Shannon entropy of brain functional complex networks under the influence of the psychedelic Ayahuasca

The entropic brain hypothesis holds that the key facts concerning psychedelics are partially explained in terms of increased entropy of the brain's functional connectivity. Ayahuasca is a psychedelic beverage of Amazonian indigenous origin with legal status in Brazil in religious and scientific settings. In this context, we use tools and concepts from the theory of complex networks to analyze resting state fMRI data of the brains of human subjects under two distinct conditions: (i) under ordinary waking state and (ii) in an altered state of consciousness induced by ingestion of Ayahuasca. We report an increase in the Shannon entropy of the degree distribution of the networks subsequent to Ayahuasca ingestion. We also find increased local and decreased global network integration. Our results are broadly consistent with the entropic brain hypothesis. Finally, we discuss our findings in the context of descriptions of "mind-expansion" frequently seen in self-reports of users of psychedelic drugs.Comment: 27 pages, 6 figure

Les placements des organismes d’assurance à fin 2010.

L’analyse de la composition des portefeuilles des assureurs à fin 2010 révèle l’amorce d’un recentrage de leurs positions vers les titres émis par des signatures résidentes ainsi qu’un allongement de la durée moyenne de leurs placements obligataires.organismes d’assurance, assureurs vie, assureurs vie-mixte, assureurs non-vie, provisions techniques, contrats en euros, contrats en unités de compte, placements financiers, mise en transparence, épargne des ménages, circuits de financement, titres de créance, obligations, actions.

Caveolin-1 influences human influenza A virus (H1N1) multiplication in cell culture

<p>Abstract</p> <p>Background</p> <p>The threat of recurring influenza pandemics caused by new viral strains and the occurrence of escape mutants necessitate the search for potent therapeutic targets. The dependence of viruses on cellular factors provides a weak-spot in the viral multiplication strategy and a means to interfere with viral multiplication.</p> <p>Results</p> <p>Using a motif-based search strategy for antiviral targets we identified caveolin-1 (Cav-1) as a putative cellular interaction partner of human influenza A viruses, including the pandemic influenza A virus (H1N1) strains of swine origin circulating from spring 2009 on. The influence of Cav-1 on human influenza A/PR/8/34 (H1N1) virus replication was determined in inhibition and competition experiments. RNAi-mediated Cav-1 knock-down as well as transfection of a dominant-negative Cav-1 mutant results in a decrease in virus titre in infected Madin-Darby canine kidney cells (MDCK), a cell line commonly used in basic influenza research as well as in virus vaccine production. To understand the molecular basis of the phenomenon we focussed on the putative caveolin-1 binding domain (CBD) located in the lumenal, juxtamembranal portion of the M2 matrix protein which has been identified in the motif-based search. Pull-down assays and co-immunoprecipitation experiments showed that caveolin-1 binds to M2. The data suggest, that Cav-1 modulates influenza virus A replication presumably based on M2/Cav-1 interaction.</p> <p>Conclusion</p> <p>As Cav-1 is involved in the human influenza A virus life cycle, the multifunctional protein and its interaction with M2 protein of human influenza A viruses represent a promising starting point for the search for antiviral agents.</p

Anomaly detection in electromechanical systems by means of deep-autoencoder

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksAnomaly detection in manufacturing processes is one of the main concerns in the new era of the Industry 4.0 framework. The detection of uncharacterized events represents a major challenge within the operation monitoring of electrical rotatory machinery. In this regard, although several machine learning techniques have been classically considered, the recent appearance of deep-learning approaches represents an opportunity in the field to increase the anomaly detection capabilities in front of complex electromechanical systems. However, each anomaly detection technique considers its own data interpretability and modelling strategy, which should be analyzed in front of the specificities of the data generated in an industrial environment and, specifically, by an electromechanical actuator. Thus, in this study, a comparison framework is considered including multiple fault scenarios in order to analyze the performance of four representative anomaly detection techniques, that is, one-class support vector machine, k-nearest neighbor, Gaussian mixture model and, finally, deep-autoencoder. The experimental results suggest that the use of the deep-autoencoder in the task of detecting anomalies of operation in electromechanical systems has a higher performance compared to the state of the art methods.Peer ReviewedPostprint (published version

Diagnosis electromechanical system by means CNN and SAE: an interpretable-learning study

