A graphical approach to relational reasoning

Relational reasoning is concerned with relations over an unspecified domain of discourse. Two limitations to which it is customarily subject are: only dyadic relations are taken into account; all formulas are equations, having the same expressive power as first-order sentences in three variables. The relational formalism inherits from the Peirce-Schröder tradition, through contributions of Tarski and many others. Algebraic manipulation of relational expressions (equations in particular) is much less natural than developing inferences in first-order logic; it may in fact appear to be overly machine-oriented for direct hand-based exploitation. The situation radically changes when one resorts to a convenient representation of relations based on labeled graphs. The paper provides details of this representation, which abstracts w.r.t. inessential features of expressions. Formal techniques illustrating three uses of the graph representation of relations are discussed: one technique deals with translating first-order specifications into the calculus of relations; another one, with inferring equalities within this calculus with the aid of convenient diagram-rewriting rules; a third one with checking, in the specialized framework of set theory, the definability of particular set operations. Examples of use of these techniques are produced; moreover, a promising approach to mechanization of graphical relational reasoning is outlined

A methodology for the structural and functional analysis of signaling and regulatory networks

BACKGROUND: Structural analysis of cellular interaction networks contributes to a deeper understanding of network-wide interdependencies, causal relationships, and basic functional capabilities. While the structural analysis of metabolic networks is a well-established field, similar methodologies have been scarcely developed and applied to signaling and regulatory networks. RESULTS: We propose formalisms and methods, relying on adapted and partially newly introduced approaches, which facilitate a structural analysis of signaling and regulatory networks with focus on functional aspects. We use two different formalisms to represent and analyze interaction networks: interaction graphs and (logical) interaction hypergraphs. We show that, in interaction graphs, the determination of feedback cycles and of all the signaling paths between any pair of species is equivalent to the computation of elementary modes known from metabolic networks. Knowledge on the set of signaling paths and feedback loops facilitates the computation of intervention strategies and the classification of compounds into activators, inhibitors, ambivalent factors, and non-affecting factors with respect to a certain species. In some cases, qualitative effects induced by perturbations can be unambiguously predicted from the network scheme. Interaction graphs however, are not able to capture AND relationships which do frequently occur in interaction networks. The consequent logical concatenation of all the arcs pointing into a species leads to Boolean networks. For a Boolean representation of cellular interaction networks we propose a formalism based on logical (or signed) interaction hypergraphs, which facilitates in particular a logical steady state analysis (LSSA). LSSA enables studies on the logical processing of signals and the identification of optimal intervention points (targets) in cellular networks. LSSA also reveals network regions whose parametrization and initial states are crucial for the dynamic behavior. We have implemented these methods in our software tool CellNetAnalyzer (successor of FluxAnalyzer) and illustrate their applicability using a logical model of T-Cell receptor signaling providing non-intuitive results regarding feedback loops, essential elements, and (logical) signal processing upon different stimuli. CONCLUSION: The methods and formalisms we propose herein are another step towards the comprehensive functional analysis of cellular interaction networks. Their potential, shown on a realistic T-cell signaling model, makes them a promising tool

Three-points interfacial quadrature for geometrical source terms on nonuniform grids

International audienceThis paper deals with numerical (finite volume) approximations, on nonuniform meshes, for ordinary differential equations with parameter-dependent fields. Appropriate discretizations are constructed over the space of parameters, in order to guarantee the consistency in presence of variable cells' size, for which $L^p$-error estimates, $1\le p < +\infty$, are proven. Besides, a suitable notion of (weak) regularity for nonuniform meshes is introduced in the most general case, to compensate possibly reduced consistency conditions, and the optimality of the convergence rates with respect to the regularity assumptions on the problem's data is precisely discussed. This analysis attempts to provide a basic theoretical framework for the numerical simulation on unstructured grids (also generated by adaptive algorithms) of a wide class of mathematical models for real systems (geophysical flows, biological and chemical processes, population dynamics)

Lower urinary tract dysfunction in Parkinsonian syndromes

Purpose of review: The aim of this review is to outline the clinical presentation, pathophysiology and evaluation of lower urinary tract (LUT) dysfunction in Parkinson’s disease and other parkinsonian syndromes including multiple system atrophy, dementia with Lewy bodies, progressive supranuclear palsy and corticobasal degeneration. // Recent findings: LUT dysfunction commonly occurs in neurological disorders, including patients with parkinsonian syndromes. The pattern of LUT dysfunction and its severity are variable, depending upon the site of lesion within the neural pathways. Parkinsonian syndromes are broadly divided into Parkinson’s disease (PD) and a typical parkinsonian syndromes such as multiple system atrophy (MSA), dementia with Lewy bodies (DLB), progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). Different parkinsonian syndromes have distinct clinical features (e.g. dysautonomia, early dementia, supranuclear gaze palsy, higher cortical signs), and the pattern of LUT dysfunction and its severity can differ. // Conclusions: LUT dysfunction is a common feature in patients with parkinsonian syndromes. Recognising the pattern of LUT dysfunction during the assessment of these patients can help management and possibly facilitate an earlier diagnosis

Multiple aspect trajectories: A case study on fishing vessels in the northern adriatic sea

In this paper we build, implement and analyze a spatio-temporal database describing the fishing activities in the Northern Adriatic Sea over four years. The database results from the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and the corresponding fish catch reports (i.e., the quantity and type of fish caught). We present all the phases of the dataset creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. Moreover, we formalise and compare different techniques to distribute the fish caught by the fishing vessels along their trajectories. We implement the database with MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, guided by our ecological experts, we perform some analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management

