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The International Conference on Green and Human Information Technology (ICGHIT) is an international conference focusing on green and information technologies oriented toward humanity [...]

I take this opportunity to discuss a few things that I have learned from being Guest Editor of this special issue of Sensors devoted to Neural Networks and Sensors. The advancement in artificial neural network (ANN) technology is very impressive. The wide variety of fields in which this technology applies in the form of practical applications to clearly identifiable real problems demonstrates that ANNs are being routinely used to solve non-trivial problems. I mention that because A. K. Dewdney wrote in 1997 that while ANNs have been used to solve a few toy problems, he was surprised that anyone takes them seriously as general problem-solving tools [1]. The ANN applications reported by Yu Liu et al. [2], Erkan Beşdok [3], Guillermo Zatorre et al. [4], Amir Jabbari et al. [5], Mohamed Lamine Hafiane et al. [6], Kai-Wei Chiang et al. [7], Raúl Vicen-Bueno et al. [8], Juan L. Pedreño-Molina et al. [9], and P. B. Garcia-Allende et al. [10] are far more than toy applications. The lesson to be learned here is that it is a bad idea to publically bet against technological progress in computer applications. [...]

Despite the growing use of renewable and sustainable biofuels in transportation, their combustion chemistry is poorly understood, limiting our efforts to reduce harmful emissions. Here we report on the (dissociative) ionization and the thermal decomposition mechanism of cyclopentanone, studied using imaging photoelectron photoion coincidence spectroscopy. The fragmentation of the ions is dominated by loss of CO, C2H4, and C2H5, leading to daughter ions at m/z 56 and 55. Exploring the C5H8O. + potential energy surface reveals hydrogen tunneling to play an important role in low‐energy decarbonylation and probably also in the ethene‐loss processes, yielding 1‐butene and methylketene cations, respectively. At higher energies, pathways without a reverse barrier open up to oxopropenyl and cyclopropanone cations by ethyl‐radical loss and a second ethene‐loss channel, respectively. A statistical Rice–Ramsperger–Kassel–Marcus model is employed to test the viability of this mechanism. The pyrolysis of cyclopentanone is studied at temperatures ranging from about 800 to 1100 K. Closed‐shell pyrolysis products, namely 1,3‐butadiene, ketene, propyne, allene, and ethene, are identified based on their photoion mass‐selected threshold photoelectron spectrum. Furthermore, reactive radical species such as allyl, propargyl, and methyl are found. A reaction mechanism is derived incorporating both stable and reactive species, which were not predicted in prior computational studies.

Because cerebrospinal fluid (CSF) is the biofluid which interacts most closely with the central nervous system, it holds promise as a reporter of neurological disease, for example multiple sclerosis (MScl). To characterize the metabolomics profile of neuroinflammatory aspects of this disease we studied an animal model of MScl—experimental autoimmune/allergic encephalomyelitis (EAE). Because CSF also exchanges metabolites with blood via the blood–brain barrier, malfunctions occurring in the CNS may be reflected in the biochemical composition of blood plasma. The combination of blood plasma and CSF provides more complete information about the disease. Both biofluids can be studied by use of NMR spectroscopy. It is then necessary to perform combined analysis of the two different datasets. Mid-level data fusion was therefore applied to blood plasma and CSF datasets. First, relevant information was extracted from each biofluid dataset by use of linear support vector machine recursive feature elimination. The selected variables from each dataset were concatenated for joint analysis by partial least squares discriminant analysis (PLS-DA). The combined metabolomics information from plasma and CSF enables more efficient and reliable discrimination of the onset of EAE. Second, we introduced hierarchical models fusion, in which previously developed PLS-DA models are hierarchically combined. We show that this approach enables neuroinflamed rats (even on the day of onset) to be distinguished from either healthy or peripherally inflamed rats. Moreover, progression of EAE can be investigated because the model separates the onset and peak of the disease. Figure Graphical representation of Hierarchical Models Fusion applied to concatenated plasma and CSF datasets. Electronic supplementary material The online version of this article (doi:10.1007/s00216-012-5871-4) contains supplementary material, which is available to authorized users.

