Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

International audience

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.

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.

Detection of African Swine Fever Virus Antibodies in Serum and Oral Fluid Specimens Using a Recombinant Protein 30 (p30) Dual Matrix Indirect ELISA

In the absence of effective vaccine(s), control of African swine fever caused by African swine fever virus (ASFV) must be based on early, efficient, cost-effective detection and strict control and elimination strategies. For this purpose, we developed an indirect ELISA capable of detecting ASFV antibodies in either serum or oral fluid specimens. The recombinant protein used in the ELISA was selected by comparing the early serum antibody response of ASFV-infected pigs (NHV-p68 isolate) to three major recombinant polypeptides (p30, p54, p72) using a multiplex fluorescent microbead-based immunoassay (FMIA). Non-hazardous (non-infectious) antibody-positive serum for use as plate positive controls and for the calculation of sample-to-positive (S:P) ratios was produced by inoculating pigs with a replicon particle (RP) vaccine expressing the ASFV p30 gene. The optimized ELISA detected anti-p30 antibodies in serum and/or oral fluid samples from pigs inoculated with ASFV under experimental conditions beginning 8 to 12 days post inoculation. Tests on serum (n = 200) and oral fluid (n = 200) field samples from an ASFV-free population demonstrated that the assay was highly diagnostically specific. The convenience and diagnostic utility of oral fluid sampling combined with the flexibility to test either serum or oral fluid on the same platform suggests that this assay will be highly useful under the conditions for which OIE recommends ASFV antibody surveillance, i.e., in ASFV-endemic areas and for the detection of infections with ASFV isolates of low virulence.

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.

Assessment of resting energy expenditure in pediatric mitochondrial diseases with indirect calorimetry

Mitochondrial diseases (MD) are the most frequent inborn errors of metabolism. In affected tissues, MD can alter cellular oxygen consumption rate leading to potential decreases in whole-body resting energy expenditure (REE), but data on pediatric children are absent. We determined, using indirect calorimetry (IC), whole-body oxygen consumption (VO2), carbon dioxide production (VCO2), respiratory quotient (RQ) and REE in pediatric patients with MD and healthy controls. Another goal was to assess the accuracy of available predictive equations for REE estimation in this patient population. IC data were obtained under fasting and resting conditions in 20 MD patients and 27 age and gender-matched healthy peers. We determined the agreement between REE measured with IC and REE estimated with Schofield weight and FAO/WHO/UNU equations. Mean values of VO2, VCO2 (mL·min-1·kg-1) or RQ did not differ significantly between patients and controls (P = 0.085, P = 0.055 and P = 0.626 respectively). Accordingly, no significant differences (P = 0.086) were found for REE (kcal·day-1 kg-1) either. On the other hand, although we found no significant differences between IC-measured REE and Schofield or FAO/WHO/UNU-estimated REE, Bland-Altman analysis revealed wide limits of agreement and there were some important individual differences between IC and equation-derived REE. VO2, VCO2, RQ and REE are not significantly altered in pediatric patients with MD compared with healthy controls. The energy demands of pediatric patients with MD should be determined based on IC data in order to provide the best possible personalized nutritional management for these children.

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

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).

Indirect and expedite assessment of Ilex paraguariensis commercial yield.

A produção de biomassa comercial é uns dos principais indicadores para seleção de progênies e clones de erva-mate. Técnicas tradicionais para se obter tais informações dependem da colheita das árvores e apresentam, dentre outras limitações, elevado custo e reduzida praticidade. Assim, objetivou-se avaliar a eficiência de métodos indiretos, por meio de estimativa de biomassa comercial e escore de produtividade em função de diferentes procedências, sexo e morfotipos. Em um teste de progênies e procedências instalado em 1997, foram avaliadas em agosto de 2015 (dois anos após a última colheita) duas metodologias de análise visual. Para tanto, participaram cinco avaliadores treinados que determinaram, para cada planta, uma estimativa de biomassa comercial (kg), e uma nota, com base em um escore de produtividade (0-10). Para avaliação da produtividade pelas técnicas tradicionais, todas as plantas foram podadas e tiveram sua biomassa comercial (folhas e ramos finos menores de 7 mm de diâmetro) colhida e avaliada por meio de pesagem (Kg.planta-1). As avaliações foram realizadas em experimento instalado em blocos ao acaso, com cinco repetições, sete procedências e 126 progênies, totalizando 5292 plantas avaliadas. Os métodos avaliados foram eficientes na estimativa da biomassa comercial. Os avaliadores apresentaram boa acuidade nas estimativas, expressando de forma eficiente a maior produtividade determinada por comparação de médias entre as procedências, sexo das matrizes e morfotipos. As maiores correlações foram verificadas na análise geral das médias e a estimativa de biomassa comercial é a melhor metodologia para estimar a biomassa comercial aferida em plantas adultas de erva-mate.

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

New perspectives in statistical mechanics and high-dimensional inference

The main purpose of this thesis is to go beyond two usual assumptions that accompany theoretical analysis in spin-glasses and inference: the i.i.d. (independently and identically distributed) hypothesis on the noise elements and the finite rank regime. The first one appears since the early birth of spin-glasses. The second one instead concerns the inference viewpoint. Disordered systems and Bayesian inference have a well-established relation, evidenced by their continuous cross-fertilization. The thesis makes use of techniques coming both from the rigorous mathematical machinery of spin-glasses, such as the interpolation scheme, and from Statistical Physics, such as the replica method. The first chapter contains an introduction to the Sherrington-Kirkpatrick and spiked Wigner models. The first is a mean field spin-glass where the couplings are i.i.d. Gaussian random variables. The second instead amounts to establish the information theoretical limits in the reconstruction of a fixed low rank matrix, the “spike”, blurred by additive Gaussian noise. In chapters 2 and 3 the i.i.d. hypothesis on the noise is broken by assuming a noise with inhomogeneous variance profile. In spin-glasses this leads to multi-species models. The inferential counterpart is called spatial coupling. All the previous models are usually studied in the Bayes-optimal setting, where everything is known about the generating process of the data. In chapter 4 instead we study the spiked Wigner model where the prior on the signal to reconstruct is ignored. In chapter 5 we analyze the statistical limits of a spiked Wigner model where the noise is no longer Gaussian, but drawn from a random matrix ensemble, which makes its elements dependent. The thesis ends with chapter 6, where the challenging problem of high-rank probabilistic matrix factorization is tackled. Here we introduce a new procedure called "decimation" and we show that it is theoretically to perform matrix factorization through it.

Hierarchical active inference for cognitive architectures

This thesis explores the methods based on the free energy principle and active inference for modelling cognition. Active inference is an emerging framework for designing intelligent agents where psychological processes are cast in terms of Bayesian inference. Here, I appeal to it to test the design of a set of cognitive architectures, via simulation. These architectures are defined in terms of generative models where an agent executes a task under the assumption that all cognitive processes aspire to the same objective: the minimization of variational free energy. Chapter 1 introduces the free energy principle and its assumptions about self-organizing systems. Chapter 2 describes how from the mechanics of self-organization can emerge a minimal form of cognition able to achieve autopoiesis. In chapter 3 I present the method of how I formalize generative models for action and perception. The architectures proposed allow providing a more biologically plausible account of more complex cognitive processing that entails deep temporal features. I then present three simulation studies that aim to show different aspects of cognition, their associated behavior and the underlying neural dynamics. In chapter 4, the first study proposes an architecture that represents the visuomotor system for the encoding of actions during action observation, understanding and imitation. In chapter 5, the generative model is extended and is lesioned to simulate brain damage and neuropsychological patterns observed in apraxic patients. In chapter 6, the third study proposes an architecture for cognitive control and the modulation of attention for action selection. At last, I argue how active inference can provide a formal account of information processing in the brain and how the adaptive capabilities of the simulated agents are a mere consequence of the architecture of the generative models. Cognitive processing, then, becomes an emergent property of the minimization of variational free energy.

Considerations on indirect adhesive restorations at the material and tooth interfaces

Objective: Lithium-silicate (LiSi) ceramic is nowadays widely used in dentistry. However, for the longevity of LiSi indirect restorations, it is important to pretreat the material and the dental substrate adequately. However, is not certain how the simplification of the manufacturing and conditioning procedures influences the bonding performances of LiSi ceramic restorations. Accordingly, the aims of this thesis were to investigate the effect of: 1) different LiSi ceramic surface decontamination procedures on the shear bond strength (SBS) to resin composite; 2) different types of lithium-disilicate (LiDi) (pressed vs CAD-CAM) on SBS to resin composite; 3) an experimental metal salt-based zirconium oxynitrate etchant [ZrO(NO3)2] on bonding performances to dentin. Materials and Methods: SBS test was used to investigate the influence of different cleaning protocols applied, or different processing techniques (CAD or PRESS) on the bond strength to composite resin. The third study tackled the interface between restorative materials and dentin, and investigated the microtensile bond strength test (µTBS), nanoleakage expression analysis (NL), gelatin zymography and in situ zymography of dentin conditioned with an experimental metal salt-based zirconium oxynitrate etchant [ZrO(NO3)2]. Results: MEP showed comparable bond strength to the double HP etching and higher compared to other groups. BS of press LiSi to composite was higher than that of CAD/CAM LiSi. ZON pretreatment increased bond strength to dentin when used with a universal adhesive, and inhibited dentinal endogenous enzymes. Conclusions: While simplification of the LiSi conditioning and cleaning procedures seems to yield bond strength comparable to the traditional procedures, it could be recommended in the clinical practice. However, pressed LiSi still seems to perform better in terms of bond strength compared to the CAD/CAM LiSi. Further, the novel ZON etchant seems to perform better compared to the traditional phosphoric dentin etching.