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cyber-physical systems are the response to the adaptability, scalability and accurate demands of the new era of manufacturing called Industry 4.0. They will become the core technology of control and monitoring in smart manufacturing processes. In this regard, the complexity of industrial systems implies a challenge for the implementation of monitoring and diagnosis schemes. Moreover, the challenges that is presented in technological aspects regarding connectivity, data management and computing are being resolved through different IT-OT (information technology and operational technology) convergence proposals. These solutions are making it possible to have large computing capacities and low response latency. However, regarding the logical part of information processing and analysis, this still requires additional studies to identify the options with a better complexity-performance trade-off. The emergence of techniques based on artificial intelligence, especially those based on deep-learning, has provided monitoring schemes with the capacity for characterization and recognition in front of complex electromechanical systems. However, most deep learning-based schemes suffer from critical lack of interpretability lying to low generalization capabilities and overfitted responses. This paper proposes a study of two of the main deep learning-based techniques applied to fault diagnosis in electromechanical systems. An analysis of the interpretability of the learning processes is carried out, and the approaches are evaluated under common performance metrics.Peer ReviewedPostprint (published version

Distally based sural fasciomusculocutaneous flap for treatment of wounds of the distal third of the leg and ankle with exposed internal hardware

Soft tissue reconstruction of the distal third of the lower limb with exposure of the internal hardware is a challenging problem with several potential complications, such as exposure of the fracture line, fracture instability and bacterial contamination. The treatment of these lesions usually consists of substitution of the internal hardware with external fixation devices and further flap coverage. We propose a different reconstructive approach, characterized by harvesting a sural fasciomusculocutaneous flap on the exposed internal hardware once a sterile ground has been obtained. Four patients were retrospectively analyzed. Soft tissue reconstruction was achieved in all cases. In one case hardware removal was necessary for complete healing. The sural fasciomusculocutaneous flap is a safe alternative to other pedicled and free flaps. Moreover, it allows direct coverage of internal fixators, thus completing the reconstruction in less time. This flap fits best to the morphology of the wound and internal hardware, leaving the main vascular trunk of the leg intact and at the same time providing a reliable vascular supply

Design and Characterization of a Prototype Stripline Beam Position Monitor for the Clic Drive Beam*

The prototype of a stripline Beam Position Monitor (BPM) with its associated readout electronics is under development at CERN, in collaboration with SLAC, LAPP and IFIC. The anticipated position resolution and accuracy are expected to be below 2μm and 20μm respectively for operation of the BPM in the CLIC drive beam (DB) linac. This paper describes the particular CLIC DB conditions with respect to the beam position monitoring, presents the measurement concept, and summarizes electromagnetic simulations and RF measurements performed on the prototype

Variability in lutetium-177 SPECT quantification between different state-of-the-art SPECT/CT systems

Background: Quantitative SPECT imaging in targeted radionuclide therapy with lutetium-177 holds great potential for individualized treatment based on dose assessment. The establishment of dose-effect relations requires a standardized method for SPECT quantification. The purpose of this multi-center study is to evaluate quantitative accuracy and inter-system variations of different SPECT/CT systems with corresponding commercially available quantitative reconstruction algorithms. This is an important step towards a vendor-independent standard for quantitative lutetium-177 SPECT. Methods: Four state-of-the-art SPECT/CT systems were included: Discovery™ NM/CT 670Pro (GE Healthcare), Symbia Intevo™, and two Symbia™ T16 (Siemens Healthineers). Quantitative accuracy and inter-system variations were evaluated by repeatedly scanning a cylindrical phantom with 6 spherical inserts (0.5 – 113 ml). A sphere-to-background activity concentration ratio of 10:1 was used. Acquisition settings were standardized: medium energy collimator, body contour trajectory, photon energy window of 208 keV (± 10%), adjacent 20% lower scatter window, 2 × 64 projections, 128 × 128 matrix size, and 40 s projection time. Reconstructions were performed using GE Evolution with Q.Metrix™, Siemens xSPECT Quant™, Siemens Broad Quantification™ or Siemens Flash3D™ algorithms using vendor recommended settings. In addition, projection data were reconstructed using Hermes SUV SPECT™ with standardized reconstruction settings to obtain a vendor-neutral quantitative reconstruction for all systems. Volumes of interest (VOI) for the spheres were obtained by applying a 50% threshold of the sphere maximum voxel value corrected for background activity. For each sphere, the mean and maximum recovery coefficient (RCmean and RCmax) of three repeated measurements was calculated, defined as the imaged activity concentration divided by the actual activity concentration. Inter-system variations were defined as the range of RC over all systems. Re

Desenvolvimento de revestimentos fotocatalíticos à base de TiO2 nanométricos pelo método de revestimento por imersão

O artigo descreve o estágio inicial do desenvolvimento de revestimentos fotocatalíticos à base de TiO2 nanométricos utilizando o processo de revestimento por imersão. Os resultados alcançados demonstram que é possível formar revestimentos aderentes, homogêneos e transparentes no espectro vísivil, mantendo os desempenhos fotocatalíticos dos pós precursores