Bacterial growth kinetics estimation by fluorescence in situ hybridization and spectrofluorometric quantification

Aims: The aim of this study was to develop a specific and rapid method to identify and quantify relevant bacterial populations in mixed biomass by spectrofluorometric quantification (SQ) of whole cells hybridized with fluorescently labelled oligonucleotide probes targeting mature 16S ribosomal RNA (rRNA). Probe targeting the precursor of rRNA synthesis was also employed because it was being suggested as more indicative of the activity state of the micro-organisms. Methods and Results: Original fluorescence in situ hybridization protocol was modified to be applied to liquid samples and the fluorescence emission from the Cy3-labelled cells was measured by spectrofluorometry. The method was calibrated on an exponentially growing cell suspension of Acinetobacter johnsonii and was successfully applied to generate kinetic data. No substantial difference in the estimated maximum specific growth rate (mu(max)) values was found between the SQ method and the classical method, using absorbance at 420 nm (6.2 d(-1)). The preliminary validation tests showed their direct applicability to target enriched cultures. Conclusions: This study demonstrated the validity of the SQ method to easily quantify the concentration and to determine the growth rate of specific micro-organisms present in mixed cultures. Significan ce and Impact of the Study: The proposed method can be directly utilized for quantification and kinetic characterization of microbial enrichments. It has the advantage of being easily applicable using simple, inexpensive equipment suitable for routine analysis

Analysing the Multiple Timescale Recurrent Neural Network for Embodied Language Understanding

Abstract How the human brain understands natural language and how we can ex-ploit this understanding for building intelligent grounded language systems is open research. Recently, researchers claimed that language is embodied in most – if not all – sensory and sensorimotor modalities and that the brain’s architecture favours the emergence of language. In this chapter we investigate the characteristics of such an architecture and propose a model based on the Multiple Timescale Recurrent Neural Network, extended by embodied visual perception, and tested in a real world sce-nario. We show that such an architecture can learn the meaning of utterances with respect to visual perception and that it can produce verbal utterances that correctly describe previously unknown scenes. In addition we rigorously study the timescale mechanism (also known as hysteresis) and explore the impact of the architectural connectivity in the language acquisition task.

An Agrobacterium catalase is a virulence factor involved in tumorigenesis

10.1046/j.1365-2958.2000.01709.xMolecular Microbiology352407-414MOMI