Argument-predicate distance as a filter for enhancing precision in extracting predications on the genetic etiology of disease

BACKGROUND: Genomic functional information is valuable for biomedical research. However, such information frequently needs to be extracted from the scientific literature and structured in order to be exploited by automatic systems. Natural language processing is increasingly used for this purpose although it inherently involves errors. A postprocessing strategy that selects relations most likely to be correct is proposed and evaluated on the output of SemGen, a system that extracts semantic predications on the etiology of genetic diseases. Based on the number of intervening phrases between an argument and its predicate, we defined a heuristic strategy to filter the extracted semantic relations according to their likelihood of being correct. We also applied this strategy to relations identified with co-occurrence processing. Finally, we exploited postprocessed SemGen predications to investigate the genetic basis of Parkinson's disease. RESULTS: The filtering procedure for increased precision is based on the intuition that arguments which occur close to their predicate are easier to identify than those at a distance. For example, if gene-gene relations are filtered for arguments at a distance of 1 phrase from the predicate, precision increases from 41.95% (baseline) to 70.75%. Since this proximity filtering is based on syntactic structure, applying it to the results of co-occurrence processing is useful, but not as effective as when applied to the output of natural language processing. In an effort to exploit SemGen predications on the etiology of disease after increasing precision with postprocessing, a gene list was derived from extracted information enhanced with postprocessing filtering and was automatically annotated with GFINDer, a Web application that dynamically retrieves functional and phenotypic information from structured biomolecular resources. Two of the genes in this list are likely relevant to Parkinson's disease but are not associated with this disease in several important databases on genetic disorders. CONCLUSION: Information based on the proximity postprocessing method we suggest is of sufficient quality to be profitably used for subsequent applications aimed at uncovering new biomedical knowledge. Although proximity filtering is only marginally effective for enhancing the precision of relations extracted with co-occurrence processing, it is likely to benefit methods based, even partially, on syntactic structure, regardless of the relation

Rapid Bacteria Detection from Patients' Blood Bypassing Classical Bacterial Culturing

Sepsis is a life-threatening condition mostly caused by a bacterial infection resulting in inflammatory reaction and organ dysfunction if not treated effectively. Rapid identification of the causing bacterial pathogen already in the early stage of bacteremia is therefore vital. Current technologies still rely on time-consuming procedures including bacterial culturing up to 72 h. Our approach is based on ultra-rapid and highly sensitive nanomechanical sensor arrays. In measurements we observe two clearly distinguishable distributions consisting of samples with bacteria and without bacteria respectively. Compressive surface stress indicates the presence of bacteria. For this proof-of-concept, we extracted total RNA from EDTA whole blood samples from patients with blood-culture-confirmed bacteremia, which is the reference standard in diagnostics. We determined the presence or absence of bacterial RNA in the sample through 16S-rRNA hybridization and species-specific probes using nanomechanical sensor arrays. Via both probes, we identified two clinically highly-relevant bacterial species i.e., Escherichia coli and Staphylococcus aureus down to an equivalent of 20 CFU per milliliter EDTA whole blood. The dynamic range of three orders of magnitude covers most clinical cases. We correctly identified all patient samples regarding the presence or absence of bacteria. We envision our technology as an important contribution to early and sensitive sepsis diagnosis directly from blood without requirement for cultivation. This would be a game changer in diagnostics, as no commercial PCR or POCT device currently exists who can do this

Fiacre: an Intermediate Language for Model Verification in the Topcased Environment

International audienceFiacre was designed in the framework of the TOPCASED project dealing with model-driven engineering and gathering numerous partners, from both industry and academics. Therefore, Fiacre is designed both as the target language of model transformation engines from various models such as SDL, UML, AADL, and as the source language of compilers into the targeted verification toolboxes, namely CADP and Tina in the first step. In this paper, we present the Fiacre language. Then transformations from AADL to Fiacre are illustrated on a small example

Proteus, des web services pour les systèmes de maintenance

Colloque avec actes et comité de lecture. nationale.National audienceLe projet PROTEUS a comme objectif de fournir une plate-forme et les concepts génériques pour construire des systèmes de e-maintenance industrielle incluant les systèmes existants d'acquisition de données, de contrôle - commande, de gestion de la maintenance, d'aide au diagnostic, de gestion de la documentation, etc. Le but de la plate-forme est non seulement d'intégrer des outils existants, mais aussi de prévoir l'évolution de celle-ci au travers de l'introduction de nouveaux services. Les concepts de Web services, d'ontologie et de services génériques associés à des modèles génériques des données sont au centre de la solution en cours de développement. En effet, ces techniques permettent de garantir l'interopérabilité de systèmes hétérogènes. Cet article décrit les principes de la solution en s'appuyant sur un exemple de processus et un scénario typique de maintenance corrective. De plus, il décrit les résultats préliminaires obtenus lors des premières expérimentations

The Hospital Anxiety and Depression Scale: low sensitivity for depression screening in demented and non-demented hospitalized elderly

Background: We currently use the depression subscale (HADD) of the Hospital Anxiety and Depression Scale (HADS) for depression screening in elderly inpatients. Given recent concerns about the performance of the HADD in this age group, we performed a quality-control study retrospectively comparing HADD with the diagnosis of depression by a psychiatrist. We also studied the effect of dementia on the scale's performance. Methods: HADS produces two 7-item subscales assessing depression or anxiety. The HADD was administered by a neuropsychologist. As "gold standard” we considered the psychiatrist's diagnosis based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria. Patients older than 65 years, assessed by both the HADD and the psychiatrist, with a clinical dementia rating (CDR) score lower than 3, were included. The effect of dementia was assessed by forming three groups according to the CDR score (CDR0-0.5, CDR1, and CDR2). Simple and multiple logistic regression models were applied to predict the psychiatrist's depression diagnosis from HADD scores. Areas under the receiver operating characteristics curve (AUC) were plotted and compared by χ2 tests. Results: On both univariate and multiple analyses, HADD predicted depression diagnosis but performed poorly (univariate: p = 0.009, AUC = 0.60 (95% confidence interval (CI) = 0.53-0.66); multiple: p = 0.007, AUC = 0.65 (95% CI = 0.58-0.71)), regardless of cognitive status. Because mood could have changed between the two assessments (they occurred at different points of the hospital stay), the multiple analyses were repeated after limiting time interval at 28, 21, and 14 days. No major improvements were noted. Conclusion: The HADD performed poorly in elderly inpatients regardless of cognitive status. It cannot be recommended in this population for depression screening without further stud

Knowledge-Based Programs as Succinct Policies for Partially Observable Domains

International audienceWe suggest to express policies for contingent planning by knowledge-based programs (KBPs). KBPs, introduced by Fagin et al. [Reasoning about Knowledge, MIT Press, 1995], are high-level protocols describing the actions that the agent should perform as a function of their current knowledge: branching conditions are epistemic formulas that are interpretable by the agent. The main aim of our paper is to show that KBPs can be seen as a succinct language for expressing 15 policies in single-agent contingent planning. KBP are conceptually very close to languages used for expressing policies in the partially observable planning literature: like them, they have conditional and looping structures, with actions as atomic programs and Boolean formulas on beliefs for choosing the execution path. Now, the specificity of KBPs is that 20 branching conditions refer to the belief state and not to the observations. Because of their structural proximity, KBPs and standard languages for representing policies have the same power of expressivity: every standard policy can be expressed as a KBP, and every KBP can be "unfolded" into a standard policy. However, KBPs are more succinct, more readable, and more explainable than standard policies. On the other hand, they require more online computation time, but we show that this is an unavoidable tradeoff. We study knowledge-based programs along four criteria: expressivity, succinctness, complexity of online execution, and complexity of verification

Preclinical and early clinical development of GNbAC1, a humanized IgG4 monoclonal antibody targeting endogenous retroviral MSRV-Env protein

Monoclonal antibodies (mAbs) play an increasing important role in the therapeutic armamentarium against multiple sclerosis (MS), an inflammatory and degenerative disorder of the central nervous system. Most of the mAbs currently developed for MS are immunomodulators blocking the inflammatory immune process. In contrast with mAbs targeting immune function, GNbAC1, a humanized IgG4 mAb, targets the multiple sclerosis associated retrovirus envelope (MSRVEnv) protein, an upstream factor in the pathophysiology of MS. MSRV-Env protein is of endogenous retroviral origin, expressed in MS brain lesions, and it is pro-inflammatory and toxic to the remyelination process, by preventing the differentiation of oligodendrocyte precursor cells. We present the preclinical and early clinical development results of GNbAC1. The specificity of GNbAC1 for its endogenous retroviral target is described. Efficacy of different mAb versions of GNbAC1 were assessed in MSRV-Env induced experimental allergic encephalitis (EAE), an animal model of MS. Because the target MSRV-Env is not expressed in animals, no relevant animal model exists for a proper in vivo toxicological program. An off-target 2-week toxicity study in mice was thus performed, and it showed an absence of safety risk. Additional in vitro analyses showed an absence of complement or antibody-dependent cytotoxicity as well as a low level of cross-reactivity to human tissues. The first-in-man clinical study in 33 healthy subjects and a long-term clinical study in 10 MS patients showed that GNbAC1 is well tolerated in humans without induction of immunogenicity and that it induces a pharmacodynamic response on MSRV biomarkers. These initial results suggest that the mAb GNbAC1 could be a safe long-term treatment for patients with MS with a unique therapeutic mechanism of action.GeNeuro SA, Geneva, Switzerlandhttp://www.tandfonline.com/loi/kmab202016-01-31hb201