Sex and race differences of cerebrospinal fluid metabolites in healthy individuals.

INTRODUCTION: Analyses of cerebrospinal fluid (CSF) metabolites in large, healthy samples have been limited and potential demographic moderators of brain metabolism are largely unknown. OBJECTIVE: Our objective in this study was to examine sex and race differences in 33 CSF metabolites within a sample of 129 healthy individuals (37 African American women, 29 white women, 38 African American men, and 25 white men). METHODS: CSF metabolites were measured with a targeted electrochemistry-based metabolomics platform. Sex and race differences were quantified with both univariate and multivariate analyses. Type I error was controlled for by using a Bonferroni adjustment (0.05/33 = .0015). RESULTS: Multivariate Canonical Variate Analysis (CVA) of the 33 metabolites showed correct classification of sex at an average rate of 80.6% and correct classification of race at an average rate of 88.4%. Univariate analyses revealed that men had significantly higher concentrations of cysteine (p < 0.0001), uric acid (p < 0.0001), and N-acetylserotonin (p = 0.049), while women had significantly higher concentrations of 5-hydroxyindoleacetic acid (5-HIAA) (p = 0.001). African American participants had significantly higher concentrations of 3-hydroxykynurenine (p = 0.018), while white participants had significantly higher concentrations of kynurenine (p < 0.0001), indoleacetic acid (p < 0.0001), xanthine (p = 0.001), alpha-tocopherol (p = 0.007), cysteine (p = 0.029), melatonin (p = 0.036), and 7-methylxanthine (p = 0.037). After the Bonferroni adjustment, the effects for cysteine, uric acid, and 5-HIAA were still significant from the analysis of sex differences and kynurenine and indoleacetic acid were still significant from the analysis of race differences. CONCLUSION: Several of the metabolites assayed in this study have been associated with mental health disorders and neurological diseases. Our data provide some novel information regarding normal variations by sex and race in CSF metabolite levels within the tryptophan, tyrosine and purine pathways, which may help to enhance our understanding of mechanisms underlying sex and race differences and potentially prove useful in the future treatment of disease.

Source Attribution of Cyanides Using Anionic Impurity Profiling, Stable Isotope Ratios, Trace Elemental Analysis and Chemometrics.

Chemical attribution signatures (CAS) for chemical threat agents (CTAs), such as cyanides, are being investigated to provide an evidentiary link between CTAs and specific sources to support criminal investigations and prosecutions. Herein, stocks of KCN and NaCN were analyzed for trace anions by high performance ion chromatography (HPIC), carbon stable isotope ratio (δ(13)C) by isotope ratio mass spectrometry (IRMS), and trace elements by inductively coupled plasma optical emission spectroscopy (ICP-OES). The collected analytical data were evaluated using hierarchical cluster analysis (HCA), Fisher-ratio (F-ratio), interval partial least-squares (iPLS), genetic algorithm-based partial least-squares (GAPLS), partial least-squares discriminant analysis (PLSDA), K nearest neighbors (KNN), and support vector machines discriminant analysis (SVMDA). HCA of anion impurity profiles from multiple cyanide stocks from six reported countries of origin resulted in cyanide samples clustering into three groups, independent of the associated alkali metal (K or Na). The three groups were independently corroborated by HCA of cyanide elemental profiles and corresponded to countries each having one known solid cyanide factory: Czech Republic, Germany, and United States. Carbon stable isotope measurements resulted in two clusters: Germany and United States (the single Czech stock grouped with United States stocks). Classification errors for two validation studies using anion impurity profiles collected over five years on different instruments were as low as zero for KNN and SVMDA, demonstrating the excellent reliability associated with using anion impurities for matching a cyanide sample to its factory using our current cyanide stocks. Variable selection methods reduced errors for those classification methods having errors greater than zero; iPLS-forward selection and F-ratio typically provided the lowest errors. Finally, using anion profiles to classify cyanides to a specific stock or stock group for a subset of United States stocks resulted in cross-validation errors ranging from 0 to 5.3%.

