Rapid discrimination and determination of polyunsaturated fatty acid composition in marine oils by FTIR Spectroscopy and Multivariate Data Analysis

A rapid analytical approach for discrimination and quantitative determination of polyunsaturated fatty acid (PUFA) contents, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), in a range of oils extracted from marine resources has been developed by using attenuated total reflection Fourier transform infrared spectroscopy and multivariate data analysis. The spectral data were collected without any sample preparation; thus, no chemical preparation was involved, but data were rather processed directly using the developed spectral analysis platform, making it fast, very cost effective, and suitable for routine use in various biotechnological and food research and related industries. Unsupervised pattern recognition techniques, including principal component analysis and unsupervised hierarchical cluster analysis, discriminated the marine oils into groups by correlating similarities and differences in their fatty acid (FA) compositions that corresponded well to the FA profiles obtained from traditional lipid analysis based on gas chromatography (GC). Furthermore, quantitative determination of unsaturated fatty acids, PUFAs, EPA and DHA, by partial least square regression analysis through which calibration models were optimized specifically for each targeted FA, was performed in both known marine oils and totally independent unknown n - 3 oil samples obtained from an actual commercial product in order to provide prospective testing of the developed models towards actual applications. The resultant predicted FAs were achieved at a good accuracy compared to their reference GC values as evidenced through (1) low root mean square error of prediction, (2) good coefficient of determination close to 1 (i.e., R 2≥ 0.96), and (3) the residual predictive deviation values that indicated the predictive power at good and higher levels for all the target FAs. © 2014 Springer Science+Business Media New York.

Analysis of fetal heart rate asymmetry before and after 35 weeks of gestation

In this study, we have investigated the evidence of fetal heart rate asymmetry and how the fetal heart rate asymmetry changes before and after 35 weeks of gestation. Noninvasive fetal electrocardiogram (fECG) signals from 45 pregnant women at the gestational age from16 to 41 weeks with normal single pregnancies were analysed. A nonlinear parameter called heart rate asymmetry (HRA) index that measures time asymmetry of RR interval time-series signal was used to understand the changes of HRA in early and late fetus groups. Results indicate that fetal HRA measured by Porta's Index (PI) consistently increases after 35 weeks gestation compared to foetus before 32 weeks of gestation. It might be due to significant changes of sympatho-vagal balance towards delivery with more sympathetic surge. On the other hand, Guzik's Index (GI) showed a mixed effect i.e., increases at lower lags and decreases at higher lags. Finally, fHRA could potentially help identify normal and the pathological autonomic nervous system development.

A spectroscopy of texts for effective clustering

For many clustering algorithms, such as k-means, EM, and CLOPE, there is usually a requirement to set some parameters. Often, these parameters directly or indirectly control the number of clusters to return. In the presence of different data characteristics and analysis contexts, it is often difficult for the user to estimate the number of clusters in the data set. This is especially true in text collections such as Web documents, images or biological data. The fundamental question this paper addresses is: ldquoHow can we effectively estimate the natural number of clusters in a given text collection?rdquo. We propose to use spectral analysis, which analyzes the eigenvalues (not eigenvectors) of the collection, as the solution to the above. We first present the relationship between a text collection and its underlying spectra. We then show how the answer to this question enhances the clustering process. Finally, we conclude with empirical results and related work.

Fourier transform infrared microspectroscopy as a tool to identify macroalgal propagules

Understanding of macroalgal dispersal has been hindered by the difficulty in identifying propagules. Different carrageenans typically occur in gametophytes and tetrasporophytes of the red algal family Gigartinaceae, and we may expect that carpospores and tetraspores also differ in composition of carrageenans. Using Fourier transform infrared (FT-IR) microspectroscopy, we tested the model that differences in carrageenans and other cellular constituents between nuclear phases should allow us to discriminate carpospores and tetraspores of Chondrus verrucosus Mikami. Spectral data suggest that carposporophytes isolated from the pericarp and female gametophytes contained κ-carrageenan, whereas tetrasporophytes contained λ-carrageenan. However, both carpospores and tetraspores exhibited absorbances in wave bands characteristic of κ-,ι-, and λ-carrageenans. Carpospores contained more proteins and may be more photosynthetically active than tetraspores, which contained more lipid reserves. We draw analogies to planktotrophic and lecithotrophic larvae. These differences in cellular chemistry allowed reliable discrimination of spores, but pretreatment of spectral data affected the accuracy of classification. The best classification of spores was achieved with extended multiplicative signal correction (EMSC) pretreatment using partial least squares discrimination analysis, with correct classification of 86% of carpospores and 83% of tetraspores. Classification may be further improved by using synchrotron FT-IR microspectroscopy because of its inherently higher signal-to-noise ratio compared with microspectroscopy using conventional sources of IR. This study demonstrates that FT-IR microspectroscopy and bioinformatics are useful tools to advance our understanding of algal dispersal ecology through discrimination of morphologically similar propagules both within and potentially between species.

