title: Vitamin D and innate immunity in pneumonia and COPD

authors: Simpson, Talia Mary

subjects: Vitamin D

abstract: A resurgence of interest in vitamin D research has led to the discovery that it plays a role in an unexpectedly large number of biological processes, and that reduced levels of this hormone are implicated in a range of diseases. In fact, it is estimated that vitamin D is involved in the regulation of 3% of the human genome. Two genes containing the target sequence indicative of vitamin D regulation are those encoding LL-37 and hBD-2. These antimicrobial peptides are integral components of the innate immune system and act as natural antibiotics to help combat infection.
The respiratory epithelium exposes a large surface area to environmental pathogens, making the innate immune response extremely important in its defence. Microbial infection of the respiratory tract is the cause of pneumonia, and is implicated in cases of COPD exacerbation. This study aimed to determine whether a relationship existed between vitamin D, LL-37 and hBD-2 in 185 patients admitted to Waikato hospital with either condition. It was hypothesised that low vitamin D would correlate with reduced peptide levels, and that this would be associated with increased infection severity and higher mortality rates.
Peptide concentrations in patient plasma were measured by indirect ELISA and compared to 25D levels. Statistical analysis revealed no significant associations between vitamin D status, peptide levels and severity, but did show increased mortality in individuals with severe vitamin D deficiency or low LL-37.
Based on the significance of LL-37 as a predictor of mortality (particularly in COPD), development of a plasma screening method using MALDI-TOF mass spectrometry was attempted, as a potential means of identifying patients most at risk. The success of this method was limited however, as the low abundance and small size of the mature peptide caused detection problems.
A protocol for assessing the vitamin D binding protein (DBP) genotype was developed, as it influences baseline 25D levels and response to supplementation. The association between low vitamin D and mortality suggests that supplementation could improve survival rates and, as the supplement dose required for effectiveness is genotype-dependent, this method could allow determination of the appropriate amount to administer to at-risk individuals.

text: Vitamin D and innate immunity in pneumonia and COPD A resurgence of interest in vitamin D research has led to the discovery that it plays a role in an unexpectedly large number of biological processes, and that reduced levels of this hormone are implicated in a range of diseases. In fact, it is estimated that vitamin D is involved in the regulation of 3% of the human genome. Two genes containing the target sequence indicative of vitamin D regulation are those encoding LL-37 and hBD-2. These antimicrobial peptides are integral components of the innate immune system and act as natural antibiotics to help combat infection.
The respiratory epithelium exposes a large surface area to environmental pathogens, making the innate immune response extremely important in its defence. Microbial infection of the respiratory tract is the cause of pneumonia, and is implicated in cases of COPD exacerbation. This study aimed to determine whether a relationship existed between vitamin D, LL-37 and hBD-2 in 185 patients admitted to Waikato hospital with either condition. It was hypothesised that low vitamin D would correlate with reduced peptide levels, and that this would be associated with increased infection severity and higher mortality rates.
Peptide concentrations in patient plasma were measured by indirect ELISA and compared to 25D levels. Statistical analysis revealed no significant associations between vitamin D status, peptide levels and severity, but did show increased mortality in individuals with severe vitamin D deficiency or low LL-37.
Based on the significance of LL-37 as a predictor of mortality (particularly in COPD), development of a plasma screening method using MALDI-TOF mass spectrometry was attempted, as a potential means of identifying patients most at risk. The success of this method was limited however, as the low abundance and small size of the mature peptide caused detection problems.
A protocol for assessing the vitamin D binding protein (DBP) genotype was developed, as it influences baseline 25D levels and response to supplementation. The association between low vitamin D and mortality suggests that supplementation could improve survival rates and, as the supplement dose required for effectiveness is genotype-dependent, this method could allow determination of the appropriate amount to administer to at-risk individuals.

