Anatomical atlas of the upper part of the human head for electroencephalography and bioimpedance applications

Objective. The objective of this work is to develop a 4D (3D+T) statistical anatomical atlas of the electrical properties of the upper part of the human head for cerebral electrophysiology and bioimpedance applications. Approach. The atlas was constructed based on 3D magnetic resonance images (MRI) of 107 human individuals and comprises the electrical properties of the main internal structures and can be adjusted for specific electrical frequencies. T1w+T2w MRI images were used to segment the main structures of the head while angiography MRI was used to segment the main arteries. The proposed atlas also comprises a time-varying model of arterial brain circulation, based on the solution of the Navier-Stokes equation in the main arteries and their vascular territories. Main results. High-resolution, multi-frequency and time-varying anatomical atlases of resistivity, conductivity and relative permittivity were created and evaluated using a forward problem solver for EIT. The atlas was successfully used to simulate electrical impedance tomography measurements indicating the necessity of signal-to-noise between 100 and 125 dB to identify vascular changes due to the cardiac cycle, corroborating previous studies. The source code of the atlas and solver are freely available to download. Significance. Volume conductor problems in cerebral electrophysiology and bioimpedance do not have analytical solutions for nontrivial geometries and require a 3D model of the head and its electrical properties for solving the associated PDEs numerically. Ideally, the model should be made with patient-specific information. In clinical practice, this is not always the case and an average head model is often used. Also, the electrical properties of the tissues might not be completely known due to natural variability. Anatomical atlases are important tools for in silico studies on cerebral circulation and electrophysiology that require statistically consistent data, e.g. machine learning, sensitivity analyses, and as a benchmark to test inverse problem solvers.Peer reviewe

Next-generation regulatory T cell therapy

Regulatory T cells (Treg cells) are a small subset of immune cells that are dedicated to curbing excessive immune activation and maintaining immune homeostasis. Accordingly, deficiencies in Treg cell development or function result in uncontrolled immune responses and tissue destruction and can lead to inflammatory disorders such as graft-versus-host disease, transplant rejection and autoimmune diseases. As Treg cells deploy more than a dozen molecular mechanisms to suppress immune responses, they have potential as multifaceted adaptable smart therapeutics for treating inflammatory disorders. Indeed, early-phase clinical trials of Treg cell therapy have shown feasibility, tolerability and potential efficacy in these disease settings. In the meantime, progress in the development of chimeric antigen receptors and in genome editing (including the application of CRISPR-Cas9) over the past two decades has facilitated the genetic optimization of primary T cell therapy for cancer. These technologies are now being used to enhance the specificity and functionality of Treg cells. In this Review, we describe the key advances and prospects in designing and implementing Treg cell-based therapy in autoimmunity and transplantation

Nutrição de tithonia diversifolia e atributos do solo adubado com biofertilizante em sistema irrigado

The fertilization with biofertilizer associated with the use of irrigation favors nutrient uptake by plants and soil chemical properties; however, these effects are little studied in Tithonia diversifolia in semiarid regions. This study evaluated the effect of doses of bovine biofertilizer and irrigation on accumulation of nutrients in the leaves of Tithonia diversifolia plants and on soil chemical attributes. The study was carried out from December 3, 2014 to November 28, 2015, and arranged in a 2 x 5 factorial scheme, consisting of five doses of bovine biofertilizer (0, 40, 80, 120 and 160 m3 ha-1), combined with and without irrigation. The experiment was set in a randomized block design, using three replicates. Irrigation promoted increased accumulation of N, P, K, Ca, Mg, S, Zn, Fe, Mn, Cu and B in leaves of Tithonia diversifolia in the first cutting. However, the high bicarbonate concentration in the irrigation water and the occurrence of rainfall during the second crop increased the accumulation of Cu in the leaves of Tithonia diversifolia under rainfed condition, compared with irrigated plants. The increase in biofertilizer doses contributed to the increment of base saturation and the contents of organic matter, P and K in soil201110081013Associada ao uso de irrigação, a adubação com biofertilizante favorece a absorção de nutrientes pelas plantas e as propriedades químicas dos solos, porém tais efeitos são pouco estudados no cultivo de Tithonia diversifolia em regiões semiáridas. Avaliaram-se os efeitos de doses de biofertilizante bovino e da irrigação no acúmulo foliar de nutrientes em plantas de Tithonia diversifolia e nos atributos químicos do solo. O estudo foi conduzido entre 3 de dezembro de 2014 e 28 de novembro de 2015 e distribuído em esquema fatorial 5 x 2, consistindo de cinco doses de biofertilizante bovino (0, 40, 80, 120 e 160 m3 ha-1), combinado com e sem irrigação. O delineamento estatístico do experimento foi em blocos casualizados com três repetições. A irrigação promoveu aumento no acúmulo de N, P, K, Ca, Mg, S, Zn, Fe, Mn, Cu e B em folhas de Tithonia diversifolia no primeiro corte; entretanto, a alta concentração de bicarbonato na água de irrigação e a presença de chuvas durante o segundo cultivo aumentaram o acúmulo de Cu nas folhas de Tithonia diversifolia em sequeiro quando comparado às plantas irrigadas. O aumento das doses de biofertilizante contribuiu para o incremento da saturação por base e do teor de matéria orgânica, P e K no sol

Nicotinic acid induces antinociceptive and anti-inflammatory effects in different experimental models