UAVS TO ASSESS THE EVOLUTION OF EMBRYO DUNES

The balance of a coastal environment is particularly complex: the continuous formation of dunes, their destruction as a result of violent storms, the growth of vegetation and the consequent growth of the dunes themselves are phenomena that significantly affect this balance. This work presents an approach to the long-term monitoring of a complex dune system by means of Unmanned Aerial Vehicles (UAVs). Four different surveys were carried out between November 2015 and November 2016. Aerial photogrammetric data were acquired during flights by a DJI Phantom 2 and a DJI Phantom 3 with cameras in a nadiral arrangement. GNSS receivers in Network Real Time Kinematic (NRTK) mode were used to frame models in the European Terrestrial Reference System. Processing of the captured images consisted in reconstruction of a three-dimensional model using the principles of Structure from Motion (SfM). Particular care was necessary due to the vegetation: filtering of the dense cloud, mainly based on slope detection, was performed to minimize this issue. Final products of the SfM approach were represented by Digital Elevation Models (DEMs) of the sandy coastal environment. Each model was validated by comparison through specially surveyed points. Other analyses were also performed, such as cross sections and computing elevation variations over time. The use of digital photogrammetry by UAVs is particularly reliable: fast acquisition of the images, reconstruction of high-density point clouds, high resolution of final elevation models, as well as flexibility, low cost and accuracy comparable with other available techniques

Safety, pharmacokinetics, and pharmacodynamics of escalating repeat doses of GSK249320 in patients with stroke.

Background and purposeRestorative therapies have the potential to improve function and reduce disability after stroke with a wide therapeutic window. The current study evaluated GSK249320, a monoclonal antibody that blocks the axon outgrowth inhibition molecule myelin-associated glycoprotein and also protects oligodendrocytes.MethodsPatients with mild-moderate stroke were randomized to intravenous GSK249320 (1, 5, or 15 mg/kg per infusion, in escalating cohorts of 8-9 subjects) versus placebo (n=17). Infusion 1 was 24 to 72 hours after stroke; infusion 2 was 9 ± 1 days later. The primary objective evaluated safety and tolerability, and the secondary objectives evaluated immunogenicity, pharmacokinetics, biomarkers, neurophysiology, and motor function.ResultsBaseline (n=42) characteristics were similar across treatment groups. No safety concerns were found based on adverse events, examination, vital signs, ECG, nerve conduction tests, brain imaging, motor function testing, and laboratory studies. Two of the 25 subjects dosed with GSK249320 developed transient antidrug antibodies after infusion 1. The pharmacokinetics profile was as expected for an IgG1 type monoclonal antibody. Serum levels of the biomarker S100β did not differ between groups. Global outcome measures were similar across groups. Modality-specific end points could be consistently measured in the first few days after stroke, and one of these, gait velocity, demonstrated a trend toward improvement with GSK249320 compared with placebo.ConclusionsGSK249320 was generally well tolerated. No major safety issues were identified in this first study of a monoclonal antibody to modulate the neurobiology of brain repair after stroke. Future studies might explore the efficacy of GSK249320 as a restorative therapy for stroke. Clinical Trial Registration- URL: http://www.clinicaltrials.gov. Unique Identifier: NCT00833989

Association between the A-2518G polymorphism in the monocyte chemoattractant protein-1 gene and insulin resistance and Type 2 diabetes mellitus

Aims/hypothesis: The molecular mechanisms of obesity-related insulin resistance are incompletely understood. Macrophages accumulate in adipose tissue of obese individuals. In obesity, monocyte chemoattractant protein-1 (MCP-1), a key chemokine in the process of macrophage accumulation, is overexpressed in adipose tissue. MCP-1 is an insulin-responsive gene that continues to respond to exogenous insulin in insulin-resistant adipocytes and mice. MCP-1 decreases insulin-stimulated glucose uptake into adipocytes. The A-2518G polymorphism in the distal regulatory region of MCP-1 may regulate gene expression. The aim of this study was to investigate the impact of this gene polymorphism on insulin resistance. Methods: We genotyped the Ludwigshafen Risk and Cardiovascular Health (LURIC) cohort (n=3307). Insulin resistance, estimated by homeostasis model assessment, and Type 2 diabetes were diagnosed in 803 and 635 patients respectively. Results: Univariate analysis revealed that plasma MCP-1 levels were significantly and positively correlated with WHR (p=0.011), insulin resistance (p=0.0097) and diabetes (p<0.0001). Presence of the MCP-1 G-2518 allele was associated with decreased plasma MCP-1 (p=0.017), a decreased prevalence of insulin resistance (odds ratio [OR]=0.82, 95% CI: 0.70-0.97, p=0.021) and a decreased prevalence of diabetes (OR=0.80, 95% CI: 0.67-0.96, p=0.014). In multivariate analysis, the G allele retained statistical significance as a negative predictor of insulin resistance (OR=0.78, 95% CI: 0.65-0.93, p=0.0060) and diabetes (OR=0.80, 95% CI: 0.66-0.96, p=0.018). Conclusions/interpretation: In a large cohort of Caucasians, the MCP-1 G-2518 gene variant was significantly and negatively correlated with plasma MCP-1 levels and the prevalence of insulin resistance and Type 2 diabetes. These results add to recent evidence supporting a role for MCP-1 in pathologies associated with hyperinsulinaemi