Likelihood-Based Inference for Partially Observed Multi-Type Markov Branching Processes

Thesis (Ph.D.)--University of Washington, 2016-08

Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity

This paper considers identification of treatment effects when the outcome variables and covari-ates are not observed in the same data sets. Ecological inference models, where aggregate out-come information is combined with individual demographic information, are a common example of these situations. In this context, the counterfactual distributions and the treatment effects are not point identified. However, recent results provide bounds to partially identify causal effects. Unlike previous works, this paper adopts the selection on unobservables assumption, which means that randomization of treatment assignments is not achieved until time fixed unobserved heterogeneity is controlled for. Panel data models linear in the unobserved components are con-sidered to achieve identification. To assess the performance of these bounds, this paper provides a simulation exercise.

Caracterización del agente causal de la producción radicular del aguacate y Trichoderma sp., como potencial agente de control biológico en Nicaragua.

En el cultivo de aguacate (Persea americana Mill.) se presentan problemas fitosanitarios importantes dentro de los cuales sobresalen por su relevancia las enfermedades de la raíz. Un fitopatógeno limitante de este cultivo es el oomicete Phytophthora cinnamomi Rands, que puede causar pérdidas hasta del 90%. Por tal razón el principal objetivo del estudio fue generar información acerca de la etiología del agente causal de la pudrición radicular del aguacate utilizando marcadores morfológicos y moleculares, además de proponer alternativas de manejo de carácter biológico que estén enmarcadas dentro de un programa de manejo integrado de la enfermedad. Se realizaron colectas de muestras de suelo en cuatro localidades del departamento de Masaya. La identificación morfológica del patógeno se realizó mediante claves taxonómicas y se confirmó a través de la técnica PCR-RFLP. Se identificó a P. cinnamomi como el principal agente causal de la pudrición radicular del aguacate. Los aislados de P. cinnamomi fueron enfrentados con Trichoderma sp por el método de cultivo dual en cajas Petri con medio PDA. Se determinó el porcentaje de inhibición de crecimiento radial (PICR) a las 72 horas, así como el grado de antagonismo de cada una de las cepas de Trichoderma sp utilizadas en el estudio. Las cepas de Trichoderma al enfrentarlas a aislados del patógeno P. cinnamomi se ubicaron en las Clases 1 y 2 de la escala de evaluación, por lo tanto se consideraron altamente antagonistas. Existe la posibilidad de manejo biológico de las poblaciones de P. cinnamomi con microorganismos antagonistas del género Trichoderma no solamente en agroecosistemas de aguacate, sino también en otros sistemas agrícolas y forestales donde el patógeno esté presente.

The renormalization group via statistical inference

In physics, one attempts to infer the rules governing a system given only the results of imperfect measurements. Hence, microscopic theories may be effectively indistinguishable experimentally. We develop an operationally motivated procedure to identify the corresponding equivalence classes of states, and argue that the renormalization group (RG) arises from the inherent ambiguities associated with the classes: one encounters flow parameters as, e.g., a regulator, a scale, or a measure of precision, which specify representatives in a given equivalence class. This provides a unifying framework and reveals the role played by information in renormalization. We validate this idea by showing that it justifies the use of low-momenta n-point functions as statistically relevant observables around a Gaussian hypothesis. These results enable the calculation of distinguishability in quantum field theory. Our methods also provide a way to extend renormalization techniques to effective models which are not based on the usual quantum-field formalism, and elucidates the relationships between various type of RG.

Causal structure of the entanglement renormalization ansatz

We show that the multiscale entanglement renormalization ansatz (MERA) can be reformulated in terms of a causality constraint on discrete quantum dynamics. This causal structure is that of de Sitter space with a flat space-like boundary, where the volume of a spacetime region corresponds to the number of variational parameters it contains. This result clarifies the nature of the ansatz, and suggests a generalization to quantum field theory. It also constitutes an independent justification of the connection between MERA and hyperbolic geometry which was proposed as a concrete implementation of the AdS-CFT correspondence.

