Metastability and dynamics of stem cells: from direct observations to inference at the single cell level

Organismal development, homeostasis, and pathology are rooted in inherently probabilistic events. From gene expression to cellular differentiation, rates and likelihoods shape the form and function of biology. Processes ranging from growth to cancer homeostasis to reprogramming of stem cells all require transitions between distinct phenotypic states, and these occur at defined rates. Therefore, measuring the fidelity and dynamics with which such transitions occur is central to understanding natural biological phenomena and is critical for therapeutic interventions.

While these processes may produce robust population-level behaviors, decisions are made by individual cells. In certain circumstances, these minuscule computing units effectively roll dice to determine their fate. And while the 'omics' era has provided vast amounts of data on what these populations are doing en masse, the behaviors of the underlying units of these processes get washed out in averages.

Therefore, in order to understand the behavior of a sample of cells, it is critical to reveal how its underlying components, or mixture of cells in distinct states, each contribute to the overall phenotype. As such, we must first define what states exist in the population, determine what controls the stability of these states, and measure in high dimensionality the dynamics with which these cells transition between states.

To address a specific example of this general problem, we investigate the heterogeneity and dynamics of mouse embryonic stem cells (mESCs). While a number of reports have identified particular genes in ES cells that switch between 'high' and 'low' metastable expression states in culture, it remains unclear how levels of many of these regulators combine to form states in transcriptional space. Using a method called single molecule mRNA fluorescent in situ hybridization (smFISH), we quantitatively measure and fit distributions of core pluripotency regulators in single cells, identifying a wide range of variabilities between genes, but each explained by a simple model of bursty transcription. From this data, we also observed that strongly bimodal genes appear to be co-expressed, effectively limiting the occupancy of transcriptional space to two primary states across genes studied here. However, these states also appear punctuated by the conditional expression of the most highly variable genes, potentially defining smaller substates of pluripotency.

Having defined the transcriptional states, we next asked what might control their stability or persistence. Surprisingly, we found that DNA methylation, a mark normally associated with irreversible developmental progression, was itself differentially regulated between these two primary states. Furthermore, both acute or chronic inhibition of DNA methyltransferase activity led to reduced heterogeneity among the population, suggesting that metastability can be modulated by this strong epigenetic mark.

Finally, because understanding the dynamics of state transitions is fundamental to a variety of biological problems, we sought to develop a high-throughput method for the identification of cellular trajectories without the need for cell-line engineering. We achieved this by combining cell-lineage information gathered from time-lapse microscopy with endpoint smFISH for measurements of final expression states. Applying a simple mathematical framework to these lineage-tree associated expression states enables the inference of dynamic transitions. We apply our novel approach in order to infer temporal sequences of events, quantitative switching rates, and network topology among a set of ESC states.

Taken together, we identify distinct expression states in ES cells, gain fundamental insight into how a strong epigenetic modifier enforces the stability of these states, and develop and apply a new method for the identification of cellular trajectories using scalable in situ readouts of cellular state.

Integration of a Sedimentation Module to a Hydrologic Model and its Application to a Mercury TMDL Analysis

This research is part of continued efforts to correlate the hydrology of East Fork Poplar Creek (EFPC) and Bear Creek (BC) with the long term distribution of mercury within the overland, subsurface, and river sub-domains. The main objective of this study was to add a sedimentation module (ECO Lab) capable of simulating the reactive transport mercury exchange mechanisms within sediments and porewater throughout the watershed. The enhanced model was then applied to a Total Maximum Daily Load (TMDL) mercury analysis for EFPC. That application used historical precipitation, groundwater levels, river discharges, and mercury concentrations data that were retrieved from government databases and input to the model. The model was executed to reduce computational time, predict flow discharges, total mercury concentration, flow duration and mercury mass rate curves at key monitoring stations under various hydrological and environmental conditions and scenarios. The computational results provided insight on the relationship between discharges and mercury mass rate curves at various stations throughout EFPC, which is important to best understand and support the management mercury contamination and remediation efforts within EFPC.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.

