7,131 research outputs found

    Converging organoids and extracellular matrix::New insights into liver cancer biology

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    Single-cell time-series analysis of metabolic rhythms in yeast

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    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

    Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

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    This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

    Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification

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    The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks

    Knowledge Distillation and Continual Learning for Optimized Deep Neural Networks

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    Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations. Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally learning new tasks, the model performance on old tasks significantly drops. The ability to accommodate new knowledge while retaining previously learned knowledge is called continual learning. Since the realworld environments in which the model operates are always evolving, a robust neural network needs to have this continual learning ability for adapting to new changes

    Understanding Personal Determinants of Lifting Strategy to Inform Movement-Focused Ergonomic Interventions

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    Introduction: Lift training interventions are needed to reduce risk in jobs with non-modifiable demands, but to date have been generally ineffective. The lack of lift training effectiveness has been partially attributed to insufficient quality of content in the training programs. One way to improve the effectiveness of future lift training interventions may be to first understand what factors influence how a lifter chooses to move in the workplace (i.e., root causes). Previous research has identified that some lifters seem to consistently minimize resultant biomechanical exposures at the low back, but it is unclear why. If we can understand what personal factors influence how a lifter moves, lift training may be better targeted to address modifiable personal factors to minimize exposures during lifting. The overarching objective of this thesis was to quantify the variability in low back exposures during lifting and to further determine if variability could be explained by personal factors including ability to perceive proprioceptive information, expertise, and a range of structural (i.e., body mass and stature) and functional (i.e., strength and flexibility) factors. With this understanding, I then aimed to identify which modifiable personal factors have the greatest prospective benefit of biasing a lifter to adopt a movement strategy with lower resultant biomechanical exposures using a computational modelling approach. The impetus for this thesis is to develop critical evidence as needed to inform the development of future, more efficacious lift training interventions. Methods: A cross-sectional between-subjects experimental design was used to address the thesis objectives. A sample of 72 participants were recruited to perform a lifting protocol consisting of both job-specific and generic lifting tasks. Purposive sampling was used to recruit participants with a range of experience and demographics. Ability to perceive sensory feedback was assessed using lift force and lift posture matching tests. The average and variability in resultant peak low back compression and A-P shear force, as well as kinematic features of whole-body movement strategy, during lifting were quantified as dependent variables. Consistently lower magnitudes of biomechanical exposures within a personal factor group would support that this group defines a movement objective that aims to minimize resultant exposures on the low back. Using the experimentally obtained data, a probabilistic model was then developed that predicts the range of movement strategies and corresponding biomechanical exposures that are likely given a combination of underlying personal factors. Simulations were run to determine if improvements in any of ability to perceive sensory feedback, expertise, flexibility and/or strength capacity resulted in predicted reductions of low back exposure magnitude. Simulations were also conducted across a range of non-modifiable structural factors (i.e., sex, stature, and body mass) to evaluate whether the prospective benefit of improving modifiable factors to reduce low back exposures is generalizable across a working population. Results: Ability to perceive proprioceptive information (both force- and posture-sense) was associated with lower average and variability of low back loads. This suggests that individuals with better ability to perceive proprioceptive information may be more likely to define a movement objective to consistently minimize exposures. Albeit small effect sizes were observed with a maximum of 16% of variance in low back loads explained by proprioceptive ability. Both structural and functional factors were significant predictors of average peak low back loads in lifting. However, except for females having lower variability in exposures than males, no other associations of personal factors to variability in loads was observed. These findings support that the investigated structural and functional factors can bias the range of available movement strategies to lifters, but don’t necessarily influence towards a movement objective aiming to minimize low back loading. No differences in average or variability in peak low back loads were observed across expertise groups. While this finding highlights that expertise doesn’t seem to influence resultant exposures in lifting, differences in lifting kinematics were observed across groups suggesting other movement objectives may be defined as a function of expertise. The prospective ability of reducing peak low back loads by improving modifiable personal factors was assessed using the developed probabilistic model. While improving proprioceptive ability, functional knee range of motion and strength were statistically associated with reducing low back loads, only improving functional knee range of motion was interpreted to have clinically significant effects on reducing low back loads during lifting. Conclusion: In this thesis the variance in peak low back loads during lifting that could be explained independently and inter-dependently by personal factors was investigated. These findings have implications for the development of future lift training interventions where improvements to functional knee range of motion may lead to retained lifting behaviour changes to reduce resultant peak low back loads during lifting. Secondary benefits may also come from improving proprioceptive ability and strength. Future lift training interventions can be developed to leverage these findings in practice where these results support that improvements to underlying flexibility, strength and proprioceptive ability seem to be important factors allowing individuals to adopt lower exposure lifting strategy

    Anwendungen maschinellen Lernens fĂŒr datengetriebene PrĂ€vention auf Populationsebene

