Comments on "Solar System constraints to general f(R) gravity"

We comment on, and complete, the analysis of the weak field limit of metric f(R) gravity in T. Chiba, T.L. Smith, and A.L. Erickcek, Phys. Rev. D 75, 124014 (2007).Comment: 2 latex pages, to appear in the Comments section of Phys. Rev. D. A statement corrected, acknowledgments update

Spliceosome SNRNP200 promotes viral RNA sensing and IRF3 activation of antiviral response

Le système immunitaire innée est la première ligne de défense de l’organisme contre une multitude d’agents pathogènes tel que les bactéries, les virus, les parasites et les champignons. Afin d’identifier de nouveau régulateur de l’immunité antivirale innée, nous avons complété le premier criblage pangénomique par ARN interférent (RNAi) s’intéressant à la réponse transcriptionnelle de l’interféron-β (IFNB1) suite à une infection par le virus Sendai (SeV). De façon surprenante, une analyse d’enrichissement génomique (GESA) nous a permis d’identifier 114 gènes régulateurs dont plusieurs facteurs du splicéosome. Par eux, nous avons priorisé la caractérisation de SRNNP200, une protéine clé de la machinerie d’épissage des introns et une hélicase de la famille Ski2, sur la base de similitudes entre sa structure et celle d’autres hélicases antivirales tel que RIG-I et MDA5. Dans cette thèse, nous montrons, pour la première fois, un rôle distinct, pour SNRNP200, de sa fonction canonique dans l’épissage des pré-ARNs. En effet, le silençage de l’expression de SNRNP200 dans des lignées de cellules humaines primaires entraîne une réduction de l’immunité antivirale et une augmentation de la susceptibilité à une infection virale. Plus spécifiquement, nous montrons que SNRNP200 est un régulateur positif de l’activation de IRF3 via une interaction protéine-protéine avec la sérine/thréonine-kinase TBK1. Additionnement, nous avons montré que, lors d’une infection, SNRNP200 est capable de lier l’ARN viral cytoplasmique et qu’il relocalise, du noyau au cytoplasme, avec TBK1 dans des structures périnucléaires distinctes et spécifiques. En lien avec la clinique, nous avons observé une réponse antivirale réduite dans les cellules mononucléées du sang périphérique (PBMC) de patients atteints de rétinite pigmentaire de type 33 (RP33) causée par des mutations dans le gène SNRNP200. De plus, nous avons démontré qu’un mutant de SNRNP200 associé à RP33 n’était plus en mesure de lier l’ARN viral cytoplasmique ou de rétablir l’immunité antivirale de cellules ciblée par un RNAi lors d’une expérience de sauvetage. Ainsi, cette thèse présente les premiers travaux portant sur la fonction immunomodulatrice de SNRNP200 et de son rôle comme senseur d’ARN viral et de protéines adaptatrice de TBK1 et d’IRF3.The innate immune system is the first line of defense against invading pathogens of many kind such as bacteria, viruses, parasites and fungi. Its role is straightforward: it acts within minutes of a pathogenic engagement to control and restrict the microscopic invasion using non-specific mechanisms while the host mounts an induced, and specific, innate and adaptive response. To identify new regulators of antiviral innate immunity, we have completed the first genome-wide gene RNAi screen assessing the transcriptional response at the interferon-β (IFNB1) promoter following Sendai virus (SeV) infection. Interestingly, a Gene Set Enrichment Analysis (GSEA) of the 114 gene hits revealed that many of these proteins were spliceosome-associated. Among them, we further prioritized the characterization of SNRNP200, a core and unique spliceosomal member of the Ski2-like RNA helicase family based on its structural similarities to other antiviral RNA helicase like RIG-I and MDA5. In this thesis, we provide evidence for a role of the spliceosomal SNRNP200 that is clearly distinguishable of the one in pre-mRNA splicing. Indeed, the depletion of SNRNP200 in human cells resulted in a reduced antiviral response and increased susceptibility to viral infection. We specifically showed that SNRNP200 positively regulates activation of the key antiviral transcriptional factor IRF3 via a protein-protein interaction with the serine/threonine-protein kinase TBK1. Additionally, we showed that upon infection, SNRNP200 binds viral RNA and relocalizes into TBK1-containing cytoplasmic structures to promote innate signaling. Of clinical relevance, we observed a significantly hindered antiviral response of PBMCs from patients carrying a dominant SNRNP200 mutation associated to the retina pigmentosa type 33 (RP33), an inherited degenerative eye disease. We showed that expression of the RP33-associated mutant has lost the ability to bind RNA and to rescue antiviral response in SNRNP200 silenced cells. Thus, this thesis provides new insights into an immunoregulatory role of spliceosome SNRNP200 acting as an RNA sensor and adaptor of TBK1 to promote IRF3 signaling in antiviral response

