title: Diversity of methyl halide-degrading microorganisms in oceanic and coastal waters

authors: Cox, Michael J.

subjects: methyl halide

abstract: Methyl halides have a significant impact on atmospheric chemistry, particularly in the degradation of stratospheric ozone. Bacteria are known to contribute to the degradation of methyl halides in the oceans and marine bacteria capable of using methyl bromide and methyl chloride as sole carbon and energy source have been isolated. A genetic marker for microbial degradation of methyl bromide ( cmuA ) was used to examine the distribution and diversity of these organisms in the marine environment. Three novel marine clades of cmuA were identified in unamended seawater and in marine enrichment cultures degrading methyl halides. Two of these cmuA clades are not represented in extant bacteria, demonstrating the utility of this molecular marker in identifying uncultivated marine methyl halide-degrading bacteria. The detection of populations of marine bacteria containing cmuA genes suggests that marine bacteria employing the CmuA enzyme contribute to methyl halide cycling in the ocean.

text: Diversity of methyl halide-degrading microorganisms in oceanic and coastal waters Methyl halides have a significant impact on atmospheric chemistry, particularly in the degradation of stratospheric ozone. Bacteria are known to contribute to the degradation of methyl halides in the oceans and marine bacteria capable of using methyl bromide and methyl chloride as sole carbon and energy source have been isolated. A genetic marker for microbial degradation of methyl bromide ( cmuA ) was used to examine the distribution and diversity of these organisms in the marine environment. Three novel marine clades of cmuA were identified in unamended seawater and in marine enrichment cultures degrading methyl halides. Two of these cmuA clades are not represented in extant bacteria, demonstrating the utility of this molecular marker in identifying uncultivated marine methyl halide-degrading bacteria. The detection of populations of marine bacteria containing cmuA genes suggests that marine bacteria employing the CmuA enzyme contribute to methyl halide cycling in the ocean.

year: 2012

title: Propositionalisation of multi-instance data using random forests

authors: Frank, Eibe

subjects: Machine learning

abstract: Multi-instance learning is a generalisation of attribute-value learning where examples for learning consist of labeled bags (i.e. multi-sets) of instances. This learning setting is more computationally challenging than attribute-value learning and a natural fit for important application areas of machine learning such as classification of molecules and image classification. One approach to solve multi-instance learning problems is to apply propositionalisation, where bags of data are converted into vectors of attribute-value pairs so that a standard propositional (i.e. attribute-value) learning algorithm can be applied. This approach is attractive because of the large number of propositional learning algorithms that have been developed and can thus be applied to the propositionalised data. In this paper, we empirically investigate a variant of an existing propositionalisation method called TLC. TLC uses a single decision tree to obtain propositionalised data. Our variant applies a random forest instead and is motivated by the potential increase in robustness that this may yield. We present results on synthetic and real-world data from the above two application domains showing that it indeed yields increased classification accuracy when applying boosting and support vector machines to classify the propositionalised data.

text: Propositionalisation of multi-instance data using random forests Multi-instance learning is a generalisation of attribute-value learning where examples for learning consist of labeled bags (i.e. multi-sets) of instances. This learning setting is more computationally challenging than attribute-value learning and a natural fit for important application areas of machine learning such as classification of molecules and image classification. One approach to solve multi-instance learning problems is to apply propositionalisation, where bags of data are converted into vectors of attribute-value pairs so that a standard propositional (i.e. attribute-value) learning algorithm can be applied. This approach is attractive because of the large number of propositional learning algorithms that have been developed and can thus be applied to the propositionalised data. In this paper, we empirically investigate a variant of an existing propositionalisation method called TLC. TLC uses a single decision tree to obtain propositionalised data. Our variant applies a random forest instead and is motivated by the potential increase in robustness that this may yield. We present results on synthetic and real-world data from the above two application domains showing that it indeed yields increased classification accuracy when applying boosting and support vector machines to classify the propositionalised data.

year: 2013

title: Kaupapa Maori research: An indigenous approach to creating knowledge

authors: Bishop, Russell

subjects: Maori

abstract: This paper seeks to identify how issues of epistemological racism are addressed in practice
within an indigenous Kaupapa (philosophy) Maori approach to research, and how such
considerations may impact on the Western trained and positioned researcher. One fundamental
understanding to a Kaupapa Maori approach to research is that it is the discursive practice
that is Kaupapa Maori that positions researchers in such a way as to operationalise selfdetermination
(agentic positioning and behaviour) for research participants. This is because the
cultural aspirations, understandings and practices of Maori people implement and organise the
research process. Further, the research issues of power; initiation, benefits, representation,
legitimation, and accountability are addressed and understood in practice by practitioners of
Kaupapa Maori research through the development of a participatory mode of consciousness.

text: Kaupapa Maori research: An indigenous approach to creating knowledge This paper seeks to identify how issues of epistemological racism are addressed in practice
within an indigenous Kaupapa (philosophy) Maori approach to research, and how such
considerations may impact on the Western trained and positioned researcher. One fundamental
understanding to a Kaupapa Maori approach to research is that it is the discursive practice
that is Kaupapa Maori that positions researchers in such a way as to operationalise selfdetermination
(agentic positioning and behaviour) for research participants. This is because the
cultural aspirations, understandings and practices of Maori people implement and organise the
research process. Further, the research issues of power; initiation, benefits, representation,
legitimation, and accountability are addressed and understood in practice by practitioners of
Kaupapa Maori research through the development of a participatory mode of consciousness.

year: 1999