Pyrobitumen occurrence and formation in a Cambro–Ordovician sandstone reservoir, Fahud Salt Basin, North Oman

The Cambro–Ordovician Barik Sandstone reservoirs in the Fahud Salt Basin in Oman contain bitumen which may fill up to 40% of the porosity. In well Jaleel-1, this bitumen was isolated (according to kerogen procedure) and typed by NMR, elemental analysis and density measurements. The isolated bitumen is characterized by: (1) a highly aromatic character (NMR 75% CAro, H/C atomic ratio: 0.65), (2) a very high sulphur content (4.2%) and (3) a relatively high density (1.3–1.4 g/cm3). The insolubility and the reflectivity of the bitumen (1.2% Vr) qualify it as a low mature pyrobitumen. The combination of Rock-Eval and density data was used to calculate the actual volume of the pyrobitumen in the rock, as a percentage of porosity. It was found that the pyrobitumen volume shows a negative correlation with total porosity, indicating that small pores are more invaded by bitumen than larger ones. Finally, closed system pyrolysis experiments, performed on oils with different NSO contents, indicate that an in situ oil with a very high content of NSO compounds is required to generate such large amounts of pyrobitumen in the pore system. These observations suggest that the precursor oil of the current pyrobitumen was a very heavy oil tentatively assumed to be the result of a severe biodegradation. Basin modeling shows that the reservoir was charged already in Devonian times. A major uplift brought the oil accumulation near the surface during the Carboniferous and a rather regular burial to the present day position (4500 m, 140°C) (Loosveld et al., 1996). This scenario, involving a residence time at shallow depth, strengthens the biodegradation hypothesis. The numerical modeling, which involves the IFP kinetic model for secondary oil cracking, suggests that pyrobitumen formation is a very recent event. Inclusion of pyrobitumen particles within quartz overgrowth, containing fluid inclusions, provides an upper temperature limit for the beginning of pyrobitumen formation which comforts the result of kinetic modelling

Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck:Bayesian probability versus neural network

Purpose: The intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least-squares regression: Bayesian probability estimation, a recently introduced neural network approach, IVIM-NET, and a version of the neural network modified to increase consistency, IVIM-NETmod. Methods: Ten healthy volunteers underwent two imaging sessions of the neck, two weeks apart, with two DWI acquisitions per session. Model parameters (ADC, diffusion coefficient (Formula presented.), perfusion fraction (Formula presented.), and pseudo-diffusion coefficient (Formula presented.)) from each fit method were determined in the tonsils and in the pterygoid muscles. Within-subject coefficients of variation (wCV) were calculated to assess repeatability. Training of the neural network was repeated 100 times with random initialization to investigate consistency, quantified by the coefficient of variance. Results: The Bayesian and neural network approaches outperformed nonlinear regression in terms of wCV. Intersession wCV of (Formula presented.) in the tonsils was 23.4% for nonlinear regression, 9.7% for Bayesian estimation, 9.4% for IVIM-NET, and 11.2% for IVIM-NETmod. However, results from repeated training of the neural network on the same data set showed differences in parameter estimates: The coefficient of variances over the 100 repetitions for IVIM-NET were 15% for both (Formula presented.) and (Formula presented.), and 94% for (Formula presented.); for IVIM-NETmod, these values improved to 5%, 9%, and 62%, respectively. Conclusion: Repeatabilities from the Bayesian and neural network approaches are superior to that of nonlinear regression for estimating IVIM parameters in the head and neck

Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.Peer reviewe

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients

Neurodegenerative diseases are challenging for systems biology because of the lack of reliable animal models or patient samples at early disease stages. Induced pluripotent stem cells (iPSCs) could address these challenges. We investigated DNA, RNA, epigenetics, and proteins in iPSC-derived motor neurons from patients with ALS carrying hexanucleotide expansions in C9ORF72. Using integrative computational methods combining all omics datasets, we identified novel and known dysregulated pathways. We used a C9ORF72 Drosophila model to distinguish pathways contributing to disease phenotypes from compensatory ones and confirmed alterations in some pathways in postmortem spinal cord tissue of patients with ALS. A different differentiation protocol was used to derive a separate set of C9ORF72 and control motor neurons. Many individual -omics differed by protocol, but some core dysregulated pathways were consistent. This strategy of analyzing patient-specific neurons provides disease-related outcomes with small numbers of heterogeneous lines and reduces variation from single-omics to elucidate network-based signatures.Genetics of disease, diagnosis and treatmen

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients

Neurodegenerative diseases are challenging for systems biology because of the lack of reliable animal models or patient samples at early disease stages. Induced pluripotent stem cells (iPSCs) could address these challenges. We investigated DNA, RNA, epigenetics, and proteins in iPSC-derived motor neurons from patients with ALS carrying hexanucleotide expansions in C9ORF72. Using integrative computational methods combining all omics datasets, we identified novel and known dysregulated pathways. We used a C9ORF72 Drosophila model to distinguish pathways contributing to disease phenotypes from compensatory ones and confirmed alterations in some pathways in postmortem spinal cord tissue of patients with ALS. A different differentiation protocol was used to derive a separate set of C9ORF72 and control motor neurons. Many individual -omics differed by protocol, but some core dysregulated pathways were consistent. This strategy of analyzing patient-specific neurons provides disease-related outcomes with small numbers of heterogeneous lines and reduces variation from single-omics to elucidate network-based signatures

Sûreté des transports de matières radioactives

15 000 colis sont transportés chaque année pour les besoins du programme nucléaire français. C'est très peu en regard des 200 000 colis contenant des radio-isotopes et des 15 000 000 colis de matières dangereuses qui voyagent chaque année. Ce sont des recommandations internationales (ONU/AIEA) qui servent de base aux réglementations françaises pour protéger les personnes et les biens contre les effets possibles des rayonnements ionisants émis ou susceptibles d'être émis par les matières transportées. Ces risques sont les risques d'irradiation, de criticité et de contamination. La sûreté repose en premier lieu sur la résistance des emballages face aux diverses agressions envisageables (chutes, feux prolongés d'hydrocarbures etc.), mais aussi sur le respect des exigences de sûreté, le suivi des transports et, en cas de crise, la gestion de l'intervention

Ильменская Ilmenskaya Елена Elena Михайловна Mikhaelovna

Abstract: Окончила факультет экономической кибернетики Московского экономико-статистического института (1985)Note: Научные исследования в области экономик

Ильменская Ilmenskaya Елена Elena Михайловна Mikhaelovna

Abstract: Окончила факультет экономической кибернетики Московского экономико-статистического института (1985)Note: Научные исследования в области экономик

Shallow Sparsely-Connected Autoencoders for Gene Set Projection

When analyzing biological data, it can be helpful to consider gene sets, or predefined groups of biologically related genes. Methods exist for identifying gene sets that are differential between conditions, but large public datasets from consortium projects and single-cell RNA-Sequencing have opened the door for gene set analysis using more sophisticated machine learning techniques, such as autoencoders and variational autoencoders. We present shallow sparsely-connected autoencoders (SSCAs) and variational autoencoders (SSCVAs) as tools for projecting gene-level data onto gene sets. We tested these approaches on single-cell RNA-Sequencing data from blood cells and on RNA-Sequencing data from breast cancer patients. Both SSCA and SSCVA can recover known biological features from these datasets and the SSCVA method often outperforms SSCA (and six existing gene set scoring algorithms) on classification and prediction tasks.National Institutes of Health (U.S.) (Grant R01NS089076)National Institutes of Health (U.S.) (Grant 1U01CA18498