Adaptação do paradigma orientado a notificações para desenvolvimento de sistemas fuzzy

This work proposes to adjust the Notification Oriented Paradigm (NOP) so that it provides support to fuzzy concepts. NOP is inspired by elements of imperative and declarative paradigms, seeking to solve some of the drawbacks of both. By decomposing an application into a network of smaller computational entities that are executed only when necessary, NOP eliminates the need to perform unnecessary computations and helps to achieve better logical-causal uncoupling, facilitating code reuse and application distribution over multiple processors or machines. In addition, NOP allows to express the logical-causal knowledge at a high level of abstraction, through rules in IF-THEN format. Fuzzy systems, in turn, perform logical inferences on causal knowledge bases (IF-THEN rules) that can deal with problems involving uncertainty. Since PON uses IF-THEN rules in an alternative way, reducing redundant evaluations and providing better decoupling, this research has been carried out to identify, propose and evaluate the necessary changes to be made on NOP allowing to be used in the development of fuzzy systems. After that, two fully usable materializations were created: a C++ framework, and a complete programming language (LingPONFuzzy) that provide support to fuzzy inference systems. From there study cases have been created and several tests cases were conducted, in order to validate the proposed solution. The test results have shown a significant reduction in the number of rules evaluated in comparison to a fuzzy system developed using conventional tools (frameworks), which could represent an improvement in performance of the applications.

Using Causal Models for the Calculation of Direct and Indirect Effects: An Example From Occupational Health

International audience

Phylogenetic inference's algorithms

Phylogenetic inference consist in the search of an evolutionary tree to explain the best way possible genealogical relationships of a set of species. Phylogenetic analysis has a large number of applications in areas such as biology, ecology, paleontology, etc. There are several criterias which has been defined in order to infer phylogenies, among which are the maximum parsimony and maximum likelihood. The first one tries to find the phylogenetic tree that minimizes the number of evolutionary steps needed to describe the evolutionary history among species, while the second tries to find the tree that has the highest probability of produce the observed data according to an evolutionary model. The search of a phylogenetic tree can be formulated as a multi-objective optimization problem, which aims to find trees which satisfy simultaneously (and as much as possible) both criteria of parsimony and likelihood. Due to the fact that these criteria are different there won't be a single optimal solution (a single tree), but a set of compromise solutions. The solutions of this set are called "Pareto Optimal". To find this solutions, evolutionary algorithms are being used with success nowadays.This algorithms are a family of techniques, which aren’t exact, inspired by the process of natural selection. They usually find great quality solutions in order to resolve convoluted optimization problems. The way this algorithms works is based on the handling of a set of trial solutions (trees in the phylogeny case) using operators, some of them exchanges information between solutions, simulating DNA crossing, and others apply aleatory modifications, simulating a mutation. The result of this algorithms is an approximation to the set of the “Pareto Optimal” which can be shown in a graph with in order that the expert in the problem (the biologist when we talk about inference) can choose the solution of the commitment which produces the higher interest. In the case of optimization multi-objective applied to phylogenetic inference, there is open source software tool, called MO-Phylogenetics, which is designed for the purpose of resolving inference problems with classic evolutionary algorithms and last generation algorithms. REFERENCES [1] C.A. Coello Coello, G.B. Lamont, D.A. van Veldhuizen. Evolutionary algorithms for solving multi-objective problems. Spring. Agosto 2007 [2] C. Zambrano-Vega, A.J. Nebro, J.F Aldana-Montes. MO-Phylogenetics: a phylogenetic inference software tool with multi-objective evolutionary metaheuristics. Methods in Ecology and Evolution. En prensa. Febrero 2016.

