal response, and Pearson\'s correlation analysis to test for associations with the psychotherapy outcome. Data analyses revealed large effect sizes for CBT in the clinical measures (η2 ≥ 0.321), main effects of time for cortisol and ACTH with no differences between both groups, and significant associations between cortisol release and agoraphobic cognitions for the patients. PD diagnosis had no impact on the hormonal response. However, those patients with higher cortisol release showed less improvement after CBT (significantly for agoraphobic cognitions). Clinical implications of these findings are the prediction of the therapy success from a potential endocrine correlate whose persistency (if assessed repeatedly) during the treatment may predict (non-)response to the current treatment, possibly representing a decision support for a change in treatment to avoid the continuation of an inefficient treatment.

Over the past decades, quantitative methods linking theory and observation became increasingly important in many areas of life science. Subsequently, a large number of mathematical and computational models has been developed. The BioModels database alone lists more than 140,000 Systems Biology Markup Language (SBML) models. However, while the exchange within specific model classes has been supported by standardisation and database efforts, the generic application and especially the re-use of models is still limited by practical issues such as easy and straight forward model execution. MAGPIE, a Modeling and Analysis Generic Platform with Integrated Evaluation, closes this gap by providing a software platform for both, publishing and executing computational models without restrictions on the programming language, thereby combining a maximum on flexibility for programmers with easy handling for non-technical users. MAGPIE goes beyond classical SBML platforms by including all models, independent of the underlying programming language, ranging from simple script models to complex data integration and computations. We demonstrate the versatility of MAGPIE using four prototypic example cases. We also outline the potential of MAGPIE to improve transparency and reproducibility of computational models in life sciences. A demo server is available at magpie.imb.medizin.tu-dresden.de.

The objective of this study was to analyze patterns, dynamics and processes of land-use/cover changes in the transboundary Mara River Basin in East Africa. We specifically focused on deforestation and expansion of agriculture in the watershed. The intensity analysis approach was used to analyze data from satellite imagery-derived land-use/cover maps. Results indicate that swap change accounted for more than 50% of the overall change, which shows a very dynamic landscape transformation. Transition from closed forest to open forest was found to be a dominant landscape change, as opposed to a random change. Similarly, transition from open forest to small-scale agriculture was also found to be a dominant transition. This suggests a trend (pathway) of deforestation from closed forest to small-scale agriculture, with open forest as a transitional land cover. The observed deforestation may be attributed to continuous encroachment and a series of excisions of the forest reserve. Transition from rangeland to mechanized agriculture was found to be a dominant land-use change, which was attributed to change in land tenure. These findings are crucial for designing strategies and integrated watershed management policies to arrest further deforestation in the forest reserves as well as to sustainably control expansion of agriculture.

It is far from conclusive what distinguishes the inattentive (ADD) and the combined (ADHD-C) subtype of ADHD on the neuronal level. Theoretical considerations suggest that especially interval timing processes may dissociate these subtypes from each other. Combining high-density EEG recordings with source localization analyses, we examine whether there are ADHD-subtype specific modulations of neurophysiological processes subserving interval timing in matched groups of ADD (n = 16), ADHD-C (n = 16) and controls (n = 16). Patients with ADD and ADHD-C show deficits in interval timing, which was correlated with the degree of inattention in ADD patients. Compared to healthy controls, patients with ADHD-C display a somewhat weaker, yet consistent response preparation process (contingent negative variation, CNV). In patients with ADD, the early CNV is interrupted, indicating an oscillatory disruption of the interval timing process. This is associated with activations in the supplemental motor areas and the middle frontal gyrus. Patients with ADD display adequate feedback learning mechanisms (feedback-related negativity, FRN), which is not the case in patients with ADHD-C. The results suggest that altered pacemaker-accumulation processes in medial frontal structures distinguish the ADD from the ADHD-C subtype. Particularly in patients with ADD phasic interruptions of preparatory neurophysiological processes are evident, making this a possible diagnostic feature.