Odor-structure relationship studies of tetralin and indan musks.

A list of 147 tetralin- and indan-like compounds was compiled from the literature for investigating the relationship between molecular structure and musk odor. Each compound in the data set was represented by 374 CODESSA and 970 TAE descriptors. A genetic algorithm (GA) for pattern recognition analysis was used to identify a subset of molecular descriptors that could differentiate musks from nonmusks in a plot of the two largest principal components (PCs) of the data. A PC map of the 110 compounds in the training set using 45 molecular descriptors identified by the pattern recognition GA revealed an asymmetric data structure. Tetralin and indan musks were found to occupy a small, but well-defined region of the PC (descriptor) space, with the nonmusks randomly distributed in the PC plot. A three-layer feed-forward neural network trained by back propagation was used to develop a discriminant that correctly classified all the compounds in the training set as musk or nonmusk. The neural network was successfully validated using an external prediction of 37 compounds.

Elemental source attribution signatures for calcium ammonium nitrate (CAN) fertilizers used in homemade explosives.

Calcium ammonium nitrate (CAN) is a widely available fertilizer composed of ammonium nitrate (AN) mixed with some form of calcium carbonate such as limestone or dolomite. CAN is also frequently used to make homemade explosives. The potential of using elemental profiling and chemometrics to match both pristine and reprocessed CAN fertilizers to their factories of origin for use in future forensic investigations was examined. Inductively coupled plasma-mass spectrometry (ICP-MS) was used to determine the concentrations of 64 elements in 125 samples from 11 CAN stocks from 6 different CAN factories. Using Fisher ratio and degree-of-class-separation, the elements Na, V, Mn, Cu, Ga, Sr, Ba and U were selected for classification of the CAN samples into 5 factory groups; one group was two factories from the same fertilizer company. Partial least squares discriminant analysis (PLSDA) was used to develop a classification model which was tested on a separate set of samples. The test set included samples that were analyzed at a different time period and samples from factory stocks that were not part of the training set. For pristine CAN samples, i.e., unadulterated prills, 73% of the test samples were matched to their correct factory group with the remaining 27% undetermined using strict classification. The same PLSDA model was used to correctly match all CAN samples that were reprocessed by mixing with powdered sugar. For CAN samples that were reprocessed by mixing with aluminum or by extraction of AN with tap or bottled water, correct classification was observed for one factory group, but source matching was confounded with adulterant interference for two other factories. The elemental signatures of the water-insoluble (calcium carbonate) portions of CAN provided a greater degree of discrimination between factories than the water-soluble portions of CAN. In summary, this work illustrates the strong potential for matching unadulterated CAN fertilizer samples to their manufacturing facility using elemental profiling and chemometrics. The effectiveness of this method for source determination of reprocessed CAN is dependent on how much an adulterant alters the recovered elemental profile of CAN.

Search prefilters to assist in library searching of infrared spectra of automotive clear coats.

Clear coat searches of the infrared (IR) spectral library of the paint data query (PDQ) forensic database often generate an unusable number of hits that span multiple manufacturers, assembly plants, and years. To improve the accuracy of the hit list, pattern recognition methods have been used to develop search prefilters (i.e., principal component models) that differentiate between similar but non-identical IR spectra of clear coats on the basis of manufacturer (e.g., General Motors, Ford, Chrysler) or assembly plant. A two step procedure to develop these search prefilters was employed. First, the discrete wavelet transform was used to decompose each IR spectrum into wavelet coefficients to enhance subtle but significant features in the spectral data. Second, a genetic algorithm for IR spectral pattern recognition was employed to identify wavelet coefficients characteristic of the manufacturer or assembly plant of the vehicle. Even in challenging trials where the paint samples evaluated were all from the same manufacturer (General Motors) within a limited production year range (2000-2006), the respective assembly plant of the vehicle was correctly identified. Search prefilters to identify assembly plants were successfully validated using 10 blind samples provided by the Royal Canadian Mounted Police (RCMP) as part of a study to populate PDQ to current production years, whereas the search prefilter to discriminate among automobile manufacturers was successfully validated using IR spectra obtained directly from the PDQ database.

Pattern recognition-assisted infrared library searching of automotive clear coats.

Pattern recognition techniques have been developed to search the infrared (IR) spectral libraries of the paint data query (PDQ) database to differentiate between similar but nonidentical IR clear coat paint spectra. The library search system consists of two separate but interrelated components: search prefilters to reduce the size of the IR library to a specific assembly plant or plants corresponding to the unknown paint sample and a cross-correlation searching algorithm to identify IR spectra most similar to the unknown in the subset of spectra identified by the prefilters. To develop search prefilters with the necessary degree of accuracy, IR spectra from the PDQ database were preprocessed using wavelets to enhance subtle but significant features in the data. Wavelet coefficients characteristic of the assembly plant of the vehicle were identified using a genetic algorithm for pattern recognition and feature selection. A search algorithm was then used to cross-correlate the unknown with each IR spectrum in the subset of library spectra identified by the search prefilters. Each cross-correlated IR spectrum was simultaneously compared to an autocorrelated IR spectrum of the unknown using several spectral windows that span different regions of the cross-correlated and autocorrelated data from the midpoint. The top five hits identified in each search window are compiled, and a histogram is computed that summarizes the frequency of occurrence for each selected library sample. The five library samples with the highest frequency of occurrence are selected as potential hits. Even in challenging trials where the clear coat paint samples evaluated were all the same make (e.g., General Motors) within a limited production year range, the model of the automobile from which the unknown paint sample was obtained could be identified from its IR spectrum.

Simulation of attenuated total reflection infrared absorbance spectra: applications to automotive clear coat forensic analysis.

Attenuated total reflection (ATR) is a widely used sampling technique in infrared (IR) spectroscopy because minimal sample preparation is required. Since the penetration depth of the ATR analysis beam is quite shallow, the outer layers of a laminate or multilayered paint sample can be preferentially analyzed with the entire sample intact. For this reason, forensic laboratories are taking advantage of ATR to collect IR spectra of automotive paint systems that may consist of three or more layers. However, the IR spectrum of a paint sample obtained by ATR will exhibit distortions, e.g., band broadening and lower relative intensities at higher wavenumbers, compared with its transmission counterpart. This hinders library searching because most library spectra are measured in transmission mode. Furthermore, the angle of incidence for the internal reflection element, the refractive index of the clear coat, and surface contamination due to inorganic contaminants can profoundly influence the quality of the ATR spectrum obtained for automotive paints. A correction algorithm to allow ATR spectra to be searched using IR transmission spectra of the paint data query (PDQ) automotive database is presented. The proposed correction algorithm to convert transmission spectra from the PDQ library to ATR spectra is able to address distortion issues such as the relative intensities and broadening of the bands, and the introduction of wavelength shifts at lower frequencies, which prevent library searching of ATR spectra using archived IR transmission data.

Development of search prefilters for infrared library searching of clear coat paint smears.

Search prefilters developed from spectral data collected on two 6700 Thermo-Nicolet FTIR spectrometers were able to identify the respective manufacturing plant and the production line of an automotive vehicle from its clear coat paint smear using IR transmission spectra collected on a Bio-Rad 40A or Bio-Rad 60 FTIR spectrometer. All four spectrometers were equipped with DTGS detectors. An approach based on instrumental line functions was used to transfer the classification model between the Thermo-Nicolet and Bio-Rad instruments. In this study, 209 IR spectra of clear coat paint smears comprising the training set were collected using two Thermo-Nicolet 6700 IR spectrometers, whereas the validation set consisted of 242 IR spectra of clear coats obtained using two Bio-Rad FTIR instruments.

Characterization of swellable molecularly imprinted polymer particles by surface plasmon resonance spectroscopy.