AbstractAlthough in vitro studies have shown that nicotinic acid inhibits some aspects of the inflammatory response, a reduced number of in vivo studies have investigated this activity. To the best of our knowledge, the effects induced by nicotinic acid in models of nociceptive and inflammatory pain are not known. Per os (p.o.) administration of nicotinic acid (250, 500 or 1000mg/kg, −1h) inhibited the first and the second phases of the nociceptive response induced by formalin in mice. Nicotinic acid (250 or 500mg/kg, −1 and 3h) also inhibited the mechanical allodynia induced by carrageenan in rats, a model of inflammatory pain. However, in a model of nociceptive pain, exposure of mice to a hot-plate, nicotinic acid was devoid of activity. In addition to inhibiting the nociceptive response in models of inflammatory pain, nicotinic acid (250 or 500mg/kg, p.o., −1 and 3h) inhibited paw edema induced by carrageenan in mice and rats. Picolinic acid (62.5 or 125mg/kg, p.o., −1h), a nicotinic acid isomer, inhibited both phases of the nociceptive response induced by formalin, but not paw edema induced by carrageenan in mice. The other nicotinic acid isomer, isonicotinic acid, was devoid of activity in these two models. In conclusion, our results represent the first demonstration of the activity of nicotinic acid in experimental models of nociceptive and inflammatory pain and also provide further support to its anti-inflammatory activity. It is unlikely that conversion to nicotinamide represents an important mechanism to explain the antinociceptive and anti-inflammatory activities of nicotinic acid. The demonstration of new activities of nicotinic acid, a drug that has already been approved for clinical use and presents a positive safety record, may contribute to raise the interest in conducting clinical trials to investigate its usefulness in the treatment of painful and inflammatory diseases

Global Fire Season Severity Analysis and Forecasting

Global fire activity has a huge impact on human lives. In recent years, many fire models have been developed to forecast fire activity. They present good results for some regions but require complex parametrizations and input variables that are not easily obtained or estimated. In this paper, we evaluate the possibility of using historical data from 2003 to 2017 of active fire detections (NASA's MODIS MCD14ML C6) and time series forecasting methods to estimate global fire season severity (FSS), here defined as the accumulated fire detections in a season. We used a hexagonal grid to divide the globe, and we extracted time series of daily fire counts from each cell. We propose a straightforward method to estimate the fire season lengths. Our results show that in 99% of the cells, the fire seasons have lengths shorter than seven months. Given this result, we extracted the fire seasons defined as time windows of seven months centered in the months with the highest fire occurrence. We define fire season severity (FSS) as the accumulated fire detections in a season. A trend analysis suggests a global decrease in length and severity. Since FSS time series are concise, we used the monthly-accumulated fire counts (MA-FC) to train and test the seven forecasting models. Results show low forecasting errors in some areas. Therefore we conclude that many regions present predictable variations in the FSS

Environmental impact monitoring of a minero-chemical complex in Catalão urban area of PTS, PM10 and PM2.5 by EDX characterization

Depending on its nature, particulate matter has very different size, composition and morphology. By the combination of these criteria it is possible to distinguish the emitting sources (primary or secondary). The shape and the dimension of the particles have also a direct interaction with the risk assessment for human health. The minero-chemical complex consists of phosphate fertilizer manufacturing, rock phosphate and niobium mining open pits and it is located northeast of the urban area of the city. Environmental issues associated with it include the following: fugitive emissions which are primarily associated with operational leaks from tubing, valves, connections, flanges, packings, open ended lines, floating roof storage tank and pump seals, gas conveyance systems, compressor seals, pressure relief valves, tanks or open its/containments, and loading and unloading operations of products. Furthermore the area of study is characterized by a predominantly northeast winds direction. The monitoring was performed weekly particulates samples were collected in two seasonal episodes at one representative places in the urban area of Catalao (a Brazilian city located in Goias state) in the period from August to November of 2014. Suspended particles were sampled on pure fiberglass filters by using a High Volume air sampler and were analyzed via an energy dispersive X-ray microanalysis system (EDX). The airborne particulate matter was characterized from a physico-chemical point of view to supply information on the particle composition and the compounds carried on their surfaces. The microanalysis enables identification of several groups of particles such as: soot, Si-rich, metal-rich and biological particules. These results may help in controlling and preventing fugitive emissions in atmospheric air.Depending on its nature, particulate matter has very different size, composition and morphology. By the combination of these criteria it is possible to distinguish the emitting sources (primary or secondary). The shape and the dimension of the particles h4319091914CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPEG - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE GOIÁSsem informaçãosem informaçãoFinancial support and scholarships from the Brazilian funding agencies CNPq, FAPEG and Environment Office City of Catalão, City Council Environmental Protection of Catalão and Public Ministry of Goiás State are gratefully acknowledged

Answer Set Programming for Non-Stationary Markov Decision Processes

Non-stationary domains, where unforeseen changes happen, present a challenge for agents to find an optimal policy for a sequential decision making problem. This work investigates a solution to this problem that combines Markov Decision Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming (ASP) in a method we call ASP(RL). In this method, Answer Set Programming is used to find the possible trajectories of an MDP, from where Reinforcement Learning is applied to learn the optimal policy of the problem. Results show that ASP(RL) is capable of efficiently finding the optimal solution of an MDP representing non-stationary domains