year: 2010

title: Evaluation of New Zealand estuarine water properties using remote sensing

authors: Shao, Zhanchao

subjects: esutaries

abstract: Estuaries and shallow lagoons are the most productive marine systems in the world and contribute to the maintenance of coastal biodiversity by providing various unique habitats for aquatic species. However, due to the inputs of nutrients, sediments and pollutants from the surrounding landscape, light availability and primary productivity can be strongly restricted, which may trigger rapid ecological change over time and cause substantial loss of coastal flora and fauna. This thesis explores the use of Sentinel-2 remote sensing imagery to monitor water properties of shallow intertidal tidal estuaries, for the purposes of managing the estuaries of New Zealand. 
The first goal (Chapter 2) was to develop a method to remove the influence of water bottom substrate reflectance in order to detect the true water colour (represented by dominant wavelength) and diffuse attenuation coefficients from Sentinel-2 imagery, in the case study estuary of Tauranga Harbour. The new methodology used direct measurement of bottom reflectance of intertidal areas while exposed, and used a regression estimator to derive subtidal bottom reflectance from particle size (developed using the intertidal properties). The method required the water depth to be known (from LiDAR) and reflectance observations from multiple water depths (either extracted along transects or at the same location at different tides). The method was applied to all available Sentinel-2 images and showed seasonal fluctuations and strong correlations with chlorophyll-a, suspended sediments and coloured metal ions collected by the regional council monitoring programme. This methodology was then applied to 12 estuaries and the results were interpreted in terms of ecological changes (Chapter 5).
The second goal (Chapter 3) was to detect the distribution or density of seagrass/sandflats (where microphytobenthos (MPB) thrive) and use this as a basis to estimate their gross primary productivity (GPP). The new methodology combined Sentinel-2 imagery, machine learning and literature-derived photosynthesis-irradiance (P – I) curves. The machine-learning model included (1) supervised classification with random forest to delineate seagrass and sandflat areas (2) and three machine learning regressions (artificial neural network (ANN), support vector machine (SVM) and random forest regression (RFR)) to predict the density of seagrass. The result showed ANN was the optimal algorithm to predict seagrass coverage. By adjusting the input water depth and light intensity, the methodology could be further developed to predict the response of seagrass and MPB to sea level rise.  To counteract the negative impact of increased turbidity caused by coastal erosion and sediment resuspension due to sea level rise and climate change, controlling sediment loading in coastal waters could be an effective solution for maintaining current productivity throughout the entire harbour.
Considering monitoring suspended sediment concentration (SSC) is essential for understanding the resilience of coastal wetlands, a prediction model in Chapter 4 consisting of satellite imagery, numerical simulation and machine learning was developed to enable continuous estimation of SSC. The prediction model included two steps: (1) comparing the Delft3D-derived SSC with corrected satellite data and using K-means classification to categorise the differences into classes; (2) developing a random forest regression model for each class to predict the satellite-derived SSC using Delft3D-derived SSC and other physical parameters. Comparison of the prediction model with in situ measurement showed high accuracy, and the model provided the basis for estimating the accumulated sediment in wetlands. The results showed strong sensitivity to different ways of accounting for incoming SSC supply coastal wetlands. Therefore, employing a model based on real-time observations, like the one developed here, would substantially improve sediment budget estimates in coastal wetlands.
Dominant wavelength and diffuse attenuation coefficients (Kd) can form the basis of ecologically relevant indicators. Therefore, in Chapter 5, classification based on these two indicators was developed to cluster New Zealand estuaries with similar states into groups as a basis for management. The dominant wavelength and Kd were derived from the Sentinel-2 images using the seabed correction model developed in Chapter 2. Three groups, including less impacted, moderately impacted and highly impacted, were created which were in broad agreement with other in situ measurements and indicator models such as those focused on light availability and benthic health. Therefore, satellite-derived dominant wavelength and Kd can be two good indicators to reflect estuarine water properties for management.

