Computational and Biological Evaluation of N-octadecyl-N'-propylsulfamide, a Selective PPARα Agonist Structurally Related to N-acylethanolamines.

To further understand the pharmacological properties of N-oleoylethanolamine (OEA), a naturally occurring lipid that activates peroxisome proliferator-activated receptor alpha (PPARα), we designed sulfamoyl analogs based on its structure. Among the compounds tested, N-octadecyl-N'-propylsulfamide (CC7) was selected for functional comparison with OEA. The performed studies include the following computational and biological approaches: 1) molecular docking analyses; 2) molecular biology studies with PPARα; 3) pharmacological studies on feeding behavior and visceral analgesia. For the docking studies, we compared OEA and CC7 data with crystallization data obtained with the reference PPARα agonist GW409544. OEA and CC7 interacted with the ligand-binding domain of PPARα in a similar manner to GW409544. Both compounds produced similar transcriptional activation by in vitro assays, including the GST pull-down assay and reporter gene analysis. In addition, CC7 and OEA induced the mRNA expression of CPT1a in HpeG2 cells through PPARα and the induction was avoided with PPARα-specific siRNA. In vivo studies in rats showed that OEA and CC7 had anorectic and antiobesity activity and induced both lipopenia and decreases in hepatic fat content. However, different effects were observed when measuring visceral pain; OEA produced visceral analgesia whereas CC7 showed no effects. These results suggest that OEA activity on the PPARα receptor (e.g., lipid metabolism and feeding behavior) may be dissociated from other actions at alternative targets (e.g., pain) because other non cannabimimetic ligands that interact with PPARα, such as CC7, do not reproduce the full spectrum of the pharmacological activity of OEA. These results provide new opportunities for the development of specific PPARα-activating drugs focused on sulfamide derivatives with a long alkyl chain for the treatment of metabolic dysfunction.

Forest fires cluster detection with space-time scan statistics

Forest fires are defined as uncontrolled fires often occurring in wildland areas, but that can also affect houses or agricultural resources. Causes are both natural (e.g.,lightning phenomena) and anthropogenic (human negligence or arsons).Major environmental factors influencing the fire ignition and propagation are climate and vegetation. Wildfires are most common and severe during drought period and on windy days. Moreover, under water-stress conditions, which occur after a long hot and dry period, the vegetation is more vulnerable to fire. These conditions are common in the United State and Canada, where forest fires represent a big problem. We focused our analysis on the state of Florida, for which a big dataset on forest fires detection is readily available. USDA Forest Service Remote Sensing Application Center, in collaboration with NASA-Goddard Space Flight Center and the University of Maryland, has compiled daily MODIS Thermal Anomalies (fires and biomass burning images) produced by NASA using a contextual algorithm that exploits the strong emission of mid-infrared radiation from fires. Fire classes were converted in GIS format: daily MODIS fire detections are provided as the centroids of the 1 kilometer pixels and compiled into daily Arc/INFO point coverage.

Qualité des statistiques de causes de décès: problèmes actuels et perspectives. [Quality of statistics on causes of death: current problems and perspectives].

Statistics of causes of death remain an important source of epidemiological data for the evaluation of various medical and health problems. The improvement of analytical techniques and, above all, the transformation of demographic and morbid structures of populations have prompted researchers in the field to give more importance to the quality of death certificates. After describing the data collection system presently used in Switzerland, the paper discusses various indirect estimations of the quality of Swiss data and reviews the corresponding international literature.

Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors

Reverse transcriptase (RT) is a multifunctional enzyme in the human immunodeficiency virus (HIV)-1 life cycle and represents a primary target for drug discovery efforts against HIV-1 infection. Two classes of RT inhibitors, the nucleoside RT inhibitors (NRTIs) and the nonnucleoside transcriptase inhibitors are prominently used in the highly active antiretroviral therapy in combination with other anti-HIV drugs. However, the rapid emergence of drug-resistant viral strains has limited the successful rate of the anti-HIV agents. Computational methods are a significant part of the drug design process and indispensable to study drug resistance. In this review, recent advances in computer-aided drug design for the rational design of new compounds against HIV-1 RT using methods such as molecular docking, molecular dynamics, free energy calculations, quantitative structure-activity relationships, pharmacophore modelling and absorption, distribution, metabolism, excretion and toxicity prediction are discussed. Successful applications of these methodologies are also highlighted.

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

Vital Statistics of Iowa, 2001

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.

Computational anatomy for studying use-dependant brain plasticity.

In this article we provide a comprehensive literature review on the in vivo assessment of use-dependant brain structure changes in humans using magnetic resonance imaging (MRI) and computational anatomy. We highlight the recent findings in this field that allow the uncovering of the basic principles behind brain plasticity in light of the existing theoretical models at various scales of observation. Given the current lack of in-depth understanding of the neurobiological basis of brain structure changes we emphasize the necessity of a paradigm shift in the investigation and interpretation of use-dependent brain plasticity. Novel quantitative MRI acquisition techniques provide access to brain tissue microstructural properties (e.g., myelin, iron, and water content) in-vivo, thereby allowing unprecedented specific insights into the mechanisms underlying brain plasticity. These quantitative MRI techniques require novel methods for image processing and analysis of longitudinal data allowing for straightforward interpretation and causality inferences.

