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Artificial neural network approaches for modelling complex biological network – Mammalian cell cycle : A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Applied Science at Lincoln University
No full textAn important process in the growth of any biological organism is its ability to proliferate, a tightly controlled process in which a cell divides into two genetically identical daughter cells. This happens within a dynamic environment, where a cell responds to various internal and external signals through a well-ordered sequence of events called the cell cycle. Underlying these events is a complex and elegantly orchestrated web of interactions that function as an integrated system with various sub-systems that specialise in various tasks. Two such important tasks include cell cycle initiation in response to proliferative signals and the interaction of numerous elements for the completion of the cell cycle. This results in a highly complex system. Any malfunctioning during cell cycle division can cause diseases like Cancer. For gaining insights into biological reactions and their effects, cellular modelling approaches have contributed immensely.
A few gaps are recognised in the field after reviewing the literature on mammalian cell cycle modelling. Most models are based on mathematical formulation representing the dynamic behaviour of the cell cycle which includes varied equations ranging from a few to tens of equations. They produce accurate systems dynamics, but the models are complex to solve and require the knowledge of many parameters. On the other hand, Discrete Models are simpler and use a qualitative approach but have numerous limitations to represent the continuous dynamics of the Mammalian Cell cycle. Therefore, there is a need for a modelling approach that is simplified but comprehensively represents the system. Mainly, the representation of a complex system in a robust way is a crucial demand. Our research mainly aims to introduce Artificial Neural Network approaches that mimic the mammalian cell cycle in an intuitive way. The goal is to explore the updated biological knowledge and develop ANN-based mathematical models to check their capabilities for mimicking cell signalling mechanisms
Polar tourism
No full textPolar tourism refers to visits, excluding those for scientific research or support, to the Arctic (typically comprised of the states, water bodies, and islands north of the tree-line) or the Antarctic (often described as the continent itself, ice shelves, water, and islands south of the Antarctic Convergence). The geographic remoteness associated with unique biota, landscapes, and climate forms the appeal of the polar regions
In-stream habitat unit additions: If you build it, will they stay?
No full textRiver restoration in New Zealand is often focussed on riparian planting, hoping water quality improvements will improve overall ecosystem health. These interventions are important, but successful community recovery requires additional steps to improve aquatic habitat. We trialled the addition of simple, instream habitat units as a practicable restoration tool and opportunity to investigate community recovery mechanisms. Habitat units were designed to be simple to construct using sustainable, readily available materials, and optimised to create heterogeneous habitat and refugia for invertebrates. Here, we present the outcome of three trials: (1) a preliminary trial as a proof of concept that habitat addition can facilitate
establishment of drifting colonists; (2) a trial in streams with varying physical characteristics to identify methodological constraints; and (3) a project co developed with local iwi (indigenous people) in NZ, demonstrating the value of indigenous knowledge in
restoration
Improving supply chain resilience against price volatility by long-term contracts and price adjustment formulas – Case study of wood processing industry in New Zealand
No full textShort-term supply agreements and quarterly pricing based on the log export price are dissuading investment into New Zealand's wood processing, perpetuating the commoditisation of the forest industry and concentrating market risk rather than encouraging adding value onshore and market diversification. Supply, demand, cost, and revenue uncertainties reduce business confidence and discourage investments. A long-term contract with price adjustment is not a zero-sum game. The transparent information, calculability, steadiness, and time for adjustments across the supply chains provide synergies and advantages for the strategic allies. To mitigate risks, long-term contracts with price adjustment clauses are frequently implemented in power generation, coal, nuclear, construction, and oil industries. This price adjustment method benefits commodity suppliers and buyers with steady product flow and long payback period capital investments. Beyond cost reduction and productivity, in these lean supply chains, risk mitigation is essential for the entire payback period. Usually, the allies take significant roles in each other's businesses and have a symmetric relationship. Therefore, they want to build long-term partnerships rather than realise one-time gains. That paper introduces the benefits of the above mentioned solution in decreasing risks and improving supply chain resilience and profitability for all partie
