Radiomics in radiooncology – Challenging the medical physicist

PurposeNoticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions.MethodsBased on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach.ResultsClinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data (‘panomics’) challenges the medical physicist as member of the radiooncology team.ConclusionsThe new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.     

Using food network unfolding to evaluate food–web complexity in terms of biodiversity: theory and applications

Food–web complexity often hinders disentangling functionally relevant aspects of food–web structure and its relationships to biodiversity. Here, we present a theoretical framework to evaluate food–web complexity in terms of biodiversity. Food network unfolding is a theoretical method to transform a complex food web into a linear food chain based on ecosystem processes. Based on this method, we can define three biodiversity indices, horizontal diversity (D_H), vertical diversity (D_V) and range diversity (D_R), which are associated with the species diversity within each trophic level, diversity of trophic levels, and diversity in resource use, respectively. These indices are related to Shannon's diversity index (H′), where H′ = D_H + D_V ? D_R. Application of the framework to three riverine macroinvertebrate communities revealed that D indices, calculated from biomass and stable isotope features, captured well the anthropogenic, seasonal, or other within‐site changes in food–web structures that could not be captured with H′ alone.     

Generalism in the interaction of Tulasnellaceae mycobionts with orchids characterizes a biodiversity hotspot in the tropical Andes of Southern Ecuador

Biotic interactions play an important role in the assembly and stability of communities. All orchids depend on mycobionts for early establishment, but whether individual orchid species depend on a specific or broad spectrum of mycobionts is still a matter of debate. Tulasnellaceae (Basidiomycota) is the richest and most widespread mycobiont worldwide. We assessed Tulasnellaceae richness in epiphytic and terrestrial orchids in different habitats, and evaluated the degree of generalism in orchid-Tulasnellaceae interactions and the robustness of this mutualistic system to the extinction of mycobiont partners. We sampled 114 orchid individuals including all common and rare species in 56 plots of 1 m² in 3 habitats: pristine forest, regenerating forest and a landslide site in a tropical montane rainforest in Southern Ecuador. We found 52 orchid and 29 Tulasnellaceae species. The composition of Tulasnellaceae OTUs was moderately to highly similar across habitats and between orchid growth forms. A significantly nested network architecture indicated the existence of a core of generalist Tulasnellaceae OTUs interacting with both rare and common orchids. Terrestrial and epiphytic orchids showed significant differences in robustness to the extinction of their Tulasnellaceae mycobionts. Thus, generalist mycobionts may be relevant for the preservation of hyperdiverse orchid communities in the tropics.     

Traveling behavior of a termite in tunnels with different curvatures and base surface roughness

This study focused on understanding the termite traveling behavior. Artificial tunnels with different curvatures and base surface roughness were constructed. Each tunnel was 50 mm in length, with widths of W (W = 2, 3, or 4 mm). The distance between the two ends of the tunnel was D (D = 20, 30, 40, or 50 mm). A higher value of D means a lower curvature. The roughness, R (R = 60, 120, 240 and ∞), was generated by uniformly sanding the substrate with a sanding machine. A higher R value means a finer base surface. The symbol ∞ represents a smooth surface. Time (τ) taken by a termite to pass through the tunnel was measured. τ was longer in tunnels with a smooth surface compared with the tunnels with a rough surface. When W = 2, there was no effect of the roughness on speed. This is because the narrow tunnel width diluted the effects of the smoothness of the surface. When W = 3, τ was statistically shorter for R = 240, than for R = 60 and 120. This suggests that an appropriate surface roughness could positively contribute to the traveling speed.     

Simple foraging rules in competitive environments can generate socially structured populations

Social vertebrates commonly form foraging groups whose members repeatedly interact with one another and are often genetically related. Many species also exhibit within‐population specializations, which can range from preferences to forage in particular areas through to specializing on the type of prey they catch. However, within‐population structure in foraging groups, behavioral homogeneity in foraging behavior, and relatedness could be outcomes of behavioral interactions rather than underlying drivers. We present a simple process by which grouping among foragers emerges and is maintained across generations. We introduce agent‐based models to investigate (1) whether a simple rule (keep foraging with the same individuals when you were successful) leads to stable social community structure, and (2) whether this structure is robust to demographic changes and becomes kin‐structured over time. We find the rapid emergence of kin‐structured populations and the presence of foraging groups that control, or specialize on, a particular food resource. This pattern is strongest in small populations, mirroring empirical observations. Our results suggest that group stability can emerge as a product of network self‐organization and, in doing so, may provide the necessary conditions for the evolution of more sophisticated processes, such as social learning. This taxonomically general social process has implications for our understanding of the links between population, genetic, and social structures.     

Size-Dependent Axonal Bouton Dynamics following Visual Deprivation In Vivo

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