Natural caprine whey oligosaccharides separated by membrane technology and profile evaluation by capillary electrophoresis

The functional food market is growing rapidly and membrane processing offers several advantages over conventional methods for separation, fractionation and recovery of bioactive components. The aim of the present study was to select a process that could be implemented easily on an industrial scale for the isolation of natural lactose-derived oligosaccharides (OS) from caprine whey, enabling the development of functional foods for clinical and infant nutrition. The most efficient process was the combination of a pre-treatment to eliminate proteins and fat, using an ultrafiltration (UF) membrane of 25 kDa molecular weight cut off (MWCO), followed by a tighter UF membrane with 1 kDa MWCO. Circa 90% of the carbohydrates recovered in the final retentate were OS. Capillary electrophoresis was used to evaluate the OS profile in this retentate. The combined membrane-processing system is thus a promising technique for obtaining natural concentrated OS from whey. Powered

Anionic metabolic profiling of urine from antibiotic-treated rats by capillary electrophoresis–mass spectrometry

A recently developed capillary electrophoresis (CE)-negative-ionisation mass spectrometry (MS) method was used to profile anionic metabolites in a microbial-host co-metabolism study. Urine samples from rats receiving antibiotics (penicillin G and streptomycin sulfate) for 0, 4, or 8 days were analysed. A quality control sample was measured repeatedly to monitor the performance of the applied CE-MS method. After peak alignment, relative standard deviations (RSDs) for migration time of five representative compounds were below 0.4 %, whereas RSDs for peak area were 7.9–13.5 %. Using univariate and principal component analysis of obtained urinary metabolic profiles, groups of rats receiving different antibiotic treatment could be distinguished based on 17 discriminatory compounds, of which 15 were downregulated and 2 were upregulated upon treatment. Eleven compounds remained down- or upregulated after discontinuation of the antibiotics administration, whereas a recovery effect was observed for others. Based on accurate mass, nine compounds were putatively identified; these included the microbial-mammalian co-metabolites hippuric acid and indoxyl sulfate. Some discriminatory compounds were also observed by other analytical techniques, but CE-MS uniquely revealed ten metabolites modulated by antibiotic exposure, including aconitic acid and an oxocholic acid. This clearly demonstrates the added value of CE-MS for nontargeted profiling of small anionic metabolites in biological samples.

Updated empirical model of genetically-modified maize grain production practices to achieve European Union labeling thresholds

An updated empirical approach is proposed for specifying coexistence requirements for genetically modified (GM) maize (Zea mays L.) production to ensure compliance with the 0.9% labeling threshold for food and feed in the European Union. The model improves on a previously published (Gustafson et al., 2006) empirical model by adding recent data sources to supplement the original database and including the following additional cases: (i) more than one GM maize source field adjacent to the conventional or organic field, (ii) the possibility of so-called “stacked” varieties with more than one GM trait, and (iii) lower pollen shed in the non-GM receptor field. These additional factors lead to the possibility for somewhat wider combinations of isolation distance and border rows than required in the original version of the empirical model. For instance, in the very conservative case of a 1-ha square non-GM maize field surrounded on all four sides by homozygous GM maize with 12 m isolation (the effective isolation distance for a single GM field), non-GM border rows of 12 m are required to be 95% confident of gene flow less than 0.9% in the non-GM field (with adventitious presence of 0.3%). Stacked traits of higher GM mass fraction and receptor fields of lower pollen shed would require a greater number of border rows to comply with the 0.9% threshold, and an updated extension to the model is provided to quantify these effects.

A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method

In this paper a modified algorithm is suggested for developing polynomial neural network (PNN) models. Optimal partial description (PD) modeling is introduced at each layer of the PNN expansion, a task accomplished using the orthogonal least squares (OLS) method. Based on the initial PD models determined by the polynomial order and the number of PD inputs, OLS selects the most significant regressor terms reducing the output error variance. The method produces PNN models exhibiting a high level of accuracy and superior generalization capabilities. Additionally, parsimonious models are obtained comprising a considerably smaller number of parameters compared to the ones generated by means of the conventional PNN algorithm. Three benchmark examples are elaborated, including modeling of the gas furnace process as well as the iris and wine classification problems. Extensive simulation results and comparison with other methods in the literature, demonstrate the effectiveness of the suggested modeling approach.

