7,726 research outputs found

    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

    Non-Intrusive Disaggregation of Advanced Metering Infrastructure Signals for Demand-Side Management

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    As intermittent renewable energy generation resources become more prevalent, innovative ways to manage the electric grid are sought. In the past, much of the grid balancing effort has been focused on the supply side or on demand-side management of large commercial or industrial electricity customers. Today, with the increase in enabling technologies such as Internet-connected appliances, home energy management systems, and advanced metering infrastructure (AMI) smart meters, residential demand-side management is also a possibility. For a utility to assess the potential capacity of residential demand-side flexibility, power data from controllable appliances from a large sample of houses is required. These data may be collected by installing time- and cost-intensive monitoring equipment at every site, or, alternatively, by disaggregating the signals communicated to the utility by AMI meters. In this study, non-intrusive load monitoring algorithms are used to disaggregate low-resolution real power signals from AMI smart meters. Disaggregation results using both supervised and unsupervised versions of a graph signal processing (GSP) -based algorithm are presented. The effects of varying key parameters in each GSP algorithm, including scaling factor, sequence, and classifier threshold are also presented, and limitations of the algorithm based on energy use patterns are discussed. FM values greater than 0.8 were achieved for the electric resistance water heater and electric vehicle charger using the unsupervised GSP algorithm. The disaggregated signals are then used to develop energy forecasting models for predicting the load of controllable appliances over a given demand response period. ARIMA, SVR, and LSTM forecasting methods were evaluated and compared to a baseline model developed using the mean hourly power draw values. The minimum MAAPE was achieved for the water heater, with an approximate range of 10 < MAAPE < 20. The total energy flexibility of each appliance and the associated uncertainty of the combined disaggregation and forecast are characterized to assess the feasibility of this approach for demand-side management applications. The framework presented in this study may be used to characterize the ability of signals to be disaggregated from a larger dataset of AMI data, based on the whole-house signal characteristics. This analysis can aid grid managers in assessing the viability of selected devices, such as the water heater, for demand response activities.Ph.D

    Mathematical Problems in Rock Mechanics and Rock Engineering

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    With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

    A Generalized Look at Federated Learning: Survey and Perspectives

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    Federated learning (FL) refers to a distributed machine learning framework involving learning from several decentralized edge clients without sharing local dataset. This distributed strategy prevents data leakage and enables on-device training as it updates the global model based on the local model updates. Despite offering several advantages, including data privacy and scalability, FL poses challenges such as statistical and system heterogeneity of data in federated networks, communication bottlenecks, privacy and security issues. This survey contains a systematic summarization of previous work, studies, and experiments on FL and presents a list of possibilities for FL across a range of applications and use cases. Other than that, various challenges of implementing FL and promising directions revolving around the corresponding challenges are provided.Comment: 9 pages, 2 figure

    Predicting the outcome of heart failure against chronic-ischemic heart disease in elderly population – Machine learning approach based on logistic regression, case to Villa Scassi hospital Genoa, Italy

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    Totally 167 patients were admitted at cardiology ward in Villa Scassi hospital, Genoa, Italy. We worked with two control groups: heart failure 59 patients (mean age: 71.37 ± 13.27 years) and chronic-ischemic heart disease 108 patients (mean age: 68.85 ± 11.3 years). Nine parameters: Hb, Serum Creatinine, LDL, HDL, Triglycerides, ALT, AST, hs-cTnI, CRP were evaluated onset to hospitalization. We aimed to identify significant independent predictors relative to the outcome of heart failure versus chronic-ischemic heart disease and select combination of biochemical parameters in logistic regression-based model that would provide on average excellent discrimination to the outcome of heart failure versus chronic-ischemic heart disease in elderly population

    DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record

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    Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant. Healthcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable and reliable models that integrate with clinical workflow. Such governance frameworks require an accompanying technical framework to deploy models in a resource efficient manner. Here we present DEPLOYR, a technical framework for enabling real-time deployment and monitoring of researcher created clinical ML models into a widely used electronic medical record (EMR) system. We discuss core functionality and design decisions, including mechanisms to trigger inference based on actions within EMR software, modules that collect real-time data to make inferences, mechanisms that close-the-loop by displaying inferences back to end-users within their workflow, monitoring modules that track performance of deployed models over time, silent deployment capabilities, and mechanisms to prospectively evaluate a deployed model's impact. We demonstrate the use of DEPLOYR by silently deploying and prospectively evaluating twelve ML models triggered by clinician button-clicks in Stanford Health Care's production instance of Epic. Our study highlights the need and feasibility for such silent deployment, because prospectively measured performance varies from retrospective estimates. By describing DEPLOYR, we aim to inform ML deployment best practices and help bridge the model implementation gap

    Mortality Prediction of Various Cancer Patients via Relevant Feature Analysis and Machine Learning

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    Breast, lung, prostate, and stomach cancers are the most frequent cancer types globally. Early-stage detection and diagnosis of these cancers pose a challenge in the literature. When dealing with cancer patients, physicians must select among various treatment methods that have a risk factor. Since the risks of treatment may outweigh the benefits, treatment schedule is critical in clinical decision making. Manually deciding which medications and treatments are going to be successful takes a lot of expertise and can be hard. In this paper, we offer a computational solution to predict the mortality of various types of cancer patients. The solution is based on the analysis of diagnosis, medication, and treatment parameters that can be easily acquired from electronic healthcare systems. A classification-based approach introduced to predict the mortality outcome of cancer patients. Several classifiers evaluated on the Medical Information Mart in Intensive Care IV (MIMIC-IV) dataset. Diagnosis, medication, and treatment features extracted for breast, lung, prostate, and stomach cancer patients and relevant feature selection done with Logistic Regression. Best F1 scores were 0.74 for breast, 0.73 for lung, 0.82 for prostate, and 0.79 for stomach cancer. Best AUROC scores were 0.94 for breast, 0.91 for lung, 0.96 for prostate, and 0.88 for stomach cancer. In addition, using relevant features, results were very similar to the baseline for each cancer type. Using less features and a robust machine-learning model, the proposed approach can be easily implemented in hospitals when there are limited data and resources available.publishedVersionPeer reviewe

    Anwendungen maschinellen Lernens für datengetriebene Prävention 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 Prävention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung für ein präventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und Maßnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da für die meisten Krankheiten keine Risikomodelle existieren und sich verfüg- bare Modelle auf einzelne Krankheiten beschränken. Weil für deren Berechnung jeweils spezielle Sets an Prädiktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe Datenmodalitäten, wie Bilder oder -omics- Messungen, systemische Informationen über zukünftige Gesundheitsverläufe, mit poten- tieller Relevanz für viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch für die Risikomod- ellierung unzugänglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier Datenmodalitäten in der Primärpräven- tion zu untersuchen: polygene Risikoscores für die kardiovaskuläre Prävention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- ität wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenüber üblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der Datenmodalität zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores für die kardiovaskuläre Prävention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen über den zukünftigen Ausbruch von Krankheiten. Unter Einsatz einer phänomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prädik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert für deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, präventionsori- entierten Medizin zu verändern
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