Multilevel Contracts for Trusted Components

This article contributes to the design and the verification of trusted components and services. The contracts are declined at several levels to cover then different facets, such as component consistency, compatibility or correctness. The article introduces multilevel contracts and a design+verification process for handling and analysing these contracts in component models. The approach is implemented with the COSTO platform that supports the Kmelia component model. A case study illustrates the overall approach.Comment: In Proceedings WCSI 2010, arXiv:1010.233

Symmetric dithiodigalactoside: strategic combination of binding studies and detection of selectivity between a plant toxin and human lectins

Thioglycosides offer the advantage over O-glycosides to be resistant to hydrolysis. Based on initial evidence of this recognition ability for glycosyldisulfides by screening dynamic combinatorial libraries, we have now systematically studied dithiodigalactoside on a plant toxin (Viscum album agglutinin) and five human lectins (adhesion/growth-regulatory galectins with medical relevance e.g. in tumor progression and spread). Inhibition assays with surface-presented neoglycoprotein and in solution monitored by saturation transfer difference NMR spectroscopy, flanked by epitope mapping, as well as isothermal titration calorimetry revealed binding properties to VAA (Ka: 1560 ± 20 M-1). They were reflected by the structural model and the affinity on the level of toxin-exposed cells. In comparison, galectins were considerably less reactive, with intrafamily grading down to very minor reactivity for tandem-repeat-type galectins, as quantitated by radioassays for both domains of galectin-4. Model building indicated contact formation to be restricted to only one galactose moiety, in contrast to thiodigalactoside. The tested lycosyldisulfide exhibits selectivity between the plant toxin and the tested human lectins, and also between these proteins. Therefore, glycosyldisulfides have potential as chemical platform for inhibitor design

Impact of glycemic control on the incidence of acute kidney injury in critically ill patients: a comparison of two strategies using the RIFLE criteria

OBJECTIVE: To compare the renal outcome in patients submitted to two different regimens of glycemic control, using the RIFLE criteria to define acute kidney injury. INTRODUCTION: The impact of intensive insulin therapy on renal function outcome is controversial. The lack of a criterion for AKI definition may play a role on that. METHODS: Included as the subjects were 228 randomly selected, critically ill patients engaged in intensive insulin therapyor in a carbohydrate-restrictive strategy. Renal outcome was evaluated through the comparison of the last RIFLE score obtained during the ICU stay and the RIFLE score at admission; the outcome was classified as favorable, stable or unfavorable. RESULTS: The two groups were comparable regarding demographic data. AKI developed in 52% of the patients and was associated with a higher mortality (39.4%) compared with those who did not have AKI (8.2%) (p<0.001). Renal function outcome was comparable between the two groups (p=0.37). We observed a significant correlation between blood glucose levels and the incidence of acute kidney injury (p=0.007). In the multivariate logistic regression analysis, only APACHE III scores higher than 60 were identified as an independent risk factor for unfavorable renal outcome. APACHE III scores>60, acute kidney injury and hypoglycemia were risk factors for mortality. CONCLUSION: Intensive insulin therapy and a carbohydrate-restrictive strategy were comparable regarding the incidence of acute kidney injury evaluated using RIFLE criteria

Formation of proto-clusters and star formation within clusters: apparent universality of the initial mass function ?

It is believed that the majority of stars form in clusters. Therefore it is likely that the gas physical conditions that prevail in forming clusters, largely determine the properties of stars that form and in particular the initial mass function. We develop an analytical model to account for the formation of low mass clusters and the formation of stars within clusters. The formation of clusters is determined by an accretion rate, the virial equilibrium and energy as well as thermal balance. For this latter both molecular and dust cooling are considered using published rates. The star distribution is computed within the cluster using the physical conditions inferred from this model and the Hennebelle & Chabrier theory. Our model reproduces well the mass-size relation of low mass clusters (up to few $\simeq 10^3$ M$_\odot$ of stars corresponding to about 5 times more gas) and an initial mass function which is $i)$ very close to the Chabrier's IMF, $ii)$ weakly dependent on the mass of the clusters, $iii)$ relatively robust to (i.e. not too steeply dependent on) variations of physical quantities as accretion rate, radiation and cosmic rays abundances. The weak dependence of the mass distribution of stars with the cluster mass results from the compensation between varying clusters densities, velocity dispersions and temperatures all inferred from first physical principles. This constitutes a possible explanation for the apparent universality of the IMF within the Galaxy though variations with the local conditions could certainly be observed.Comment: accepted for publication in A&