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Langmuir, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/la804216z, Graphene nanosheets produced in the form of stable aqueous dispersions by chemical reduction of graphene oxide and deposited onto graphite substrates have been investigated by atomic force and scanning tunneling microscopy (AFM/STM). The chemically reduced graphene oxide nanosheets were hardly distinguishable from their unreduced counterparts in the topographic AFM images. However, they could be readily discriminated through phase imaging in the attractive regime of tapping-mode AFM, probably because of differences in hydrophilicity arising from their distinct oxygen contents. The chemically reduced nanosheets displayed a smoothly undulated, globular morphology on the nanometer scale, with typical vertical variations in the subnanometer range and lateral feature sizes of ∼5−10 nm. Such morphology was attributed to be the result of significant structural disorder in the carbon skeleton, which originates during the strong oxidation that leads to graphene oxide and remains after chemical reduction. Direct evidence of structural disorder was provided by atomic-scale STM imaging, which revealed an absence of long-range periodicity in the graphene nanosheets. Only structured domains a few nanometers large were observed instead. Likewise, the nanosheet edges appeared atomically rough and ill-defined, though smooth on the nanometer scale. The unreduced graphene oxide nanosheets could only be imaged by STM at very low tunneling currents (∼1 pA), being visualized in some cases with inverted contrast relative to the graphite substrate, a result that was attributed to their extremely low conductivity. Complementary characterization of the unreduced and chemically reduced nanosheets was carried out by thermogravimetric analysis as well as UV−visible absorption and X-ray photoelectron and Raman spectroscopies. In particular, the somewhat puzzling Raman results were interpreted to be the result of an amorphous character of the graphene oxide material., Partial funding of this work by MEC (Project CTQ2005-09105-C04-02), CSIC (I3 Project 200680I198), and MICINN (Project MAT 2008-05700) is gratefully acknowledged. P.S.-F. also acknowledges receipt of an I3P predoctoral contract from CSIC.

All papers published in the Journal of Radioanalytical and Nuclear Chemistry (JRNC) in the period 2005–2009 (source dataset) and all papers citing these papers and published in other journals (target dataset) have been compiled. A scientometric analysis of the datasets has been performed using Hirsch-type statistics. A comprehensive bibliography of the citing papers is presented.

BackgroundRecent controversies highlighting substandard peer review in Open Access (OA) and traditional (subscription) journals have increased the need for authors, funders, publishers, and institutions to assure quality of peer-review in academic journals. I propose that transparency of the peer-review process may be seen as an indicator of the quality of peer-review, and develop and validate a tool enabling different stakeholders to assess transparency of the peer-review process.Methods and FindingsBased on editorial guidelines and best practices, I developed a 14-item tool to rate transparency of the peer-review process on the basis of journals’ websites. In Study 1, a random sample of 231 authors of papers in 92 subscription journals in different fields rated transparency of the journals that published their work. Authors’ ratings of the transparency were positively associated with quality of the peer-review process but unrelated to journal’s impact factors. In Study 2, 20 experts on OA publishing assessed the transparency of established (non-OA) journals, OA journals categorized as being published by potential predatory publishers, and journals from the Directory of Open Access Journals (DOAJ). Results show high reliability across items (α = .91) and sufficient reliability across raters. Ratings differentiated the three types of journals well. In Study 3, academic librarians rated a random sample of 140 DOAJ journals and another 54 journals that had received a hoax paper written by Bohannon to test peer-review quality. Journals with higher transparency ratings were less likely to accept the flawed paper and showed higher impact as measured by the h5 indexfrom Google Scholar.ConclusionsThe tool to assess transparency of the peer-review process at academic journals shows promising reliability and validity. The transparency of the peer-review process can be seen as an indicator of peer-review quality allowing the tool to be used to predict academic quality in new journals.

The speed and angular distribution of O atoms arising from the photofragmentation of C(5)H(8)-O(2), the isoprene-oxygen van der Waals complex, in the wavelength region of 213-277 nm has been studied with the use of a two-color dissociation-probe method and the velocity map imaging technique. Dramatic enhancement in the O atoms photo-generation cross section in comparison with the photodissociation of individual O(2) molecules has been observed. Velocity map images of these "enhanced" O atoms consisted of five channels, different in their kinetic energy, angular distribution, and wavelength dependence. Three channels are deduced to be due to the one-quantum excitation of the C(5)H(8)-O(2) complex into the perturbed Herzberg III state ((3)Δ(u)) of O(2). This excitation results in the prompt dissociation of the complex giving rise to products C(5)H(8)+O+O when the energy of exciting quantum is higher than the complex photodissociation threshold, which is found to be 41740 ± 200 cm(-1) (239.6±1.2 nm). This last threshold corresponds to the photodissociation giving rise to an unexcited isoprene molecule. The second channel, with threshold shifted to the blue by 1480 ± 280 cm(-1), corresponds to dissociation with formation of rovibrationally excited isoprene. A third channel was observed at wavelengths up to 243 nm with excitation below the upper photodissociation threshold. This channel is attributed to dissociation with the formation of a bound O atom C(5)H(8)-O(2) + hv → C(5)H(8)-O(2)((3)Δ(u)) → C(5)H(8)O + O and/or to dissociation of O(2) with borrowing of the lacking energy from incompletely cooled complex internal degrees of freedom C(5)H(8)*-O(2) + hv → C(5)H(8)*-O(2)((3)Δ(u)) → C(5)H(8) + O + O. The kinetic energy of the O atoms arising in two other observed channels corresponds to O atoms produced by photodissociation of molecular oxygen in the excited a (1)Δ(g) and b (1)Σ(g)(+) singlet states as the precursors. This indicates the formation of singlet oxygen O(2)(a (1)Δ(g)) and O(2)(b (1)Σ(g)(+)) after excitation of the C(5)H(8)-O(2) complex. Cooperative excitation of the complex with a simultaneous change of the spin of both partners (1)X-(3)O(2) + hν → (3)X-(1)O(2) → (3)X + (1)O(2) is suggested as a source of singlet oxygen O(2)(a (1)Δ(g)) and O(2)(b (1)Σ(g)(+)). This cooperative excitation is in agreement with little or no vibrational excitation of O(2)(a (1)Δ(g)), produced from the C(5)H(8)-O(2) complex as studied in the current paper as well as from the C(3)H(6)-O(2) and CH(3)I-O(2) complexes reported in our previous paper [Baklanov et al., J. Chem. Phys. 126, 124316 (2007)]. The formation of O(2)(a (1)Δ(g)) from C(5)H(8)-O(2) was observed at λ(pump) = 213-277 nm with the yield going down towards the long wavelength edge of this interval. This spectral profile is interpreted as the red-side wing of the band of a cooperative transition (1)X-(3)O(2) + hν → (3)X(T(2))-(1)O(2)(a (1)Δ(g)) in the C(5)H(8)-O(2) complex.

The estimation of the pseudorank of a matrix, i.e., the rank of a matrix in the absence of measurement error, is a major problem in multivariate data analysis. In the practice of analytical chemistry it is often even the only problem. An important example is the determination of the purity of a chromatographic peak. In this paper we discuss three pseudorank estimation methods that make use of prior knowledge about the size of the measurement error. The first method (Method A) is based on the standard errors in the diagonal elements of the row-echelon form of the matrix, the second method (Method B) is based on the eigenvalues of principal component analysis (PCA) and the third method (a t-test) is based on the singular values. Methods A and B are modifications of methods that are well known in analytical chemistry. However, these methods cannot provide significance levels for the estimated pseudorank. This holds for the original methods as well as the present modifications. The main reason for introducing these modifications is that in this way relationships are established between the t-test and methods that are already known. The aspects that are covered in this paper include the sampling distribution of the test statistic, the number of degrees of freedom to be used in the test, the adequacy of theoretical predictions and the bias that results from random measurement noise. The object of this paper is to demonstrate that using prior knowledge about the size of the measurement error may yield powerful pseudorank estimation methods. This is illustrated by comparing the significance levels obtained by the t-test and Malinowski's F-test. The t-test yields sharper significance levels for experimental data obtained from the literature as well as simulated data. This can be satisfactorily explained by the larger number of degrees of freedom that is employed in this test. The viability of the new t-test is supported by a thorough evaluation of the test data by a large number of conventional methods. As a remarkable by-product of the present investigation we find that a plot of the singular values yields a promising graphical pseudorank estimation method. Graphical methods have proved their use in the past in the case that the size of the measurement error is unknown. This new graphical method therefore provides a natural complement to the t-test.

