title: Self-supervised Feature Extractor Training for Alzheimer’s Disease Classification

authors: Zheng, Chen

abstract: Deep learning achieves encouraging performance in natural image classification. It has huge potential for detecting neural degenerative anomalies, but this is often limited by the availability of well-segmented neuroimages and computational
resources. One way to address this problem is to apply self-supervised learning methods that utilise unsegmented neuroimages and artificial labels. Another solution is developing surrogate tasks to learn the representations of neuroimages for classification. This thesis reports the investigations of different variants of self-supervised learning and pretext tasks to train feature extractors for downstream Alzheimer’s Disease classification.
Firstly, this thesis reviews the literature regarding Alzheimer’s Disease classification and possible data leakage issues. Then, a lightweight 3D CNN-based ensemble is trained to predict brain age using the 3D MRI data of cognitively normal subjects from the OASIS-3 dataset. The extracted features are evaluated in the binary classification of CN vs. AD patients from their brain MRI scans. This approach achieved competitive performance compared with state-of-the-art methods in the literature. 
The next part of this thesis developed four different self-supervised learning pretext tasks for feature extractor training: brain age prediction, brain sMRI reconstruction, brain sMRI rotation classification, as well as a combination of all three approaches into one single multi-task predictor. To further explore the feasibility of employing synthetic neuroimaging data in the self-supervised learning setting, the proposed approaches are trained on the LDM100K dataset followed by evaluation using real-world OASIS and ADNI datasets. The real-world data training and testing leads to the best classification performance. The random cropping data augmentation technique can improve feature extractor training on 3D MRI data. Due to high computational expense and time limitations, the results of the training using synthetic data are not as satisfactory as those using real-world data. Future research is needed to develop more advanced feature extractor architectures and more complex pretext tasks that can learn more discriminative features. Another area of research to improve training efficiency would involve developing specialised software and hardware for processing 3D neuroimaging data.

text: Self-supervised Feature Extractor Training for Alzheimer’s Disease Classification Deep learning achieves encouraging performance in natural image classification. It has huge potential for detecting neural degenerative anomalies, but this is often limited by the availability of well-segmented neuroimages and computational
resources. One way to address this problem is to apply self-supervised learning methods that utilise unsegmented neuroimages and artificial labels. Another solution is developing surrogate tasks to learn the representations of neuroimages for classification. This thesis reports the investigations of different variants of self-supervised learning and pretext tasks to train feature extractors for downstream Alzheimer’s Disease classification.
Firstly, this thesis reviews the literature regarding Alzheimer’s Disease classification and possible data leakage issues. Then, a lightweight 3D CNN-based ensemble is trained to predict brain age using the 3D MRI data of cognitively normal subjects from the OASIS-3 dataset. The extracted features are evaluated in the binary classification of CN vs. AD patients from their brain MRI scans. This approach achieved competitive performance compared with state-of-the-art methods in the literature. 
The next part of this thesis developed four different self-supervised learning pretext tasks for feature extractor training: brain age prediction, brain sMRI reconstruction, brain sMRI rotation classification, as well as a combination of all three approaches into one single multi-task predictor. To further explore the feasibility of employing synthetic neuroimaging data in the self-supervised learning setting, the proposed approaches are trained on the LDM100K dataset followed by evaluation using real-world OASIS and ADNI datasets. The real-world data training and testing leads to the best classification performance. The random cropping data augmentation technique can improve feature extractor training on 3D MRI data. Due to high computational expense and time limitations, the results of the training using synthetic data are not as satisfactory as those using real-world data. Future research is needed to develop more advanced feature extractor architectures and more complex pretext tasks that can learn more discriminative features. Another area of research to improve training efficiency would involve developing specialised software and hardware for processing 3D neuroimaging data.

year: 2024

title: Agrobiodiversity Enhancement for the sustainability of the tropical uplands:  An evaluation of agricultural land use  in Liliw, Laguna, Philippines

authors: Wagan, Amparo M.

