Implications of Inconsistencies between fMRI and dMRI on Multimodal Connectivity Estimation

International audienceThere is a recent trend towards integrating resting state functional magnetic resonance imaging (RS-fMRI) and diffusion MRI (dMRI) for brain connectivity estimation, as motivated by how estimates from these modalities are presumably two views reflecting the same underlying brain circuitry. In this paper, we show on a cohort of 60 subjects that conventional functional connectivity (FC) estimates based on Pearson's correlation and anatomical connectivity (AC) estimates based on fiber counts are actually not that highly correlated for typical RS-fMRI (~7 min) and dMRI (~32 gradient directions) data. The FC-AC correlation can be significantly increased by considering sparse partial correlation and modeling fiber endpoint uncertainty, but the resulting FC-AC correlation is still rather low in absolute terms. We further exemplify the inconsistencies between FC and AC estimates by integrating them as priors into activation detection and demonstrating significant differences in their detection sensitivity. Importantly, we illustrate that these inconsistencies can be useful in fMRI-dMRI integration for improving brain connectivity estimation

A Novel Sparse Group Gaussian Graphical Model for Functional Connectivity Estimation

International audienceThe estimation of intra-subject functional connectivity is greatly complicated by the small sample size and complex noise structure in functional magnetic resonance imaging (fMRI) data. Pooling samples across subjects improves the conditioning of the estimation, but loses subject-specific connectivity information. In this paper, we propose a new sparse group Gaussian graphical model (SGGGM) that facilitates joint estimation of intra-subject and group-level connectivity. This is achieved by casting functional connectivity estimation as a regularized consensus optimization problem, in which information across subjects is aggregated in learning group-level connectivity and group information is propagated back in estimating intra-subject connectivity. On synthetic data, we show that incorporating group information using SGGGM significantly enhances intra-subject connectivity estimation over existing techniques. More accurate group-level connectivity is also obtained. On real data from a cohort of 60 subjects, we show that integrating intra-subject connectivity estimated with SGGGM significantly improves brain activation detection over connectivity priors derived from other graphical modeling approaches

Long-term viral competition monitoring: a case of epidemiological rescue

Biological invasions are major threats to biodiversity and the main causes of emerging viral diseases. The ongoing spread of Tomato yellow leaf curl virus is a major concern to the sustainable tomato production throughout the world. The two main strains of TYLCV have been successively introduced in Reunion Island providing a fortuitous field experiment to study the invasion and competition of these two emerging strains in a tropical and insular environment. In this study, a seven-year field survey was performed following the introduction of the Israel strain of TYLCV (TYLCV-IL) into a niche occupied by the Mild strain of TYLCV (TYLCV-Mld). A displacement of the resident TYLCV-Mld by the newcomer TYLCV-IL was observed in this short period. To understand the factors associated with this displacement, biological traits related to fitness were measured to compare these strains. Besides demonstrating a better ecological aptitude of TYLCV-IL, which explains its rapid spread and increasing prevalence, the first estimate of the number of viral particles efficiently transmitted by an insect vector for a circulative virus was obtained. However, TYLCV-Mld persistence in the field (especially in mixed infections with TYLCV-IL) spurred further experiments regarding the effects of the mixed infections on these biological traits. Our study revealed complex interplay between these two strains of one of the most emergent plant virus following their successive introductions in the insular and tropical environment of Reunion Island. This rare case of unilateral facilitation between two pathogens led to the epidemiological rescue and maintenance of the less fit strain. (Texte intégral

An Empirical Study of the Usage of Checksums for Web Downloads

Checksums, typically provided on webpages and generated from cryptographic hash functions (e.g., MD5, SHA256) or signature schemes (e.g., PGP), are commonly used on websites to enable users to verify that the files they download have not been tampered with when stored on possibly untrusted servers. In this paper, we shed light on the current practices regarding the usage of checksums for web downloads (hash functions used, visibility and validity of checksums, type of websites and files, presence of instructions, etc.), as this has been mostly overlooked so far. Using a snowball-sampling strategy for the 200,000 most popular domains of the Web, we first crawled a dataset of 8.5M webpages, from which we built, through an active-learning approach, a unique dataset of 277 diverse webpages that contain checksums. Our analysis of these webpages reveals interesting findings about the usage of checksums. For instance, it shows that checksums are used mostly to verify program files, that weak hash functions are frequently used and that a non-negligible proportion of the checksums provided on webpages do not match that of their associated files. We make freely available our dataset and the code for collecting and analyzing it. Finally, we complement our analysis with a survey of the webmasters of the considered webpages (26 complete responses), shedding light on the reasons behind the checksum-related choices they make