Surface plasmon resonance (SPR) has been used to investigate template binding at sites in theophylline-imprinted poly-[N-(N-propyl) acrylamide] particles. At concentrations as low as 10(-6) M theophylline, particle swelling is detected as a shift in the angle of minimum reflectance. The binding constant of theophylline estimated from the inflection point of the theophylline calibration curve is approximately 10,000. The imprinted polymer particles do not respond to caffeine or theobromine (which differs from theophylline by a single methyl group) at concentrations as high as 10(-2) M. Full-scale response of the imprinted polymer particles to theophylline (template) occurs in less than 15 minutes, and swelling is reversible. The immobilized imprinted polymer particles can undergo approximately 20 to 25 swelling and shrinking cycles before there is significant loss in functionality. A unique aspect of these imprinted polymer particles is that template binding causes the angle of minimum reflectance to decrease, not increase, in magnitude. Adsorption, which causes an increase in the angle of minimum reflectance, can be readily discriminated from template binding.

Pattern recognition assisted infrared library searching.

Pattern recognition methods have been used to develop search prefilters for infrared (IR) library searching. A two-step procedure has been employed. First, the wavelet packet tree is used to decompose each spectrum into wavelet coefficients that represent both the high and low frequency components of the signal. Second, a genetic algorithm for pattern recognition analysis is used to identify wavelet coefficients characteristic of functional group. Even in challenging trials involving carboxylic acids, compounds that possess both carbonyl and hydroxyl functionalities can be readily differentiated from carboxylic acids. The proposed search prefilters allow for the use of more sophisticated and correspondingly more time-consuming algorithms in IR spectral library matching because the size of the library can be culled down for a specific match using information from the search prefilter about the presence or absence of specific functional groups in the unknown.

Wavelets and genetic algorithms applied to search prefilters for spectral library matching in forensics.

Currently, the identification of the make, model and year of a motor vehicle involved in a hit and run collision from only a clear coat paint smear left at a crime scene is not possible. Search prefilters for searching infrared (IR) spectral libraries of the paint data query (PDQ) automotive database to differentiate between similar but nonidentical Fourier transform infrared (FTIR) paint spectra are proposed. Applying wavelets, FTIR spectra of clear coat paint smears can be denoised and deconvolved by decomposing each spectrum into wavelet coefficients which represent the sample's constituent frequencies. A genetic algorithm for pattern recognition analysis is used to identify wavelet coefficients for underdetermined data that are characteristic of the model and manufacturer of the automobile from which the spectra of the clear coats were obtained. Even in challenging trials where the samples evaluated were all the same manufacturer (Chrysler) with a limited production year range, the respective models and manufacturing plants were correctly identified. Search prefilters for spectral library matching are necessary to extract investigative lead information from a clear coat paint smear; unlike the undercoat and color coat paint layers, which can be identified using the text based portion of the PDQ database.

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques.

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the overall hydrocarbon profile pattern can obscure information about colony in the GC traces of cuticular hydrocarbon extracts obtained from red fire ants. Re-analysis of temporal caste and time period data on the cuticular hydrocarbon patterns demonstrates that sampling time and social caste must be taken into account to avoid unnecessary variability and possible confounding. This and the fact that foragers could not be separated from reserves and brood-tenders in all five laboratory colonies studied suggests that cuticular hydrocarbons as a class of sociochemicals cannot model every facet of nestmate recognition in Solenopsis invicta which in turn suggests a potential role for other compounds in the discrimination of alien conspecifics from nestmates.

Analysis of chemical signals in red fire ants by gas chromatography and pattern recognition techniques.

