Sparse, hierarchical and shared-factors priors for representation learning

La représentation en caractéristiques est une préoccupation centrale des systèmes d’apprentissage automatique d’aujourd’hui. Une représentation adéquate peut faciliter une tâche d’apprentissage complexe. C’est le cas lorsque par exemple cette représentation est de faible dimensionnalité et est constituée de caractéristiques de haut niveau. Mais comment déterminer si une représentation est adéquate pour une tâche d’apprentissage ? Les récents travaux suggèrent qu’il est préférable de voir le choix de la représentation comme un problème d’apprentissage en soi. C’est ce que l’on nomme l’apprentissage de représentation. Cette thèse présente une série de contributions visant à améliorer la qualité des représentations apprises. La première contribution élabore une étude comparative des approches par dictionnaire parcimonieux sur le problème de la localisation de points de prises (pour la saisie robotisée) et fournit une analyse empirique de leurs avantages et leurs inconvénients. La deuxième contribution propose une architecture réseau de neurones à convolution (CNN) pour la détection de points de prise et la compare aux approches d’apprentissage par dictionnaire. Ensuite, la troisième contribution élabore une nouvelle fonction d’activation paramétrique et la valide expérimentalement. Finalement, la quatrième contribution détaille un nouveau mécanisme de partage souple de paramètres dans un cadre d’apprentissage multitâche.Feature representation is a central concern of today’s machine learning systems. A proper representation can facilitate a complex learning task. This is the case when for instance the representation has low dimensionality and consists of high-level characteristics. But how can we determine if a representation is adequate for a learning task? Recent work suggests that it is better to see the choice of representation as a learning problem in itself. This is called Representation Learning. This thesis presents a series of contributions aimed at improving the quality of the learned representations. The first contribution elaborates a comparative study of Sparse Dictionary Learning (SDL) approaches on the problem of grasp detection (for robotic grasping) and provides an empirical analysis of their advantages and disadvantages. The second contribution proposes a Convolutional Neural Network (CNN) architecture for grasp detection and compares it to SDL. Then, the third contribution elaborates a new parametric activation function and validates it experimentally. Finally, the fourth contribution details a new soft parameter sharing mechanism for multitasking learning

Cross-validation of ELISA and a portable surface plasmon resonance instrument for IgG antibody serology with SARS-CoV-2 positive individuals.

We report on the development of surface plasmon resonance (SPR) sensors and matching ELISAs for the detection of nucleocapsid and spike antibodies specific to the novel coronavirus 2019 (SARS-CoV-2) in human serum, plasma and dried blood spots (DBS)

Inferring Biochemical Reactions and Metabolite Structures to Understand Metabolic Pathway Drift

International audienceInferring genome-scale metabolic networks in emerging model organisms is challenged by incomplete biochemical knowledge and partial conservation of biochemical pathways during evolution. Therefore, specific bioinformatic tools are necessary to infer biochemical reactions and metabolic structures that can be checked experimentally. Using an integrative approach combining genomic and metabolomic data in the red algal model Chondrus crispus, we show that, even metabolic pathways considered as conserved, like sterols or mycosporine-like amino acid synthesis pathways, undergo substantial turnover. This phenomenon, here formally defined as “metabolic pathway drift,” is consistent with findings from other areas of evolutionary biology, indicating that a given phenotype can be conserved even if the underlying molecular mechanisms are changing. We present a proof of concept with a methodological approach to formalize the logical reasoning necessary to infer reactions and molecular structures, abstracting molecular transformations based on previous biochemical knowledge

Cross-Validation of ELISA and a Portable Surface Plasmon Resonance Instrument for IgG Antibodies Serology with SARS-CoV-2 Positive Individuals

We report on the development of surface plasmon resonance (SPR) sensors and matching ELISAs for the detection of nucleocapsid and spike antibodies specific against the novel coronavirus 2019 (SARS-CoV-2) in human serum, plasma and dried blood spots (DBS). When exposed to SARS-CoV-2 or a vaccine against SARS-CoV-2, the immune system responds by expressing antibodies at levels that can be detected and monitored to identify the fraction of the population potentially immunized against SARS-CoV-2 and support efforts to deploy a vaccine strategically. A SPR sensor coated with a peptide monolayer and functionalized with various sources of SARS-CoV-2 recombinant proteins expressed in different cell lines detected human anti-SARS-CoV-2 IgG in the nanomolar range. Nucleocapsid expressed in different cell lines did not significantly change the sensitivity of the assays, whereas the use of a CHO cell line to express spike ectodomain led to excellent performance. This bioassay was performed on a portable SPR instrument capable of measuring 4 biological samples within 30 minutes of sample/sensor contact and the chip could be regenerated at least 9 times. Multi-site validation was then performed with in-house and commercial ELISA, which revealed excellent cross-correlations with Pearson’s coefficients exceeding 0.85 in all cases, for measurements in DBS and plasma. This strategy paves the way to point-of-care and rapid testing for antibodies in the context of viral infection and vaccine efficacy monitoring

The genome of Ectocarpus subulatus – a highly stress-tolerant brown alga

International audienceBrown algae are multicellular photosynthetic stramenopiles that colonize marine rocky shores worldwide. Ectocarpus sp. Ec32 has been established as a genomic model for brown algae. Here we present the genome and metabolic network of the closely related species, Ectocarpus subulatus Kützing, which is characterized by high abiotic stress tolerance. Since their separation, both strains show new traces of viral sequences and the activity of large retrotransposons, which may also be related to the expansion of a family of chlorophyll-binding proteins. Further features suspected to contribute to stress tolerance include an expanded family of heat shock proteins, the reduction of genes involved in the production of halogenated defence compounds, and the presence of fewer cell wall polysaccharide-modifying enzymes. Overall, E. subulatus has mainly lost members of gene families down-regulated in low salinities, and conserved those that were up-regulated in the same condition. However, 96% of genes that differed between the two examined Ectocarpus species, as well as all genes under positive selection, were found to encode proteins of unknown function. This underlines the uniqueness of brown algal stress tolerance mechanisms as well as the significance of establishing E. subulatus as a comparative model for future functional studies