Degradation of dibutyl phthalate by Paenarthrobacter sp. Shss isolated from Saravan landfill, Hyrcanian Forests, Iran.

Phthalic acid esters are predominantly used as plasticizers and are industrially produced on the million ton scale per year. They exhibit endocrine-disrupting, carcinogenic, teratogenic, and mutagenic effects on wildlife and humans. For this reason, biodegradation, the major process of phthalic acid ester elimination from the environment, is of global importance. Here, we studied bacterial phthalic acid ester degradation at Saravan landfill in Hyrcanian Forests, Iran, an active disposal site with 800 tons of solid waste input per day. A di-n-butyl phthalate degrading enrichment culture was established from which Paenarthrobacter sp. strain Shss was isolated. This strain efficiently degraded 1 g L-1 di-n-butyl phthalate within 15 h with a doubling time of 5 h. In addition, dimethyl phthalate, diethyl phthalate, mono butyl phthalate, and phthalic acid where degraded to CO2, whereas diethyl hexyl phthalate did not serve as a substrate. During the biodegradation of di-n-butyl phthalate, mono-n-butyl phthalate was identified in culture supernatants by ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry. In vitro assays identified two cellular esterase activities that converted di-n-butyl phthalate to mono-n-butyl phthalate, and the latter to phthalic acid, respectively. Our findings identified Paenarthrobacter sp. Shss amongst the most efficient phthalic acid esters degrading bacteria known, that possibly plays an important role in di-n-butyl phthalate elimination at a highly phthalic acid esters contaminated landfill.

Monte Carlo methods for localization of cones given multielectrode retinal ganglion cell recordings.

It has recently become possible to identify cone photoreceptors in primate retina from multi-electrode recordings of ganglion cell spiking driven by visual stimuli of sufficiently high spatial resolution. In this paper we present a statistical approach to the problem of identifying the number, locations, and color types of the cones observed in this type of experiment. We develop an adaptive Markov Chain Monte Carlo (MCMC) method that explores the space of cone configurations, using a Linear-Nonlinear-Poisson (LNP) encoding model of ganglion cell spiking output, while analytically integrating out the functional weights between cones and ganglion cells. This method provides information about our posterior certainty about the inferred cone properties, and additionally leads to improvements in both the speed and quality of the inferred cone maps, compared to earlier "greedy" computational approaches.