AtomSurf : Surface Representation for Learning on Protein Structures

Recent advancements in Cryo-EM and protein structure prediction algorithms have made large-scale protein structures accessible, paving the way for machine learning-based functional annotations.The field of geometric deep learning focuses on creating methods working on geometric data. An essential aspect of learning from protein structures is representing these structures as a geometric object (be it a grid, graph, or surface) and applying a learning method tailored to this representation. The performance of a given approach will then depend on both the representation and its corresponding learning method. In this paper, we investigate representing proteins as $\textit{3D mesh surfaces}$ and incorporate them into an established representation benchmark. Our first finding is that despite promising preliminary results, the surface representation alone does not seem competitive with 3D grids. Building on this, we introduce a synergistic approach, combining surface representations with graph-based methods, resulting in a general framework that incorporates both representations in learning. We show that using this combination, we are able to obtain state-of-the-art results across $\textit{all tested tasks}$. Our code and data can be found online: https://github.com/Vincentx15/atom2D .Comment: 10 page

3D-based RNA function prediction tools in rnaglib

Understanding the connection between complex structural features of RNA and biological function is a fundamental challenge in evolutionary studies and in RNA design. However, building datasets of RNA 3D structures and making appropriate modeling choices remains time-consuming and lacks standardization. In this chapter, we describe the use of rnaglib, to train supervised and unsupervised machine learning-based function prediction models on datasets of RNA 3D structures

VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network Motifs

RNA 3D motifs are recurrent substructures, modelled as networks of base pair interactions, which are crucial for understanding structure-function relationships. The task of automatically identifying such motifs is computationally hard, and remains a key challenge in the field of RNA structural biology and network analysis. State of the art methods solve special cases of the motif problem by constraining the structural variability in occurrences of a motif, and narrowing the substructure search space. Here, we relax these constraints by posing the motif finding problem as a graph representation learning and clustering task. This framing takes advantage of the continuous nature of graph representations to model the flexibility and variability of RNA motifs in an efficient manner. We propose a set of node similarity functions, clustering methods, and motif construction algorithms to recover flexible RNA motifs. Our tool, VeRNAl can be easily customized by users to desired levels of motif flexibility, abundance and size. We show that VeRNAl is able to retrieve and expand known classes of motifs, as well as to propose novel motifs

Le dimanche à Paris en 2030: Enquête sur les rythmes urbains

Economic, social and technical development have transformed the urban rhythms during the last 30 years and determining a new horizon for the cities. Among those rhythms, Sundays'one occupy a special symbolic place. This research program aims at questioning the transformation of Parisian rhythms. Specifically: How does socio-economical evolution change Sunday activities? What makes it still different from the other days of the week? Is it becoming normal to offer a city without interruption, constantly opened, on a 24/7? How will our Sundays look like in 2030? Confronting territorial changes with the customs and representations of the populations and with placed-bases projects will help to understand urban rhythms, as well as Sunday evolution trends on the 2030 horizon.Les évolutions économiques, sociales, religieuses et techniques bouleversent l’ensemble des rythmes urbains depuis une trentaine d'années et interrogent le devenir des villes. Parmi ceux-ci le dimanche occupe une place singulière. Cette recherche vise à interroger les transformations des rythmes urbains parisiens. De quelles manières les évolutions socio-économiques en cours transforment-elles le dimanche ? En quoi celui-ci reste-il différent des autres jours de la semaine ? Tend-t-il à se banaliser pour offrir une ville sans interruption, vivant 24 heures sur 24, 7 jours sur 7 ? Quel(s) dimanche(s) se profilent à l’horizon 2030

Tropical polyhedra are equivalent to mean payoff games

We show that several decision problems originating from max-plus or tropical convexity are equivalent to zero-sum two player game problems. In particular, we set up an equivalence between the external representation of tropical convex sets and zero-sum stochastic games, in which tropical polyhedra correspond to deterministic games with finite action spaces. Then, we show that the winning initial positions can be determined from the associated tropical polyhedron. We obtain as a corollary a game theoretical proof of the fact that the tropical rank of a matrix, defined as the maximal size of a submatrix for which the optimal assignment problem has a unique solution, coincides with the maximal number of rows (or columns) of the matrix which are linearly independent in the tropical sense. Our proofs rely on techniques from non-linear Perron-Frobenius theory.Comment: 28 pages, 5 figures; v2: updated references, added background materials and illustrations; v3: minor improvements, references update

