Measurement of the extent of strain relief in InGaAs layers grown under tensile strain on InP(100) substrates

International audienceHigh resolution x‐ray diffraction has been used to investigate the structural properties of InxGa1−xAs epitaxial layers grown under tension on InP(100) substrates. The nominal indium composition (x=0.42) corresponds to a small lattice mismatch and a two dimensional growth mode. We have also included for comparison two samples grown under compression covering the mostly strained and the mostly relaxed regimes. Our results show that the residual strain and the asymmetry in strain relaxation along 〈011〉 directions are always larger for layers under tension. This can be explained by the difference in dislocation glide velocity induced by a different indium content, by the dissociation of perfect dislocations and partially by the difference in thermal expansion coefficients between substrate and epilayer. The larger asymmetry in strain relaxation for tensile strain layers is interpreted by the existence of microcracks aligned in the [011] direction

A Distributed Hierarchical Structure for Object Networks Supporting Activity Recognition

Pervasive environments will witness heterogeneous smart embedded devices (e.g. sensors, actuators) integrated into user’s living environment (e.g. smart homes and hospitals) and provide a multitude of information that can transparently support user’s lifestyle. One promising application resulting from the management and exploitation of this information is the human activity recognition. In this paper we briefly describe our activity recognition architecture and focus on an important management component of this architecture using the concept of object networks. We explore how object networks can integrate various sensor networks and heterogeneous devices into a coherent network through embedded context and role profile and at the same time support distributed context reasoning. The paper also describes the mechanisms used to eliminate and refine context information that is deemed irrelevant due to user behaviour changes over time, by employing the idea of role fitness

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset

PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity

Statistical expression deconvolution from mixed tissue samples

Motivation: Global expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately, tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. However, statistical analysis can be used to identify differentially expressed genes that are related to the biological question being studied

Three-dimensional cultured ampullae from rats as a screening tool for vestibulotoxicity: Proof of concept using styrene.

peer reviewedNumerous ototoxic drugs, such as some antibiotics and chemotherapeutics, are both cochleotoxic and vestibulotoxic (causing hearing loss and vestibular disorders). However, the impact of some industrial cochleotoxic compounds on the vestibular receptor, if any, remains unknown. As in vivo studies are long and expensive, there is considerable need for predictive and cost-effective in vitro models to test ototoxicity. Here, we present an organotypic model of cultured ampullae harvested from rat neonates. When cultured in a gelatinous matrix, ampulla explants form an enclosed compartment that progressively fills with a high-potassium (K+) endolymph-like fluid. Morphological analyses confirmed the presence of a number of cell types, sensory epithelium, secretory cells, and canalar cells. Treatments with inhibitors of potassium transporters demonstrated that the potassium homeostasis mechanisms were functional. To assess the potential of this model to reveal the toxic effects of chemicals, explants were exposed for either 2 or 72 h to styrene at a range of concentrations (0.5-1 mM). In the 2-h exposure condition, K+ concentration was significantly reduced, but ATP levels remained stable, and no histological damage was visible. After 72 h exposure, variations in K+ concentration were associated with histological damage and decreased ATP levels. This in vitro 3D neonatal rat ampulla model therefore represents a reliable and rapid means to assess the toxic properties of industrial compounds on this vestibular tissue, and can be used to investigate the specific underlying mechanisms

Styrene alters potassium endolymphatic concentration in a model of cultured utricle explants.

peer reviewedDespite well-documented neurotoxic and ototoxic properties, styrene remains commonly used in industry. Its effects on the cochlea have been extensively studied in animals, and epidemiological and animal evidence indicates an impact on balance. However, its influence on the peripheral vestibular receptor has yet to be investigated. Here, we assessed the vestibulotoxicity of styrene using an in vitro model, consisting of three-dimensional cultured newborn rat utricles filled with a high‑potassium (K+) endolymph-like fluid, called "cysts". K+ entry in the cyst ("influx") and its exit ("efflux") are controlled by secretory cells and hair cells, respectively. The vestibular epithelium's functionality is thus linked to K+ concentration, measured using a microelectrode. Known inhibitors of K+ efflux and influx validated the model. Cysts were subsequently exposed to styrene (0.25; 0.5; 0.75 and 1 mM) for 2 h or 72 h. The decrease in K+ concentration measured after both exposure durations was dose-dependent, and significant from 0.75 mM styrene. Vacuoles were visible in the cytoplasm of epithelial cells from 0.5 mM after 2 h and from 0.25 mM after 72 h. The results presented here are the first evidence that styrene may deregulate K+ homeostasis in the endolymphatic space, thereby altering the functionality of the vestibular receptor

Generic Combination of Heap and Value Analyses in Abstract Interpretation

Abstract. Abstract interpretation has been widely applied to approx-imate data structures and (usually numerical) value information. One needs to combine them to effectively apply static analysis to real software. Nevertheless, they have been studied mainly as orthogonal problems so far. In this context, we introduce a generic framework that, given a heap and a value analysis, combines them, and we formally prove its soundness. The heap analysis approximates concrete locations with heap identifiers, that can be materialized or merged. Meanwhile, the value analysis tracks information both on variable and heap identifiers, taking into account when heap identifiers are merged or materialized. We show how existing pointer and shape analyses, as well as numerical domains, can be plugged in our framework. As far as we know, this is the first sound generic automatic framework combining heap and value analyses that allows to freely manage heap identifiers.