Correction: A control theory approach to optimal pandemic mitigation.

[This corrects the article DOI: 10.1371/journal.pone.

Testing the effectiveness of different wash protocols to remove body surface contaminants in invertebrate food web studies.

Molecular gut content analysis diagnostic PCR or high-throughput sequencing (metabarcoding) of consumers allows unravelling of feeding interactions in a wide range of animals. This is of particular advantage for analyzing the diet of small invertebrates living in opaque habitats such as the soil. Due to their small body size, which complicates dissection, microarthropods are subjected to whole-body DNA extraction-step before their gut content is screened for DNA of their food.

A control theory approach to optimal pandemic mitigation.

In the framework of homogeneous susceptible-infected-recovered (SIR) models, we use a control theory approach to identify optimal pandemic mitigation strategies. We derive rather general conditions for reaching herd immunity while minimizing the costs incurred by the introduction of societal control measures (such as closing schools, social distancing, lockdowns, etc.), under the constraint that the infected fraction of the population does never exceed a certain maximum corresponding to public health system capacity.

Topology determines force distributions in one-dimensional random spring networks.

Networks of elastic fibers are ubiquitous in biological systems and often provide mechanical stability to cells and tissues. Fiber-reinforced materials are also common in technology. An important characteristic of such materials is their resistance to failure under load.

Topology Counts: Force Distributions in Circular Spring Networks.

Filamentous polymer networks govern the mechanical properties of many biological materials. Force distributions within these networks are typically highly inhomogeneous, and, although the importance of force distributions for structural properties is well recognized, they are far from being understood quantitatively. Using a combination of probabilistic and graph-theoretical techniques, we derive force distributions in a model system consisting of ensembles of random linear spring networks on a circle.