Automating risk analysis of software design models

The growth of the internet and networked systems has exposed software to an increased amount of security threats. One of the responses from software developers to these threats is the introduction of security activities in the software development lifecycle. This paper describes an approach to reduce the need for costly human expertise to perform risk analysis in software, which is common in secure development methodologies, by automating threat modeling. Reducing the dependency on security experts aims at reducing the cost of secure development by allowing non-security-aware developers to apply secure development with little to no additional cost,making secure development more accessible. To automate threat modeling two data structures are introduced, identification trees and mitigation trees, to identify threats in software designs and advise mitigation techniques, while taking into account specification requirements and cost concerns. These are the components of our model for automated threat modeling, AutSEC.We validated AutSEC by implementing it in a tool based on data flow diagrams, from theMicrosoft security development methodology, and applying it to VOMS, a grid middleware component, to evaluate our model's performance

Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl

Automating risk analysis of software design models

The growth of the internet and networked systems has exposed software to an increased amount of security threats. One of the responses from software developers to these threats is the introduction of security activities in the software development lifecycle. This paper describes an approach to reduce the need for costly human expertise to perform risk analysis in software, which is common in secure development methodologies, by automating threat modeling. Reducing the dependency on security experts aims at reducing the cost of secure development by allowing non-security-aware developers to apply secure development with little to no additional cost,making secure development more accessible. To automate threat modeling two data structures are introduced, identification trees and mitigation trees, to identify threats in software designs and advise mitigation techniques, while taking into account specification requirements and cost concerns. These are the components of our model for automated threat modeling, AutSEC.We validated AutSEC by implementing it in a tool based on data flow diagrams, from theMicrosoft security development methodology, and applying it to VOMS, a grid middleware component, to evaluate our model's performance

¹³C NMR detection of metabolic mixtures enhanced by Dynamic Nuclear Polarization

Dynamic Nuclear Polarization (DNP) shows a high potential to boost the sensitivity of NMR experiments. Particularly promising is the dissolution DNP approach, which can increase the sensitivity of certain liquid-state NMR experiments by factors in excess of 104. Usual applications of this promising technique rely on polarizing a 13C-labeled tracer, and following the latter’s metabolic fate after injection into an MRI scanner. This paper explores a different aspect pertaining the use of hyperpolarized metabolites, with a preliminary report exploring the potential of dissolution DNP in metabolomics analyses. To this end synthetic samples involving several common metabolites were hyperpolarized, and the analytical performance of the ensuing DNP NMR experiment was evaluated under a variety of different experimental conditions. These analyses revealed average signal enhancements of ~5000x to 10000x for non-protonated 13C sites, with a repeatability better than 10% on the 13C NMR peak areas. These are promising results, opening interesting application perspectives in the field of metabolomics analyses of biological extracts.The authors from France thank the French National Research Agency for a young investigator starting grant (ANR Grant 2010-JCJC-0804-01) and the CORSAIRE metabolomics platform from the Biogenouest network. The authors from Israel acknowledge ERC Advanced Grant #246754, EU'S BioNMR Grant #261863, DIP Project 710907 (Ministry of Education and Research, Germany), and the generosity of the Perlman Family Foundation

Diffusion tensor distribution imaging of an in vivo mouse brain at ultrahigh magnetic field by spatiotemporal encoding

Diffusion tensor distribution (DTD) imaging builds on principles from diffusion, solid-state and low-field NMR spectroscopies, to quantify the contents of heterogeneous voxels as nonparametric distributions, with tensor “size”, “shape” and orientation having direct relations to corresponding microstructural properties of biological tissues. The approach requires the acquisition of multiple images as a function of the magnitude, shape and direction of the diffusion-encoding gradients, leading to long acquisition times unless fast image read-out techniques like EPI are employed. While in previous in vivo human brain studies performed at 3 T this proved a viable option, porting these measurements to very high magnetic fields and/or to heterogeneous organs induces B0- and B1-inhomogeneity artifacts that challenge the limits of EPI. To overcome such challenges, we demonstrate here that high spatial resolution DTD of mouse brain can be carried out at 15.2 T with a surface-cryoprobe, by relying on SPatiotemporal ENcoding (SPEN) imaging sequences. These new acquisition and data-processing protocols are demonstrated with measurements on in vivo mouse brain, and validated with synthetic phantoms designed to mimic the diffusion properties of white matter, gray matter and cerebrospinal fluid. While still in need of full extensions to 3D mappings and of scanning additional animals to extract more general physiological conclusions, this work represents another step towards the model-free, noninvasive in vivo characterization of tissue microstructure and heterogeneity in animal models, at ≈0.1 mm resolutions

Hyperpolarized NMR of plant and cancer cell extracts at natural abundance

International audienceNatural abundance C-13 NMR spectra of biological extracts are recorded in a single scan provided that the samples are hyperpolarized by dissolution dynamic nuclear polarization combined with cross polarization. Heteronuclear 2D correlation spectra of hyperpolarized breast cancer cell extracts can also be obtained in a single scan. Hyperpolarized NMR of extracts opens many perspectives for metabolomics