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering.publishedVersio

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering

Formation of low-mass stars and brown dwarfs

These lectures attempt to expose the most important ideas, which have been proposed to explain the formation of stars with particular emphasis on the formation of brown dwarfs and low-mass stars. We first describe the important physical processes which trigger the collapse of a self-gravitating piece of fluid and regulate the star formation rate in molecular clouds. Then we review the various theories which have been proposed along the years to explain the origin of the stellar initial mass function paying particular attention to four models, namely the competitive accretion and the theories based respectively on stopped accretion, MHD shocks and turbulent dispersion. As it is yet unsettled whether the brown dwarfs form as low-mass stars, we present the theory of brown dwarfs based on disk fragmentation stressing all the uncertainties due to the radiative feedback and magnetic field. Finally, we describe the results of large scale simulations performed to explain the collapse and fragmentation of molecular clouds.Comment: proceedings of the Evry Schatzman School on "Low-mass stars and the transition between stars and brown dwarfs" (Roscoff 2011), to appear in EAS Publication Series (Eds C.Reyl\'e, C.Charbonnel, & M.Schultheis

Fecal Microbiota Transplantation (FMT) as an Adjunctive Therapy for Depression-Case Report

Depression is a debilitating disorder, and at least one third of patients do not respond to therapy. Associations between gut microbiota and depression have been observed in recent years, opening novel treatment avenues. Here, we present the first two patients with major depressive disorder ever treated with fecal microbiota transplantation as add-on therapy. Both improved their depressive symptoms 4 weeks after the transplantation. Effects lasted up to 8 weeks in one patient. Gastrointestinal symptoms, constipation in particular, were reflected in microbiome changes and improved in one patient. This report suggests further FMT studies in depression could be worth pursuing and adds to awareness as well as safety assurance, both crucial in determining the potential of FMT in depression treatment

An Infrared/X-ray Survey for New Members of the Taurus Star-Forming Region

We present the results of a search for new members of the Taurus star-forming region using data from the Spitzer Space Telescope and the XMM-Newton Observatory. We have obtained optical and near-infrared spectra of 44 sources that exhibit red Spitzer colors that are indicative of stars with circumstellar disks and 51 candidate young stars that were identified by Scelsi and coworkers using XMM-Newton. We also performed spectroscopy on four possible companions to members of Taurus that were reported by Kraus and Hillenbrand. Through these spectra, we have demonstrated the youth and membership of 41 sources, 10 of which were independently confirmed as young stars by Scelsi and coworkers. Five of the new Taurus members are likely to be brown dwarfs based on their late spectral types (>M6). One of the brown dwarfs has a spectral type of L0, making it the first known L-type member of Taurus and the least massive known member of the region (M=4-7 M_Jup). Another brown dwarf exhibits a flat infrared spectral energy distribution, which indicates that it could be in the protostellar class I stage (star+disk+envelope). Upon inspection of archival images from various observatories, we find that one of the new young stars has a large edge-on disk (r=2.5=350 AU). The scattered light from this disk has undergone significant variability on a time scale of days in optical images from the Canada-France-Hawaii Telescope. Using the updated census of Taurus, we have measured the initial mass function for the fields observed by XMM-Newton. The resulting mass function is similar to previous ones that we have reported for Taurus, showing a surplus of stars at spectral types of K7-M1 (0.6-0.8 M_sun) relative to other nearby star-forming regions like IC 348, Chamaeleon I, and the Orion Nebula Cluster