GEOTRACES IC1 (BATS) contamination-prone trace element isotopes Cd, Fe, Pb, Zn, Cu, and Mo intercalibration

International audienceWe report data on the isotopic composition of cadmium, copper, iron, lead, zinc, and molybdenum at the GEOTRACES IC1 BATS Atlantic intercalibration station. In general, the between lab and within-lab precisions are adequate to resolve global gradients and vertical gradients at this station for Cd, Fe, Pb, and Zn. Cd and Zn isotopes show clear variations in the upper water column and more subtle variations in the deep water; these variations are attributable, in part, to progressive mass fractionation of isotopes by Rayleigh distillation from biogenic uptake and/or adsorption. Fe isotope variability is attributed to heavier crustal dust and hydrothermal sources and light Fe from reducing sediments. Pb isotope variability results from temporal changes in anthropogenic source isotopic compositions and the relative contributions of U.S. and European Pb sources. Cu and Mo isotope variability is more subtle and close to analytical precision. Although the present situation is adequate for proceeding with GEOTRACES, it should be possible to improve the within-lab and between-lab precisions for some of these properties

Homeostatic Scaling of Excitability in Recurrent Neural Networks

Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which neurons reside. However, most neurons are embedded in recurrent networks, which require a delicate balance between excitation and inhibition to maintain network stability. This balance could be disrupted when neurons independently adjust their intrinsic excitability. Here, we study the functioning of activity-dependent homeostatic scaling of intrinsic excitability (HSE) in a recurrent neural network. Using both simulations of a recurrent network consisting of excitatory and inhibitory neurons that implement HSE, and a mean-field description of adapting excitatory and inhibitory populations, we show that the stability of such adapting networks critically depends on the relationship between the adaptation time scales of both neuron populations. In a stable adapting network, HSE can keep all neurons functioning within their dynamic range, while the network is undergoing several (patho)physiologically relevant types of plasticity, such as persistent changes in external drive, changes in connection strengths, or the loss of inhibitory cells from the network. However, HSE cannot prevent the unstable network dynamics that result when, due to such plasticity, recurrent excitation in the network becomes too strong compared to feedback inhibition. This suggests that keeping a neural network in a stable and functional state requires the coordination of distinct homeostatic mechanisms that operate not only by adjusting neural excitability, but also by controlling network connectivity

Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing

Homeostatic Plasticity Studied Using In Vivo Hippocampal Activity-Blockade: Synaptic Scaling, Intrinsic Plasticity and Age-Dependence

Homeostatic plasticity is thought to be important in preventing neuronal circuits from becoming hyper- or hypoactive. However, there is little information concerning homeostatic mechanisms following in vivo manipulations of activity levels. We investigated synaptic scaling and intrinsic plasticity in CA1 pyramidal cells following 2 days of activity-blockade in vivo in adult (postnatal day 30; P30) and juvenile (P15) rats. Chronic activity-blockade in vivo was achieved using the sustained release of the sodium channel blocker tetrodotoxin (TTX) from the plastic polymer Elvax 40W implanted directly above the hippocampus, followed by electrophysiological assessment in slices in vitro. Three sets of results were in general agreement with previous studies on homeostatic responses to in vitro manipulations of activity. First, Schaffer collateral stimulation-evoked field responses were enhanced after 2 days of in vivo TTX application. Second, miniature excitatory postsynaptic current (mEPSC) amplitudes were potentiated. However, the increase in mEPSC amplitudes occurred only in juveniles, and not in adults, indicating age-dependent effects. Third, intrinsic neuronal excitability increased. In contrast, three sets of results sharply differed from previous reports on homeostatic responses to in vitro manipulations of activity. First, miniature inhibitory postsynaptic current (mIPSC) amplitudes were invariably enhanced. Second, multiplicative scaling of mEPSC and mIPSC amplitudes was absent. Third, the frequencies of adult and juvenile mEPSCs and adult mIPSCs were increased, indicating presynaptic alterations. These results provide new insights into in vivo homeostatic plasticity mechanisms with relevance to memory storage, activity-dependent development and neurological diseases

Diastereoselective Synthesis of C60/Steroid Conjugates

The design and synthesis of fullerene–steroid hybrids by using Prato’s protocol has afforded new fullerene derivatives endowed with epiandrosterone, an important naturally occurring steroid hormone. Since the formation of the pyrrolidine ring resulting from the 1,3-dipolar cyloaddition reaction takes place with generation of a new stereogenic center on the C2 of the five-membered ring, the reaction proceeds with formation of a diastereomeric mixture [compounds 6 and 7 in 70:30 ratio, 8 and 9 in 26:74 ratio (HPLC)] in which the formation of the major diasteroisomers 6 and 9 is consistent with an electrophilic attack of [60]fullerene on the Re face of the azomethine ylide directed by the steroidic unit. The chiroptical properties of these conjugates reveal typical Cotton effects in CD spectra that have been used to assign the absolute configuration of the new fulleropyrrolidines. The electrochemical study of the new compounds reveals the presence of four quasi-reversible reduction waves which are cathodically shifted in comparison with the parent C60, thus ascertaining the proposed structures.Financial support by the Ministerio de Ciencia e Innovación (MINECO) of Spain (CTQ2011-24652, CTQ2011-27253, PIB2010JP-00196, and CSD2007-00010 projects) and CAM (Madrisolar-2) is acknowledged; A.R. thanks UCM for financial support; M.S. is indebted to Programa del Grupo Santander 2012

Incidence and prevalence of patellofemoral pain: a systematic review and meta-analysis

Background: Patellofemoral pain is considered one of the most common forms of knee pain, affecting adults, adolescents, and physically active populations. Inconsistencies in reported incidence and prevalence exist and in relation to the allocation of healthcare and research funding, there is a clear need to accurately understand the epidemiology of patellofemoral pain. Methods: An electronic database search was conducted, as well as grey literature databases, from inception to June 2017. Two authors independently selected studies, extracted data and appraised methodological quality. If heterogeneous, data were analysed descriptively. Where studies were homogeneous, data were pooled through a meta-analysis. Results: 23 studies were included. Annual prevalence for patellofemoral pain in the general population was reported as 22.7%, and adolescents as 28.9%. Incidence rates in military recruits ranged from 9.7 – 571.4/1,000 person-years, amateur runners in the general population at 1080.5/1,000 person-years and adolescents amateur athletes 5.1% - 14.9% over 1 season. One study reported point prevalence within military populations as 13.5%. The pooled estimate for point prevalence in adolescents was 7.2% (95% Confidence Interval: 6.3% - 8.3%), and in female only adolescent athletes was 22.7% (95% Confidence Interval 17.4% - 28.0%). Conclusion: This review demonstrates high incidence and prevalence levels for patellofemoral pain. Within the context of this, and poor long term prognosis and high disability levels, PFP should be an urgent research priority

Recent advances and perspectives on starch nanocomposites for packaging applications

Starch nanocomposites are popular and abundant materials in packaging sectors. The aim of this work is to review some of the most popular starch nanocomposite systems that have been used nowadays. Due to a wide range of applicable reinforcements, nanocomposite systems are investigated based on nanofiller type such as nanoclays, polysaccharides and carbonaceous nanofillers. Furthermore, the structures of starch and material preparation methods for their nanocomposites are also mentioned in this review. It is clearly presented that mechanical, thermal and barrier properties of plasticised starch can be improved with well-dispersed nanofillers in starch nanocomposites

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms