The Psychedelic State Induced by Ayahuasca Modulates the Activity and Connectivity of the Default Mode Network

The experiences induced by psychedelics share a wide variety of subjective features, related to the complex changes in perception and cognition induced by this class of drugs. A remarkable increase in introspection is at the core of these altered states of consciousness. Self-oriented mental activity has been consistently linked to the Default Mode Network (DMN), a set of brain regions more active during rest than during the execution of a goal-directed task. Here we used fMRI technique to inspect the DMN during the psychedelic state induced by Ayahuasca in ten experienced subjects. Ayahuasca is a potion traditionally used by Amazonian Amerindians composed by a mixture of compounds that increase monoaminergic transmission. In particular, we examined whether Ayahuasca changes the activity and connectivity of the DMN and the connection between the DMN and the task-positive network (TPN). Ayahuasca caused a significant decrease in activity through most parts of the DMN, including its most consistent hubs: the Posterior Cingulate Cortex (PCC)/Precuneus and the medial Prefrontal Cortex (mPFC). Functional connectivity within the PCC/Precuneus decreased after Ayahuasca intake. No significant change was observed in the DMN-TPN orthogonality. Altogether, our results support the notion that the altered state of consciousness induced by Ayahuasca, like those induced by psilocybin (another serotonergic psychedelic), meditation and sleep, is linked to the modulation of the activity and the connectivity of the DMN.The Brazilian Federal Agencies: CNPq, CAPES; FINEP; The Sao Paulo State financial agency (FAPESP)

Estudio Integral sobre la Evaluación del Recurso Eólico en Entornos Urbanos. Estación Anemométrica Adaptable

Como consecuencia de un intenso proceso de trabajo, coordinado con Investigadores del CIEMAT (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas), se pretende comunicar el estado del trabajo de Investigación relacionado con la Evaluación del Potencial Eólico en entornos urbanos, con vistas a la posible generación de energía eléctrica limpia

Molecular characterization of HTLV-1 and HTLV-2 and routes of virus transmission in HIV-infected patients from the southeastern and southern Brazil

MCT/CNPq # 481040/2007-2; # 303545/2012-7CAPESIAL # 33/07; # 39/0

Gramine derivatives targeting Ca2+ channels and Ser/Thr phosphatases: A new dual strategy for the treatment of neurodegenerative diseases

This document is the unedited author's version of a Submitted Work that was subsequently accepted for publication in Journal of Medicinal Chemistry , copyright © American Chemical Society after peer review. To access the final edited and published work, see http://pubs.acs.org/doi/abs/10.1021/acs.jmedchem.6b00478We describe the synthesis of gramine derivatives and their pharmacological evaluation as multipotent drugs for the treatment of Alzheimer’s disease. An innovative multitarget approach is presented, targeting both voltage-gated Ca2+ channels, classically studied for neurodegenerative diseases, and Ser/Thr phosphatases, which have been marginally aimed, even despite their key role in protein τ dephosphorylation. Twenty-five compounds were synthesized, and mostly their neuroprotective profile exceeded that offered by the head compound gramine. In general, these compounds reduced the entry of Ca2+ through VGCC, as measured by Fluo-4/AM and patch clamp techniques, and protected in Ca2+ overload-induced models of neurotoxicity, like glutamate or veratridine exposures. Furthermore, we hypothesize that these compounds decrease τ hyperphosphorylation based on the maintenance of the Ser/Thr phosphatase activity and their neuroprotection against the damage caused by okadaic acid. Hence, we propose this multitarget approach as a new and promising strategy for the treatment of neurodegenerative diseasesThis work was supported by the following grant: Proyectos de Investigación en Salud (PI13/00789, IS Carlos III). R.L.C is granted by Universidad Autónoma de Madri

Alterations of cardiorespiratory and motor profile of paralympic 5-a-side football athletes during 14-week in-season training

The aim of this study was to characterize the cardiorespiratory and motor performance characteristics of blind 5-a-side footballers from the Brazilian Paralympic Team. Seven male athletes were evaluated at before and after 14-week in-season training (weekly volume between 6.5 hours to 10.8 hours), through cardiorespiratory fitness test, Agility test (5x10), Running-based Anaerobic Sprint Test (RAST) and Standing Long Jump Test (SLJT). The VO2max ranged from 51.9 (±3.8) to 54.3 (±5.0) mL.kg-1.min-1.VO2 at ventilatory threshold ranged from 48.4 (±4.4) to 41.1 (±6.8) mL.kg-1.min-1. Heart rate at ventilatory threshold ranged from 94.7 (±2.3) to 89.9 (±3.7) bpm. Regarding motor performance the values of medium Power Output ranged from 442 (± 63) to 421.9 (±66) Watts and Fatigue Index ranged from 63.1 (±9.4) to 53.9 (±14.8) W/s. Overall, our results show that while the performance of these athletes is inferior to that of professional players, their cardiorespiratory and motor performance is superior to that typical of semi-professional futsal athletes and, this study which can potentially suggest and contribute to the prescription of future training programs

ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks

The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches to enabling deep neural networks (DNNs) in this domain, including pruning, quantization, compression, and binary neural networks (BNNs), but with the emergence of the "extreme edge", there is now a demand for even more efficient models. In order to meet the constraints of ultra-low-energy devices, we propose ULEEN, a model architecture based on weightless neural networks. Weightless neural networks (WNNs) are a class of neural model which use table lookups, not arithmetic, to perform computation. The elimination of energy-intensive arithmetic operations makes WNNs theoretically well suited for edge inference; however, they have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by BNNs to make significant strides in improving accuracy and reducing model size. We compare FPGA and ASIC implementations of an inference accelerator for ULEEN against edge-optimized DNN and BNN devices. On a Xilinx Zynq Z-7045 FPGA, we demonstrate classification on the MNIST dataset at 14.3 million inferences per second (13 million inferences/Joule) with 0.21 $\mu$s latency and 96.2% accuracy, while Xilinx FINN achieves 12.3 million inferences per second (1.69 million inferences/Joule) with 0.31 $\mu$s latency and 95.83% accuracy. In a 45nm ASIC, we achieve 5.1 million inferences/Joule and 38.5 million inferences/second at 98.46% accuracy, while a quantized Bit Fusion model achieves 9230 inferences/Joule and 19,100 inferences/second at 99.35% accuracy. In our search for ever more efficient edge devices, ULEEN shows that WNNs are deserving of consideration.Comment: 14 pages, 14 figures Portions of this article draw heavily from arXiv:2203.01479, most notably sections 5E and 5F.

Renormalization of the Inverse Square Potential

The quantum-mechanical D-dimensional inverse square potential is analyzed using field-theoretic renormalization techniques. A solution is presented for both the bound-state and scattering sectors of the theory using cutoff and dimensional regularization. In the renormalized version of the theory, there is a strong-coupling regime where quantum-mechanical breaking of scale symmetry takes place through dimensional transmutation, with the creation of a single bound state and of an energy-dependent s-wave scattering matrix element.Comment: 5 page