text: Evaluation of New Zealand estuarine water properties using remote sensing Estuaries and shallow lagoons are the most productive marine systems in the world and contribute to the maintenance of coastal biodiversity by providing various unique habitats for aquatic species. However, due to the inputs of nutrients, sediments and pollutants from the surrounding landscape, light availability and primary productivity can be strongly restricted, which may trigger rapid ecological change over time and cause substantial loss of coastal flora and fauna. This thesis explores the use of Sentinel-2 remote sensing imagery to monitor water properties of shallow intertidal tidal estuaries, for the purposes of managing the estuaries of New Zealand. 
The first goal (Chapter 2) was to develop a method to remove the influence of water bottom substrate reflectance in order to detect the true water colour (represented by dominant wavelength) and diffuse attenuation coefficients from Sentinel-2 imagery, in the case study estuary of Tauranga Harbour. The new methodology used direct measurement of bottom reflectance of intertidal areas while exposed, and used a regression estimator to derive subtidal bottom reflectance from particle size (developed using the intertidal properties). The method required the water depth to be known (from LiDAR) and reflectance observations from multiple water depths (either extracted along transects or at the same location at different tides). The method was applied to all available Sentinel-2 images and showed seasonal fluctuations and strong correlations with chlorophyll-a, suspended sediments and coloured metal ions collected by the regional council monitoring programme. This methodology was then applied to 12 estuaries and the results were interpreted in terms of ecological changes (Chapter 5).
The second goal (Chapter 3) was to detect the distribution or density of seagrass/sandflats (where microphytobenthos (MPB) thrive) and use this as a basis to estimate their gross primary productivity (GPP). The new methodology combined Sentinel-2 imagery, machine learning and literature-derived photosynthesis-irradiance (P – I) curves. The machine-learning model included (1) supervised classification with random forest to delineate seagrass and sandflat areas (2) and three machine learning regressions (artificial neural network (ANN), support vector machine (SVM) and random forest regression (RFR)) to predict the density of seagrass. The result showed ANN was the optimal algorithm to predict seagrass coverage. By adjusting the input water depth and light intensity, the methodology could be further developed to predict the response of seagrass and MPB to sea level rise.  To counteract the negative impact of increased turbidity caused by coastal erosion and sediment resuspension due to sea level rise and climate change, controlling sediment loading in coastal waters could be an effective solution for maintaining current productivity throughout the entire harbour.
Considering monitoring suspended sediment concentration (SSC) is essential for understanding the resilience of coastal wetlands, a prediction model in Chapter 4 consisting of satellite imagery, numerical simulation and machine learning was developed to enable continuous estimation of SSC. The prediction model included two steps: (1) comparing the Delft3D-derived SSC with corrected satellite data and using K-means classification to categorise the differences into classes; (2) developing a random forest regression model for each class to predict the satellite-derived SSC using Delft3D-derived SSC and other physical parameters. Comparison of the prediction model with in situ measurement showed high accuracy, and the model provided the basis for estimating the accumulated sediment in wetlands. The results showed strong sensitivity to different ways of accounting for incoming SSC supply coastal wetlands. Therefore, employing a model based on real-time observations, like the one developed here, would substantially improve sediment budget estimates in coastal wetlands.
Dominant wavelength and diffuse attenuation coefficients (Kd) can form the basis of ecologically relevant indicators. Therefore, in Chapter 5, classification based on these two indicators was developed to cluster New Zealand estuaries with similar states into groups as a basis for management. The dominant wavelength and Kd were derived from the Sentinel-2 images using the seabed correction model developed in Chapter 2. Three groups, including less impacted, moderately impacted and highly impacted, were created which were in broad agreement with other in situ measurements and indicator models such as those focused on light availability and benthic health. Therefore, satellite-derived dominant wavelength and Kd can be two good indicators to reflect estuarine water properties for management.

year: 2024-07

title: E kore e ngaro, he takere waka nui: Te mātauranga whakatere waka me ōna take nunui

authors: Tuaupiki, Jackie Whetumarama

abstract: E kapi ana te hautoru o te ao i te Moana nui a Kiwa. I ngā tau manomano ka huri, ka puea ake i Āhia ki te tonga mā rāwhiti, he iwi māia, he iwi mātau ki te hanga me te whakatere i ō rātou waka. Ka tupu ngātahi hoki i a rātou tētehi mātauranga whakatere i tarawhitia e rātou ngā tōpito katoa o te Moana nui a Kiwa. Koinei ngā tāngata tuatahi, nāna i kau ngā ara terenga waka onamata. Nō ngā tau maha i muri iho, ka whanake tēnei mātauranga me tō rātou hangarau waka, ā, ka tīmata ngā terenga whakawhiti roa e tau haeretia ana ngā moutere o Poronīhia, tatū mai ki Aotearoa. Engari ia, i roto i te wā, i tino waikauere ngā mahi whakatere waka, ā, i ētehi moutere o Poronīhia, i wairuhi kau, he mahara noa i mau i te kōrero tuku iho. Ko tātou tērā, te iwi Māori.

