3D synchrotron laminography assessment of damage evolution in blanked dual phase steels

International audienceThe mechanical performance of automotive structures made of advanced high strength steels (AHSS) is often seen reduced by the presence of cut-edges. Here an attempt is made to gain insight into the initial damage state and the damage evolution during loading of a cut-edge. This is assessed in 3D and in-situ by synchrotron laminography observation during simultaneous tensile and bending loading of a cut-edge produced by stamping. Laminography is a technique that allows to observe regions of interest in thin sheet-like objects. It is found for the DP600 laboratory steel grade that the fracture zone is very rough and that needle voids from the surface and in the material bulk follow ferrite-martensite flow lines. During loading the needle voids grow from the fracture zone surface and coalesce with voids in the bulk. The needle cracks coalesce with the burnish zone though narrow zones, called void sheets. The formed cracks are inclined by 45° compared to the load direction

Plasma neuregulin 1 as a synaptic biomarker in Alzheimer's disease: a discovery cohort study

BACKGROUND: Synaptic dysfunction is an early core feature of Alzheimer's disease (AD), closely associated with cognitive symptoms. Neuregulin 1 (NRG1) is a growth and differentiation factor with a key role in the development and maintenance of synaptic transmission. Previous reports have shown that changes in cerebrospinal fluid (CSF) NRG1 concentration are associated with cognitive status and biomarker evidence of AD pathology. Plasma biomarkers reflecting synaptic impairment would be of great clinical interest. OBJECTIVE: To measure plasma NRG1 concentration in AD patients in comparison with other neurodegenerative disorders and neurological controls (NC) and to study its association with cerebrospinal fluid (CSF) core AD and synaptic biomarkers. METHODS: This retrospective study enrolled 127 participants including patients with AD at mild cognitive impairment stage (AD-MCI, n = 27) and at dementia stage (n = 35), non-AD dementia (n = 26, Aβ-negative), non-AD MCI (n = 19), and neurological controls (n=20). Plasma and CSF NRG1, as well as CSF core AD biomarkers (Aβ 42/Aβ 40 ratio, phospho-tau, and total tau), were measured using ELISA. CSF synaptic markers were measured using ELISA for GAP-43 and neurogranin and through immunoprecipitation mass spectrometry for SNAP-25. RESULTS: Plasma NRG1 concentration was higher in AD-MCI and AD dementia patients compared with neurological controls (respectively P = 0.005 and P < 0.001). Plasma NRG1 differentiated AD MCI patients from neurological controls with an area under the curve of 88.3%, and AD dementia patients from NC with an area under the curve of 87.3%. Plasma NRG1 correlated with CSF NRG1 (β = 0.372, P = 0.0056, adjusted on age and sex). Plasma NRG1 was associated with AD CSF core biomarkers in the whole cohort and in Aβ-positive patients (β = -0.197-0.423). Plasma NRG1 correlated with CSF GAP-43, neurogranin, and SNAP-25 (β = 0.278-0.355). Plasma NRG1 concentration correlated inversely with MMSE in the whole cohort and in Aβ-positive patients (all, β = -0.188, P = 0.038; Aβ+: β = -0.255, P = 0.038). CONCLUSION: Plasma NRG1 concentration is increased in AD patients and correlates with CSF core AD and synaptic biomarkers and cognitive status. Thus, plasma NRG1 is a promising non-invasive biomarker to monitor synaptic impairment in AD

A Pragmatic, Data-Driven Method to Determine Cutoffs for CSF Biomarkers of Alzheimer Disease Based on Validation Against PET Imaging

OBJECTIVE: To elaborate a new algorithm to establish a standardized method to define cuff-offs for CSF biomarkers of Alzheimer's disease (AD) by validating the algorithm against CSF classification derived from PET imaging. METHODS: Low and high levels of CSF phosphorylated tau were first identified to establish optimal cut-offs for CSF amyloid-β peptide (Aβ) biomarkers. These Aβ cut-offs were then used to determine cut-offs for CSF tau and phosphorylated tau markers. We compared this algorithm to a reference method, based on tau and amyloid PET imaging status (ADNI study), and then applied the algorithm to 10 large clinical cohorts of patients. RESULTS: A total of 6,922 subjects with CSF biomarkers data were included (mean (SD) age: 70.6 (8.5) years, 51.0% women). In the ADNI study population (n=497), the agreement between classification based on our algorithm and one based on amyloid/tau PET imaging was high with Cohen's kappa coefficient between 0.87 and 0.99. Applying the algorithm to 10 large cohorts of patients (n=6,425), the proportion of persons with AD ranged from 25.9% to 43.5%. DISCUSSION: The proposed novel, pragmatic method to determine CSF biomarkers cut-offs for AD does not require assessment of other biomarkers or assumptions concerning the clinical diagnosis of patients. Use of this standardized algorithm is likely to reduce heterogeneity in AD classification

Accounting for Population Stratification in Practice: A Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies

