Contribution of finite element analyses to crack-like flaw assessments in a gas pipeline

Peer reviewedPostprin

Prioritizing protein complexes implicated in human diseases by network optimization.

BACKGROUND: The detection of associations between protein complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related protein complexes. RESULTS: We propose a method, MAXCOM, for the prioritization of candidate protein complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate protein complexes through a heterogeneous network that is constructed by combining protein-protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 protein complexes show that MAXCOM can rank 382 (70.87%) protein complexes at the top against protein complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze protein complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer. CONCLUSIONS: MAXCOM is an effective method for the discovery of disease-related protein complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple proteins

Exposure to ambient air pollution and cognitive decline: Results of the prospective Three-City cohort study

BACKGROUND: Growing epidemiological evidence suggests an adverse relationship between exposure to air pollutants and cognitive decline. However, there is still some heterogeneity in the findings, with inconsistent results depending on the pollutant and the cognitive domain considered. We wanted to determine whether air pollution was associated with global and domain-specific cognitive decline. METHODS: This analysis used data from the French Three-City prospective cohort (participants aged 65 and older at recruitment and followed for up to 12 years). A battery of cognitive tests was administered at baseline and every 2 years, to assess global cognition (Mini Mental State Examination, MMSE), visual memory (Benton Visual Retention Test), semantic fluency (Isaacs Set Test) and executive functions (Trail Making Tests A and B). Exposure to fine particulate matter (PM(2.5)), nitrogen dioxide (NO(2)) and black carbon (BC) at the participants' residential address during the 5 years before the baseline visit was estimated with land use regression models. Linear mixed models and latent process mixed models were used to assess the association of each pollutant with global and domain-specific cognitive decline. RESULTS: The participants' (n = 6380) median age was 73.4 years (IQR: 8.0), and 61.5% were women. At baseline, the median MMSE score was 28 (IQR: 3). Global cognition decline, assessed with the MMSE, was slightly accelerated among participants with higher PM(2.5) exposure: one IQR increment in PM(2.5) (1.5 µg/m(3)) was associated with accelerated decline (β: -0.0060 [-0.0112; -0.0007] standard unit per year). Other associations were inconsistent in direction, and of small magnitude. CONCLUSION: In this large population-based cohort, higher PM(2.5) exposure was associated with accelerated global cognition decline. We did not detect any significant association for the specific cognitive domains or the other pollutants. Evidence concerning PM(2.5) effects on cognition is growing, but more research is needed on other ambient air pollutants

N-glycosylation of mouse TRAIL-R and human TRAIL-R1 enhances TRAIL-induced death.

APO2L/TRAIL (TNF-related apoptosis-inducing ligand) induces death of tumor cells through two agonist receptors, TRAIL-R1 and TRAIL-R2. We demonstrate here that N-linked glycosylation (N-glyc) plays also an important regulatory role for TRAIL-R1-mediated and mouse TRAIL receptor (mTRAIL-R)-mediated apoptosis, but not for TRAIL-R2, which is devoid of N-glycans. Cells expressing N-glyc-defective mutants of TRAIL-R1 and mouse TRAIL-R were less sensitive to TRAIL than their wild-type counterparts. Defective apoptotic signaling by N-glyc-deficient TRAIL receptors was associated with lower TRAIL receptor aggregation and reduced DISC formation, but not with reduced TRAIL-binding affinity. Our results also indicate that TRAIL receptor N-glyc impacts immune evasion strategies. The cytomegalovirus (CMV) UL141 protein, which restricts cell-surface expression of human TRAIL death receptors, binds with significant higher affinity TRAIL-R1 lacking N-glyc, suggesting that this sugar modification may have evolved as a counterstrategy to prevent receptor inhibition by UL141. Altogether our findings demonstrate that N-glyc of TRAIL-R1 promotes TRAIL signaling and restricts virus-mediated inhibition

Point Collocation Methods for Linear Elasticity Problems

Point collocation is the oldest way to solve partial differential equations. Methods based on collocation have been studied since decades and many variations have been proposed over the years. More recently, those methods have shown a greater interest thanks to the advances in computing hardware. The collocation methods offer a great flexibility with regards to the discretization of a defined domain and the approximation of the field derivatives. This presentation will introduce the bases of the collocation methods and of the generalized finite difference method. The importance of the selection of the nodes involved in the approximation of the field derivatives will then be presented. Finally two aspects for which the method is particularly attractive will be detailed: the solution of a PDE from a given geometry with minimum discretization effort and the adaptivity of a model based on a posteriori error estimation

Smart cloud collocation: a unified workflow from CAD to enhanced solutions

Computer Aided Design (CAD) software packages are used in the industry to design mechanical systems. Then, calculations are often performed using simulation software packages to improve the quality of the design. To speed up the development costs, companies and research centers have been trying to ease the integration of the computation phase in the design phase. The collocation methods have the potential of easing such integration thanks to their meshless nature. The geometry discretization step which is a key element of all computational method is simplified compared to mesh-based methods such as the finite element method. We propose in this thesis a unified workflow that allows the solution of engineering problems defined by partial differential equations (PDEs) directly from input CAD files. The scheme is based on point collocation methods and proposed techniques to enhance the solution. We introduce the idea of “smart clouds”. Smart clouds refer to point cloud discretizations that are aware of the exact CAD geometry, appropriate to solve a defined problem using a point collocation method and that contain information used to improve locally the solution. We introduce a unified node selection algorithm based on a generalization of the visibility criterion. The proposed algorithm leads to a significant reduction of the error for concave problems and does not have any drawback for convex problems. The point collocation methods rely on many parameters. We select in this thesis parameters for the Generalized Finite Difference (GFD) method and the Discretization-Corrected Particle Strength Exchange (DC PSE) method that we deem appropriate for most problems from the field of linear elasticity. We also show that solution improvement techniques, based on the use of Voronoi diagrams or on a stabilization of the PDE, do not lead to a reduction of the error for all of the considered benchmark problems. These methods shall therefore be used with care. We propose two types of a posteriori error indicators that both succeed in identifying the areas of the domain where the error is the greatest: a ZZ-type and a residual-type error indicator. We couple these indicators to a h-adaptive refinement scheme and show that the approach is effective. Finally, we show the performance of Algebraic Multigrid (AMG) preconditions on the solution of linear systems compared to other preconditioning/solution methods. This family of preconditioners necessitates the selection of a large number of parameters. We assess the impact of some of them on the solution time for a 3D problem from the field of linear elasticity. Despite the performance of AMG preconditions, ILU preconditioners may be preferred thanks to their ease of usage and robustness to lead to a convergence of the solution

Walking on a Tissue-Specific Disease-Protein-Complex Heterogeneous Network for the Discovery of Disease-Related Protein Complexes

Besides the pinpointing of individual disease-related genes, associating protein complexes to human inherited diseases is also of great importance, because a biological function usually arises from the cooperative behaviour of multiple proteins in a protein complex. Moreover, knowledge about disease-related protein complexes could also enhance the inference of disease genes and pathogenic genetic variants. Here, we have designed a computational systems biology approach to systematically analyse potential relationships between diseases and protein complexes. First, we construct a heterogeneous network which is composed of a disease-disease similarity layer, a tissue-specific protein-protein interaction layer, and a protein complex membership layer. Then, we propose a random walk model on this disease-protein-complex network for identifying protein complexes that are related to a query disease. With a series of leave-one-out cross-validation experiments, we show that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We further predict a landscape of associations between human diseases and protein complexes. This landscape can be used to facilitate the inference of disease genes, thereby benefiting studies on pathology of diseases