Probing real sensory worlds of receivers with unsupervised clustering.

The task of an organism to extract information about the external environment from sensory signals is based entirely on the analysis of ongoing afferent spike activity provided by the sense organs. We investigate the processing of auditory stimuli by an acoustic interneuron of insects. In contrast to most previous work we do this by using stimuli and neurophysiological recordings directly in the nocturnal tropical rainforest, where the insect communicates. Different from typical recordings in sound proof laboratories, strong environmental noise from multiple sound sources interferes with the perception of acoustic signals in these realistic scenarios. We apply a recently developed unsupervised machine learning algorithm based on probabilistic inference to find frequently occurring firing patterns in the response of the acoustic interneuron. We can thus ask how much information the central nervous system of the receiver can extract from bursts without ever being told which type and which variants of bursts are characteristic for particular stimuli. Our results show that the reliability of burst coding in the time domain is so high that identical stimuli lead to extremely similar spike pattern responses, even for different preparations on different dates, and even if one of the preparations is recorded outdoors and the other one in the sound proof lab. Simultaneous recordings in two preparations exposed to the same acoustic environment reveal that characteristics of burst patterns are largely preserved among individuals of the same species. Our study shows that burst coding can provide a reliable mechanism for acoustic insects to classify and discriminate signals under very noisy real-world conditions. This gives new insights into the neural mechanisms potentially used by bushcrickets to discriminate conspecific songs from sounds of predators in similar carrier frequency bands.

Novel s-triazine-degrading bacteria isolated from agricultural soils of central Chile for herbicide bioremediation

s-Triazine-degrading bacterial strains were isolated from long-term simazine-treated agricultural soils of central Chile. The number of culturable heterotrophic bacteria of these agricultural soils (7 x 10(6) CFU/g of dry soil) was not affected by simazine application on field. The simazine-degrading bacterial strains P51, P52 and C53 were isolated by enrichment in minimal medium using simazine as the sole nitrogen source. Resting cells of strains P51 and P52 degraded >80 percent of simazine within 48 hrs, whereas strain C53 was able to remove >60 percent of the herbicide. The atzA and atzD genes of the s-triazine upper and lower catabolic pathways were detected in strains P51 and C53, while only atzD gene was observed in strain P52. To compare the bacterial 16S rRNA gene sequence structure, ARDRA were performed using the restriction enzymes Msp1 and Hha1. ARDRA indicated that strain P52 was a different ribotype than C53 and P51 strains. For further characterization the novel isolates were identified by 16S rRNA gene sequencing. Strains C53 and P51 belong to the genus Stenotrophomonas and the strain P52 belongs to the genus Arthrobacter . s -Triazine-degrading bacterial strains isolated from contaminated soils could be used as biocatalysts for bioremediation of these herbicides.