title: Expression, purification and characterisation of GIGANTEA: A circadian clock-controlled regulator of photoperiodic flowering in plants

authors: Black, Moyra M.

subjects: flowering

abstract: The Arabidopsis thaliana (Arabidopsis) GIGANTEA (GI) gene is a central component of the photoperiodic flowering pathway. While it has been 40 years since the first mutant alleles of GI were described much is still unknown about the molecular mechanism of GI action. To investigate the biochemistry and domain organisation (and ultimately to give a greater understanding of the role of GI in floral induction), it is first necessary to produce significant quantities of purified protein. Soluble affinity-tagged full-length GI was expressed in Escherichia coli (E. coli) and was stabilised by the addition of the detergent n-dodecyl-β-d-maltoside (DDM) to storage and purification buffers. Stabilised GI was purified using a variety of chromatographic methods, and characterised using a selection of biochemical techniques including circular dichroism, and dynamic light scattering. This showed that purified GI contained secondary structure, but was polydisperse in solution. Electron microscopy suggests a possible tetramer arrangement of GI. Limited proteolytic digests and mass spectrometry were used to identify potential GI domains. This led to the identification of a predicted 46 kDa amino-terminal GI domain. GI was also expressed in Sf9 insect cells using the baculovirus expression system. GI produced via this route gave insoluble protein.

text: Expression, purification and characterisation of GIGANTEA: A circadian clock-controlled regulator of photoperiodic flowering in plants The Arabidopsis thaliana (Arabidopsis) GIGANTEA (GI) gene is a central component of the photoperiodic flowering pathway. While it has been 40 years since the first mutant alleles of GI were described much is still unknown about the molecular mechanism of GI action. To investigate the biochemistry and domain organisation (and ultimately to give a greater understanding of the role of GI in floral induction), it is first necessary to produce significant quantities of purified protein. Soluble affinity-tagged full-length GI was expressed in Escherichia coli (E. coli) and was stabilised by the addition of the detergent n-dodecyl-β-d-maltoside (DDM) to storage and purification buffers. Stabilised GI was purified using a variety of chromatographic methods, and characterised using a selection of biochemical techniques including circular dichroism, and dynamic light scattering. This showed that purified GI contained secondary structure, but was polydisperse in solution. Electron microscopy suggests a possible tetramer arrangement of GI. Limited proteolytic digests and mass spectrometry were used to identify potential GI domains. This led to the identification of a predicted 46 kDa amino-terminal GI domain. GI was also expressed in Sf9 insect cells using the baculovirus expression system. GI produced via this route gave insoluble protein.

year: 2010

title: Super-resolution of satellite imagery

authors: Bull, Daniel

subjects: Super-resolution

abstract: Can deep neural networks super-resolve satellite imagery to a high perceptual quality? This thesis explores the juxtaposition between the pixel accuracy and perceptual qualities of super-resolved imagery by comparing and combining a discriminative and a generative network. Rather than solving a theoretical problem, we tackle a real-world low-resolution scenario: Sentinel-2 imagery is super-resolved and evaluated against high-resolution aerial photos as ground truth; this is in contrast to super-resolving previously down-sampled data, which is the methodology used in most other studies. An existing feed-forward network architecture designed for super-resolution, called DeepSUM, is used to super-resolve multiple low-resolution images by a factor of four to obtain a single high-resolution image. DeepSUM is trained using a range of loss functions, to assess the e ect on network accuracy. A novel loss function is created, called variation loss, to help better define edges and textures to create a sharper, perceptually better product. Using an SSIM loss function gives the best result in terms of pixel-based performance. Running DeepSUM alone creates a superior output compared to bicubically up-sampling the input data, but the output is blurry and not photo-realistic. A probabilistic model from the literature, ESRGAN (Enhanced SRGAN), a Generative Adversarial Network, is trained against both raw Sentinel-2 data and the output of DeepSUM. Using ERS-GAN for super-resolution, creates a perceptually better, more realistic looking output. However, the ESRGAN output is less accurate than the DeepSUM output, as measured using pixel-based metrics. Combining ESRGAN with DeepSUM is found to inherit some of the advantages of both approaches. In an end-to-end process, using ESRGAN with the output of DeepSUM trained using variation loss is found to super-resolve an image to better show boundaries, textures and detail.

text: Super-resolution of satellite imagery Can deep neural networks super-resolve satellite imagery to a high perceptual quality? This thesis explores the juxtaposition between the pixel accuracy and perceptual qualities of super-resolved imagery by comparing and combining a discriminative and a generative network. Rather than solving a theoretical problem, we tackle a real-world low-resolution scenario: Sentinel-2 imagery is super-resolved and evaluated against high-resolution aerial photos as ground truth; this is in contrast to super-resolving previously down-sampled data, which is the methodology used in most other studies. An existing feed-forward network architecture designed for super-resolution, called DeepSUM, is used to super-resolve multiple low-resolution images by a factor of four to obtain a single high-resolution image. DeepSUM is trained using a range of loss functions, to assess the e ect on network accuracy. A novel loss function is created, called variation loss, to help better define edges and textures to create a sharper, perceptually better product. Using an SSIM loss function gives the best result in terms of pixel-based performance. Running DeepSUM alone creates a superior output compared to bicubically up-sampling the input data, but the output is blurry and not photo-realistic. A probabilistic model from the literature, ESRGAN (Enhanced SRGAN), a Generative Adversarial Network, is trained against both raw Sentinel-2 data and the output of DeepSUM. Using ERS-GAN for super-resolution, creates a perceptually better, more realistic looking output. However, the ESRGAN output is less accurate than the DeepSUM output, as measured using pixel-based metrics. Combining ESRGAN with DeepSUM is found to inherit some of the advantages of both approaches. In an end-to-end process, using ESRGAN with the output of DeepSUM trained using variation loss is found to super-resolve an image to better show boundaries, textures and detail.