Kinetic effects of halide ions on the morphological evolution of silver nanoplates

Time-resolved extinction spectroscopy is employed to study the reaction kinetics in the shape-conversion reaction involving halide ions (including Cl-, Br- and I-) etching (sculpturing) silver nanoplates. A series of time-resolved extinction spectra are obtained during the in situ etching process and the evolution of surface plasmon resonance (SPR) of the silver nanoparticles is analyzed. Spectral analysis indicates that the conversion of nanoprisms starts simultaneously with the emergence of nanodisks when the halide ions are added. The etching rate of different halide ions is evaluated through the in-plane dipole resonance peak intensity of silver nanoplates vs. the reaction time (dI/dt). The relationship between the etching rate and the halide ion concentration shows that the halide ion etching reaction can be considered as a pseudo-first-order reaction. The effect of different halide ions on the shape-conversion of silver nanoplates is compared in detail. The activation energy of the etching reaction is calculated, which indicates that the etching ability of different halide ions is on the order of Cl - < I- < Br-.

Financial development, shocks, and growth volatility

This paper uses spectral theory to develop the following two testable hypotheses in a unified framework for the predictions of business-cycle and endogenous growth models: (i) financial development affects only business-cycle volatility; and (ii) shocks affect both business-cycle volatility and long-run volatility of GDP growth. In other words, volatility caused by shocks is more persistent than that caused by financial underdevelopment. We decompose the business-cycle and long-run volatility by the spectral method and then test the hypotheses at the cross-country level. Empirical evidence provides support for both hypotheses. Higher private credit, a bank-based measure of financial development, dampens business-cycle volatility but not long-run volatility. Volatility of shocks, as measured by the volatility of changes in the terms of trade, magnifies both business-cycle and long-run volatility. The results are robust to accounting for endogeneity, a market-based measure of financial development, and an alternative method of volatility decomposition.

Mixing layer oscillations for a submerged horizontal wall-jet

Measurements of the horizontal velocity component were made for a horizontal wall-jet emanating from a submerged sluice gate forming one side of a large flow compartment. The existence of large-scale vortex structures was quantified by spectral analysis of the velocity measurements taken at various distances from the floor of the flow compartment, for different measurement stations from the jet exit. Close to the jet exit, the spectra of the velocity measurements within the potential core exhibit multiple peaks. Further downstream, the spectra are more defined and peak at the same frequency, irrespective of whether the measurements were made within the potential core or the mixing layer. The spectral peak corresponds to the passage frequency of large-scale vortex structures. Downstream of the potential core, the peak frequencies of the velocity spectra increase as the measurement location was moved towards the floor of the flow compartment. The increase in peak frequencies is attributed to fluctuations associated with the wall boundary layer. Predictions of the mixing layer instabilities were made using linear stability analysis. The predictions are in good agreement with the observed vortex shedding frequencies in the mixing layer

An improved understanding of the dispersion of multi-walled carbon nanotubes in non-aqueous solvents

The homogeneous and stable dispersion of carbon nanotubes (CNTs) in solvents is often a prerequisite for their use in advanced materials. Dispersion procedures, reagent concentration as well as the interactions among reagent, defective CNTs and near-perfect CNTs will affect the resulting CNT dispersion properties. This study, for the first time, presents a detailed comparison between two different approaches for dispersing CNTs. The results enhance our understanding of the interactions between surfactant, defective CNTs and near-perfect CNTs and thus provide insight into the mechanism of CNT dispersion. Dispersions of "as-produced" short multi-walled carbon nanotubes (MWCNTs) in N,N-dimethylformamide were prepared by two different surfactant (Triton X-100) assisted methods: ultrasonication and ultrasonication followed by centrifugation, decanting the supernatant and redispersing the precipitate. Visual observation and UV-visible spectroscopy results showed that the latter method produce a more stable dispersion with higher MWCNT content compared to dispersions produced by ultrasonication alone. Transmission electron microscopy and Raman spectroscopic investigations revealed that the centrifugation/ decanting step removed highly defective nanotubes, amorphous carbon and excess surfactant from the readily re-dispersible near-perfect CNT precipitate. This is contrary to other published findings where the dispersed MWCNTs were found in the supernatant. Thermogravimetric analysis showed that 95 % of Triton X-100 was removed by centrifugation/decanting step, and the remainder of the Triton X-100 molecules is likely randomly adsorbed onto the MWCNT surface. Infrared spectral analysis suggests that the methylene groups of the polyoxyethylene (aliphatic ether) chains of the residual Triton X-100 molecules are interacting with the MWCNTs. © 2014 Springer Science+Business Media.

