Ecological Guild Evolution and the Discovery of the World's Smallest Vertebrate

Living vertebrates vary drastically in body size, yet few taxa reach the extremely minute size of some frogs and teleost fish. Here we describe two new species of diminutive terrestrial frogs from the megadiverse hotspot island of New Guinea, one of which represents the smallest known vertebrate species, attaining an average body size of only 7.7 mm. Both new species are members of the recently described genus Paedophryne, the four species of which are all among the ten smallest known frog species, making Paedophryne the most diminutive genus of anurans. This discovery highlights intriguing ecological similarities among the numerous independent origins of diminutive anurans, suggesting that minute frogs are not mere oddities, but represent a previously unrecognized ecological guild

Pregnancy after cardiac transplantation. Report of one case and review

A 14-year-old female patient became pregnant 6 years after heart transplantation. The pregnancy evolved uneventfully, and the newborn infant was healthy. Five months after delivery, the mother was in good condition with preserved ventricular function, and the baby had normal neuro-psychomotor development. Even though the case reported here was a success, pregnancy following cardiac transplantation is considered a high-risk condition and remains contraindicated

Ion-Selective Electrode Array Based on a Bayesian Nonlinear Source Separation Method

International audienceIon-selective electrodes (ISE) offer a practical approach for estimating ionic activities. Nonetheless, such devices are not selective, i.e., the ISE response can be affected by nterfering ions other than the target one. With the aim of overcoming this problem, we propose a Bayesian nonlinear source separation method for processing the data acquired by an ISE array. The Bayesian framework permits us to easily incorporate prior information such as the non-negativity of the sources into the separation method. The effectiveness of our proposal is attested by experiments using artificial and real data

A Bayesian Nonlinear Source Separation Method for Smart Ion-Selective Electrode Arrays

International audiencePotentiometry with ion-selective electrodes (ISEs) provides a simple and cheap approach for estimating ionic activities. However, a well-known shortcoming of ISEs regards their lack of selectivity. Recent works have suggested that smart sensor arrays equipped with a blind source separation (BSS) algorithm offer a promising solution to the interference problem. In fact, the use of blind methods eases the time-demanding calibration stages needed in the typical approaches. In this work, we develop a Bayesian source separation method for processing the outputs of an ISE array. The major benefit brought by the Bayesian framework is the possibility of taking into account some prior information, which can result in more realistic solutions. Concerning the inference stage, it is conducted by means of Markov chain Monte Carlo (MCMC) methods. The validity of our approach is supported by experiments with artificial data and also in a scenario with real data