Improved inference in the evaluation of mutual fund performance using bootstrap methods

Two new methodologies are introduced to improve inference in the evaluation of mutual fund performance against benchmarks. First, the benchmark models are estimated using panel methods with both fund and time effects. Second, the non-normality of individual mutual fund returns is accounted for by using panel bootstrap methods. We also augment the standard benchmark factors with fund-specific characteristics, such as fund size. Using a dataset of UK equity mutual fund returns, we find that fund size has a negative effect on the average fund manager’s benchmark-adjusted performance. Further, when we allow for time effects and the non-normality of fund returns, we find that there is no evidence that even the best performing fund managers can significantly out-perform the augmented benchmarks after fund management charges are taken into account.

Preference inference based on Pareto models

In this paper, we consider Preference Inference based on a generalised form of Pareto order. Preference Inference aims at reasoning over an incomplete specification of user preferences. We focus on two problems. The Preference Deduction Problem (PDP) asks if another preference statement can be deduced (with certainty) from a set of given preference statements. The Preference Consistency Problem (PCP) asks if a set of given preference statements is consistent, i.e., the statements are not contradicting each other. Here, preference statements are direct comparisons between alternatives (strict and non-strict). It is assumed that a set of evaluation functions is known by which all alternatives can be rated. We consider Pareto models which induce order relations on the set of alternatives in a Pareto manner, i.e., one alternative is preferred to another only if it is preferred on every component of the model. We describe characterisations for deduction and consistency based on an analysis of the set of evaluation functions, and present algorithmic solutions and complexity results for PDP and PCP, based on Pareto models in general and for a special case. Furthermore, a comparison shows that the inference based on Pareto models is less cautious than some other types of well-known preference model.

Analyzing the Effect of Soiling on the Performance of a Photovoltaic System of Different Module Technologies in Kalkbult, South Africa

The fact that most of the large scale solar PV plants are built in arid and semi-arid areas where land availability and solar radiation is high, it is expected the performance of the PV plants in such locations will be affected significantly due to high cell temperature as well as due to soiling. Therefore, it is essential to study how the different PV module technologies will perform in such geographical locations to ensure a consistent and reliable power delivery over the lifetime of the PV power plants. As soiling is strongly dependent on the climatic conditions of a particular location a test station, consisted of about 24 PV modules and a well-equipped weather station, was built within the fences of Scatec’s 75 MW Kalkbult solar PV plant in South Africa. This study was performed to a better understand the effect of soiling by comparing the relative power generation by the cleaned modules to the un-cleaned modules. Such knowledge can enable more quantitative evaluations of the cleaning strategies that are going to be implemented in bigger solar PV power plants. The data collected and recorded from the test station has been analyzed at IFE, Norway using a MatLab script written for this thesis project. This thesis work has been done at IFE, Norway in collaboration with Stellenbosch University in South Africa and Scatec Solar a Norwegian independent power producer company. Generally for the polycrystalline modules it is found that the average temperature corrected efficiency during the period of the experiment has been 15.00±0.08 % and for the thin film-CdTe with ARC is 11.52% and for the thin film without ARC is about 11.13% with standard uncertainty of ±0.01 %. Besides, by comparing the initial relative average efficiency of the polycrystalline-Si modules when all the modules have been cleaned for the first time and the final relative efficiency; after the last cleaning schedule which is when all the reference modules E, F, G, and H have been cleaned for the last time it is found that poly3 performs 2 % and 3 % better than poly1 and poly16 respectively, poly13 performs 1 % better than poly15 as well as poly5 and poly12 performs 1 % and 2 % better than poly10 respectively. Besides, poly5 and poly12 performs a 9 % and 11 % better than poly7. Furthermore, there is no change in performance between poly6 and poly9 as well as poly4 and poly15. However, the increase in performance of poly3 to poly1, poly13 to poly15 as well as poly5 and poly12 to poly10 is insignificant. In addition, it is found that TF22 perform 7% better than the reference un-cleaned module TF24 and similarly; TF21 performs 7% higher than TF23. Furthermore, modules with ARC glass (TF17, TF18, TF19, and TF20) shows that cleaning the modules with only distilled water (TF19) or dry-cleaned after cleaned with distilled water(TF20) decreases the performance of the modules by 5 % and 4 % comparing to its respective reference uncleanedmodules TF17 and TF18 respectively.

