Monitoring cryptic amphibians and reptiles in a Florida state park

We monitored cryptic herpetofauna at Savannas Preserve State Park, Florida, by combining artificial cover counts with a quantitative paradigm for constructing and calculating population indices. Weekly indices were calculated from two consecutive days of data collection each week for 7 months from mid-winter to mid-summer in three habitats. Seventeen species were observed at least once, and time trends using index values were followed for six species. Among these, abundance and seasonal pattern information were obtained for an exotic species (greenhouse frog) and a species identified by the Florida Committee on Rare and Endangered Plants and Animals as threatened (Florida scrub lizard). We identified winter as the optimal time in this area to monitor populations for conducting annual assessments. This combined observation and indexing approach could provide managers or researchers with an economical means to quantitatively index population trends for multiple cryptic herpetofauna species simultaneously. Using artificial cover to sample within a population indexing design can be generalized beyond monitoring herpetofauna. Other forms of artificial cover that can be used as observation stations include aquatic artificial substrates, artificial tree cavities, artificial reefs, and other artificial aquatic structures and artificial sea grass units, among many others, and a wide range of taxa are suitable for population monitoring using artificial cover as observation stations in the approach we present, including insects, soil invertebrates, micro and macro aquatic invertebrates, fish, crustaceans, and small mammals

Exploiting Clinical Trial Data Drastically Narrows the Window of Possible Solutions to the Problem of Clinical Adaptation of a Multiscale Cancer Model

The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem