title: Survey of Scytonema (Cyanobacteria) and associated saxitoxins in the littoral zone of recreational lakes in Canterbury, New Zealand

authors: Smith, Francine M. J.

subjects: 16S rRNA

abstract: The recent identification of saxitoxin-producing Scytonema cf. crispum triggered a survey of metaphyton and periphyton for Scytonema spp. in 34 high-use recreational lakes across Canterbury, New Zealand. Scytonema was observed in 10 of the lakes surveyed. Three morphospecies were identified: Scytonema cf. crispum, Scytonema cf. chiastum and Scytonema cf.fritschii. Environmental samples containing Scytonema were analysed for saxitoxins using the Jellett rapid test for paralytic shellfish poisoning, and saxitoxin variants were identified in positive samples using high-performance liquid chromatography with fluorescence detection (HPLC-FD). Cultures were established from selected sites and their phylogeny investigated using partial 16S rRNA gene sequences. These cultures were also screened for a region of sxtA, a gene involved in saxitoxin production. Cultures containing the sxtA gene were analysed for saxitoxins with HPLC-FD. Saxitoxins were only identified in cultures of S. cf. crispum and environmental samples containing this species. HPLC-FD analysis of these environmental samples and cultures identified saxitoxin and the variants gonyautoxins (GTX1-5), neosaxitoxin, decarbamoyl saxitoxin and decarbamoyl gonyautoxins (dcGTX2/3). This was the first report of these saxitoxin variants from cyanobacteria in New Zealand. All cultures of S. cf. crispum contained the sxtA gene segment. The partial 16S rRNA gene sequence of Scytonema C. Agardh ex Bornet & Flahault cultures were compared with cyanobacterial sequences from GenBank, only S. cf.fritschii clustered amongst other Scytonema species. The identification of metaphytic saxitoxin-producing S. cf. crispum highlighted a new freshwater habitat where toxic cyanobacteria may need to be monitored.

text: Survey of Scytonema (Cyanobacteria) and associated saxitoxins in the littoral zone of recreational lakes in Canterbury, New Zealand The recent identification of saxitoxin-producing Scytonema cf. crispum triggered a survey of metaphyton and periphyton for Scytonema spp. in 34 high-use recreational lakes across Canterbury, New Zealand. Scytonema was observed in 10 of the lakes surveyed. Three morphospecies were identified: Scytonema cf. crispum, Scytonema cf. chiastum and Scytonema cf.fritschii. Environmental samples containing Scytonema were analysed for saxitoxins using the Jellett rapid test for paralytic shellfish poisoning, and saxitoxin variants were identified in positive samples using high-performance liquid chromatography with fluorescence detection (HPLC-FD). Cultures were established from selected sites and their phylogeny investigated using partial 16S rRNA gene sequences. These cultures were also screened for a region of sxtA, a gene involved in saxitoxin production. Cultures containing the sxtA gene were analysed for saxitoxins with HPLC-FD. Saxitoxins were only identified in cultures of S. cf. crispum and environmental samples containing this species. HPLC-FD analysis of these environmental samples and cultures identified saxitoxin and the variants gonyautoxins (GTX1-5), neosaxitoxin, decarbamoyl saxitoxin and decarbamoyl gonyautoxins (dcGTX2/3). This was the first report of these saxitoxin variants from cyanobacteria in New Zealand. All cultures of S. cf. crispum contained the sxtA gene segment. The partial 16S rRNA gene sequence of Scytonema C. Agardh ex Bornet & Flahault cultures were compared with cyanobacterial sequences from GenBank, only S. cf.fritschii clustered amongst other Scytonema species. The identification of metaphytic saxitoxin-producing S. cf. crispum highlighted a new freshwater habitat where toxic cyanobacteria may need to be monitored.

year: 2012

title: Interim guidance on the use of new sea-level rise projections

authors: Bell, Robert G.

subjects: sea-level rise

abstract: The recently updated future projections from the Intergovernmental Panel on Climate Change (IPCC) in 2021–22 confirm that sea-level rise is accelerating. The updated projections show an increase in projected sea-level rise compared with the earlier projections in the 2017 Ministry for the Environment (the Ministry) Coastal hazards and climate change guidance. New localised projections are also now available, which for the first time explicitly include the effect of local upward or downward movement of land locally on sea-level rise.

text: Interim guidance on the use of new sea-level rise projections The recently updated future projections from the Intergovernmental Panel on Climate Change (IPCC) in 2021–22 confirm that sea-level rise is accelerating. The updated projections show an increase in projected sea-level rise compared with the earlier projections in the 2017 Ministry for the Environment (the Ministry) Coastal hazards and climate change guidance. New localised projections are also now available, which for the first time explicitly include the effect of local upward or downward movement of land locally on sea-level rise.

year: 2022

title: Machine translation for te reo Māori

authors: Keegan, Te Taka Adrian Gregory

abstract: What is Machine Translation? Machine Translation (MT) is a process where computer software is used to translate texts from one natural (or spoken) language to another. Early research centred on two distinct approaches: Rules Based Machine Translation (RBMT) and Statistical Machine Translation (SMT). In simple terms RBMT makes use of large sets of linguistic rules that define languages whereas SMT uses statistical techniques to build language models from large language corpora. Increases in computing power and the amount of language corpora available has meant that SMT had become the preferred option with recent advances in neural networks also being applied to improve the accuracy of SMT. For commercial reasons, this is an area of research that has generated a lot of interest and funding support from some major international computer companies, including Google¹,Microsoft² and Facebook³.
Why is Machine Translation important for te reo Māori? A te reo Māori purist may argue that it is not important to focus activities on having a machine undertake translations for te reo Māori; if people want to understand te reo Māori then they should put in the effort to learn the language. This line of reasoning is difficult to argue with. But, from a Māori language activist perspective the value of MT is not so much in the translation of te reo Māori to (say) English, but rather the translation of English to te reo Māori. If this translation can be done efficiently, with low costs, it will assist in the proliferation of te reo Māori into new contexts, new environments and will assist its normalisation in New Zealand's society.
At this time, two of the major international companies, Google and Microsoft, have invested significantly in MT for te reo Māori. This paper summarises their endeavours and reports on the quality of translations they have been able to generate.

text: Machine translation for te reo Māori What is Machine Translation? Machine Translation (MT) is a process where computer software is used to translate texts from one natural (or spoken) language to another. Early research centred on two distinct approaches: Rules Based Machine Translation (RBMT) and Statistical Machine Translation (SMT). In simple terms RBMT makes use of large sets of linguistic rules that define languages whereas SMT uses statistical techniques to build language models from large language corpora. Increases in computing power and the amount of language corpora available has meant that SMT had become the preferred option with recent advances in neural networks also being applied to improve the accuracy of SMT. For commercial reasons, this is an area of research that has generated a lot of interest and funding support from some major international computer companies, including Google¹,Microsoft² and Facebook³.
Why is Machine Translation important for te reo Māori? A te reo Māori purist may argue that it is not important to focus activities on having a machine undertake translations for te reo Māori; if people want to understand te reo Māori then they should put in the effort to learn the language. This line of reasoning is difficult to argue with. But, from a Māori language activist perspective the value of MT is not so much in the translation of te reo Māori to (say) English, but rather the translation of English to te reo Māori. If this translation can be done efficiently, with low costs, it will assist in the proliferation of te reo Māori into new contexts, new environments and will assist its normalisation in New Zealand's society.
At this time, two of the major international companies, Google and Microsoft, have invested significantly in MT for te reo Māori. This paper summarises their endeavours and reports on the quality of translations they have been able to generate.

year: 2017