Contributing to Understand the Crosstalk between Brain and Periphery in Methylmercury Intoxication: Neurotoxicity and Extracellular Vesicles

Human exposure to methylmercury (MeHg) is currently high in regions such as the Amazon. Understanding the molecular changes associated with MeHg-induced neurotoxicity and the crosstalk with the periphery is essential to support early diagnoses. This work aimed to evaluate cellular and molecular changes associated with behavioral alterations in MeHg acute exposure and the possible changes in extracellular vesicles (EVs) number and S100β content. Adults male Wistar rats were orally treated with 5 mg/kg for four days. Behavioral performance, molecular and histological changes in the cerebellum, and plasma EVs were assessed. MeHg-intoxicated animals performed significantly worse in behavioral tests. MeHg increased the number of GFAP+ cells and GFAP and S100β mRNA expression in the cerebellum but no change in NeuN+ or IBA-1+ cells number was detected. The number of exosomes isolated from plasma were decreased by the metal. S100B mRNA was detected in circulating plasma EVs cargo in MeHg exposure. Though preliminary, our results suggest astrocytic reactivity is displaying a protective role once there was no neuronal death. Interestingly, the reduction in exosomes number could be a new mechanism associated with MeHg-induced neurotoxicity and plasma EVs could represent a source of future biomarkers in MeHg intoxication.     

Prevention of steel corrosion in concrete exposed to seawater with submerged sacrificial anodes

This paper presents the results of a study of the effectiveness of submerged sacrificial anodes in preventing the onset of pitting corrosion in the emerged part of marine piles. Experimental tests were carried out on reinforced concrete columns with steel embedded both in chloride free concrete and chloride contaminated concrete in order to compare the effects of sacrificial anodes on passive steel and on corroding steel. Results have shown, at least under the present testing conditions, that sacrificial anodes may be more effective in preventing corrosion initiation (i.e. in providing cathodic prevention) than in controlling ongoing pitting corrosion (i.e. in guaranteeing cathodic protection). Monitoring criteria for this type of prevention are also discussed.     

不同热变形工艺大塑性变形加工超细晶1570C铝合金的组织、强度和超塑性

将Al-5Mg-0.18Mn-0.2Sc-0.08Zr-0.01Fe-0.01Si(质量分数,％)合金铸锭进行多向等温锻造(应变12)或等径角挤压(应变10,325℃),再进行热轧(325℃)和冷轧(20℃),对比研究合金变形后的组织和力学行为.结果表明,对(亚)晶粒尺寸dUFG=2μm的超细晶组织合金进行多向等温锻造...     

Using chemistry and microfluidics to understand the spatial dynamics of complex biological networks

Understanding the spatial dynamics of biochemical networks is both fundamentally important for understanding life at the systems level and also has practical implications for medicine, engineering, biology, and chemistry. Studies at the level of individual reactions provide essential information about the function, interactions, and localization of individual molecular species and reactions in a network. However, analyzing the spatial dynamics of complex biochemical networks at this level is difficult. Biochemical networks are nonequilibrium systems containing dozens to hundreds of reactions with nonlinear and time-dependent interactions, and these interactions are influenced by diffusion, flow, and the relative values of state-dependent kinetic parameters. To achieve an overall understanding of the spatial dynamics of a network and the global mechanisms that drive its function, networks must be analyzed as a whole, where all of the components and influential parameters of a network are simultaneously considered. Here, we describe chemical concepts and microfluidic tools developed for network-level investigations of the spatial dynamics of these networks. Modular approaches can be used to simplify these networks by separating them into modules, and simple experimental or computational models can be created by replacing each module with a single reaction. Microfluidics can be used to implement these models as well as to analyze and perturb the complex network itself with spatial control on the micrometer scale. We also describe the application of these network-level approaches to elucidate the mechanisms governing the spatial dynamics of two networkshemostasis (blood clotting) and early patterning of the Drosophila embryo. To investigate the dynamics of the complex network of hemostasis, we simplified the network by using a modular mechanism and created a chemical model based on this mechanism by using microfluidics. Then, we used the mechanism and the model to predict the dynamics of initiation and propagation of blood clotting and tested these predictions with human blood plasma by using microfluidics. We discovered that both initiation and propagation of clotting are regulated by a threshold response to the concentration of activators of clotting, and that clotting is sensitive to the spatial localization of stimuli. To understand the dynamics of patterning of the Drosophila embryo, we used microfluidics to perturb the environment around a developing embryo and observe the effects of this perturbation on the expression of Hunchback, a protein whose localization is essential to proper development. We found that the mechanism that is responsible for Hunchback positioning is asymmetric, time-dependent, and more complex than previously proposed by studies of individual reactions. Overall, these approaches provide strategies for simplifying, modeling, and probing complex networks without sacrificing the functionality of the network. Such network-level strategies may be most useful for understanding systems with nonlinear interactions where spatial dynamics is essential for function. In addition, microfluidics provides an opportunity to investigate the mechanisms responsible for robust functioning of complex networks. By creating nonideal, stressful, and perturbed environments, microfluidic experiments could reveal the function of pathways thought to be nonessential under ideal conditions.     

FTIR Characterization of Fe3+–OH Groups in Fe–H–BEA Zeolite: Interaction with CO and NO

Fe³⁺–OH groups of a Fe–H–BEA sample prepared by conventional ion-exchange method are characterized by an IR band at 3686–3684 cm^?1. They exhibit a weak acidity: upon low-temperature CO adsorption the O–H stretching modes are blue shifted by 100 cm^?1 and the respective carbonyl adducts are observed at 2158 cm^?1. The Fe³⁺–OH groups are reduced at room temperature by NO to form Fe²⁺–NO species and NO⁺ groups in cationic positions. Desorption of pre-adsorbed NO at temperatures above 373 K regenerates the Fe³⁺–OH groups. The relation of the Fe³⁺–OH species to the so-called α-oxygen is discussed.     

High efficiency and reusability of iridium complexes adsorbed in montmorillonite clay on catalytic hydrogenation of imines

Heterogenised homogeneous catalytic hydrogenation of aldimines by removable and reusable immobilised iridium complexes on montmorillonite clay. This revised version was published online in July 2006 with corrections to the Cover Date.     

Sandwiched-Beam Procedure for Precracking Brittle Materials

A technique for precracking brittle materials is presented. This procedure, which is called the sandwiched-beam (SB) technique, allows the production of sharp through-thickness cracks with predetermined length in specimens with a rectangular section. A bar, in which an initial notch is produced by using a conventional saw, is inserted between two supporting beams and the sandwich assembly is loaded in three-point bending. Conditions can be defined that allow the stable propagation of a sharp flaw from the notch as the applied load is increased. Then, the cracked bar can be used to determine the fracture toughness. The SB technique is applied to different brittle materials, including soda-lime-silica glass, alumina, Si₃N₄, a SiC _w -Si₃N₄ composite, graphite, a Ti-Al intermetallic, and Carrara marble.     

Robust Estimation in Vector Autoregressive Moving-Average Models

Bustos and Yohai proposed a class of robust estimates for autoregressive moving-average (ARMA) models based on residual autocovariances (RA estimates). In this paper an affine equivariant generalization of the RA estimates for vector ARMA processes is given. These estimates are asymptotically normal and, when the innovations have an elliptical distribution, their asymptotic covariance matrix differs only by a scalar factor from the covariance matrix corresponding to the maximum likelihood estimate. A Monte Carlo study confirms that the RA estimates are efficient under normal errors and robust when the sample contains outliers. A robust multivariate goodness-of-fit test based on the RA estimates is also obtained.