Benchmarking network propagation methods for disease gene identification

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genesPeer ReviewedPostprint (published version

Demodulation of Spatial Carrier Images: Performance Analysis of Several Algorithms Using a Single Image

http://link.springer.com/article/10.1007%2Fs11340-013-9741-6#Optical full-field techniques have a great importance in modern experimental mechanics. Even if they are reasonably spread among the university laboratories, their diffusion in industrial companies remains very narrow for several reasons, especially a lack of metrological performance assessment. A full-field measurement can be characterized by its resolution, bias, measuring range, and by a specific quantity, the spatial resolution. The present paper proposes an original procedure to estimate in one single step the resolution, bias and spatial resolution for a given operator (decoding algorithms such as image correlation, low-pass filters, derivation tools ...). This procedure is based on the construction of a particular multi-frequential field, and a Bode diagram representation of the results. This analysis is applied to various phase demodulating algorithms suited to estimate in-plane displacements.GDR CNRS 2519 “Mesures de Champs et Identification en Mécanique des Solide

The contribution of age structure to cell population responses to targeted therapeutics

Cells grown in culture act as a model system for analyzing the effects of anticancer compounds, which may affect cell behavior in a cell cycle position-dependent manner. Cell synchronization techniques have been generally employed to minimize the variation in cell cycle position. However, synchronization techniques are cumbersome and imprecise and the agents used to synchronize the cells potentially have other unknown effects on the cells. An alternative approach is to determine the age structure in the population and account for the cell cycle positional effects post hoc. Here we provide a formalism to use quantifiable age distributions from live cell microscopy experiments to parameterize an age-structured model of cell population response

Magnetic-responsive hydrogels for cartilage tissue engineering

Publicado em "Journal of Tissue Engineering and Regenerative Medicine", vol. 7, supp. 1 (2013)The use of magnetic nanoparticles (MNPs) has been explored as an alternative approach to overcome current limitations of regenerative medicine strategies. Cell engineering approaches where MNPs are incorporated within three-dimensional constructs, such as scaffolds or hydrogels may constitute a novel and attractive approach towards the development of a magnetically-responsive system. These systems would enable remote controlled actions over tissue engineered constructs in vitro and in vivo. Moreover, growing evidence suggests that the application of a magnetic field may enhance biological performance over commonly used static culture conditions providing stimulation for cell proliferation, migration and differentiation. In this work we analyze the role of magnetic stimulation on the behavior of human adipose derived stem cells (hASCs) laden in k-carrageenan hydrogels aiming at cartilage tissue engineering approaches. Thermo-responsive natural-based κ-carrageenan hydrogels were used as 3D templates since previous studies(1) report the adequate environment provided by these materials to support the viability and chondrogenic differentiation of several types of cells

Multi-Layered Films Containing a Biomimetic Stimuli-Responsive Recombinant Protein

Electrostatic self-assembly was used to fabricate new smart multi-layer coatings, using a recombinant elastin-like polymer (ELP) and chitosan as the counterion macromolecule. The ELP was bioproduced, purified and its purity and expected molecular weight were assessed. Aggregate size measurements, obtained by light scattering of dissolved ELP, were performed as a function of temperature and pH to assess the smart properties of the polymer. The build-up of multi-layered films containing ELP and chitosan, using a layer-by-layer methodology, was followed by quartz-crystal microbalance with dissipation monitoring. Atomic force microscopy analysis permitted to demonstrate that the topography of the multi-layered films could respond to temperature. This work opens new possibilities for the use of ELPs in the fabrication of biodegradable smart coatings and films, offering new platforms in biotechnology and in the biomedical area

AVLaughterCycle: Enabling a virtual agent to join in laughing with a conversational partner using a similarity-driven audiovisual laughter animation

peer reviewedThe AVLaughterCycle project aims at developing an audiovisual laughing machine, able to detect and respond to user's laughs. Laughter is an important cue to reinforce the engagement in human-computer interactions. As a first step toward this goal, we have implemented a system capable of recording the laugh of a user and responding to it with a similar laugh. The output laugh is automatically selected from an audiovisual laughter database by analyzing acoustic similarities with the input laugh. It is displayed by an Embodied Conversational Agent, animated using the audio-synchronized facial movements of the subject who originally uttered the laugh. The application is fully implemented, works in real time and a large audiovisual laughter database has been recorded as part of the project. This paper presents AVLaughterCycle, its underlying components, the freely available laughter database and the application architecture. The paper also includes evaluations of several core components of the application. Objective tests show that the similarity search engine, though simple, significantly outperforms chance for grouping laughs by speaker or type. This result can be considered as a first measurement for computing acoustic similarities between laughs. A subjective evaluation has also been conducted to measure the influence of the visual cues on the users' evaluation of similarity between laughs

HyperProbe consortium: innovate tumour neurosurgery with innovative photonic solutions

Recent advancements in imaging technologies (MRI, PET, CT, among others) have significantly improved clinical localisation of lesions of the central nervous system (CNS) before surgery, making possible for neurosurgeons to plan and navigate away from functional brain locations when removing tumours, such as gliomas. However, neuronavigation in the surgical management of brain tumours remains a significant challenge, due to the inability to maintain accurate spatial information of pathological and healthy locations intraoperatively. To answer this challenge, the HyperProbe consortium have been put together, consisting of a team of engineers, physicists, data scientists and neurosurgeons, to develop an innovative, all-optical, intraoperative imaging system based on (i) hyperspectral imaging (HSI) for rapid, multiwavelength spectral acquisition, and (ii) artificial intelligence (AI) for image reconstruction, morpho-chemical characterisation and molecular fingerprint recognition. Our HyperProbe system will (1) map, monitor and quantify biomolecules of interest in cerebral physiology; (2) be handheld, cost-effective and user-friendly; (3) apply AI-based methods for the reconstruction of the hyperspectral images, the analysis of the spatio-spectral data and the development and quantification of novel biomarkers for identification of glioma and differentiation from functional brain tissue. HyperProbe will be validated and optimised with studies in optical phantoms, in vivo against gold standard modalities in neuronavigational imaging, and finally we will provide proof of principle of its performances during routine brain tumour surgery on patients. HyperProbe aims at providing functional and structural information on biomarkers of interest that is currently missing during neuro-oncological interventions