Ship type classification is an essential task in maritime navigation domains, contributing to shipping monitoring, analysis, and forecasting. Presently, with the development of ship positioning and monitoring systems, many ship trajectory acquisitions make it possible to classify ships according to their movement pattern. Existing methods of ship classification based on trajectory include classical sequence analysis and deep learning methods. However, the real ship trajectories are unevenly distributed in geographical space, which leads to many problems in inferring the ship movement mode on the original ship trajectory. This paper proposes a hierarchical spatial-temporal embedding method based on enhanced trajectory features for ship type classification. We first preprocess the trajectory and combine the port information to transform the original ship trajectory into the moored records of ships, removing the unevenly distributed points in the trajectory data and enhancing key points’ semantic information. Then, we propose a Hierarchical Spatial-Temporal Embedding Method (Hi-STEM) for ship classification. Hi-STEM maps moored records in the original geographical space into the feature space and can efficiently find the classification plane in the feature space. Experiments are conducted on real-world datasets and compared with several existing methods. The result shows that our approach has high accuracy in ship classification on ship moored records. We make the source code and datasets publicly available.

ESCA, Expert Systems Applied to Chemical Analysis, started its research in March 1987, with the aim of building prototype expert systems for HPLC method development. Results of this research have been published as the work has progressed. The project is now completed and this paper summarises some of the overall project conclusions. Seven different expert systems have been built which tackle problems throughout the process of method development, four stand-alone systems and three integrated systems. The object of ESCA was to evaluate the applicability of expert system technology to analytical chemistry and not all the systems were built for commercial uses. Many of the systems tackle problems specific to one or more of the partners and thus may not be useful outside this environment. However, the results of the work are still pertinent to analysts wishing to build their own systems. These results are described, however, the emphasis of the paper is on those systems developed for method validation.

The authors make the following corrections to the published paper [...]

The calibration performance of Partial Least Squares regression (PLS) can be improved by eliminating uninformative variables. For PLS, many variable elimination methods have been developed. One is the Uninformative-Variable Elimination for PLS (UVE-PLS). However, the number of variables retained by UVE-PLS is usually still large. In UVE-PLS, variable elimination is repeated as long as the root mean squared error of cross validation (RMSECV) is decreasing. The set of variables in this first local minimum is retained. In this paper, a modification of UVE-PLS is proposed and investigated, in which UVE is repeated until no further reduction in variables is possible, followed by a search for the global RMSECV minimum. The method is called Global-Minimum Error Uninformative-Variable Elimination for PLS, denoted as GME-UVE-PLS or simply GME-UVE. After each iteration, the predictive ability of the PLS model, built with the remaining variable set, is assessed by RMSECV. The variable set with the global RMSECV minimum is then finally selected. The goal is to obtain smaller sets of variables with similar or improved predictability than those from the classical UVE-PLS method. The performance of the GME-UVE-PLS method is investigated using four data sets, i.e. a simulated set, NIR and NMR spectra, and a theoretical molecular descriptors set, resulting in twelve profile-response (X-y) calibrations. The selective and predictive performances of the models resulting from GME-UVE-PLS are statistically compared to those from UVE-PLS and 1-step UVE, one-sided paired t-tests. The results demonstrate that variable reduction with the proposed GME-UVE-PLS method, usually eliminates significantly more variables than the classical UVE-PLS, while the predictive abilities of the resulting models are better. With GME-UVE-PLS, a lower number of uninformative variables, without a chemical meaning for the response, may be retained than with UVE-PLS. The selectivity of the classical UVE method thus can be improved by the application of the proposed GME-UVE method resulting in more parsimonious models.