Compressive Spectral Clustering

Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object, and run k-means on these features to separate objects into k classes. Each of these three steps becomes computationally intensive for large N and/or k. We propose to speed up the last two steps based on recent results in the emerging field of graph signal processing: graph filtering of random signals, and random sampling of bandlimited graph signals. We prove that our method, with a gain in computation time that can reach several orders of magnitude, is in fact an approximation of spectral clustering, for which we are able to control the error. We test the performance of our method on artificial and real-world network data.Comment: 12 pages, 2 figure

Random sampling of bandlimited signals on graphs

We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performance depends on a parameter called the graph coherence. On the contrary, the second strategy is adaptive but yields optimal results. Indeed, no more than O(k log(k)) measurements are sufficient to ensure an accurate and stable recovery of all k-bandlimited signals. This second strategy is based on a careful choice of the sampling distribution, which can be estimated quickly. Then, we propose a computationally efficient decoder to reconstruct k-bandlimited signals from their samples. We prove that it yields accurate reconstructions and that it is also stable to noise. Finally, we conduct several experiments to test these techniques

Accelerated Spectral Clustering Using Graph Filtering Of Random Signals

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first k eigenvectors of the similarity matrix' Laplacian, whose computation cost, even for sparse matrices, becomes prohibitive for large datasets. We show that we can estimate the spectral clustering distance matrix without computing these eigenvectors: by graph filtering random signals. Also, we take advantage of the stochasticity of these random vectors to estimate the number of clusters k. We compare our method to classical spectral clustering on synthetic data, and show that it reaches equal performance while being faster by a factor at least two for large datasets

Approximating Spectral Clustering via Sampling: a Review

International audienceSpectral clustering refers to a family of well-known unsupervised learning algorithms. Rather than attempting to cluster points in their native domain, one constructs a (usually sparse) similarity graph and computes the principal eigenvec-tors of its Laplacian. The eigenvectors are then interpreted as transformed points and fed into a k-means clustering algorithm. As a result of this non-linear transformation , it becomes possible to use a simple centroid-based algorithm in order to identify non-convex clusters, something that was otherwise impossible. Unfortunately , what makes spectral clustering so successful is also its Achilles heel: forming a graph and computing its dominant eigenvectors can be computationally prohibitive when dealing with more that a few tens of thousands of points. In this chapter, we review the principal research efforts aiming to reduce this computational cost. We focus on methods that come with a theoretical control on the clustering performance and incorporate some form of sampling in their operation. Such methods abound in the machine learning, numerical linear algebra, and graph signal processing literature and, amongst others, include Nyström-approximation, landmarks, coarsening, coresets, and compressive spectral clustering. We present the approximation guarantees available for each and discuss practical merits and limitations. Surprisingly, despite the breadth of the literature explored, we conclude that there is still a gap between theory and practice: the most scalable methods are only intuitively motivated or loosely controlled, whereas those that come with end-to-end guarantees rely on strong assumptions or enable a limited gain of computation time

Approximating Spectral Clustering via Sampling: a Review

Spectral clustering refers to a family of unsupervised learning algorithms that compute a spectral embedding of the original data based on the eigenvectors of a similarity graph. This non-linear transformation of the data is both the key of these algorithms' success and their Achilles heel: forming a graph and computing its dominant eigenvectors can indeed be computationally prohibitive when dealing with more that a few tens of thousands of points. In this paper, we review the principal research efforts aiming to reduce this computational cost. We focus on methods that come with a theoretical control on the clustering performance and incorporate some form of sampling in their operation. Such methods abound in the machine learning, numerical linear algebra, and graph signal processing literature and, amongst others, include Nystr\"om-approximation, landmarks, coarsening, coresets, and compressive spectral clustering. We present the approximation guarantees available for each and discuss practical merits and limitations. Surprisingly, despite the breadth of the literature explored, we conclude that there is still a gap between theory and practice: the most scalable methods are only intuitively motivated or loosely controlled, whereas those that come with end-to-end guarantees rely on strong assumptions or enable a limited gain of computation time

Estimating the inverse trace using random forests on graphs

Some data analysis problems require the computation of (regularised) inverse traces, i.e. quantities of the form \Tr (q \bI + \bL)^{-1}. For large matrices, direct methods are unfeasible and one must resort to approximations, for example using a conjugate gradient solver combined with Girard's trace estimator (also known as Hutchinson's trace estimator). Here we describe an unbiased estimator of the regularized inverse trace, based on Wilson's algorithm, an algorithm that was initially designed to draw uniform spanning trees in graphs. Our method is fast, easy to implement, and scales to very large matrices. Its main drawback is that it is limited to diagonally dominant matrices \bL.Comment: Submitted to GRETSI conferenc