Causal factors of anxiety symptoms in children

Aim: The present work aimed to investigate the impact of the child’s cognitions associated with ambiguous stimuli that refer to anxiety, both parents’ fears and anxiety, and parents’ attributions to the child’s interpretations of ambiguous stimuli on child anxiety. The influence of parental modelling on child’s cognitions was also analyzed. Method: The final sample was composed of 111 children (62 boys; 49 girls) with ages between 10 and 11 years (M = 10.6, SD = 0.5) from a community population, and both their parents. The variables identified as most significant were included in a predictive model of anxiety. Results: Results revealed the children’s thoughts (positive and negative) related to ambiguous stimuli that describe anxiety situations. Parents’ fears and mothers’ anxiety significantly predict children’s anxiety. Those variables explain 29% of the variance in children general anxiety. No evidence was found for a direct parental modeling of child cognitions. Conclusion: Children’s positive thoughts seem to be cognitive aspects that buffer against anxiety. Negative thoughts are vulnerability factors for the development of child anxiety. Parents’ fears and anxiety should be analyzed in separate as they have distinct influences over children’s anxiety. Mothers’ fears contribute to children’s anxiety by reducing it, revealing a possible protective effect. It is suggested that the contribution of both parents’ fears to children’s anxiety may be interpreted acknowledging the existence of “psychological and/or behavioral filters”. Mothers’ filters seem to be well developed while fathers’ filters seem to be compromised. The contribution of mothers’ anxiety (but not fathers’ anxiety) to children’s anxiety is also understood in light of the possible existence of a “proximity space” between the child and parents, which is wider with mothers than with fathers.

Pesquisa de fungos endofíticos presentes em videira (Vitis vinifera L.) com capacidade para inibir o crescimento do agente causal da podridão negra (Guignardia bidwellii)

Neste trabalho testou-se o potencial antagonista de 16 fungos endofíticos isolados de videiras (Vitis vinifera L.), de castas representativas do Alentejo produzidas em modo de proteção integrada e em modo biológico, contra Guignardia bidwellii. Os isolados identificados após ITS-PCR e sequenciação pertencem aos géneros Epicoccum, Alternaria, Botrytis, Athelia, Phoma e Gibberella. Os isolados testados mostraram atividade antagonista contra G. bidwellii quer por inibição direta, quer através da produção de compostos voláteis, à exceção dos dois isolados de B. cinerea. No entanto, todos os isolados produziram alguns compostos voláteis com reconhecida atividade antimicrobiana, tais como benzaldeído, 3-metil-1-butanol e derivados de ácido propanoico. Foi ainda observado que seis dos isolados produziram também metabolitos não voláteis com capacidade de inibir o crescimento de G. bidwellii. Os resultados obtidos vêm mostrar o potencial dos fungos endofíticos como agentes de luta biológica no controlo de G. bidwellii, podendo constituir novas alternativas no âmbito de Proteção de Plantas; ABSTRACT: Endophytic fungi present in grapevines (Vitis vinifera L.) with the ability to inhibit the growth of the causal agent of black rot (Guignardia bidwellii) In this work the antagonistic potential of 16 endophytic grapevine fungi isolates (Vitis vinifera L.), from representative cultivars of the Alentejo region produced either under integrated pest management or organic mode, was tested against Guignardia bidwellii. Isolates were identified through ITS-PCR and sequencing, as belonging to the genera Epicoccum, Alternaria, Botrytis, Athelia, Phoma and Gibberella. Isolates showed antagonist activity against G. bidwellii either by direct inhibition or through the production of volatile compounds, with the exception of two isolates of B. cinerea. Nevertheless, all isolates produced volatile compounds with known antimicrobial activity such as benzaldehyde, 3-methyl-1-butanol and propionic acid derivatives. Additionally, six isolates produced non-volatile metabolites with the ability to inhibit G. bidwellii growth. These results show the potential that endophytic fungi have as agents for biological control of G. bidwellii, opening new options in the field of Plant Protection.

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.

Post-transcriptional silencing of the SGE1 gene induced by a dsRNA hairpin in Fusarium oxysporum f. sp cubense, the causal agent of Panama disease.

Fusarium oxysporum f. sp cubense (Foc), the causal agent of Panama disease, is responsible for economic losses in banana crops worldwide. The identification of genes that effectively act on pathogenicity and/or virulence may contribute to the development of different strategies for disease control and the production of resistant plants. The objective of the current study was to analyze the importance of SGE1 gene expression in Foc virulence through post-transcriptional silencing using a double-stranded RNA hairpin.