Application of normal-phase high-performance liquid chromatography followed by gas chromatography for analytics of diesel fuel additives

The paper presents the results of investigations on new procedures of determination of selected cleaning additives in diesel fuel. Two procedures: one-step analysis using gas chromatography with flame ionization detection (GC-FID) or mass spectrometry (GC-MS) and a two-step procedure in which normal-phase high-performance liquid chromatography (NP-HPLC) was used for preliminary separation of the additives, were compared. The additive fraction was collected using either simple elution or eluent backflush. Final determinations were performed by GC-FID and GC-MS. The studies revealed that it was impossible to determine the investigated analytes by one-step procedures, i.e. by using solely HPLC or GC. On the other hand, the use of a two-step procedure ensures reproducible results of determinations, and the limits of quantitation are, depending on the method of fraction collection by HPLC, from 1.4–2.2 ppm (GC-MS in SIM mode) to 9.6–24.0 ppm (GC-FID). Precision and accuracy of the developed procedures are compared, and possible determination errors and shortcomings discussed. [Figure: see text

A Graph Algorithmic Approach to Separate Direct from Indirect Neural Interactions

Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to represent and analyze functional interactions between neural sources. Interactions are often reconstructed using pairwise bivariate analyses, overlooking their multivariate nature: it is neglected that investigating the effect of one source on a target necessitates to take all other sources as potential nuisance variables into account; also combinations of sources may act jointly on a given target. Bivariate analyses produce networks that may contain spurious interactions, which reduce the interpretability of the network and its graph metrics. A truly multivariate reconstruction, however, is computationally intractable due to combinatorial explosion in the number of potential interactions. Thus, we have to resort to approximative methods to handle the intractability of multivariate interaction reconstruction, and thereby enable the use of networks in neuroscience. Here, we suggest such an approximative approach in the form of an algorithm that extends fast bivariate interaction reconstruction by identifying potentially spurious interactions post-hoc: the algorithm flags potentially spurious edges, which may then be pruned from the network. This produces a statistically conservative network approximation that is guaranteed to contain non-spurious interactions only. We describe the algorithm and present a reference implementation to test its performance. We discuss the algorithm in relation to other approximative multivariate methods and highlight suitable application scenarios. Our approach is a tractable and data-efficient way of reconstructing approximative networks of multivariate interactions. It is preferable if available data are limited or if fully multivariate approaches are computationally infeasible.Comment: 24 pages, 8 figures, published in PLOS On

Genetic variability of the grey wolf Canis lupus in the Caucasus in comparison with Europe and the Middle East: distinct or intermediary population?

Despite continuous historical distribution of the grey wolf (Canis lupus) throughout Eurasia, the species displays considerable morphological differentiation that resulted in delimitation of a number of subspecies. However, these morphological discontinuities are not always consistent with patterns of genetic differentiation. Here we assess genetic distinctiveness of grey wolves from the Caucasus (a region at the border between Europe and West Asia) that have been classified as a distinct subspecies C. l. cubanensis. We analysed their genetic variability based on mtDNA control region, microsatellite loci and genome-wide SNP genotypes (obtained for a subset of the samples), and found similar or higher levels of genetic diversity at all these types of loci as compared with other Eurasian populations. Although we found no evidence for a recent genetic bottleneck, genome-wide linkage disequilibrium patterns suggest a long-term demographic decline in the Caucasian population – a trend consistent with other Eurasian populations. Caucasian wolves share mtDNA haplotypes with both Eastern European and West Asian wolves, suggesting past or ongoing gene flow. Microsatellite data also suggest gene flow between the Caucasus and Eastern Europe. We found evidence for moderate admixture between the Caucasian wolves and domestic dogs, at a level comparable with other Eurasian populations. Taken together, our results show that Caucasian wolves are not genetically isolated from other Eurasian populations, share with them the same demographic trends, and are affected by similar conservation problems

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis

Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis

Characterizing how different cortical rhythms interact and how their interaction changes with sensory stimulation is important to gather insights into how these rhythms are generated and what sensory function they may play. Concepts from information theory, such as Transfer Entropy (TE), offer principled ways to quantify the amount of causation between different frequency bands of the signal recorded from extracellular electrodes; yet these techniques are hard to apply to real data. To address the above issues, in this study we develop a method to compute fast and reliably the amount of TE from experimental time series of extracellular potentials. The method consisted in adapting efficiently the calculation of TE to analog signals and in providing appropriate sampling bias corrections. We then used this method to quantify the strength and significance of causal interaction between frequency bands of field potentials and spikes recorded from primary visual cortex of anaesthetized macaques, both during spontaneous activity and during binocular presentation of naturalistic color movies. Causal interactions between different frequency bands were prominent when considering the signals at a fine (ms) temporal resolution, and happened with a very short (ms-scale) delay. The interactions were much less prominent and significant at coarser temporal resolutions. At high temporal resolution, we found strong bidirectional causal interactions between gamma-band (40–100 Hz) and slower field potentials when considering signals recorded within a distance of 2 mm. The interactions involving gamma bands signals were stronger during movie presentation than in absence of stimuli, suggesting a strong role of the gamma cycle in processing naturalistic stimuli. Moreover, the phase of gamma oscillations was playing a stronger role than their amplitude in increasing causations with slower field potentials and spikes during stimulation. The dominant direction of causality was mainly found in the direction from MUA or gamma frequency band signals to lower frequency signals, suggesting that hierarchical correlations between lower and higher frequency cortical rhythms are originated by the faster rhythms

Investigating large-scale brain dynamics using field potential recordings: Analysis and interpretation

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (EEG, MEG, ECoG and LFP) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide best-practice recommendations for the analyses and interpretations using a forward model and an inverse model. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems