4,567 research outputs found
Single-cell time-series analysis of metabolic rhythms in yeast
The yeast metabolic cycle (YMC) is a biological rhythm in budding yeast (Saccharomyces cerevisiae). It entails oscillations in the concentrations and redox states of intracellular metabolites, oscillations in transcript levels, temporal partitioning of biosynthesis, and, in chemostats, oscillations in oxygen consumption. Most studies on the YMC have been based on chemostat experiments, and it is unclear whether YMCs arise from interactions between cells or are generated independently by each cell. This thesis aims at characterising the YMC in single cells and its response to nutrient and genetic perturbations. Specifically, I use microfluidics to trap and separate yeast cells, then record the time-dependent intensity of flavin autofluorescence, which is a component of the YMC.
Single-cell microfluidics produces a large amount of time series data. Noisy and short time series produced from biological experiments restrict the computational tools that are useful for analysis. I developed a method to filter time series, a machine learning model to classify whether time series are oscillatory, and an autocorrelation method to examine the periodicity of time series data.
My experimental results show that yeast cells show oscillations in the fluorescence of flavins. Specifically, I show that in high glucose conditions, cells generate flavin oscillations asynchronously within a population, and these flavin oscillations couple with the cell division cycle. I show that cells can individually reset the phase of their flavin oscillations in response to abrupt nutrient changes, independently of the cell division cycle. I also show that deletion strains generate flavin oscillations that exhibit different behaviour from dissolved oxygen oscillations from chemostat conditions.
Finally, I use flux balance analysis to address whether proteomic constraints in cellular metabolism mean that temporal partitioning of biosynthesis is advantageous for the yeast cell, and whether such partitioning explains the timing of the metabolic cycle. My results show that under proteomic constraints, it is advantageous for the cell to sequentially synthesise biomass components because doing so shortens the timescale of biomass synthesis. However, the degree of advantage of sequential over parallel biosynthesis is lower when both carbon and nitrogen sources are limiting.
This thesis thus confirms autonomous generation of flavin oscillations, and suggests a model in which the YMC responds to nutrient conditions and subsequently entrains the cell division cycle. It also emphasises the possibility that subpopulations in the culture explain chemostat-based observations of the YMC. Furthermore, this thesis paves the way for using computational methods to analyse large datasets of oscillatory time series, which is useful for various fields of study beyond the YMC
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea
ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
Revisiting the capitalization of public transport accessibility into residential land value: an empirical analysis drawing on Open Science
Background: The delivery and effective operation of public transport is fundamental for a for a transition to low-carbon emission transport systems’. However, many cities face budgetary challenges in providing and operating this type of infrastructure. Land value capture (LVC) instruments, aimed at recovering all or part of the land value uplifts triggered by actions other than the landowner, can alleviate some of this pressure. A key element of LVC lies in the increment in land value associated with a particular public action. Urban economic theory supports this idea and considers accessibility to be a core element for determining residential land value. Although the empirical literature assessing the relationship between land value increments and public transport infrastructure is vast, it often assumes homogeneous benefits and, therefore, overlooks relevant elements of accessibility. Advancements in the accessibility concept in the context of Open Science can ease the relaxation of such assumptions.
Methods: This thesis draws on the case of Greater Mexico City between 2009 and 2019. It focuses on the effects of the main public transport network (MPTN) which is organised in seven temporal stages according to its expansion phases. The analysis incorporates location based accessibility measures to employment opportunities in order to assess the benefits of public transport infrastructure. It does so by making extensive use of the open-source software OpenTripPlanner for public transport route modelling (≈ 2.1 billion origin-destination routes). Potential capitalizations are assessed according to the hedonic framework. The property value data includes individual administrative mortgage records collected by the Federal Mortgage Society (≈ 800,000). The hedonic function is estimated using a variety of approaches, i.e. linear models, nonlinear models, multilevel models, and spatial multilevel models. These are estimated by the maximum likelihood and Bayesian methods. The study also examines possible spatial aggregation bias using alternative spatial aggregation schemes according to the modifiable areal unit problem (MAUP) literature.
Results: The accessibility models across the various temporal stages evidence the spatial heterogeneity shaped by the MPTN in combination with land use and the individual perception of residents. This highlights the need to transition from measures that focus on the characteristics of transport infrastructure to comprehensive accessibility measures which reflect such heterogeneity. The estimated hedonic function suggests a robust, positive, and significant relationship between MPTN accessibility and residential land value in all the modelling frameworks in the presence of a variety of controls. The residential land value increases between 3.6% and 5.7% for one additional standard deviation in MPTN accessibility to employment in the final set of models. The total willingness to pay (TWTP) is considerable, ranging from 0.7 to 1.5 times the equivalent of the capital costs of the bus rapid transit Line-7 of the Metrobús system. A sensitivity analysis shows that the hedonic model estimation is sensitive to the MAUP. In addition, the use of a post code zoning scheme produces the closest results compared to the smallest spatial analytical scheme (0.5 km hexagonal grid).
Conclusion: The present thesis advances the discussion on the capitalization of public transport on residential land value by adopting recent contributions from the Open Science framework. Empirically, it fills a knowledge gap given the lack of literature around this topic in this area of study. In terms of policy, the findings support LVC as a mechanism of considerable potential. Regarding fee-based LVC instruments, there are fairness issues in relation to the distribution of charges or exactions to households that could be addressed using location based measures. Furthermore, the approach developed for this analysis serves as valuable guidance for identifying sites with large potential for the implementation of development based instruments, for instance land readjustments or the sale/lease of additional development rights
Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study
Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the method’s value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits
An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains
This research aimed to develop an empirical understanding of the relationships between integration,
dynamic capabilities and performance in the supply chain domain, based on which, two conceptual
frameworks were constructed to advance the field. The core motivation for the research was that, at
the stage of writing the thesis, the combined relationship between the three concepts had not yet
been examined, although their interrelationships have been studied individually.
To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative
study, which was undertaken via multiple case studies to investigate lines of enquiry that would
address the research questions formulated. This is consistent with the author’s philosophical
adoption of the ontology of relativism and the epistemology of constructionism, which was considered
appropriate to address the research questions. Empirical data and evidence were collected, and
various triangulation techniques were employed to ensure their credibility. Some key features of
grounded theory coding techniques were drawn upon for data coding and analysis, generating two
levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in
improving performance, the performance also informed the former. This reflects a cyclical and
iterative approach rather than one purely based on linearity. Adopting a holistic approach towards
the relationship was key in producing complementary strategies that can deliver sustainable supply
chain performance.
The research makes theoretical, methodological and practical contributions to the field of supply
chain management. The theoretical contribution includes the development of two emerging
conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it
allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed
insight into their correlations. The latter gives a holistic view of their relationships and how they are
connected, reflecting a middle-range theory that bridges theory and practice. The methodological
contribution lies in presenting models that address gaps associated with the inconsistent use of
terminologies in philosophical assumptions, and lack of rigor in deploying case study research
methods. In terms of its practical contribution, this research offers insights that practitioners could
adopt to enhance their performance. They can do so without necessarily having to forgo certain
desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
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