L'Intégration d'un Module d'Étude Internet Dans la Classe de Comptabilité: Perceptions des Élèves et les Résultats Académiques

Implemented in the context of Business Administration students enrolled in a college level three year technology program, this research investigated students’ perceptions and academic results concurrent with the implementation of an online web module designed to facilitate student self-study. The students involved in this research were enrolled in a program that, while offering a broad education in business disciplines, specialized in the field of accounting. As a result, students were enrolled in academically rigorous accounting courses in each of the six semesters of the program. The weighting of these accounting courses imposes a significant self-study component – typically matching or exceeding the time spent in class. In this context many of the students enrolled in the Business Administration Program have faced difficulties completing the self-study component of the course effectively as demonstrated in low homework completion rates, low homework grade averages and ultimately low success rates in the courses. In an attempt to address this situation this research studied the implementation of a web-based self-study module. Through this module students could access a number of learning tools that were designed to facilitate the self-study process under the premise that more effective self-study learning tools will help remove obstacles and provide more timely confirmation of learning during student self-study efforts. This research collected data from a single cohort of students drawn from the first three sequential accounting courses of the Business Administration Program. The web-based self-study module was implemented in the third of the three sequential accounting courses. The first two of these courses implemented a traditional manual self-study environment. Data collected from the three accounting courses included homework completion rates, homework, exam and final grades for the respective courses. In addition the web-study module allowed the automatic reporting of student usage of a number of specific online learning tools. To complement the academic data, students were surveyed to gain insight into their perceptions of the effectiveness of the web-based system. The research provided a number of interesting insights. First among these was a confirmation of the importance of the self-study process in the academic achievement of the learners. Regardless of the self-study environment, manual or web-enhanced, a significant positive correlation existed between the students’ self-study results, demonstrated in both homework completion rates and homework averages and the corresponding final grades. These results confirm the importance of self-study found generally in the prevailing academic literature regarding students enrolled in higher education. In addition, the web-enhanced learning environment implemented during the third accounting course coincided with significantly higher homework completion rates and corresponding homework averages: homework completion rates in particular increased from a combined average of 63% in the first two accounting courses to 93% in the web-enhanced context of the third accounting course. Moreover, the homework completion rates of the web-enhanced course were evenly distributed across the cohort of students. A quartile-based analysis was subsequently completed. Quartiles were constructed by ranking the students according to their combined average homework completion rates from the first two manual self-study courses, Accounting I and II. The quartile-based homework completion rates for the manual self-study courses Accounting I and II were subsequently compared to the results these same quartiles of students achieved in the web-based self-study within Accounting III. While the first two courses demonstrated significantly uneven homework completion rates across the quartiles ranging from 31% to 91% homework completion rates, the differences among the four quartiles within the web-enhanced module, with an average homework completion rate of 93%, were statistically insignificant. Congruent with the positive academic results observed in the third, web-enhanced course, through the corresponding survey, students expressed a strong attitude in favor of the online self-study environment. This research was designed to add to the existing research that studies the implementation of learning in an online setting. Specifically, the research was designed to explore a middle ground of online learning – a web-enhanced course – a context that supplements the classroom experience rather than replacing it. The web-enhanced accounting course demonstrated impressive favorable results, both academically and in terms of students' perception of the system; these results suggest that a web-enhanced environment can provide learning tools that facilitate the self-study process while providing a structured learning environment that can help developing learners reach their potential.