Rescue of gene expression by modified REST decoy oligonucleotides in a cellular model of Huntington's disease

Transcriptional dysfunction is a prominent hallmark of Huntington's disease (HD). Several transcription factors have been implicated in the aetiology of HD progression and one of the most prominent is repressor element 1 (RE1) silencing transcription factor (REST). REST is a global repressor of neuronal gene expression and in the presence of mutant Huntingtin increased nuclear REST levels lead to elevated RE1 occupancy and a concomitant increase in target gene repression, including brain-derived neurotrophic factor. It is of great interest to devise strategies to reverse transcriptional dysregulation caused by increased nuclear REST and determine the consequences in HD. Thus far, such strategies have involved RNAi or mutant REST constructs. Decoys are double-stranded oligodeoxynucleotides corresponding to the DNA-binding element of a transcription factor and act to sequester it, thereby abrogating its transcriptional activity. Here, we report the use of a novel decoy strategy to rescue REST target gene expression in a cellular model of HD. We show that delivery of the decoy in cells expressing mutant Huntingtin leads to its specific interaction with REST, a reduction in REST occupancy of RE1s and rescue of target gene expression, including Bdnf. These data point to an alternative strategy for rebalancing the transcriptional dysregulation in HD.

Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks

Human minds often wander away from their immediate sensory environment. It remains unknown whether such mind wandering is unsystematic or whether it lawfully relates to an individual’s tendency to attend to salient stimuli such as pain and their associated brain structure/function. Studies of pain–cognition interactions typically examine explicit manipulation of attention rather than spontaneous mind wandering. Here we sought to better represent natural fluctuations in pain in daily life, so we assessed behavioral and neural aspects of spontaneous disengagement of attention from pain. We found that an individual’s tendency to attend to pain related to the disruptive effect of pain on his or her cognitive task performance. Next, we linked behavioral findings to neural networks with strikingly convergent evidence from functional magnetic resonance imaging during pain coupled with thought probes of mind wandering, dynamic resting state activity fluctuations, and diffusion MRI. We found that (i) pain-induced default mode network (DMN) deactivations were attenuated during mind wandering away from pain; (ii) functional connectivity fluctuations between the DMN and periaqueductal gray (PAG) dynamically tracked spontaneous attention away from pain; and (iii) across individuals, stronger PAG–DMN structural connectivity and more dynamic resting state PAG–DMN functional connectivity were associated with the tendency to mind wander away from pain. These data demonstrate that individual tendencies to mind wander away from pain, in the absence of explicit manipulation, are subserved by functional and structural connectivity within and between default mode and antinociceptive descending modulation networks.

Silica templating of a self-assembling peptide amphiphile that forms nanotapes

The peptide amphiphile C16-KTTKS templates silica polymerization, enabling the production of silica nanotape structures, imaged via electron microscopy (TEM and SEM). X-ray scattering shows that the nanotapes comprise stacked layers, as for the parent peptide amphiphile, but with a substantially increased layer spacing resulting from silica polymerization.

Computing Fresnel integrals via modified trapezium rules

In this paper we propose methods for computing Fresnel integrals based on truncated trapezium rule approximations to integrals on the real line, these trapezium rules modified to take into account poles of the integrand near the real axis. Our starting point is a method for computation of the error function of complex argument due to Matta and Reichel (J Math Phys 34:298–307, 1956) and Hunter and Regan (Math Comp 26:539–541, 1972). We construct approximations which we prove are exponentially convergent as a function of N , the number of quadrature points, obtaining explicit error bounds which show that accuracies of 10−15 uniformly on the real line are achieved with N=12 , this confirmed by computations. The approximations we obtain are attractive, additionally, in that they maintain small relative errors for small and large argument, are analytic on the real axis (echoing the analyticity of the Fresnel integrals), and are straightforward to implement.

