The effect of flow speed and food size on the capture efficiency and feeding behaviour of the cold-water coral Lophelia pertusa

The capture efficiency and feeding behaviour of the cold-water coral (CWC) Lophelia pertusa (Linnaeus, 1758) were investigated considering: (1) different food types, (2) different food sizes and (3) different current speeds and temperatures. This study used two different multifactorial experimental approaches: (1) Corals were subjected to three different flow speeds (2, 5 and 10 cm s− 1) in 5 l volume tanks, and three different food types (alive zooplankton, alive algae, and dry particulate organic carbon) were offered to the corals under each current regime, analysing the capture rates of the corals under these different flow velocities. (2) In a flume, the feeding behaviour of the coral polyps was studied under different current speed regimes (1, 7, 15 and 27 cm s− 1) and a temperature change over a range of 8–12 °C. The obtained results confirm that low flow speeds (below 7 cm s− 1) appear optimal for a successful prey capture, and temperature did not have an effect on polyp expansion behaviour for L. pertusa. In conclusion, flow speeds clearly impact food capture efficiency in L. pertusa, with zooplankton predominantly captured prey at low flow velocities (2 cm s− 1) and phytoplankton captured at higher flow velocities of 5 cm s− 1. This split in capture efficiency may allow corals to exploit different food sources under different tidal and flow conditionsVersión del editor2,263

Therapeutic implications of improved molecular diagnostics for rare CNS-embryonal tumor entities: results of an international, retrospective study

BACKGROUND: Only few data are available on treatment-associated behavior of distinct rare CNS-embryonal tumor entities previously treated as "CNS-primitive neuroectodermal tumors" (CNS-PNET). Respective data on specific entities, including CNS neuroblastoma, FOXR2 activated (CNS NB-FOXR2), and embryonal tumor with multi-layered rosettes (ETMR) are needed for development of differentiated treatment strategies. METHODS: Within this retrospective, international study, tumor samples of clinically well-annotated patients with the original diagnosis of CNS-PNET were analyzed using DNA methylation arrays (n=307). Additional cases (n=66) with DNA methylation pattern of CNS NB-FOXR2 were included irrespective of initial histological diagnosis. Pooled clinical data (n=292) were descriptively analyzed. RESULTS: DNA methylation profiling of "CNS-PNET" classified 58(19%) cases as ETMR, 57(19%) as HGG, 36(12%) as CNS NB-FOXR2, and 89(29%) cases were classified into 18 other entities. Sixty-seven (22%) cases did not show DNA methylation patterns similar to established CNS tumor reference classes. Best treatment results were achieved for CNS NB-FOXR2 patients (5-year PFS: 63%±7%, OS: 85%±5%, n=63), with 35/42 progression-free survivors after upfront craniospinal irradiation (CSI) and chemotherapy. The worst outcome was seen for ETMR and HGG patients with 5-year PFS of 18%±6% and 22%±7%, and 5-year OS of 24%±6% and 25%±7%, respectively. CONCLUSION: The historically reported poor outcome of CNS-PNET patients becomes highly variable when tumors are molecularly classified based on DNA methylation profiling. Patients with CNS NB-FOXR2 responded well to current treatments and a standard-risk-CSI based regimen may be prospectively evaluated. The poor outcome of ETMR across applied treatment strategies substantiates the necessity for evaluation of novel treatments

nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments

Longitudinal data from factorial experiments frequently arise in various fields of study, ranging from medicine and biology to public policy and sociology. In most practical situations, the distribution of observed data is unknown and there may exist a number of atypical measurements and outliers. Hence, use of parametric and semi-parametric procedures that impose restrictive distributional assumptions on observed longitudinal samples becomes questionable. This, in turn, has led to a substantial demand for statistical procedures that enable us to accurately and reliably analyze longitudinal measurements in factorial experiments with minimal conditions on available data, and robust nonparametric methodology offering such a possibility becomes of particular practical importance. In this article, we introduce a new R package nparLD which provides statisticians and researchers from other disciplines an easy and user-friendly access to the most up-to-date robust rank-based methods for the analysis of longitudinal data in factorial settings. We illustrate the implemented procedures by case studies from dentistry, biology, and medicine