Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks

The metabolism of organisms can be studied with comprehensive stoichiometric models of their metabolic networks. Flux balance analysis (FBA) calculates optimal metabolic performance of stoichiometric models. However, detailed biological interpretation of FBA is limited because, in general, a huge number of flux patterns give rise to the same optimal performance. The complete description of the resulting optimal solution spaces was thus far a computationally intractable problem. Here we present CoPE-FBA: Comprehensive Polyhedra Enumeration Flux Balance Analysis, a computational method that solves this problem. CoPE-FBA indicates that the thousands to millions of optimal flux patterns result from a combinatorial explosion of flux patterns in just a few metabolic sub-networks. The entire optimal solution space can now be compactly described in terms of the topology of these sub-networks. CoPE-FBA simplifies the biological interpretation of stoichiometric models of metabolism, and provides a profound understanding of metabolic flexibility in optimal states

Learning low-dimensional representations of shape data sets with diffeomorphic autoencoders

Auteur collectif : Alzheimer’s Disease Neuroimaging InitiativeInternational audienceContemporary deformation-based morphometry offers parametric classes of diffeomorphisms that can be searched to compute the optimal transformation that warps a shape into another, thus defining a similarity metric for shape objects. Extending such classes to capture the geometrical variability in always more varied statistical situations represents an active research topic. This quest for genericity however leads to computationally-intensive estimation problems. Instead, we propose in this work to learn the best-adapted class of diffeomorphisms along with its parametrization, for a shape data set of interest. Optimization is carried out with an auto-encoding variational inference approach, offering in turn a coherent model-estimator pair that we name diffeomorphic auto-encoder. The main contributions are: (i) an original network-based method to construct diffeomorphisms, (ii) a current-splatting layer that allows neural network architectures to process meshes, (iii) illustrations on simulated and real data sets that show differences in the learned statistical distributions of shapes when compared to a standard approach

Deep Group-wise Variational Diffeomorphic Image Registration

Deep neural networks are increasingly used for pair-wise image registration. We propose to extend current learning-based image registration to allow simultaneous registration of multiple images. To achieve this, we build upon the pair-wise variational and diffeomorphic VoxelMorph approach and present a general mathematical framework that enables both registration of multiple images to their geodesic average and registration in which any of the available images can be used as a fixed image. In addition, we provide a likelihood based on normalized mutual information, a well-known image similarity metric in registration, between multiple images, and a prior that allows for explicit control over the viscous fluid energy to effectively regularize deformations. We trained and evaluated our approach using intra-patient registration of breast MRI and Thoracic 4DCT exams acquired over multiple time points. Comparison with Elastix and VoxelMorph demonstrates competitive quantitative performance of the proposed method in terms of image similarity and reference landmark distances at significantly faster registration

Prevalence and Factors Associated with Intestinal Parasitic Infection among Children in an Urban Slum of Karachi

Background:Intestinal parasitic infections are endemic worldwide and have been described as constituting the greatest single worldwide cause of illness and disease. Poverty, illiteracy, poor hygiene, lack of access to potable water and hot and humid tropical climate are the factors associated with intestinal parasitic infections. The study aimed to estimate prevalence and identify factors associated with intestinal parasitic infections among 1 to 5 years old children residing in an urban slum of Karachi Pakistan. Methods And PrincipalFindings:A cross sectional survey was conducted from February to June 2006 in Ghosia Colony Gulshan Town Karachi, Pakistan. A simple random sample of 350 children aged 1-5 years was collected. The study used structured pre-tested questionnaire, anthropometric tools and stool tests to obtain epidemiological and disease data. Data were analyzed using appropriate descriptive, univariate and multivariable logistic regression methods. The mean age of participants was 2.8 years and 53% were male. The proportions of wasted, stunted and underweight children were 10.4%, 58.9% and 32.7% respectively. The prevalence of Intestinal parasitic infections was estimated to be 52.8% (95% CI: 46.1, 59.4). Giardia lamblia was the most common parasite followed by Ascaris lumbricoides, Blastocystis hominis and Hymenolepis nana. About 43% children were infected with single parasite and 10% with multiple parasites. Age {Adjusted Odds Ratio (aOR) = 1.5, 95% CI: 1.1, 1.9}, living in rented households (aOR = 2.0, 95% CI: 1.0, 3.9) and history of excessive crying (aOR = 1.9, 95% CI: 1.0, 3.4) were significantly associated with intestinal parasitic infections.Conclusion:Intestinal parasites are highly prevalent in this setting and poverty was implicated as an important risk factor for infection. Effective poverty reduction programmes and promotion of deworming could reduce intestinal parasite carriage. There is a need for mass scale campaigns to create awareness about health and hygiene

Spectral Log-Demons: Diffeomorphic Image Registration with Very Large Deformations

International audienceThis paper presents a new framework for capturing large and complex deformations in image registration and atlas construction. This challenging and recurrent problem in computer vision and medical imaging currently relies on iterative and local approaches, which are prone to local minima and, therefore, limit present methods to relatively small deformations. Our general framework introduces to this effect a new direct feature matching technique that finds global correspondences between images via simple nearest-neighbor searches. More specifically, very large image deformations are captured in Spectral Forces, which are derived from an improved graph spectral representation. We illustrate the benefits of our framework through a new enhanced version of the popular Log-Demons algorithm, named the Spectral Log-Demons, as well as through a groupwise extension, named the Groupwise Spectral Log-Demons, which is relevant for atlas construction. The evaluations of these extended versions demonstrate substantial improvements in accuracy and robustness to large deformations over the conventional Demons approaches

Human malarial disease: a consequence of inflammatory cytokine release

Malaria causes an acute systemic human disease that bears many similarities, both clinically and mechanistically, to those caused by bacteria, rickettsia, and viruses. Over the past few decades, a literature has emerged that argues for most of the pathology seen in all of these infectious diseases being explained by activation of the inflammatory system, with the balance between the pro and anti-inflammatory cytokines being tipped towards the onset of systemic inflammation. Although not often expressed in energy terms, there is, when reduced to biochemical essentials, wide agreement that infection with falciparum malaria is often fatal because mitochondria are unable to generate enough ATP to maintain normal cellular function. Most, however, would contend that this largely occurs because sequestered parasitized red cells prevent sufficient oxygen getting to where it is needed. This review considers the evidence that an equally or more important way ATP deficency arises in malaria, as well as these other infectious diseases, is an inability of mitochondria, through the effects of inflammatory cytokines on their function, to utilise available oxygen. This activity of these cytokines, plus their capacity to control the pathways through which oxygen supply to mitochondria are restricted (particularly through directing sequestration and driving anaemia), combine to make falciparum malaria primarily an inflammatory cytokine-driven disease

Parallel transport in shape analysis : a scalable numerical scheme

International audienceThe analysis of manifold-valued data requires efficient tools from Riemannian geometry to cope with the computational complexity at stake. This complexity arises from the always-increasing dimension of the data, and the absence of closed-form expressions to basic operations such as the Riemannian logarithm. In this paper, we adapt a generic numerical scheme recently introduced for computing parallel transport along geodesics in a Riemannian manifold to finite-dimensional manifolds of diffeomorphisms. We provide a qualitative and quantitative analysis of its behavior on high-dimensional manifolds, and investigate an application with the prediction of brain structures progression