Bayesian estimation of speciation and extinction from incomplete fossil occurrence data.

The temporal dynamics of species diversity are shaped by variations in the rates of speciation and extinction, and there is a long history of inferring these rates using first and last appearances of taxa in the fossil record. Understanding diversity dynamics critically depends on unbiased estimates of the unobserved times of speciation and extinction for all lineages, but the inference of these parameters is challenging due to the complex nature of the available data. Here, we present a new probabilistic framework to jointly estimate species-specific times of speciation and extinction and the rates of the underlying birth-death process based on the fossil record. The rates are allowed to vary through time independently of each other, and the probability of preservation and sampling is explicitly incorporated in the model to estimate the true lifespan of each lineage. We implement a Bayesian algorithm to assess the presence of rate shifts by exploring alternative diversification models. Tests on a range of simulated data sets reveal the accuracy and robustness of our approach against violations of the underlying assumptions and various degrees of data incompleteness. Finally, we demonstrate the application of our method with the diversification of the mammal family Rhinocerotidae and reveal a complex history of repeated and independent temporal shifts of both speciation and extinction rates, leading to the expansion and subsequent decline of the group. The estimated parameters of the birth-death process implemented here are directly comparable with those obtained from dated molecular phylogenies. Thus, our model represents a step towards integrating phylogenetic and fossil information to infer macroevolutionary processes.

Combined radioimmunotherapy and radiotherapy of liver metastases from colorectal cancer: a feasibility study.

BACKGROUND: A combination of radioimmunotherapy (RIT) and radiotherapy (RT) should allow one to increase the dose of radiation targeting a particular tumour without the concomitant increase of toxic side effects. This might be obtained if the dose limiting side effect of each individual radiation therapy concerned different organs. METHODS: Six patients with limited liver metastatic disease from colorectal cancer were treated with 6.9 GBq (range 4.7 to 8.4 GBq) 131I-labelled anti-CEA MAb F(ab')2 fragments combined with 20 Gy RT to the liver. Both treatments were given in close association, according to timing schedules evaluated in animals that gave the best results. RESULTS: Reversible bone marrow and liver toxicity was observed in 6 and 5 patients, respectively. Three patients who first received 20 Gy RT to the liver, showed a significant platelet drop upon completion of RT. Repeat computerized tomography (CT) after 2 months showed a minor response in 1 patient and stable disease in 3 patients. CONCLUSION: The study shows potential ways of combining RIT and RT, suggesting that this combination is feasible for the treatment of liver metastases.

Morphological Study of Intracardiac Signals as a New Tool to Track the Efficiency of Stepwise Ablation of Persistent Atrial Fibrillation

Intracardiac organization indices such as atrial fibrillation (AF) cycle length (AFCL) have been used to track the efficiency of stepwise catheter ablation (step-CA) of longstanding persistent AF, however with limited success. The morphology of AF activation waves reflects the underlying activation patterns. Its temporal evolution is a local organization indicator that could be potentially used for tracking the efficiency of step-CA. We report a new method for characterizing the structure of the temporal evolution of activation wave morphology. Using recurrence plots, novel organization indices are proposed. By computing their relative evolution during the first step of ablation vs baseline, we found that these new parameters are superior to AFCL to track the effect of step-CA "en route" to AF termination.

Nonlinear analysis of human physical activity patterns in health and disease.

The reliable and objective assessment of chronic disease state has been and still is a very significant challenge in clinical medicine. An essential feature of human behavior related to the health status, the functional capacity, and the quality of life is the physical activity during daily life. A common way to assess physical activity is to measure the quantity of body movement. Since human activity is controlled by various factors both extrinsic and intrinsic to the body, quantitative parameters only provide a partial assessment and do not allow for a clear distinction between normal and abnormal activity. In this paper, we propose a methodology for the analysis of human activity pattern based on the definition of different physical activity time series with the appropriate analysis methods. The temporal pattern of postures, movements, and transitions between postures was quantified using fractal analysis and symbolic dynamics statistics. The derived nonlinear metrics were able to discriminate patterns of daily activity generated from healthy and chronic pain states.