subjects: Agrobiodiversity Enhancement ABDE

abstract: Agrobiodiversity Enhancement (ABDE) is a strategy that has been advanced for preventing environmental degradation without losing agricultural productivity. However, there is not yet sufficient evidence to support the important role that ABDE might have for managing agricultural land use in the tropical uplands. This research is an attempt to help fill this knowledge gap. The general aim of the thesis was to explore the potential of ABDE as a management alternative for agricultural land use in the uplands in terms of environmental protection, productivity and farmer acceptability. To achieve the aim, a methodological framework for evaluating agricultural land use in the uplands was developed. The methodology aimed at allowing one to understand the influence of agricultural land use on natural resources and farm productivity and the social factors that will most likely influence land users to enhance agrobiodiversity of their production. The methodological framework provided a minimum set of criteria and indicators that can be used for assessing agricultural land uses in the uplands. Main criteria for the evaluation included protection, productivity, viability security and acceptability. The following indicators were included: Shannon Diversity Index, Depth of Topsoil, Soil Organic Matter, Soil Nitrate, Crop Yields, Net Income, Trend in Income and Harvest Loss, Farmer Traits, Farm Characteristics and Farmer perceptions on the influence of farming on the health of natural resources and of the farm workers. The methodological framework also includes a range of methods and techniques for gathering environmental, economic and social data in the uplands and indicates circumstances under which each might best be utilized. 

Using the methodological framework, agricultural land use in an upland area in Liliw, Laguna Philippines was evaluated for protection of natural resources, specifically of the soil quality and for farm productivity and for the social factors that influence the way agricultural lands are managed. Results showed integration of horticultural trees and crops have potential for protecting thickness of the topsoil, reducing nutrient wastage and is more economically profitable than monocropping systems. Specifically, coconut exhibited importance in maintaining thickness of topsoil while lanzones played major role in augmenting farm income and as buffer to income losses from annual crops. Older farmers and women were found associated with agricultural land use with diversified production in the case study area. The Logit model analysis further showed that leadership quality and land ownership are the social factors that will likely influence integration of horticultural trees and crops in the uplands. Other personal characteristics like years of experience in farming, access to other sources of livelihood, land size, awareness of land degradation and effect of agrochemicals on heath of the farm workers have no likely influence on agricultural lands with integration of horticultural trees and crops. SWOT analysis of agricultural land use in the case study site showed that despite the environmental and economic advantages of agricultural lands with diversified production, there are weaknesses and barriers to its further development and implementation. From this assessment, implications for developing and implementing an ABDE intervention program for the tropical uplands were drawn.

text: Agrobiodiversity Enhancement for the sustainability of the tropical uplands:  An evaluation of agricultural land use  in Liliw, Laguna, Philippines Agrobiodiversity Enhancement (ABDE) is a strategy that has been advanced for preventing environmental degradation without losing agricultural productivity. However, there is not yet sufficient evidence to support the important role that ABDE might have for managing agricultural land use in the tropical uplands. This research is an attempt to help fill this knowledge gap. The general aim of the thesis was to explore the potential of ABDE as a management alternative for agricultural land use in the uplands in terms of environmental protection, productivity and farmer acceptability. To achieve the aim, a methodological framework for evaluating agricultural land use in the uplands was developed. The methodology aimed at allowing one to understand the influence of agricultural land use on natural resources and farm productivity and the social factors that will most likely influence land users to enhance agrobiodiversity of their production. The methodological framework provided a minimum set of criteria and indicators that can be used for assessing agricultural land uses in the uplands. Main criteria for the evaluation included protection, productivity, viability security and acceptability. The following indicators were included: Shannon Diversity Index, Depth of Topsoil, Soil Organic Matter, Soil Nitrate, Crop Yields, Net Income, Trend in Income and Harvest Loss, Farmer Traits, Farm Characteristics and Farmer perceptions on the influence of farming on the health of natural resources and of the farm workers. The methodological framework also includes a range of methods and techniques for gathering environmental, economic and social data in the uplands and indicates circumstances under which each might best be utilized. 