The authors wish to make the following corrections in their published paper in Sensors [...]

This table of contents will be updated throughout the year as new papers are published.

Understanding the local structure and ion dynamics is at the heart of ion conductor research. This paper reports on high-resolution solid-state 29Si, 23Na, and 17O NMR investigation of the structure, chemical composition, and ion dynamics of a newly discovered fast ion conductor, Na-doped SrSiO3, which exhibited a much higher ionic conductivity than most of current oxide ion conductors. Quantitative analyses reveal that with a small dose ( 10 mol% Na doping, phase separation occurs, leading to the formation of an amorphous phase β-Na2Si2O5 and a crystalline Sr-rich phase. Variable-temperature 23Na and 17O magic-angle-spinning NMR up to 618 °C have shown significant changes in Na ion dynamics at high temperatures but little oxide ion motion, suggesting that Na ions are responsible for the observed high ionic conductivity. In addition, β-Na2Si2O5 starts to crystallize at temperatures higher than 480 °C with prolonged heating, resulting in reduction in Na+ motion, and thus degradation of ionic conductivity. This study has contributed critical evidence to the understanding of ionic conduction in Na-doped SrSiO3 and demonstrated that multinuclear high-resolution and high-temperature solid-state NMR is a uniquely useful tool for investigating ion conductors at their operating conditions.

In this paper, we investigated the use of multispectral imaging technique to quantify pomegranate fruit quality. Three quality factors including total soluble solids (TSS), pH and firmness were studied at four different maturity stages of 88, 109, 124 and 143 days after full bloom (DAFB) and were correlated with the spectral information extracted from images taken at four wavelength spectra. TSS, pH and firmness of the same samples were recorded using nondestructive methods and then modeled with their corresponding spectral data using partial least squire regression (PLSR). The correlation coefficient ( r ), RMSEC and RPD for the calibration models was found to be: r = 0.97, RMSEC = 0.21 °Brix and RPD = 6.7 °Brix for TSS; r = 0.93, RMSEC = 0.035 and RPD = 5.01 for pH; r = 0.95, RMSEC = 0.65 N and RPD = 5.65 N for firmness. Also these parameters for the validation models were as follows: r = 0.97, RMSEP = 0.22 °Brix and RPD = 5.77 °Brix for TSS; r = 0.94, RMSEP = 0.038 and RPD = 4.98 for pH; r = 0.94, RMSEP = 0.68 N and RPD = 5.33 N for firmness. The results demonstrated the capability of multispectral imaging and chemometrics as useful techniques to nondestructively monitoring pomegranate main quality attributes.

In this paper, we present an adaptive ship detection method for single-look complex synthetic aperture radar (SAR) images. First, noncircularity is analyzed and adopted in ship detection task, besides, similarity variance weighted information entropy (SVWIE) is proposed for clutter reduction and target enhancement. According to the analysis of scattering of SVWIE and noncircularity, SVWIE-noncircularity (SN) decomposition is developed. Based on the decomposition, two components, the high-noncircularity SVWIE amplitude (h) and the low-noncircularity SVWIE amplitude (l), are obtained. We demonstrate that ships and clutter in SAR images are different for h detector and h detector can be effectively used for ship detection. Finally, to extract ships from the background, the generalized Gamma distribution (G &Gamma, D) is used to fit h statistics of clutter and the constant false alarm rate (CFAR) is utilized to choose an adaptive threshold. The performance of the proposed method is demonstrated on HH polarization of Alos-2 images. Experimental results show that the proposed method can accurately detect ships in complex background, i.e., ships are close to small islands or with strong noise.