Experimental and computational study on the synthesis and characterization of self-assembling meso/macroporous materials in highly concentrated emulsions

Report for the scientific sojourn carried out at Massachusetts General Hospital Cancer Center-Harvard Medical School, Estats Units, from 2010 to 2011. The project aims to study the aggregation behavior of amphiphilic molecules in the continuous phase of highly concentrated emulsions, which can be used as templates for the synthesis of meso/macroporous materials. At this stage of the project, we have investigated the self-assembly of diblock and triblock surfactants under the effect of a confined geometry being surrounded by the droplets of the dispersed phase. These droplets limit the growth of the aggregates, deeply modify their orientation and hence alter their spatial arrangement as compared to the self-assembly taking place far enough from any boundary surface, that is in the bulk. By performing Monte Carlo simulations, we have showed that the interface between the dispersed and continuous phases as well as its shape has a significant impact on the structural order of the resulting aggregates and hence on the potential applications of highly concentrated emulsions as reaction media, drug delivery systems, or templates for meso/macroporous materials. Due to the combined effect of symmetry breaking and morphological frustration, very intriguing structures, such as square columnar liquid crystals, twisted X-shaped aggregates, and helical phases of cylindrical aggregates, never observed in the bulk for the same model surfactant, have been found. The presence of other more conventional structures, such as micelles and cubic and hexagonal liquid crystals, formed at low and high amphiphilic concentrations, respectively, further enhance the interest on this already rich aggregation behavior.

The role of computational fluid dynamics in the management of unruptured intracranial aneurysms: a clinicians' view

Objective: The importance of hemodynamics in the etiopathogenesis of intracranial aneurysms (IAs) is widely accepted.Computational fluid dynamics (CFD) is being used increasingly for hemodynamic predictions. However, alogn with thecontinuing development and validation of these tools, it is imperative to collect the opinion of the clinicians. Methods: A workshopon CFD was conducted during the European Society of Minimally Invasive Neurological Therapy (ESMINT) Teaching Course,Lisbon, Portugal. 36 delegates, mostly clinicians, performed supervised CFD analysis for an IA, using the @neuFuse softwaredeveloped within the European project @neurIST. Feedback on the workshop was collected and analyzed. The performancewas assessed on a scale of 1 to 4 and, compared with experts’ performance. Results: Current dilemmas in the management ofunruptured IAs remained the most important motivating factor to attend the workshop and majority of participants showedinterest in participating in a multicentric trial. The participants achieved an average score of 2.52 (range 0–4) which was 63% (range 0–100%) of an expert user. Conclusions: Although participants showed a manifest interest in CFD, there was a clear lack ofawareness concerning the role of hemodynamics in the etiopathogenesis of IAs and the use of CFD in this context. More effortstherefore are required to enhance understanding of the clinicians in the subject.

Reliable detection of directional couplings using rank statistics

To detect directional couplings from time series various measures based on distances in reconstructed state spaces were introduced. These measures can, however, be biased by asymmetries in the dynamics' structure, noise color, or noise level, which are ubiquitous in experimental signals. Using theoretical reasoning and results from model systems we identify the various sources of bias and show that most of them can be eliminated by an appropriate normalization. We furthermore diminish the remaining biases by introducing a measure based on ranks of distances. This rank-based measure outperforms existing distance-based measures concerning both sensitivity and specificity for directional couplings. Therefore, our findings are relevant for a reliable detection of directional couplings from experimental signals.

Computational Representation of Collaborative Learning Flow Patterns Using IMS Learning Design

The identification and integration of reusable and customizable CSCL (Computer Supported Collaborative Learning) may benefit from the capture of best practices in collaborative learning structuring. The authors have proposed CLFPs (Collaborative Learning Flow Patterns) as a way of collecting these best practices. To facilitate the process of CLFPs by software systems, the paper proposes to specify these patterns using IMS Learning Design (IMS-LD). Thus, teachers without technical knowledge can particularize and integrate CSCL tools. Nevertheless, the support of IMS-LD for describing collaborative learning activities has some deficiencies: the collaborative tools that can be defined in these activities are limited. Thus, this paper proposes and discusses an extension to IMS-LD that enables to specify several characteristics of the use of tools that mediate collaboration. In order to obtain a Unit of Learning based on a CLFP, a three stage process is also proposed. A CLFP-based Unit of Learning example is used to illustrate the process and the need of the proposed extension.

Patient-specific computational hemodynamics of intracranial aneurysms from 3D rotational angiography and CT angiography: an in vivo reproducibility study

Patient-specific simulations of the hemodynamics in intracranial aneurysms can be constructed by using image-based vascular models and CFD techniques. This work evaluates the impact of the choice of imaging technique on these simulations

Estimation of the viscoelastic properties of vessel walls using a computational model and Doppler ultrasound

Human arteries affected by atherosclerosis are characterized by altered wall viscoelastic properties. The possibility of noninvasively assessing arterial viscoelasticity in vivo would significantly contribute to the early diagnosis and prevention of this disease. This paper presents a noniterative technique to estimate the viscoelastic parameters of a vascular wall Zener model. The approach requires the simultaneous measurement of flow variations and wall displacements, which can be provided by suitable ultrasound Doppler instruments. Viscoelastic parameters are estimated by fitting the theoretical constitutive equations to the experimental measurements using an ARMA parameter approach. The accuracy and sensitivity of the proposed method are tested using reference data generated by numerical simulations of arterial pulsation in which the physiological conditions and the viscoelastic parameters of the model can be suitably varied. The estimated values quantitatively agree with the reference values, showing that the only parameter affected by changing the physiological conditions is viscosity, whose relative error was about 27% even when a poor signal-to-noise ratio is simulated. Finally, the feasibility of the method is illustrated through three measurements made at different flow regimes on a cylindrical vessel phantom, yielding a parameter mean estimation error of 25%.