André Dittrich

Unbalanced fermentation of glycerol in Escherichia coli via heterologous production of an electron transport chain and electrode interaction in microbial electrochemical cells

Microbial electrochemical cells are an emerging technology for achieving unbalanced fermentations. However, organisms that can serve as potential biocatalysts for this application are limited by their narrow substrate spectrum. This study describes the reprogramming of Escherichia coli for the efficient use of anodes as electron acceptors. Electron transfer into the periplasm was accelerated by 183% via heterologous expression of the c-type cytochromes CymA, MtrA and STC from Shewanella oneidensis. STC was identified as a target for heterologous expression via a two-stage screening approach. First, mass spectroscopic analysis revealed natively expressed cytochromes in S. oneidensis. Thereafter, the corresponding genes were cloned and expressed in E. coli to quantify periplasmic electron transfer activity using methylene blue. This redox dye was further used to expand electron transfer to carbon electrode surfaces. The results demonstrate that E. coli can be reprogrammed from glycerol fermentation to respiration upon production of the new electron transport chain.

A Classification Schema for Fast Disambiguation of Spatial Prepositions

In the field of Artificial Intelligence the task of spatial language understanding is a particularly complex one. Textual spatial information is frequently represented by so-called locative expressions, incorporating spatial prepositions. However, apart from the spatial domain, these prepositions can occur in a wide range of senses (e.g., temporal, manner, cause, instrument) as well as in semantically transformed senses (e.g., metaphors and metonymies). Existing practical approaches usually disregard semantic transformations or falsely classify them as spatial, although they represent the majority of cases. For the efficient extraction of locative expressions from data streams (e.g. from social media sources), a fast filter mechanism for this non-spatial information is needed. Hence, we present a classification schema to quickly and robustly disambiguate spatial from non-spatial uses of prepositions. We conduct an inter-annotator agreement test to highlight the feasibility and comprehensibility of our schema based on examples sourced from a large social media corpus. We further identify the most promising existing natural language processing tools in order to combine machine learning features with fixed rules.

Analysing the Usage of Spatial Prepositions in Short Messages

Spatial prepositions such as in, on and near are important to describe where things are located in relation to other geographic features. Location-based services (LBS) usually disregard such spatial prepositions. Their automatic detection and interpretation is challenging, because prepositions are quite often used in non-spatial context (e.g., “in the afternoon”). This paper analyses spatial relations in short messages. Short messages typically have special characteristics (e.g., slang, abbreviations, etc.) and thus represent a special type of natural language. A sample corpus of short messages was used to extract descriptions based on spatial prepositions and to analyse their commonness of use. A frequency-based probability for each term to be spatial was calculated, which can serve as an indicator of a verbal spatial description and support the development of intelligent spatial language interpretation in automatic systems.