Associations between step duration variability and inertial measurement unit derived gait characteristics

Inertial measurement units (IMU) provide a convenient tool for gait stability assessment. However, it is unclear how various gait characteristics relate to each other and whether gait characteristics can be obtained from resultant acceleration. Therefore, step duration variability was measured in treadmill walking from 39 young ambulant volunteers (age 24.2 [± 2.5] y; height 1.79 [± 0.09] m; mass 71.6 [± 12.0] kg) using motion capture. Accelerations and gyrations were simultaneously recorded with an IMU. Harmonic ratio, maximum Lyapunov exponents, and multiscale sample entropy (MSE) were calculated. Step duration variability was positively associated with MSE with coarseness levels = 3-6 (r = -.33 to -.42, P ≤ .045). Harmonic ratio and MSE with all coarseness levels were negatively associated (r = -.45 to -.57, P ≤ .004). The MSE with coarseness level = 2 was negatively associated with short-term maximum Lyapunov exponents (r = -.32, P = .047). The agreement between resultant and vertical acceleration derived gait characteristics was excellent (ICC = 0.97-0.99). In conclusion, MSE with varying coarseness levels was associated with the other gait characteristics evaluated in the study. Resultant and vertical acceleration derived results had excellent agreement, which suggests that resultant acceleration is a viable alternative to considering the acceleration dimensions independently.

Neural signal analysis by landmark-based spectral clustering with estimated number of clusters

Spike sorting plays an important role in analysing electrophysiological data and understanding neural functions. Developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. This paper proposes an automatic unsupervised spike sorting method using the landmark-based spectral clustering (LSC) method in connection with features extracted by the locality preserving projection (LPP) technique. Gap statistics is employed to evaluate the number of clusters before the LSC can be performed. Experimental results show that LPP spike features are more discriminative than those of the popular wavelet transformation (WT). Accordingly, the proposed method LPP-LSC demonstrates a significant dominance compared to the existing method that is the combination between WT feature extraction and the superparamagnetic clustering. LPP and LSC are both linear algorithms that help reduce computational burden and thus their combination can be applied into realtime spike analysis.

Enhancing the effectiveness of clustering with spectra analysis

For many clustering algorithms, such as K-Means, EM, and CLOPE, there is usually a requirement to set some parameters. Often, these parameters directly or indirectly control the number of clusters, that is, k, to return. In the presence of different data characteristics and analysis contexts, it is often difficult for the user to estimate the number of clusters in the data set. This is especially true in text collections such as Web documents, images, or biological data. In an effort to improve the effectiveness of clustering, we seek the answer to a fundamental question: How can we effectively estimate the number of clusters in a given data set? We propose an efficient method based on spectra analysis of eigenvalues (not eigenvectors) of the data set as the solution to the above. We first present the relationship between a data set and its underlying spectra with theoretical and experimental results. We then show how our method is capable of suggesting a range of k that is well suited to different analysis contexts. Finally, we conclude with further  empirical results to show how the answer to this fundamental question enhances the clustering process for large text collections.

Maximum contrast analysis for nonnegative blind source separation

In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images.

Two-dimensional correlation localized surface plasmon resonance spectroscopy for analysis of the interaction between metal nanoparticles and bovine serum albumin

Time-resolved extinction spectra assisted with two-dimensional correlation spectroscopy (2DCOS) analysis and principal component analysis (PCA) were employed to investigate the interaction between bovine serum albumin (BSA) and metal nanoparticles (NPs). A series of localized surface plasmon resonance (LSPR) spectra of metal NPs were measured just after a small amount of BSA was added into metal colloids. Through 2DCOS analysis, remarkable changes in the intensities of the LSPR were observed. The interaction process was totally divided into three periods according to the PCA. Transmission electron microscopy, dynamic light scattering, and ζ-potential measurements were also employed to characterize the interaction between BSA and metal NPs. The addition of BSA brings silver NPs to aggregate through the electrostatic interaction between them, but it has less effect on gold NPs. In a gold and silver mixed system, gold NPs can affect the interaction of silver NPs and BSA, leading it to weaken. The combination of 2DCOS analysis and LSPR spectroscopy is powerful for exploring the LSPR spectra of the metal NP involved systems. This combined technique holds great potential in LSPR sensing through analysis of slight, slim spectral changes of metal colloids