Otirā, i roto i ngā tau e whā tekau ka pahemo atu rā, kua whakawhāiti mai tētehi ohu ki te whakahaumanu i ngā mahi whakatere waka me tōna mātauranga. He waka hourua kua hangaia i Poronīhia, i te ātārangi tawhito hei amo i ngā whakamanawatanga o ngā iwi me ngā kaupapa whakarauora i ēnei mahi tūpuna. Ka tika te oha ki te hunga hāpai kura i takere nui ai, i haumi tūporo ai ki te whakahaumanu i te mātauranga whakatere waka. Nō reira, o ngā mahi rangatira kua tutuki i te rāngai waka i Aotearoa i ngā tau tata nei, ko tā tēnei rangahau, he kohura i ētehi o ngā take nunui me ngā kaupapa whakahirahira e puta mai ana i mua i te aroaro o te mātauranga whakatere waka me ōna kaumoana i ēnei rangi, ki tā te Māori titiro. Waihoki, ko te pānga o aua take nunui ki te ahurea Māori. Koinei te matū o tēnei tuhinga kairangi. He pūtoi whakaaro, he whakaemieminga kōrero ki te whakatakoto i ngā huatau o ngā puhi kai moana Māori e tākawe ana i tēnei wānanga.

Mātua rā, e kore e tukua kia riro anō ngā mahi whakatere waka i te nenewhatanga o te rā, tau ki te rua. Kua whakatupuria e te wā he kura kaumoana Māori, māna ngā take matua o tēnei tuhinga roa e whakatutuki ki tua i te pae kai ao.

“E kore e ngaro, he takere waka nui”

text: E kore e ngaro, he takere waka nui: Te mātauranga whakatere waka me ōna take nunui E kapi ana te hautoru o te ao i te Moana nui a Kiwa. I ngā tau manomano ka huri, ka puea ake i Āhia ki te tonga mā rāwhiti, he iwi māia, he iwi mātau ki te hanga me te whakatere i ō rātou waka. Ka tupu ngātahi hoki i a rātou tētehi mātauranga whakatere i tarawhitia e rātou ngā tōpito katoa o te Moana nui a Kiwa. Koinei ngā tāngata tuatahi, nāna i kau ngā ara terenga waka onamata. Nō ngā tau maha i muri iho, ka whanake tēnei mātauranga me tō rātou hangarau waka, ā, ka tīmata ngā terenga whakawhiti roa e tau haeretia ana ngā moutere o Poronīhia, tatū mai ki Aotearoa. Engari ia, i roto i te wā, i tino waikauere ngā mahi whakatere waka, ā, i ētehi moutere o Poronīhia, i wairuhi kau, he mahara noa i mau i te kōrero tuku iho. Ko tātou tērā, te iwi Māori.

Otirā, i roto i ngā tau e whā tekau ka pahemo atu rā, kua whakawhāiti mai tētehi ohu ki te whakahaumanu i ngā mahi whakatere waka me tōna mātauranga. He waka hourua kua hangaia i Poronīhia, i te ātārangi tawhito hei amo i ngā whakamanawatanga o ngā iwi me ngā kaupapa whakarauora i ēnei mahi tūpuna. Ka tika te oha ki te hunga hāpai kura i takere nui ai, i haumi tūporo ai ki te whakahaumanu i te mātauranga whakatere waka. Nō reira, o ngā mahi rangatira kua tutuki i te rāngai waka i Aotearoa i ngā tau tata nei, ko tā tēnei rangahau, he kohura i ētehi o ngā take nunui me ngā kaupapa whakahirahira e puta mai ana i mua i te aroaro o te mātauranga whakatere waka me ōna kaumoana i ēnei rangi, ki tā te Māori titiro. Waihoki, ko te pānga o aua take nunui ki te ahurea Māori. Koinei te matū o tēnei tuhinga kairangi. He pūtoi whakaaro, he whakaemieminga kōrero ki te whakatakoto i ngā huatau o ngā puhi kai moana Māori e tākawe ana i tēnei wānanga.

Mātua rā, e kore e tukua kia riro anō ngā mahi whakatere waka i te nenewhatanga o te rā, tau ki te rua. Kua whakatupuria e te wā he kura kaumoana Māori, māna ngā take matua o tēnei tuhinga roa e whakatutuki ki tua i te pae kai ao.

“E kore e ngaro, he takere waka nui”

year: 2017