Classification d'EEG pour les interfaces cerveau-machine

- Cet article propose une méthodologie pour la classification de signaux électro-encéphalogrammes (EEG) dans le cadre des interfaces cerveau-machine. L'algorithme que nous proposons est basé sur une approche mettant en oeuvre un mélange de classifieurs SVM linéaire. Chaque SVM est entrainé sur une partie des données d'apprentissage provenant d'une même session d'acquisition des données. Ainsi, le mélange de SVM permet de prendre en compte la variabilité des EEGs lors des differentes sessions de mesures. Combiner à une méthode permettant de sélectionner automatiquement les canaux EEGs pertinents, nous montrons que notre algorithme constitue l'état de l'art pour les données provenant de la compétition BCI 2003

Risk factors for post-ICU red blood cell transfusion: a prospective study

INTRODUCTION: Factors predictive of the need for red blood cell (RBC) transfusion in the intensive care unit (ICU) have been identified, but risk factors for transfusion after ICU discharge are unknown. This study aims identifies risk factors for RBC transfusion after discharge from the ICU. METHODS: A prospective, monocentric observational study was conducted over a 6-month period in a 24-bed medical ICU in a French university hospital. Between June and December 2003, 550 critically ill patients were consecutively enrolled in the study. RESULTS: A total of 428 patients survived after treatment in the ICU; 47 (11% of the survivors, 8.5% of the whole population) required RBC transfusion within 7 days after ICU discharge. Admission for sepsis (odds ratio [OR] 341.60, 95% confidence interval [CI] 20.35–5734.51), presence of an underlying malignancy (OR 32.6, 95%CI 3.8–280.1), female sex (OR 5.4, 95% CI 1.2–24.9), Logistic Organ Dysfunction score at ICU discharge (OR 1.45, 95% CI 1.1–1.9) and age (OR 1.06, 95% CI 1.02–1.12) were independently associated with RBC transfusion after ICU stay. Haemoglobin level at discharge predicted the need for delayed RBC transfusion. Use of vasopressors (OR 0.01, 95%CI 0.001–0.17) and haemoglobin level at discharge from the ICU (OR 0.02, 95% CI 0.007–0.09; P < 0.001) were strong independent predictors of transfusion of RBC 1 week after ICU discharge. CONCLUSION: Sepsis, underlying conditions, unresolved organ failures and haemoglobin level at discharge were related to an increased risk for RBC transfusion after ICU stay. We suggest that strategies to prevent transfusion should focus on homogeneous subgroups of patients and take into account post-ICU needs for RBC transfusion

How much information is needed to infer reticulate evolutionary histories?

Phylogenetic networks are a generalization of evolutionary trees and are an important tool for analyzing reticulate evolutionary histories. Recently, there has been great interest in developing new methods to construct rooted phylogenetic networks, that is, networks whose internal vertices correspond to hypothetical ancestors, whose leaves correspond to sampled taxa, and in which vertices with more than one parent correspond to taxa formed by reticulate evolutionary events such as recombination or hybridization. Several methods for constructing evolutionary trees use the strategy of building up a tree from simpler building blocks (such as triplets or clusters), and so it is natural to look for ways to construct networks from smaller networks. In this article, we shall demonstrate a fundamental issue with this approach. Namely, we show that even if we are given all of the subnetworks induced on all proper subsets of the leaves of some rooted phylogenetic network, we still do not have all of the information required to completely determine that network. This implies that even if all of the building blocks for some reticulate evolutionary history were to be taken as the input for any given network building method, the method might still output an incorrect history. We also discuss some potential consequences of this result for constructing phylogenetic networks

Swash Bar Effects On The Response Of A Large Barrier-Spit Terminus To Extreme Wave Climate: The Cap Ferret Example

International audienceThe succession of severe storms in the North-Eastern Atlantic during the 2013-2014 winter has generated exceptional erosion along the Gironde coast (SW France). Meanwhile, at the Southern extremity of this 110-km long sandy coastal stretch, the Cap Ferret barrier-spit terminus remained relatively stable. The spit terminus is flanked by the Bay of Arcachon tidal inlet which generates strong tidal currents that help local waves to build massive swash bars. Such a bar is seen as the main explanation to the contrasting behaviour observed throughout the winter