year: 2021

title: Kaumtua Mana Motuhake: A study protocol for a peer education intervention to help Māori elders work through later-stage life transitions

authors: Oetzel, John G.

subjects: Science & Technology

abstract: Background
The Aotearoa/New Zealand population is ageing and numerous studies demonstrate with this phenomenon comes increases in non-communicable diseases, injuries and healthcare costs among other issues. Further, significant inequities exist between Māori (Indigenous peoples of Aotearoa/New Zealand) and non-Māori around poor ageing and health. Most research addressing these issues is deficit oriented; however, the current research project takes a strengths-based approach that highlights the potential of kaumātua (elders) by asserting mana motuhake (autonomy, identity and self-actualisation). We believe that the esteem of elders in Māori culture signals transformative potential. Specifically, this project utilises a ‘tuakana-teina’ (older sibling/younger sibling) peer-educator model, where kaumātua work with other kaumātua in relation to health and wellbeing. The objectives of the project are (a) to develop the capacity of kaumātua as peer educators, whilst having positive impacts on their sense of purpose, health and wellbeing; and (b) to enhance the social and health outcomes for kaumātua receiving the intervention.

Methods
The research is grounded in principles of Kaupapa Māori and community-based participatory research, and brings together an Indigenous community of kaumātua, community health researchers, and academic researchers working with two advisory boards. The project intervention involves an orientation programme for tuakana peer educators for other kaumātua (teina). The research design is a pre- and post-test, clustered staggered design. All participants will complete a baseline assessment of health and wellbeing consistent with Māori worldviews (i.e., holistic model). The tuakana and teina participants will be divided into two groups with the first group completing the intervention during the first half of the project and the second group during the second half of the project. All participants will complete post-test assessments following both interventions allowing comparison of the two groups along with repeated measures over time.

Discussion
The findings will provide an evidence base for the importance and relevancy of kaumātua knowledge to create contextually based and culturally safe age-friendly environments that facilitate engagement and participation by kaumātua for kaumātua. If the model is effective, we will seek to facilitate the dissemination and scalability of the intervention.

Trial registration
Australia New Zealand Clinical Trial Registry (ACTRN12617001396314); Date Registered: 3 October 2017 (retrospectively registered).

text: Kaumtua Mana Motuhake: A study protocol for a peer education intervention to help Māori elders work through later-stage life transitions Background
The Aotearoa/New Zealand population is ageing and numerous studies demonstrate with this phenomenon comes increases in non-communicable diseases, injuries and healthcare costs among other issues. Further, significant inequities exist between Māori (Indigenous peoples of Aotearoa/New Zealand) and non-Māori around poor ageing and health. Most research addressing these issues is deficit oriented; however, the current research project takes a strengths-based approach that highlights the potential of kaumātua (elders) by asserting mana motuhake (autonomy, identity and self-actualisation). We believe that the esteem of elders in Māori culture signals transformative potential. Specifically, this project utilises a ‘tuakana-teina’ (older sibling/younger sibling) peer-educator model, where kaumātua work with other kaumātua in relation to health and wellbeing. The objectives of the project are (a) to develop the capacity of kaumātua as peer educators, whilst having positive impacts on their sense of purpose, health and wellbeing; and (b) to enhance the social and health outcomes for kaumātua receiving the intervention.

Methods
The research is grounded in principles of Kaupapa Māori and community-based participatory research, and brings together an Indigenous community of kaumātua, community health researchers, and academic researchers working with two advisory boards. The project intervention involves an orientation programme for tuakana peer educators for other kaumātua (teina). The research design is a pre- and post-test, clustered staggered design. All participants will complete a baseline assessment of health and wellbeing consistent with Māori worldviews (i.e., holistic model). The tuakana and teina participants will be divided into two groups with the first group completing the intervention during the first half of the project and the second group during the second half of the project. All participants will complete post-test assessments following both interventions allowing comparison of the two groups along with repeated measures over time.

Discussion
The findings will provide an evidence base for the importance and relevancy of kaumātua knowledge to create contextually based and culturally safe age-friendly environments that facilitate engagement and participation by kaumātua for kaumātua. If the model is effective, we will seek to facilitate the dissemination and scalability of the intervention.

Trial registration
Australia New Zealand Clinical Trial Registry (ACTRN12617001396314); Date Registered: 3 October 2017 (retrospectively registered).

year: 2019