Support vector machines (SVMs) have become a popular technique in the chemometrics and bioinformatics field, and other fields, for the classification of complex data sets. Especially because SVMs are able to model nonlinear relationships, the usage of this technique has increased substantially. This modeling is obtained by mapping the data in a higher-dimensional feature space. The disadvantage of such a transformation is, however, that information about the contribution of the original variables in the classification is lost. In this paper we introduce an innovative method which can retrieve the information about the variables of complex data sets. We apply the proposed method to several benchmark data sets and a metabolomics data set to illustrate that we can determine the contribution of the original variables in SVM classifications. The corresponding visualization of the contribution of the variables can assist in a better understanding of the underlying chemical or biological process.

Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

A novel analytical reagent 4-(2-hydroxy phenyl ethaminodiol), benzene-1,3-diol(4-2-HPEDB-1,3,D) was synthesized for the determination of molybdenum(VI). The present paper reveals the detailed study of electroanalytical behaviour for [Mo-(4-2-HPEDB-1,3,D)] complex under optimized conditions. The peak obtained for [Mo-(4-2-HPEDB-1,3,D)] prevent the interference of foreign ions which shows the sensitivity of the proposed method. The linearity was maintained at the concentration range of 0.5–200 μg/mL at pH 4.5 with correlation factor 0.9997. The present method was successfully applied for the analysis of molybdenum(VI) in biological fluids and plant material. The results obtained from the proposed method show good agreement with reference method.

The final version of this article is available at: http://www.sciencedirect.com/science/journal/01671987, This paper presents a new type of pressure cell associated with a zigzag-shaped time domain reflectometry (TDR) probe for determining the soil-water retention (θ(ψ)) curve of disturbed thin soil samples. The pressure cell, designed for pressures ranging between 0 and −500 kPa, consisted of a zigzag copper rod (150-mm long, 2 mm in diameter) vertically installed in a clear plastic cylinder (60-mm high, 50 mm in internal diameter) with six vertical copper rods (60-mm long, 2 mm in diameter) arranged around the inner wall of the plastic cylinder. The cylinder was closed at the base with a nylon cloth and placed on a porous ceramic disc. The inner rod and the six-rod grille of the cell were connected respectively to the inner and outer conductors of a coaxial cable. The results showed that the correlation between the apparent dielectric constant measured with a standard three-rod TDR probe and the zigzag-shaped TDR probe, both immersed in five different non-conductive fluids, was excellent (R2 = 0.99). On the other hand, the volumetric water content measured with the TDR probe of the pressure cell filled up with sand, 2-mm sieved loam and clay-loam soils was highly correlated to the corresponding values calculated from the gravimetric water content and the soil bulk density (R2 = 0.97; RMSE = 2.32 × 10−2). The parameters of the θ(ψ) curves measured for these three different soils with the TDR-pressure cell were within the range of values found in the literature. The cell was also used to study the θ(ψ) changes of a 2–4-mm sample of loam soil aggregates after a slow and a fast-wetting process. While negligible changes in both the soil structure and θ(ψ) were observed following slow wetting, fast wetting resulted in disintegration of aggregates and drastic changes in the shape of the θ(ψ) curve., This research was supported by the Comisión Interministerial de Ciencia y Tecnología of Spain (grants AGL2004-07763-C02-02 and AGL2007-66320-CO2-02/AGR) and the European Union (FEDER funds).

