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Tumour spheroids are widely used as an in vitro assay for characterising the dynamics and response to treatment of different cancer cell lines. Their popularity is largely due to the reproducible manner in which spheroids grow: the diffusion of nutrients and oxygen from the surrounding culture medium, and their consumption by tumour cells, causes proliferation to be localised at the spheroid boundary. As the spheroid grows, cells at the spheroid centre may become hypoxic and die, forming a necrotic core. The pressure created by the localisation of tumour cell proliferation and death generates an cellular flow of tumour cells from the spheroid rim towards its core. Experiments by Dorie et al. showed that this flow causes inert microspheres to infiltrate into tumour spheroids via advection from the spheroid surface, by adding microbeads to the surface of tumour spheroids and observing the distribution over time. We use an off-lattice hybrid agent-based model to re-assess these experiments and establish the extent to which the spatio-temporal data generated by microspheres can be used to infer kinetic parameters associated with the tumour spheroids that they infiltrate. Variation in these parameters, such as the rate of tumour cell proliferation or sensitivity to hypoxia, can produce spheroids with similar bulk growth dynamics but differing internal compositions (the proportion of the tumour which is proliferating, hypoxic/quiescent and necrotic/nutrient-deficient). We use this model to show that the types of experiment conducted by Dorie et al. could be used to infer spheroid composition and parameters associated with tumour cell lines such as their sensitivity to hypoxia or average rate of proliferation, and note that these observations cannot be conducted within previous continuum models of microbead infiltration into tumour spheroids as they rely on resolving the trajectories of individual microbeads.

Background Previous research has linked smartphone and Instagram use to higher body dissatisfaction (BD) as well as eating disorder (ED) symptomatology. However, these studies have typically been limited to using self-report measures for technology use which, as shown by scientific literature, might not be reliable. In the present work, we combine self-reported assessments as well as tracked smartphone and Instagram use. Methods The effective sample comprised N = 119 women (34 with ED diagnosis history) who were queried about BD and ED symptomatology, and who provided the data about their smartphone and Instagram use duration for each day of the previous week. Results The study results show that women with an ED diagnosis history scored higher on both BD as well as ED scales. Although women with an ED diagnosis history had higher smartphone screen time, there were no statistically significant differences in Instagram screen time. Tracked smartphone use duration was positively correlated with both BD and ED symptomatology, but the role of Instagram use needs to be further elucidated. Conclusions The results of this study show that while BD and ED symptomatology are correlated with smartphone use, it may be that Instagram use is not the main contributor to that relationship. Plain English Summary This study looked at how using smartphones and Instagram relates to feelings of dissatisfaction with one's body and symptoms of eating disorders. We compared women who had been diagnosed with an eating disorder in the past to those without such a history. We used information about the participants' smartphone and Instagram use from a tracking app, in addition to self-report surveys. The results showed that spending more time using smartphones was linked to higher levels of eating disorder symptoms and more body dissatisfaction. However, the connection between Instagram use and these psychological factors was not strong or consistent. There were no significant differences in Instagram use between women with and without an eating disorder diagnosis history. These findings suggest that smartphone use may be related to body dissatisfaction and eating disorder symptoms. At least according to the present data, Instagram use may not be the main driver of these issues. More research is needed to understand how to prevent and address these concerns, as well as to examine how different types of eating disorders relate to smartphone and social media use patterns.

Cell migration is the driving force behind the dynamics of many diverse biological processes. Even though microscopy experiments are routinely performed today by which populations of cells are visualized in space and time, valuable information contained in image data is often disregarded because statistical analyses are performed at the level of cell populations rather than at the single-cell level. Image-based systems biology is a modern approach that aims at quantitatively analyzing and modeling biological processes by developing novel strategies and tools for the interpretation of image data. In this study, we take first steps towards a fully automated characterization and parameter-free classification of cell track data that can be generally applied to tracked objects as obtained from image data. The requirements to achieve this aim include: (i) combination of different measures for single cell tracks, such as the confinement ratio and the asphericity of the track volume, and (ii) computation of these measures in a staggered fashion to retrieve local information from all possible combinations of track segments. We demonstrate for a population of synthetic cell tracks as well as for in vitro neutrophil tracks obtained from microscopy experiment that the information contained in the track data is fully exploited in this way and does not require any prior knowledge, which keeps the analysis unbiased and general. The identification of cells that show the same type of migration behavior within the population of all cells is achieved via agglomerative hierarchical clustering of cell tracks in the parameter space of the staggered measures. The recognition of characteristic patterns is highly desired to advance our knowledge about the dynamics of biological processes.

Aspergillus fumigatus is a ubiquitous airborne fungus and opportunistic human pathogen. In immunocompromised hosts, the fungus can cause life-threatening diseases like invasive pulmonary aspergillosis. Since the incidence of fungal systemic infections drastically increased over the last years, it is a major goal to investigate the pathobiology of A. fumigatus and in particular the interactions of A. fumigatus conidia with immune cells. Many of these studies include the activity of immune effector cells, in particular of macrophages, when they are confronted with conidia of A. fumigus wild-type and mutant strains. Here, we report the development of an automated analysis of confocal laser scanning microscopy images from macrophages coincubated with different A. fumigatus strains. At present, microscopy images are often analysed manually, including cell counting and determination of interrelations between cells, which is very time consuming and error-prone. Automation of this process overcomes these disadvantages and standardises the analysis, which is a prerequisite for further systems biological studies including mathematical modeling of the infection process. For this purpose, the cells in our experimental setup were differentially stained and monitored by confocal laser scanning microscopy. To perform the image analysis in an automatic fashion, we developed a ruleset that is generally applicable to phagocytosis assays and in the present case was processed by the software Definiens Developer XD. As a result of a complete image analysis we obtained features such as size, shape, number of cells and cell-cell contacts. The analysis reported here, reveals that different mutants of A. fumigatus have a major influence on the ability of macrophages to adhere and to phagocytose the respective conidia. In particular, we observe that the phagocytosis ratio and the aggregation behaviour of pksP mutant compared to wild-type conidia are both significantly increased.

Previous research has linked smartphone and Instagram use to higher body dissatisfaction (BD) as well as eating disorder (ED) symptomatology. However, these studies have typically been limited to using self-report measures for technology use which, as shown by scientific literature, might not be reliable. In the present work, we combine self-reported assessments as well as tracked smartphone and Instagram use. The effective sample comprised N = 119 women (34 with ED diagnosis history) who were queried about BD and ED symptomatology, and who provided the data about their smartphone and Instagram use duration for each day of the previous week. The study results show that women with an ED diagnosis history scored higher on both BD as well as ED scales. Although women with an ED diagnosis history had higher smartphone screen time, there were no statistically significant differences in Instagram screen time. Tracked smartphone use duration was positively correlated with both BD and ED symptomatology, but the role of Instagram use needs to be further elucidated. The results of this study show that while BD and ED symptomatology are correlated with smartphone use, it may be that Instagram use is not the main contributor to that relationship.