The practice of science for technological innovation: Learnings and implications for Te Ara Paerangi
No full textAn effective science system needs to provide expertise and knowledge to respond to societal issues in Aotearoa New Zealand. In 2022 the government released the white paper Te Ara Paerangi: future pathways to outline a vision for a future science system. This research explores how mission-led science has operated through the National Science Challenges, using Science for Technological Innovation as a case study. In the context of Te Ara Paerangi, the research examines the elements of Science for Technological Innovation’s practice and offers implications for future mission-oriented science programmes that will be relevant to government policymakers, universities, Crown Research Institutes and science leaders
Peri-urban landscapes and the potential of integrated foodscapes to promote healthy communities
No full textImagine living in a city that has farms, orchards, market gardens – places where our communities could access local, healthy produce and … get to know the farmer. Aotearoa New Zealand is an agricultural nation producing enough calories to feed 40 million people globally. We also, however, import enough food to feed our national population. As has happened in many countries in the Global North, New Zealand has over the last 100 years actively zoned food production out of our cities, which for nearly 90 per cent of New Zealanders is where we live, purchase and consume food. There is a clear spatial disconnect between where our food is being produced and where the majority of New Zealanders live. Our research, however, has shown that there is a strong desire by urban New Zealanders to reconnect with their food. The peri-urban zone, with its scale and proximity to urban centres offers valuable potential for communities to access local food produced close to where they live. Based on an extensive survey and design critique workshop with Greenfield residents and peri-urban growers/farmers operating within the peri-urban zone of Canterbury, New Zealand, this research has developed a set of spatial land-use typologies specific to the peri-urban zone, and which addresses the question: ‘How can landscapes for both people and production prosper within peri-urban New Zealand through spatial design, reconnecting New Zealanders with the land and with food?’ The outcome of this project is a series of urban design models for the co-existence and mutual benefit of accommodating both people and food production within peri-urban zones – spatial typologies that re-prioritise local food production and local access as a vital part of cities
Porous mineral amendments enhance nitrogen mineralization via improvement of soil aeration and water retention characteristics
No full textRed soils are characterized by a clay texture, resulting in low nitrogen (N) mineralization (Nmin). Amendments provide a means to ameliorate Nmin. To investigate the effects of different types and dosages of amendments on Nmin in red soil, an incubation experiment is necessary to understand the underlying mechanisms. A 15-day batch experiment was carried out with diatomite (Si), porous ceramic (Pc), and zeolite (Zl) applied to a red soil at rates of 0%, 1%, 2%, 5%, and 10% (by weight). According to the results, Zl had superior effects on Nmin and nitrification than Si and Pc. Cumulative mineralized N (C min), cumulative nitrate N content (C nit), Nmin promoting rate (NMPR), and nitrification promoting rate (NPR) reached 24.9 mg kg¯¹, 19.4 mg kg¯¹, 94.8%, and 136.4%, respectively, with Zl at a 10% amendment rate. The NMPR and NPR of Zl increased rapidly under amendment rates > 2%. However, Nmin was inhibited at low Si and Pc dosages. C min and C nit were significantly positive with field water capacity (FWC), wilting point (WP), capillary porosity (CP), and pH, but negative with bulk density (BD) when amended with Si and Zl. Furthermore, WP and BD have been identified as the primary factors influencing Nmin and nitrification. The results indicate that Nmin and nitrification enhancement were not only linked to improved aeration and water retention in soil following amendments but also depended on amendment type; 2–5% Zl improves Nmin and nitrification in clay-textured and acidic red soil, which enhances our understanding and could facilitate N availability evaluation following mineral amendments
Soil organic nitrogen fraction and sequestration in a buried paddy soil since the Neolithic age
No full textPurpose Soil organic nitrogen (SON) biochemistry trends in paddy soils are poorly understood on a long-term scale.
Methods To explore the effect of land use on SON sequestration, SON and amino acid (AA) fractions were investigated in soil profiles comprising recent and buried paddy soil (BPS) and buried non-paddy soils (BNS). Two ancient paddy soils from Chuodun ruin site, China, were distinguished based on colour and rice phytolith abundance. ¹⁴C abundance in soil organic carbon was used to estimate the age of carbonized rice and ancient paddy soil via a liquid scintillation analysis method, dating to 3800–5500 and 960–4000 BC.