Using an evidence-based online module to improve parents’ ability to manage their child with Developmental Coordination Disorder

Background : Developmental coordination disorder (DCD) is a prevalent neurodevelopmental disorder. Best practices include raising parents’ awareness and building capacity but few interventions incorporating these best practices are documented. Objective : To examine whether an evidence-based online module can increase the perceived knowledge and skills of parents of children with DCD, and lead to behavioural changes when managing their child’s health condition. Methods : A mixed-methods, before-after-follow-up design guided by the theory of planned behaviour was employed. Data about the knowledge, skills and behaviours of parents of children with DCD were collected using questionnaires prior to completing the module, immediately after, and three months later. One-way repeated measures ANOVAs and thematic analyses were performed on data as appropriate. Results : Fifty-eight participants completed all questionnaires. There was a significant effect of time on self-reported knowledge [F(2.00,114.00)=16.37, p=0.00] and skills [F(1.81,103.03)=51.37, p=0.00] with higher post- and follow-up scores than pre-intervention scores. Thirty-seven (65%) participants reported an intention to change behaviour postintervention; 29 (50%) participants had tried recommended strategies at follow-up. Three themes emerged to describe parents’ behavioural change: sharing information, trialing strategies and changing attitudes. Factors influencing parents’ ability to implement these behavioural changes included clear recommendations, time, and ‘right’ attitude. Perceived outcomes associated with the parental behavioural changes involved improvement in well-being for the children at school, at home, and for the family as a whole. Conclusions : The online module increased parents’ self-reported knowledge and skills in DCD management. Future research should explore its impacts on children’s outcomes long-term.

Integrating a web-enhanced study module into the accounting classroom: student perceptions and academic results.

Student engagement in a course is an important precursor of academic success. Within the discipline of accounting, successful completion of the self-study component of the course is a critical aspect of student engagement and success. Web-enhanced learning offers an apportunity to provide a structured learning environment with improved access to learning tools and immediate feedback that can improve completion rates of self-study activities. This study evaluated student perceptions and academic results relating to the implementation of a web-enhanced study module in an introductory accounting course in Business Administration department at John Abbott College. The results of this study indicate both a strongly favourable student perception of the web-enhanced study module as well as improved homework completion rates and academic results, particularly among students that had previously performed poorly within a tradional, non web-enhanced seelf study environment.||Résumé : L'engagement des élèves dans un cours est un précurseur important de la réussite scolaire. Dans la discipline de la comptabilité, la réussite de la composante d'auto-apprentissage du cours est un aspect critique de l'engagement et la réussite des élèves. Amélioration de l'apprentissage par Internet offre la possibilité de fournir un environnement d'apprentissage structuré avec un meilleur accès aux outils d'apprentissage et la rétroaction immédiate qui peuvent améliorer les taux d'achèvement des activités d'auto-apprentissage. Cette étude a évalué les perceptions des élèves et les résultats scolaires relatives à la mise en oeuvre d'un module d'étude avec accès Internet à un cours d'introduction à la comptabiblilté dans le département d'administration des affaires au Cégep John Abbott. Les résultats de cette étude indiquent à la fois une perception des étudiants fortement favorable du module d'étude avec accès Internet ansi que l'amélioration des taux d'achèvement des devoirs et des résultats scolaires en particulier chez les élèves qui avaient de mauvais résultats dans un cadre traditionnel, l'environnement d'étude non accès Internet.

Using an evidence-based online module to improve parents’ ability to manage their child with Developmental Coordination Disorder

Background : Developmental coordination disorder (DCD) is a prevalent neurodevelopmental disorder. Best practices include raising parents’ awareness and building capacity but few interventions incorporating these best practices are documented. Objective : To examine whether an evidence-based online module can increase the perceived knowledge and skills of parents of children with DCD, and lead to behavioral changes when managing their child’s health condition. Methods : A mixed-methods, before-after design guided by the theory of planned behavior was employed. Data about the knowledge, skills and behaviors of parents of children with DCD were collected using questionnaires prior to completing the module, immediately after, and three months later. Paired T-tests, sensitivity analyses and thematic analyses were performed on data as appropriate. Results: One hundred-sixteen, 81 and 58 participants respectively completed the three questionnaires. For knowledge and skills, post- and follow-up scores were significantly higher than baseline scores (p<0.01). Fifty-two (64%) participants reported an intention to change behavior post-intervention and 29 (50%) participants had tried recommended strategies at follow-up. Three themes emerged to describe parents’ behavioral change: sharing information, trialing strategies and changing attitudes. Factors influencing parents’ ability to implement these behavioral changes included clear recommendations, time, and ‘right’ attitude. Perceived outcomes associated with the parental behavioral changes involved improvement in well-being for the children at school, at home, and for the family as a whole. Conclusions : The online module increased parents’ self-reported knowledge and skills in DCD management. Future research should explore its impacts on children’s long-term outcomes.

Evaluation of two methods for computational HLA haplotypes inference using a real dataset

info:eu-repo/semantics/publishedVersion

Evaluation of two methods for computational HLA haplotypes inference using a real dataset

info:eu-repo/semantics/publishedVersion

The Proces of Curriculum and Module Design

The outcome of the inductive decision -making process of the leading project management group (PMG) was the proposal to develop three modules, Human Resource Management and Knowledge Management, Quality Management and Intercultural management, each for 10 ECTS credits. As a result of the theoretical and organisational framework and analytical phase of the project, four strategies informed the development and implemen- tation of the modules: 1. Collaboration as a principle stemming from EU collaborative policy and receiving it’s expression on all implementation levels (designing the modules, modes of learning, delivering the modules, evaluation process). 2. Building on the Bologna process masters level framework to assure ap- propriate academic level of outputs. 3. Development of value -based leadership of students through transforma- tional learning in a cross -cultural setting and continual reflection of theory in practice. 4. Continual evaluation and feedback among teachers and students as a strategy to achieve a high quality programme. In the first phase of designing the modules the collaborative strategy in particular was applied, as each module was led by one university, but members from all other universities participated in the discussions and development of the mod- ules. The Bologna process masters level framework and related standards and guidelines informed the form and method of designing the modules.

ANALYSE THE VULNERABILITY IN THE MARANHÃO ILHA, BRAZIL, USING GIS TECHNIQUES AND GEOSPATIAL INFERENCE

Vulnerability and sustainability studies of an area help to assess both its level of exposure and capacity to support possible environmental impacts, and it is of primordial importance for proposals of the Legislation on Zoning, Allotment, Land Use/land cover, aiming to stimulate those areas indicated for urban growth, to discourage growth of overcrowded areas, to detect sections with restrictive use, as well as districts for permanent protection. This paper aims to analyze the vulnerability in the Maranhão Ilha, using GIS techniques, geospatial inference intersected with relevant social-environmental indicators.Estudos de vulnerabilidade e de sustentabilidade de uma área ajudam a avaliar o seu grau de exposição e sua capacidade de suporte a possíveis impactos ambientais, sendo fundamental para propostas de Lei de Zoneamento, Parcelamento, Uso e Ocupação do Solo, tendo por finalidade orientar as áreas onde deverá haver estímulo para o crescimento urbano; contenção da malha urbana; detecção de locais com possibilidade de uso restritivo, bem como locais de proteção permanente. Este trabalho propõe analisar o índice de vulnerabilidade a perda de solo da Ilha do Maranhão com base na metodologia proposta por (CREPANI, et al. 2001) e em técnicas de inferência espacial com apoio na AHP (Análise Hierárquica de Processo).

Tecniche di machine learning per type inference di linguaggi tipati dinamicamente

In questa tesi vengono discusse le principali tecniche di machine learning riguardanti l'inferenza di tipo nei linguaggi tipati dinamicamente come Python. In aggiunta è stato creato un dataset di progetti Python per l'addestramento di modelli capaci di analizzare il codice