Copyright © by Biophysical Society. Final full-text version of the paper available at: http://www.biophysj.org/cgi/content/abstract/88/5/3368, The effects on dielaidoylphosphatidylethanolamine (DEPE) bilayers of ceramides containing different N-acyl chains have been studied by differential scanning calorimetry small angle x-ray diffraction and 31P-NMR spectroscopy. N-palmitoyl (Cer16), N-hexanoyl (Cer6), and N-acetyl (Cer2) sphingosines have been used. Both the gel-fluid and the lamellarinverted hexagonal transitions of DEPE have been examined in the presence of the various ceramides in the 0-25 mol % concentration range. Pure hydrated ceramides exhibit cooperative endothermic order-disorder transitions at 93 C (Cer16), 60 C (Cer6), and 54 C (Cer2). In DEPE bilayers, Cer16 does not mix with the phospholipid in the gel phase, giving rise to highmelting ceramide-rich domains. Cer16 favors the lamellar-hexagonal transition of DEPE, decreasing the transition temperature. Cer2, on the other hand, is soluble in the gel phase of DEPE, decreasing the gel-fluid and increasing the lamellar-hexagonal transition temperatures, thus effectively stabilizing the lamellar fluid phase. In addition, Cer2 was peculiar in that no equilibrium could be reached for the Cer2-DEPE mixture above 60 C, the lamellar-hexagonal transition shifting with time to temperatures beyond the instrumental range. The properties of Cer6 are intermediate between those of the other two, this ceramide decreasing both the gel-fluid and lamellar-hexagonal transition temperatures. Temperature-composition diagrams have been constructed for the mixtures of DEPE with each of the three ceramides. The different behavior of the long- and short-chain ceramides can be rationalized in terms of their different molecular geometries, Cer16 favoring negative curvature in the monolayers, thus inverted phases, and the opposite being true of the micelle-forming Cer2. These differences may be at the origin of the different physiological effects that are sometimes observed for the long- and short-chain ceramides., This work was supported in part by grants from Ministerio de Educación y Ciencia (BCM 2002-00784) and Universidad del País Vasco (UPV 00042.310/13552). J. Sot was a predoctoral student supported by the Basque government.

Infrared spectroscopy of a variety of interstellar sources shows strong mid-IR emission bands, which are generally attributed to emission from highly vibrationally excited polycyclic aromatic hydrocarbon molecules (PAHs) in the neutral and, particularly, cationic states. Over the past decade, various experimental methods have been developed to record the infrared spectra of cationic PAHs in the laboratory. In this paper, we discuss available experimental spectra obtained with matrix isolation spectroscopy (MIS), infrared multiple-photon dissociation of trapped ions (MPD), dissociation spectroscopy of ionic PAH van der Waals clusters (VDW), and infrared emission (IRE). Moreover, we compare these experimental spectra to density functional theory (DFT) calculations. The main body of experimental data relies on MIS and MPD spectra, and we present a detailed comparison of results from these methods, providing linear and multiple-photon absorption data, respectively. The effects of multiple-photon absorption, as encountered in MPD, and multiple-photon emission, occurring in interstellar spectra, are carefully assessed with the use of mathematical models, which include the effects of vibrational anharmonicity. We show that an analysis of the multiple-photon and linear data can provide important information on the anharmonicity parameters, which is otherwise difficult to attain. This is illustrated with a detailed comparison of the linear and multiple-photon absorption spectra of the naphthalene cation, yielding experimental anharmonicity parameters for the IR-active modes in the 500-1700 cm-1 range.

This Paper is downloadable at http://www.mdpi.org/molecules/papers/50300252.pdf

The authors wish to make the following corrections to their published paper [1]. Values for thedaily intake of GRE extract, total polyphenols and aspalathin in Tables 1 and 2 and the text, includingthe Abstract, are 10 times too high as they were calculated incorrectly on the basis of 2 g extract/100 gfeed mixture instead of 2 g extract/kg feed mixture. Consequently, HED values were also 10 timestoo high.These changes do not affect the context of the discussion, except for comparison of TP intakevalues with that from a 10 week study of rats, drinking unfermented rooibos. The text on p. 22681should read “dietary intake of 6.2 mg GAE/100 g bw/day for the current study was 2.5 timeslower than that of the ten week study when unfermented rooibos was consumed (16.2 mg GAE/100 g bw/day). The TP exposure for 10 weeks increased the GSH level while in the current study, thelower dose over a period of 90 days reduced the GSH level.”The authors would like to apologize for any inconvenience caused to readers by these changes.

The authors wish to update the Acknowledgments in their paper published in Sensors [1], [...]