Getting the most out of it: optimal experiments for parameter estimation of microalgae growth models

International audienceMathematical models are expected to play a pivotal role for driving microalgal production towards a profitable process of renewable energy generation. To render models of microalgae growth useful tools for prediction and process optimization, reliable parameters need to be provided. This reliability implies a careful design of experiments that can be exploited for parameter estimation. In this paper, we provide guidelines for the design of experiments with high informative content based on optimal experiment techniques to attain an accurate parameter estimation. We study a real experimental device devoted to evaluate the effect of temperature and light on microalgae growth. On the basis of a mathematical model of the experimental system, the optimal experiment design problem was formulated and solved with both static (constant light and temperature) and dynamic (time varying light and temperature) approaches. Simulation results indicated that the optimal experiment design allows for a more accurate parameter estimation than that provided by the existing experimental protocol. For its efficacy in terms of the maximum likelihood properties and its practical aspects of implementation, the dynamic approach is recommended over the static approach

On the Use of Dependencies in Relation Classification of Text with Deep Learning

International audienceDeep Learning is more and more used in NLP tasks, such as in relation classification of texts. This paper assesses the impact of syntactic dependencies in this task at two levels. The first level concerns the generic Word Embedding (WE) as input of the classification model, the second level concerns the corpus whose relations have to be classified. In this paper, two classification models are studied, the first one is based on a CNN using a generic WE and does not take into account the dependencies of the corpus to be treated, and the second one is based on a compositional WE combining a generic WE with syntactical annotations of this corpus to classify. The impact of dependencies in relation classification is estimated using two different WE. The first one is essentially lexical and trained on the Wikipedia corpus in English, while the second one is also syntactical, trained on the same previously annotated corpus with syntactical dependencies. The two classification models are evaluated on the SemEval 2010 reference corpus using these two generic WE. The experiments show the importance of taking dependencies into account at different levels in the relation classification

Connectivity-informed Sparse Classifiers for fMRI Brain Decoding

International audienceIn recent years, sparse regularization has become a dominant means for handling the curse of dimensionality in functional magnetic resonance imaging (fMRI) based brain decoding problems. Enforcing sparsity alone, however, neglects the interactions between connected brain areas. Methods that additionally impose spatial smoothness would account for local but not long-range interactions. In this paper, we propose incorporating connectivity into sparse classifier learning so that both local and long-range connections can be jointly modeled. On real data, we demonstrate that integrating connectivity information inferred from diffusion tensor imaging (DTI) data provides higher classification accuracy and more interpretable classifier weight patterns than standard classifiers. Our results thus illustrate the benefits of adding neurologically-relevant priors in fMRI brain decoding

Rapid accumulation and low degradation: Key parameters of Tomato yellow leaf curl virus persistence in its insect vector Bemisia tabaci

Of worldwide economic importance, Tomato yellow leaf curl virus (TYLCV, Begomovirus) is responsible for one of the most devastating plant diseases in warm and temperate regions. The DNA begomoviruses (Geminiviridae) are transmitted by the whitefly species complex Bemisia tabaci. Although geminiviruses have long been described as circulative non-propagative viruses, observations such as long persistence of TYLCV in B. tabaci raised the question of their possible replication in the vector. We monitored two major TYLCV strains, Mild (Mld) and Israel (IL), in the invasive B. tabaci Middle East-Asia Minor 1 cryptic species, during and after the viral acquisition, within two timeframes (0–144 hours or 0–20 days). TYLCV DNA was quantified using real-time PCR, and the complementary DNA strand of TYLCV involved in viral replication was specifically quantified using anchored real-time PCR. The DNA of both TYLCV strains accumulated exponentially during acquisition but remained stable after viral acquisition had stopped. Neither replication nor vertical transmission were observed. In conclusion, our quantification of the viral loads and complementary strands of both Mld and IL strains of TYLCV in B. tabaci point to an efficient accumulation and preservation mechanism, rather than to a dynamic equilibrium between replication and degradation. (Résumé d'auteur