Results The proportions of D-AAs and acidic AAs in BPS, up to 6.13% and 7.73%, respectively, were higher than those in modern paddy soils. D-alanine (and the D-/L- ratio), aspartate, and glutamate increased with soil depth in BPS, and the amount of D-aspartate was linearly and significantly positively correlated with soil depth (p < 0.05). Based on phytolith stability and abundance, the N sequestration rate (NSR: residual N content as a proportion of initial N content) was proposed to indicate the residual N content varied with time. The NSR was estimated as 10.8–91.2% in BPS with a phytolith stability factor of 0.5–0.9.
Conclusion These data suggest that intermittent continuous high-intensity rice cultivation could increase soil N sequestration potential over the long term, and that N sequestration is not only associated with AA aging in the organic N fraction, but also with biogeochemical processes in BPS and paddy management. In addition, high-intensity rice cultivation can increase N loss risks, and in turn result in large fluctuations in N sequestration
Artificial Intelligence (AI) and machine learning-driven automation of complex, neurobiological model reduction: A framework based on deep learning, ensemble learning, and sensitivity analysis methodologies : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy in Computer Science at Lincoln University
No full textManaging complexity is a key challenge in systems biology modelling. While detailed models can provide valuable insights into specific biological processes, they can become computationally intensive and challenging to interpret. Conversely, overly simplistic models may lack accuracy and fail to capture essential aspects of the system's behaviour.
Hence, this study focuses on reducing the complexity of the models in systems biology while enhancing accuracy, efficiency, and interpretability. This study uses deep learning (DL), a sub field of machine learning (ML) and artificial intelligence (AI), along with sensitivity analysis using partial rank correlation coefficient (PRCC), and extended Fourier amplitude sensitivity test (eFAST) methods to identify significant system behaviour. DL models can analyse and predict system behaviour using large datasets consisting of biological measurements. They can also handle various omics data and integrate them into comprehensive systems biology models.
Key objectives of the study include reducing the complexity of computational models using DL methods, identifying significant subprocesses to reduce pathway diagrams, improving the interpretability of the reduced models using sensitivity analysis, implementing models with minimum knowledge in the parameter space, introducing a framework to automate the complexity reduction process using sensitivity analysis and DL, improving model performance with ensemble learning and automated hyperparameter tuning, and testing the reproducibility of the reduced models using simulations.
The significance and contribution of the study lie in its automated approach to developing an interpretable meta-model using AI/ML/DL techniques and reducing complexity while maintaining model accuracy. The study tests neurobiology models and uses sensitivity analysis and DL for complexity reduction. The thesis provides reduced pathway diagrams for each of the models used for testing. The study offers a framework to automate the implementation of meta-models and provides insights into input-output relationships and key processes in biological systems.
The methodological approach involves using DL/ML/AI methods, ensemble methods, and dimensionality reduction to reduce complexity in VCell models. The framework reads MATLAB files from VCell, identifies parameters and outputs, performs parameter perturbations and simulations, conducts sensitivity analysis, identifies significant reactions, and trains and validates machine learning models based on the results.
The thesis builds on previous efforts to develop more interpretable and reliable computational models that can provide meaningful insights into complex biological systems
Consumer attitudes and acceptability toward edible New Zealand native plants
No full textThis study aimed to investigate consumers’ perceptions, emotions, and acceptability of selected edible New Zealand native plants. A survey-type methodology was employed, recruiting participants voluntarily through email invitations. A total of N = 100 participants, ranging in age from 18 to 70 and with diverse ethnic backgrounds, were asked to answer questions regarding six specific edible New Zealand species and edible native plants in general. Results showed that participants had varying levels of familiarity with the specific plants, with a majority feeling “calm”, “happy”, and “interested” when presented with them. Factors deemed most important when thinking about the six selected plants included edibility and safety. When considering native plants in general, participants rated factors such as general nutrition, safety, and sustainability as important. The study found that a significant proportion of participants expressed a positive intention to consume native plants in the future, but the levels of interest varied depending on the demographic distribution. The study provides insights into consumer attitudes toward edible native plants and highlights the potential for these food ingredients to be included in mainstream diets