Genome-Wide Association Studies are powerful tools to detect genetic variants associated with diseases. Their results have, however, been questioned, in part because of the bias induced by population stratification. This is a consequence of systematic differences in allele frequencies due to the difference in sample ancestries that can lead to both false positive or false negative findings. Many strategies are available to account for stratification but their performances differ, for instance according to the type of population structure, the disease susceptibility locus minor allele frequency, the degree of sampling imbalanced, or the sample size. We focus on the type of population structure and propose a comparison of the most commonly used methods to deal with stratification that are the Genomic Control, Principal Component based methods such as implemented in Eigenstrat, adjusted Regressions and Meta-Analyses strategies. Our assessment of the methods is based on a large simulation study, involving several scenarios corresponding to many types of population structures. We focused on both false positive rate and power to determine which methods perform the best. Our analysis showed that if there is no population structure, none of the tests led to a bias nor decreased the power except for the Meta-Analyses. When the population is stratified, adjusted Logistic Regressions and Eigenstrat are the best solutions to account for stratification even though only the Logistic Regressions are able to constantly maintain correct false positive rates. This study provides more details about these methods. Their advantages and limitations in different stratification scenarios are highlighted in order to propose practical guidelines to account for population stratification in Genome-Wide Association Studies

Plos Med

Background The ε4 allele of apolipoprotein E (APOE) gene and increasing age are two of the most important known risk factors for developing Alzheimer disease (AD). The diagnosis of AD based on clinical symptoms alone is known to have poor specificity; recently developed diagnostic criteria based on biomarkers that reflect underlying AD neuropathology allow better assessment of the strength of the associations of risk factors with AD. Accordingly, we examined the global and age-specific association between APOE genotype and AD by using the A/T/N classification, relying on the cerebrospinal fluid (CSF) levels of β-amyloid peptide (A, β-amyloid deposition), phosphorylated tau (T, pathologic tau), and total tau (N, neurodegeneration) to identify patients with AD. Methods and findings This case–control study included 1,593 white AD cases (55.4% women; mean age 72.8 [range = 44–96] years) with abnormal values of CSF biomarkers from nine European memory clinics and the American Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. A total of 11,723 dementia-free controls (47.1% women; mean age 65.6 [range = 44–94] years) were drawn from two longitudinal cohort studies (Whitehall II and Three-City), in which incident cases of dementia over the follow-up were excluded from the control population. Odds ratio (OR) and population attributable fraction (PAF) for AD associated with APOE genotypes were determined, overall and by 5-year age categories. In total, 63.4% of patients with AD and 22.6% of population controls carried at least one APOE ε4 allele. Compared with non-ε4 carriers, heterozygous ε4 carriers had a 4.6 (95% confidence interval 4.1–5.2; p < 0.001) and ε4/ε4 homozygotes a 25.4 (20.4–31.2; p < 0.001) higher OR of AD in unadjusted analysis. This association was modified by age (p for interaction < 0.001). The PAF associated with carrying at least one ε4 allele was greatest in the 65–70 age group (69.7%) and weaker before 55 years (14.2%) and after 85 years (22.6%). The protective effect of APOE ε2 allele for AD was unaffected by age. Main study limitations are that analyses were based on white individuals and AD cases were drawn from memory centers, which may not be representative of the general population of patients with AD. Conclusions In this study, we found that AD diagnosis based on biomarkers was associated with APOE ε4 carrier status, with a higher OR than previously reported from studies based on only clinical AD criteria. This association differs according to age, with the strongest effect at 65–70 years. These findings highlight the need for early interventions for dementia prevention to mitigate the effect of APOE ε4 at the population level

Méthodes statistiques pour la prise en compte de différentes sources de biais dans les études d'association à grande échelle

Les études d'association à grande échelle sont devenus un outil très performant pour détecter les variants génétiques associés aux maladies. Ce manuscrit de doctorat s'intéresse à plusieurs des aspects clés des nouvelles problématiques informatiques et statistiques qui ont émergé grâce à de telles recherches. Les résultats des études d'association à grande échelle sont critiqués, en partie, à cause du biais induit par la stratification des populations. Nous proposons une étude de comparaison des stratégies qui existent pour prendre en compte ce problème. Leurs avantages et limites sont discutés en s'appuyant sur divers scénarios de structure des populations dans le but de proposer des conseils et indications pratiques. Nous nous intéressons ensuite à l'interférence de la structure des populations dans la recherche génétique. Nous avons développé au cours de cette thèse un nouvel algorithme appelé SHIPS (Spectral Hierarchical clustering for the Inference of Population Structure). Cet algorithme a été appliqué à un ensemble de jeux de données simulés et réels, ainsi que de nombreux autres algorithmes utilisés en pratique à titre de comparaison. Enfin, la question du test multiple dans ces études d'association est abordée à plusieurs niveaux. Nous proposons une présentation générale des méthodes de tests multiples et discutons leur validité pour différents designs d'études. Nous nous concertons ensuite sur l'obtention de résultats interprétables aux niveaux de gènes, ce qui correspond à une problématique de tests multiples avec des tests dépendants. Nous discutons et analysons les différentes approches dédiées à cette fin.Genome-Wide association studies have become powerful tools to detect genetic variants associated with diseases. This PhD thesis focuses on several key aspects of the new computational and methodological problematics that have arisen with such research. The results of Genome-Wide association studies have been questioned, in part because of the bias induced by population stratification. Many stratégies are available to account for population stratification scenarios are highlighted in order to propose pratical guidelines to account for population stratification. We then focus on the inference of population structure that has many applications for genetic research. We have developed and present in this manuscript a new clustering algoritm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS). This algorithm in the field to propose a comparison of their performances. Finally, the issue of multiple-testing in Genome-Wide association studies is discussed on several levels. We propose a review of the multiple-testing corrections and discuss their validity for different study settings. We then focus on deriving gene-wise interpretation of the findings that corresponds to multiple-stategy to obtain valid gene-disease association measures

Méthodes statistiques pour la prise en compte de différentes sources de biais dans les études d'association à grande échelle

Les études d'association à grande échelle sont devenus un outil très performant pour détecter les variants génétiques associés aux maladies. Ce manuscrit de doctorat s'intéresse à plusieurs des aspects clés des nouvelles problématiques informatiques et statistiques qui ont émergé grâce à de telles recherches. Les résultats des études d'association à grande échelle sont critiqués, en partie, à cause du biais induit par la stratification des populations. Nous proposons une étude de comparaison des stratégies qui existent pour prendre en compte ce problème. Leurs avantages et limites sont discutés en s'appuyant sur divers scénarios de structure des populations dans le but de proposer des conseils et indications pratiques. Nous nous intéressons ensuite à l'interférence de la structure des populations dans la recherche génétique. Nous avons développé au cours de cette thèse un nouvel algorithme appelé SHIPS (Spectral Hierarchical clustering for the Inference of Population Structure). Cet algorithme a été appliqué à un ensemble de jeux de données simulés et réels, ainsi que de nombreux autres algorithmes utilisés en pratique à titre de comparaison. Enfin, la question du test multiple dans ces études d'association est abordée à plusieurs niveaux. Nous proposons une présentation générale des méthodes de tests multiples et discutons leur validité pour différents designs d'études. Nous nous concertons ensuite sur l'obtention de résultats interprétables aux niveaux de gènes, ce qui correspond à une problématique de tests multiples avec des tests dépendants. Nous discutons et analysons les différentes approches dédiées à cette fin.Genome-Wide association studies have become powerful tools to detect genetic variants associated with diseases. This PhD thesis focuses on several key aspects of the new computational and methodological problematics that have arisen with such research. The results of Genome-Wide association studies have been questioned, in part because of the bias induced by population stratification. Many stratégies are available to account for population stratification scenarios are highlighted in order to propose pratical guidelines to account for population stratification. We then focus on the inference of population structure that has many applications for genetic research. We have developed and present in this manuscript a new clustering algoritm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS). This algorithm in the field to propose a comparison of their performances. Finally, the issue of multiple-testing in Genome-Wide association studies is discussed on several levels. We propose a review of the multiple-testing corrections and discuss their validity for different study settings. We then focus on deriving gene-wise interpretation of the findings that corresponds to multiple-stategy to obtain valid gene-disease association measures.EVRY-Bib. électronique (912289901) / SudocSudocFranceF

SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies

Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns

In situ 3D synchrotron laminography assessment of ddge fracture in dual-phase steels : quantitative and numerical analysis

International audienceThe mechanical performance of automotive structures made of advanced high strength steels (AHSS) is often seen reduced by the presence of cut edges. An attempt is made to assess and quantify the initial damage state and the damage evolution during mechanical testing of a punched edge and a machined edge via a recently developed 3D imaging technique called synchrotron radiation computed laminography. This technique allows us to observe damage in regions of interest in thin sheet-like objects at micrometer resolution. In terms of new experimental mechanics, steel sheets having sizes and mechanical boundary conditions of engineering relevance can be tested for the first time with in situ 3D damage observation and quantification. It is found for the investigated DP600 steel that the fracture zone of the punched edge is rough and that needle-shape voids at the surface and in the bulk follow ferrite-martensite flow lines. During mechanical in situ testing the needle voids grow from the fracture zone surface and coalesce with the sheared zone. In contrast, during in situ mechanical testing of a machined edge the damage starts away from the edge (∼800μ m) where substantial necking has occurred. Three-dimensional image analysis was performed to quantify the initial damage and its evolution. These data can be used as input and validation data for micromechanical damage models. To interpret the experimental findings in terms of mechanical fields, combined surface digital image correlation and 3D finite element analysis were carried out using an elasto-plastic constitutive law of the investigated DP steel. The stress triaxiality and the accumulated plastic strain were calculated in order to understand the influence of the edge profile and the hardening of the cutting-affected zone on the mechanical fields

Contingency table between two clustering <i>U</i> and <i>V</i>.

<p><i>a</i>_i and <i>b</i>_i are the numbers of samples in the <i>i-th</i> clusters <i>U_i</i> of <i>U</i> and <i>V</i>_i of <i>V</i> respectively and <i>n</i>_ij the number of samples in the <i>i-th</i> cluster <i>U_i</i> of <i>U</i> and the <i>j-th</i> cluster <i>V</i>_j of <i>V</i>.</p