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    Healthcare costs are systematically rising, and current therapy-focused healthcare systems are not sustainable in the long run. While disease prevention is a viable instrument for reducing costs and suffering, it requires risk modeling to stratify populations, identify high- risk individuals and enable personalized interventions. In current clinical practice, however, systematic risk stratification is limited: on the one hand, for the vast majority of endpoints, no risk models exist. On the other hand, available models focus on predicting a single disease at a time, rendering predictor collection burdensome. At the same time, the den- sity of individual patient data is constantly increasing. Especially complex data modalities, such as -omics measurements or images, may contain systemic information on future health trajectories relevant for multiple endpoints simultaneously. However, to date, this data is inaccessible for risk modeling as no dedicated methods exist to extract clinically relevant information. This study built on recent advances in machine learning to investigate the ap- plicability of four distinct data modalities not yet leveraged for risk modeling in primary prevention. For each data modality, a neural network-based survival model was developed to extract predictive information, scrutinize performance gains over commonly collected covariates, and pinpoint potential clinical utility. Notably, the developed methodology was able to integrate polygenic risk scores for cardiovascular prevention, outperforming existing approaches and identifying benefiting subpopulations. Investigating NMR metabolomics, the developed methodology allowed the prediction of future disease onset for many common diseases at once, indicating potential applicability as a drop-in replacement for commonly collected covariates. Extending the methodology to phenome-wide risk modeling, elec- tronic health records were found to be a general source of predictive information with high systemic relevance for thousands of endpoints. Assessing retinal fundus photographs, the developed methodology identified diseases where retinal information most impacted health trajectories. In summary, the results demonstrate the capability of neural survival models to integrate complex data modalities for multi-disease risk modeling in primary prevention and illustrate the tremendous potential of machine learning models to disrupt medical practice toward data-driven prevention at population scale.Die Kosten im Gesundheitswesen steigen systematisch und derzeitige therapieorientierte Gesundheitssysteme sind nicht nachhaltig. Angesichts vieler verhinderbarer Krankheiten stellt die PrĂ€vention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung fĂŒr ein prĂ€ventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und Maßnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da fĂŒr die meisten Krankheiten keine Risikomodelle existieren und sich verfĂŒg- bare Modelle auf einzelne Krankheiten beschrĂ€nken. Weil fĂŒr deren Berechnung jeweils spezielle Sets an PrĂ€diktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe DatenmodalitĂ€ten, wie Bilder oder -omics- Messungen, systemische Informationen ĂŒber zukĂŒnftige GesundheitsverlĂ€ufe, mit poten- tieller Relevanz fĂŒr viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch fĂŒr die Risikomod- ellierung unzugĂ€nglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier DatenmodalitĂ€ten in der PrimĂ€rprĂ€ven- tion zu untersuchen: polygene Risikoscores fĂŒr die kardiovaskulĂ€re PrĂ€vention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- itĂ€t wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenĂŒber ĂŒblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der DatenmodalitĂ€t zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores fĂŒr die kardiovaskulĂ€re PrĂ€vention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen ĂŒber den zukĂŒnftigen Ausbruch von Krankheiten. Unter Einsatz einer phĂ€nomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prĂ€dik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert fĂŒr deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, prĂ€ventionsori- entierten Medizin zu verĂ€ndern

    Perceptions and Knowledge of Information Security Policy Compliance in Organizational Personnel

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    All internet connected organizations are becoming increasingly vulnerable to cyberattacks due to information security policy noncompliance of personnel. The problem is important to information technology (IT) firms, organizations with IT integration, and any consumer who has shared personal information online, because noncompliance is the single greatest threat to cybersecurity, which leads to expensive breaches that put private information in danger. Grounded in the protection motivation theory, the purpose of this quantitative study was to use multiple regression analysis to examine the relationship between perceived importance, organizational compliance, management involvement, seeking guidance, and rate of cybersecurity attack. The research question for this study was focused on the relationship between perceived importance of cybersecurity, senior management involvement, use of organizational ISPC, seeking of information or guidance on cybersecurity, and organizational security breach incidence. Data was collected from the United Kingdom’s 2021 Cyber Security Breaches Survey. Multiple linear regression analysis yielded that the four independent variables were not predictive of instances of cybersecurity breach or attack. The implications for positive social change include the potential to actively promote and publicly address cybersecurity as personal privacy increasing becomes a matter of public safety. One key recommendation is for IT leaders to pursue methodologically rigorous and uniform operationalization throughout IT research and practice, including the pursuit of replicable data of detailed resolution. The results of this study may potentially be used to reduce the risks for cybersecurity breaches, which ultimately contributes to social change by furthering the right of privacy and the protection of personal information

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
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