This paper compares the blood-to-serum distribution (B/S ratio) of 3,4-methylenedioxymethamphetamine (MDMA) and its major metabolite 3,4-methylenedioxyamphetamine (MDA). B/S ratios were determined by liquid chromatography-tandem mass spectrometry analysis following liquid-liquid extraction as a function of the hematocrit value (experimental specimens) and in blood and corresponding serum samples (n = 63) from 16 healthy volunteers participating in a controlled driving experiment (authentic specimens). A regression analysis to calculate the B/S ratio was performed followed by an analysis of covariances (ANCOVA). A linear relationship between the hematocrit value and the B/S ratio of both MDMA and MDA could be established from the experimental data. For MDMA, the regressions provided mean B/S ratios of 1.22 and 1.26 for the experimental setting and the authentic samples, respectively. For MDA, the analysis determined slopes of 1.15 and 1.27 for the experimental setting and field study, respectively. ANCOVA revealed that the method of determination (experimental vs. authentic specimens) did not influence the resulting slopes. A conversion factor of 0.80 may give an adequate estimate to derive the serum concentration for MDMA if only the concentration in whole blood is known, whereas such a definitive factor could not be established for MDA because of its very low levels in authentic samples.

Recently, 1D NMR and IR spectra have been proposed as descriptors containing 3D information. And, as such, said to be suitable for making QSAR and QSPR models where 3D molecular geometries matter, for example, in binding affinities. This paper presents a study on the predictive power of ID NMR spectra-based QSPR models using simulated proton and carbon I D NMR spectra. It shows that the spectra-based models are outperformed by models based on theoretical molecular descriptors and that spectra-based models are not easy to interpret. We therefore conclude that the use of such NMR spectra offers no added value.

Single-layer films and multiple-layer films can be quantitatively analyzed using X-ray fluorescence. Due to the absence of adequate standards, most methods are based on the calculation of theoretical X-ray fluorescence intensities from fundamental parameter methods. These methods require as initial estimates the exact qualitative sample structure and accurate starting values for both concentrations and layer thicknesses. This paper proposes a fundamental parameter method that uses a genetic algorithm as an optimization procedure. The relaxation of the requirements on the description of the sample due to this robust optimization is discussed. Preliminary results are presented indicating possible applications as well as areas for further research.

Nowadays, analytical instruments that produce a data matrix for one chemical sample enjoy a widespread popularity. However, for a successful analysis of these data an accurate estimate of the pseudorank of the matrix is often a crucial prerequisite. A large number of methods for estimating the pseudorank are based on the eigenvalues obtained from principal component analysis (PCA) . In this paper methods are discussed that exploit the essential similarity between the residuals of PCA of the test data matrix and the elements of a random matrix. In the literature of PCA these methods are commonly denoted as parallel analysis. Attention is paid to several aspects that have to be considered when applying such methods. For some of these aspects asymptotic results can be found in the statistical literature. In this study Monte Carlo simulations are used to investigate the practical implications of these theoretical results. It is shown that for sufficiently large matrices the distribution of the measurement error does not significantly influence the results. Down to a very small signal-to-noise ratio the ratio of the number of rows and the number of columns constitutes the major influence on the expected value of the eigenvalues associated with the residuals. The consequences are illustrated for two functions of the eigenvalues, i.e. the logarithm of the eigenvalues and Malinowski’s reduced eigenvalues. Both methods are graphical and have been applied in the past with considerable success for a variety of data. Malinowski’s reduced eigenvalues are of special interest since they have been used to construct an F-test. Finally, a modification is proposed for pseudorank estimation methods that are based on the principle of parallel analysis.

The use of artificial neural networks (ANNs) to develop a model of the relation between physical structure and mechanical properties of poly(ethylene terephthalate) yarns is investigated. The relation was studied on a set of 295 drawn yarn samples, produced under an exceptionally large variety of applied process conditions. The ANN, consisting of three layers of neurons, was trained using physical structure and property measurements of these 295 yarns as input and output patterns, respectively. All ten properties were fitted to the physical structure within a variance that was acceptable for quantitative use. This paper demonstrates that ANNs may be applied as a quantitative technique in solving a real-world problem. The use of ANNs resulted in an improved understanding of the complicated relation between physical yarn structure and yarn properties.

In the original published version of this paper, the name and affiliation of the third coauthor was listed by mistake. [...]