Essays in Network Econometrics

This dissertation studies estimation and inference on models with dyadic dependence, that is models for double indexed observations where observations are correlated whenever they share an index. Data exhibiting this form of dependence are commonplace: from international trade (e.g. \cite{Rose2004}) to sales on online platforms (e.g. \cite{Bajari2023}) or social networks (\cite{FafchampsGubet2007}). Because of the particular dependence structure, very little is known about efficiency in these models. For instance, for parametric models, only a handful of examples have likelihood functions or maximum likelihood estimators that can be expressed in closed form or that are computationally feasible. The analyst is forced to sacrifice efficiency for computational ease and tractability. Unfortunately, unlike cross-sectional models, efficiency losses in dyadic models can manifest as drops in rates of convergence rather than just asymptotic variance, immensely impacting the precision of estimation. The dissertation explores new estimation methods for different dyadic models, with a particular attention to efficiency and computational feasibility. Each of The three chapters in this dissertation studies a set of dyadic models and estimators for those models. The first and last chapters present efficiency results.In the first chapter I propose a two step rate optimal estimator for an undirected dyadic linear regression model with interactive unit-specific effects. The estimator remains consistent when the individual effects are additive rather than interactive. We observe that the unit-specific effects alter the eigenvalue distribution of the data's matrix representation in significant and distinctive ways. We offer a correction for the \textit{ordinary least squares}' objective function to attenuate the statistical noise that arises due to the individual effects, and in some cases, completely eliminate it. The new objective function is similar to the \textit{least squares} estimator's objective function from the large $N$ large $T$ panel data literature (\cite{Bai2009}). In general, the objective function is ill behaved and admits multiple local minima. Following a novel proof strategy, we show that in the presence of interactive effects, an iterative process in line with \cite{Bai2009}'s converges to a global minimizer and is asymptotically normal when initiated properly. The new proof strategy suggests a computationally more advantageous and asymptotically equivalent estimator. While the iterative process does not converge when the individual effects are additive, we show that the alternative estimator remains consistent for all slope parameters. Chapter 2 proposes a general procedure to construct estimators for exchangeable network models. For any network model, consider an auxiliary i.i.d. model where each observation has the same distribution as any observation in the original model. The procedure returns estimators for the original model whenever valid estimators are known in the auxiliary i.i.d. model. The chapter then studies the asymptotic behavior of the ``the average MLE", the estimators returned by the procedure for parametric binomial network models. I show that the average MLE behaves asymptotically like the composite maximum likelihood estimator. Interestingly, the average MLE does not require the entire network to be observed. For instance, I show that for a balanced bipartite graph, observing almost any sub-graph with more than $N^{\frac{3}{2}+\epsilon}$ edges for some \epsilon>0 (out of the total $N^2$ edges) is enough for the asymptotic result to hold. These results are readily extendable beyond the binomial model.The final chapter studies the properties of the maximum likelihood estimator (MLE) for exponential families of distributions on network data. I show that, under some conditions, the MLE is asymptotically normally distributed with an asymptotic variance equal to the inverse of the information matrix. I also show that under those same conditions, the MLE is efficient compared to regular estimators with the same rate of convergence. This extends well known results on MLE for $i.i.d.$ models

The miRNA199a/SIRT1/P300/Yy1/sST2 signaling axis regulates adverse cardiac remodeling following MI.

Left ventricular remodeling following myocardial infarction (MI) is related to adverse outcome. It has been shown that an up-regulation of plasma soluble ST2 (sST2) levels are associated with lower pre-discharge left ventricular (LV) ejection fraction, adverse cardiovascular outcomes and mortality outcome after MI. The mechanisms involved in its modulation are unknown and there is not specific treatment capable of lowering plasma sST2 levels in acute-stage HF. We recently identified Yin-yang 1 (Yy1) as a transcription factor related to circulating soluble ST2 isoform (sST2) expression in infarcted myocardium. However, the underlying mechanisms involved in this process have not been thoroughly elucidated. This study aimed to evaluate the pathophysiological implication of miR-199a-5p in cardiac remodeling and the expression of the soluble ST2 isoform. Myocardial infarction (MI) was induced by permanent ligation of the left anterior coronary artery in C57BL6/J mice that randomly received antimiR199a therapy, antimiR-Ctrl or saline. A model of biomechanical stretching was also used to characterize the underlying mechanisms involved in the activation of Yy1/sST2 axis. Our results show that the significant upregulation of miR-199a-5p after myocardial infarction increases pathological cardiac hypertrophy by upregulating circulating soluble sST2 levels. AntimiR199a therapy up-regulates Sirt1 and inactivates the co-activator P300 protein, thus leading to Yy1 inhibition which decreases both expression and release of circulating sST2 by cardiomyocytes after myocardial infarction. Pharmacological inhibition of miR-199a rescues cardiac hypertrophy and heart failure in mice, offering a potential therapeutic approach for cardiac failure.This study was supported by a grant from the Seneca Foundation-Agency of Science and Technology of the Region of Murcia (20652/JLI/18) and a grant from the Instituto de Salud Carlos III (PI19/00519) which is cofinanced through the European Union's European Regional Development Fund (FEDER). Dr. Lax is a Ramon and Cajal researcher at the Department of Medicine, University of Murcia.S

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Photonic-crystal fibre modeling using fuzzy classification approach

In this paper, a fuzzy classification algorithm is proposed for photonic-crystal fibres (PCF) modeling. This approach is based on fuzzy classification decisions. A training dataset composed of the most recent commercialized PCF fibers is used. Each category of fibers is defined by a set of reference patterns. The fuzzy modeling allows eliminating many problems like ones related to the strict thresholding and helps to overcome some difficulties encountered when data are expressed in different units. Typical PCFs with specific optical properties are designed using this approach. As an example, we present index-guiding fibres with manipulated chromatic dispersion using appropriate parameters. The fuzzy classification algorithm was implemented successfully and interesting optical properties of the designed PCFs were presented.Peer reviewed: YesNRC publication: Ye

Rôle des transporteurs MRP sur la fonction des myocytes cardiaques et vasculaires via la régulation des nucléotides cycliques (implications physiopathologiques et pharmacologiques)

L AMPc et le GMPc jouent un rôle important dans de multiples processus biologiques. De récentes études ont montré qu un membre de la famille des Multidrug Resistance associated-Protein (MRP4) est capable de transporter ces nucléotides cycliques hors de la cellule. Mes travaux de thèse ont permis de montrer que que l inhibition de l expression de MRP4, par des siRNA, augmente les taux intracellulaires des nucléotides cycliques et ceci a pour conséquence une diminution de la prolifération des CML humaines in vitro ainsi que du développement de la néointima de carotides de rats in vivo. Une deuxième étude nous a permis de montrer que l inhibition de MRP4 induit l activité de la PKA dans le cardiomyocyte qui module alors l activité de plusieurs acteurs du couplage excitation-contraction. Les souris déficientes en MRP4 développent une hypertrophie modérée avec l âge et le système AMPc-dépendant est exacerbée chez ces animaux. En conclusion, notre travail est la première démonstration que l efflux des nucléotides cycliques par MRP4 permet de réguler le taux d AMPc et en conséquence la fonction des cellules cardiaques et vasculaires.PARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Lean 4.0 tools and technologies to improve companies’ maturity level: the COVID-19 context

The global pandemic triggered by the new COVID-19 led to severe limitations in daily life, both private and professional. Almost all companies have been affected in one way or another. The COVID-19 crisis imposed new challenges for enterprises. As a result, many companies have been forced to rethink how to align many of their processes and practices with the new COVID-19 context, and fulfill their mission while maintaining a safe and secure management business operating environment for both employees and customers. This paper aims to bring empirical evidence, through a questionnaire survey, of the positive influence of using Lean Management tools and Industry 4.0 technologies on five organizational dimensions (strategy, leadership, culture, operations and products, and technology). Data from 98 Algerian and French companies of different sizes and representing various activity sectors was collected. Respondents were asked to answer 5 organizational dimensions (strategy, leadership, culture, operations and products, and technology) in the context COVID-19 crisis. Statistical analysis was performed through path coefficient using a Smart PLS. The results show that Industry 4.0 technologies tend to be strongly associated with Lean management tools, and that understanding the relationship between Lean management tools and Industry 4.0 technologies can improve the organizational dimensions: leadership, strategy, operation, and production. This research provides managerial implications that can help managers to understand the synergies and benefits of integrating and implementing Lean 4.0 tools and technologies in organizations in both crises and regular contexts

Deletion of delta-like 1 homologue accelerates fibroblast-myofibroblast differentiation and induces myocardial fibrosis

Aims Myocardial fibrosis is associated with profound changes in ventricular architecture and geometry, resulting in diminished cardiac function. There is currently no information on the role of the delta-like homologue 1 (Dlk1) in the regulation of the fibrotic response. Here, we investigated whether Dlk1 is involved in cardiac fibroblast-to-myofibroblast differentiation and regulates myocardial fibrosis and explored the molecular mechanism underpinning its effects in this process. Methods and results Using Dlk1-knockout mice and adenoviral gene delivery, we demonstrate that overexpression of Dlk1 in cardio-fibroblasts resulted in inhibition of fibroblast proliferation and differentiation into myofibroblasts. This process is mediated by TGF-β1 signalling, since isolated fibroblasts lacking Dlk1 exhibited a higher activation of the TGF-β1/Smad-3 pathway at baseline, leading to an earlier acquisition of a myofibroblast phenotype. Likewise, Dlk1-null mice displayed increased TGF-β1/Smad3 cardiac activity, resulting in infiltration/accumulation of myofibroblasts, induction and deposition of extra-domain A-fibronectin isoform and collagen, and activation of pro-fibrotic markers. Furthermore, these profibrotic events were associated with disrupted myofibril integrity, myocyte hypertrophy, and cardiac dysfunction. Interestingly, Dlk1 expression was down-regulated in ischaemic human and porcine heart tissues. Mechanistically, miR-370 mediated Dlk1's regulation of cardiac fibroblast-myofibroblast differentiation by directly targeting TGFβ-R2/Smad-3 signalling, while the Dlk1 canonical target, Notch pathway, does not seem to play a role in this process. Conclusion These findings are the first to demonstrate an inhibitory role of Dlk1 of cardiac fibroblast-to-myofibroblast differentiation by interfering with TGFβ/Smad-3 signalling in the myocardium. Given the deleterious effects of continuous activation of this pathway, we propose Dlk1 as a new potential candidate for therapy in cases where aberrant TGFβ signalling leads to chronic fibrosis.National Institutes of Health (Grant R01HL097357 to D.L.); Ministerio de Economía y Competitividad (MINECO) SAF 2012-31338, CSD 2007-00020, Comunidad de Madrid ‘Fibroteam’ S2010/BMD-2321 to S.L

Post-transcriptional modulation of interleukin 8 by CNOT6L regulates skeletal muscle differentiation

International audienceCNOT6L is a deadenylase subunit belonging to the CCR4-NOT complex, a major deadenylase complex in eukaryotes involved at multiple levels in regulation of gene expression. While CNOT6L is expressed in skeletal muscle cells, its specific functions in this tissue are still largely unknown. Our previous work highlighted the functional of CNOT6L in skeletal muscle cell differentiation. To further explore how CNOT6L regulates myogenesis, we used here gene expression analysis to identify CNOT6L mRNA targets in human myoblasts. Among these novel targets, IL-8 (interleukin 8) mRNA was the most upregulated in CNOT6L knock-down (KD) cells. Biochemical approaches and poly (A) tail length assays showed that IL-8 mRNA is a direct target of CNOT6L, and further investigations by loss- and gain-of-function assays pointed out that IL-8 is an important effector of myogenesis. Therefore, we have characterized CNOT6L-IL-8 as a new signaling axis that regulates myogenesis