Using the methodological framework, agricultural land use in an upland area in Liliw, Laguna Philippines was evaluated for protection of natural resources, specifically of the soil quality and for farm productivity and for the social factors that influence the way agricultural lands are managed. Results showed integration of horticultural trees and crops have potential for protecting thickness of the topsoil, reducing nutrient wastage and is more economically profitable than monocropping systems. Specifically, coconut exhibited importance in maintaining thickness of topsoil while lanzones played major role in augmenting farm income and as buffer to income losses from annual crops. Older farmers and women were found associated with agricultural land use with diversified production in the case study area. The Logit model analysis further showed that leadership quality and land ownership are the social factors that will likely influence integration of horticultural trees and crops in the uplands. Other personal characteristics like years of experience in farming, access to other sources of livelihood, land size, awareness of land degradation and effect of agrochemicals on heath of the farm workers have no likely influence on agricultural lands with integration of horticultural trees and crops. SWOT analysis of agricultural land use in the case study site showed that despite the environmental and economic advantages of agricultural lands with diversified production, there are weaknesses and barriers to its further development and implementation. From this assessment, implications for developing and implementing an ABDE intervention program for the tropical uplands were drawn.

year: 2008

title: Coastal communities, leisure and wellbeing: Advancing a trans-disciplinary agenda for understanding ocean-human relationships in Aotearoa New Zealand

authors: Wheaton, Belinda

subjects: Science & Technology

abstract: Commentators are advocating for research to better understand relationships between healthy coastal ecosystems and human wellbeing. Doing so requires inter- and transdisciplinary approaches across humanities, arts, social sciences, and science and technology disciplines. These approaches include culturally diverse knowledge systems, such as indigenous ones, that locate sustainable use of and relationships to marine ecosystems. This paper contributes to this agenda through a case-study of relationships between coastal ecosystems and human wellbeing in Aotearoa New Zealand. This article highlights interconnected cultural and wellbeing benefits of, and socio-ecological relationships between, these coastal ecosystems drawing on a case study of one ocean-based, ‘immersive’ leisure activity, surfing. Further, it examines how these relationships impact human physical, emotional and spiritual wellbeing, and the wellbeing of communities and ecosystems. The research illustrates that surfing creates strong bonds between practitioners and coastal places, linking the health of marine environments and people. We demonstrate the value of a transdisciplinary place-based approach that integrates research across the humanities and social sciences and engages with Indigenous knowledge (Mātauranga Māori). This argument for multicultural co-learning shows the value of Western and Māori vantage points for how we understand coastal blue spaces. Indigenous perspectives, we conclude, deepen appreciation, as well as equity considerations, of how we understand place, wellbeing, and long-term sustainable relationships with marine ecosystems.

text: Coastal communities, leisure and wellbeing: Advancing a trans-disciplinary agenda for understanding ocean-human relationships in Aotearoa New Zealand Commentators are advocating for research to better understand relationships between healthy coastal ecosystems and human wellbeing. Doing so requires inter- and transdisciplinary approaches across humanities, arts, social sciences, and science and technology disciplines. These approaches include culturally diverse knowledge systems, such as indigenous ones, that locate sustainable use of and relationships to marine ecosystems. This paper contributes to this agenda through a case-study of relationships between coastal ecosystems and human wellbeing in Aotearoa New Zealand. This article highlights interconnected cultural and wellbeing benefits of, and socio-ecological relationships between, these coastal ecosystems drawing on a case study of one ocean-based, ‘immersive’ leisure activity, surfing. Further, it examines how these relationships impact human physical, emotional and spiritual wellbeing, and the wellbeing of communities and ecosystems. The research illustrates that surfing creates strong bonds between practitioners and coastal places, linking the health of marine environments and people. We demonstrate the value of a transdisciplinary place-based approach that integrates research across the humanities and social sciences and engages with Indigenous knowledge (Mātauranga Māori). This argument for multicultural co-learning shows the value of Western and Māori vantage points for how we understand coastal blue spaces. Indigenous perspectives, we conclude, deepen appreciation, as well as equity considerations, of how we understand place, wellbeing, and long-term sustainable relationships with marine ecosystems.

year: 2021