Gas chromatographic (GC) profiles of cuticular hydrocarbon extracts obtained from individual and pooled ant samples were analyzed using pattern recognition techniques. Clustering according to the biological variables of social caste and colony were observed. Pooling individual extracts enhanced the recognition of patterns in the GC profile data characteristic of colony. Evidently, the contribution of the ant's individual pattern to the overall hydrocarbon profile pattern can obscure information about colony in the GC traces of cuticular hydrocarbon extracts obtained from red fire ants. Re-analysis of temporal caste and time period data on the cuticular hydrocarbon patterns demonstrates that sampling time and social caste must be taken into account to avoid unnecessary variability and possible confounding. This and the fact that foragers could not be separated from reserves and brood-tenders in all five laboratory colonies studied suggests that cuticular hydrocarbons as a class of sociochemicals cannot model every facet of nestmate recognition in Solenopsis invicta which in turn suggests a potential role for other compounds in the discrimination of alien conspecifics from nestmates.

Swellable molecularly imprinted polyN-(N-propyl)acrylamide particles for detection of emerging organic contaminants using surface plasmon resonance spectroscopy.

Lightly crosslinked theophylline imprinted polyN-(N-propyl)acrylamide particles (ca. 300nm in diameter) that are designed to swell and shrink as a function of analyte concentration in aqueous media were spin coated onto a gold surface. The nanospheres responded selectively to the targeted analyte due to molecular imprinting. Chemical sensing was based on changes in the refractive index of the imprinted particles that accompanied swelling due to binding of the targeted analyte, which was detected using surface plasmon resonance (SPR) spectroscopy. Because swelling leads to an increase in the percentage of water in the polymer, the refractive index of the polymer nanospheres decreased as the particles swelled. In the presence of aqueous theophylline at concentrations as low as 10(-6)M, particle swelling is both pronounced and readily detectable. The full scale response of the imprinted particles to template occurs in less than 10min. Swelling is also reversible and independent of the ionic strength of the solution in contact with the polymer. Replicate precision is less than 10(-4) RI units. By comparison, there is no response to caffeine which is similar in structure to theophylline at concentrations as high as 1x10(-2)M. Changes in the refractive index of the imprinted polymer particles, as low as 10(-4) RI units could be readily detected. A unique aspect of the prepared particles is the use of light crosslinking rather than heavy crosslinking. This is a significant development as it indicates that heavy crosslinking is not entirely necessary for selectivity in molecular imprinting with polyacrylamides.

Pattern recognition analysis of differential mobility spectra with classification by chemical family.

Differential mobility spectra for alkanes, alcohols, ketones, cycloalkanes, substituted ketones, and substituted benzenes with carbon numbers between 3 and 10 were obtained from gas chromatography-differential mobility spectrometry (GC-DMS) analyses of mixtures in dilute solution. Spectra were produced in a supporting atmosphere of purified air with 0.6-0.8 ppm moisture, gas temperature of 120 degrees C, sample concentrations of approximately 0.2-5 ppm, and ion source of 5 mCi (185 MBq) 63Ni. Multiple spectra were extracted from chromatographic elution profiles for each chemical providing a library of 390 spectra from 39 chemicals. The spectra were analyzed for structural content by chemical family using two different approaches. In the one approach, the wavelet packet transform was used to denoise and deconvolute the DMS data by decomposing each spectrum into its wavelet coefficients, which represent the sample's constituent frequencies. The wavelet coefficients characteristic of the compound's structural class were identified using a genetic algorithm (GA) for pattern recognition analysis. The pattern recognition GA uses both supervised and unsupervised learning to identify coefficients which optimize clustering of the spectra in a plot of the two or three largest principal components of the data. Because principal components maximize variance, the bulk of the information encoded by the selected coefficients is about differences between chemical families in the data set. The principal component analysis routine embedded in the fitness function of the pattern recognition GA acts as an information filter, significantly reducing the size of the search space since it restricts the search to coefficients whose principal component plots show clustering on the basis of chemical family. In a second approach, a back propagation neural network was trained to categorize spectra by chemical families and the network was successfully tested using familiar and unfamiliar chemicals. Performance of the network was associated with a region of the spectrum associated with fragment ions which could be extracted from spectra and were class specific.