Assessing and monitoring impacts of genetically modified plants on agro-ecosystems: the approach of AMIGA project

The environmental impacts of genetically modified crops is still a controversial issue in Europe. The overall risk assessment framework has recently been reinforced by the European Food Safety Authority(EFSA) and its implementation requires harmonized and efficient methodologies. The EU-funded research project AMIGA − Assessing and monitoring Impacts of Genetically modified plants on Agro-ecosystems − aims to address this issue, by providing a framework that establishes protection goals and baselines for European agro-ecosystems, improves knowledge on the potential long term environmental effects of genetically modified (GM) plants, tests the efficacy of the EFSA Guidance Document for the Environmental Risk Assessment, explores new strategies for post market monitoring, and provides a systematic analysis of economic aspects of Genetically Modified crops cultivation in the EU. Research focuses on ecological studies in different EU regions, the sustainability of GM crops is estimated by analysing the functional components of the agro-ecosystems and specific experimental protocols are being developed for this scope.

Neocuproine-functionalized silica-coated magnetic nanoparticles for extraction of copper(II) from aqueous solution

Neocuproine has been covalently bound to silica-coated maghemite(c-Fe2O3) magnetic nanoparticles (MNPs) by a phenyl ether linkage. The resulting MNPs are able to remove Cu(II) from 12 ppm aqueous solution with an extraction efficiency of up to 99% at pH 2.

Can dynamically downscaled windstorm footprints be improved by observations through a probabilistic approach?

Windstorms are a main feature of the European climate and exert strong socioeconomic impacts. Large effort has been made in developing and enhancing models to simulate the intensification of windstorms, resulting footprints, and associated impacts. Simulated wind or gust speeds usually differ from observations, as regional climate models have biases and cannot capture all local effects. An approach to adjust regional climate model (RCM) simulations of wind and wind gust toward observations is introduced. For this purpose, 100 windstorms are selected and observations of 173 (111) test sites of the German Weather Service are considered for wind (gust) speed. Theoretical Weibull distributions are fitted to observed and simulated wind and gust speeds, and the distribution parameters of the observations are interpolated onto the RCM computational grid. A probability mapping approach is applied to relate the distributions and to correct the modeled footprints. The results are not only achieved for single test sites but for an area-wide regular grid. The approach is validated using root-mean-square errors on event and site basis, documenting that the method is generally able to adjust the RCM output toward observations. For gust speeds, an improvement on 88 of 100 events and at about 64% of the test sites is reached. For wind, 99 of 100 improved events and ~84% improved sites can be obtained. This gives confidence on the potential of the introduced approach for many applications, in particular those considering wind data.

Effective separation of Am(III) and Eu(III) from HNO3 Q1 Q2 solutions using CyMe4-BTPhen-functionalized silica-coated magnetic nanoparticles

It has been shown that CyMe4-BTPhen-functionalized silica-coated maghemite (c-Fe2O3) magnetic nanoparticles (MNPs) are capable of quantitative separation of Am(III) from Eu(III) from HNO3 solutions. These MNPs also show a small but significant selectivity for Am(III) over Cm(III) with a separation factor of around 2 in 4 M HNO3. The water molecule in the cavity of the BTPhen may also play an important part in the selectivity.

Probability functions to build composite indicators: a methodology to measure environmental impacts of genetically modified crops

There is an on-going debate on the environmental effects of genetically modified crops to which this paper aims to contribute. First, data on environmental impacts of genetically modified (GM) and conventional crops are collected from peer-reviewed journals, and secondly an analysis is conducted in order to examine which crop type is less harmful for the environment. Published data on environmental impacts are measured using an array of indicators, and their analysis requires their normalisation and aggregation. Taking advantage of composite indicators literature, this paper builds composite indicators to measure the impact of GM and conventional crops in three dimensions: (1) non-target key species richness, (2) pesticide use, and (3) aggregated environmental impact. The comparison between the three composite indicators for both crop types allows us to establish not only a ranking to elucidate which crop is more convenient for the environment but the probability that one crop type outperforms the other from an environmental perspective. Results show that GM crops tend to cause lower environmental impacts than conventional crops for the analysed indicators.

Effective separation of Am(III) and Eu(III) from HNO3 solutions using CyMe4-BTPhen-functionalized silica-coated magnetic nanoparticles

It has been shown that CyMe4-BTPhen-functionalized silica-coated maghemite (c-Fe2O3) magnetic nanoparticles (MNPs) are capable of quantitative separation of Am(III) from Eu(III) from HNO3 solutions. These MNPs also show a small but significant selectivity for Am(III) over Cm(III) with a separation factor of around 2 in 4 M HNO3. The water molecule in the cavity of the BTPhen may also play an important part in the selectivity.