Peak Alignment Research Articles

Inter-ribbon tunneling in graphene: An atomistic Bardeen approach

A weakly coupled system of two crossed graphene nanoribbons exhibits direct tunneling due to the overlap of the wavefunctions of both ribbons. We apply the Bardeen transfer Hamiltonian formalism, using atomistic band structure calculations to account for the effect of the atomic structure on the tunneling process. The strong quantum-size confinement of the nanoribbons is mirrored by the one-dimensional character of the electronic structure, resulting in properties that differ significantly from the case of inter-layer tunneling, where tunneling occurs between bulk two-dimensional graphene sheets. The current-voltage characteristics of the inter-ribbon tunneling structures exhibit resonance, as well as stepwise increases in current. Both features are caused by the energetic alignment of one-dimensional peaks in the density-of-states of the ribbons. Resonant tunneling occurs if the sign of the curvature of the coupled energy bands is equal, whereas a step-like increase in the current occurs if the signs are opposite. Changing the doping modulates the onset-voltage of the effects as well as their magnitude. Doping through electrostatic gating makes these structures promising for application towards steep slope switching devices. Using the atomistic empirical pseudopotentials based Bardeen transfer Hamiltonian method, inter-ribbon tunneling can be studied for the whole range of two-dimensional materials, such as transition metal dichalcogenides. The effects of resonance and of step-like increases in the current we observe in graphene ribbons are also expected in ribbons made from these alternative two-dimensional materials, because these effects are manifestations of the one-dimensional character of the density-of-states.

SMART: Statistical Metabolomics Analysis-An R Tool.

Metabolomics data provide unprecedented opportunities to decipher metabolic mechanisms by analyzing hundreds to thousands of metabolites. Data quality concerns and complex batch effects in metabolomics must be appropriately addressed through statistical analysis. This study developed an integrated analysis tool for metabolomics studies to streamline the complete analysis flow from initial data preprocessing to downstream association analysis. We developed Statistical Metabolomics Analysis-An R Tool (SMART), which can analyze input files with different formats, visually represent various types of data features, implement peak alignment and annotation, conduct quality control for samples and peaks, explore batch effects, and perform association analysis. A pharmacometabolomics study of antihypertensive medication was conducted and data were analyzed using SMART. Neuromedin N was identified as a metabolite significantly associated with angiotensin-converting-enzyme inhibitors in our metabolome-wide association analysis (p = 1.56 × 10(-4) in an analysis of covariance (ANCOVA) with an adjustment for unknown latent groups and p = 1.02 × 10(-4) in an ANCOVA with an adjustment for hidden substructures). This endogenous neuropeptide is highly related to neurotensin and neuromedin U, which are involved in blood pressure regulation and smooth muscle contraction. The SMART software, a user guide, and example data can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/metabolomics/SMART.htm .

GLS-Finder: A Platform for Fast Profiling of Glucosinolates in Brassica Vegetables.

Mass spectrometry combined with related tandem techniques has become the most popular method for plant secondary metabolite characterization. We introduce a new strategy based on in-database searching, mass fragmentation behavior study, formula predicting for fast profiling of glucosinolates, a class of important compounds in brassica vegetables. A MATLAB script-based expert system computer program, "GLS-Finder", was developed. It is capable of qualitative and semi-quantitative analyses of glucosinolates in samples using data generated by ultrahigh-performance liquid chromatography-high-resolution accurate mass with multi-stage mass fragmentation (UHPLC-HRAM/MS(n)). A suite of bioinformatic tools was integrated into the "GLS-Finder" to perform raw data deconvolution, peak alignment, glucosinolate putative assignments, semi-quantitation, and unsupervised principal component analysis (PCA). GLS-Finder was successfully applied to identify intact glucosinolates in 49 commonly consumed Brassica vegetable samples in the United States. It is believed that this work introduces a new way of fast data processing and interpretation for qualitative and quantitative analyses of glucosinolates, where great efficacy was improved in comparison to identification manually.

(1)H NMR-based metabolite profiling workflow to reduce inter-sample chemical shift variations in urine samples for improved biomarker discovery.

Metabolite profiling of urine has seen much advancement in recent years, and its analysis by nuclear magnetic resonance (NMR) spectroscopy has become well established. However, the highly variable nature of human urine still requires improved protocols despite some standardization. In particular, diseases such as kidney disease can have a profound effect on the composition of urine and generate a highly diverse sample set for clinical studies. Large variations in pH and the cationic concentration of urine play an important role in creating positional noise within datasets generated from NMR. We demonstrate positional noise to be a confounding variable for multivariate statistical tools such as statistical total correlation spectroscopy (STOCSY), thereby hindering the process of biomarker discovery. We present a two-dimensional buffering system using potassium fluoride (KF) and phosphate buffer to reduce positional noise in metabolomic data generated from urine samples with various levels of proteinuria. KF reduces positional noise in citrate peaks, by decreasing the mean relative standard deviation (RSD) from 0.17 to 0.09. By reducing positional noise with KF, STOCSY analysis of citrate peaks saw significant improvement. We further aligned spectral data using a recursive segment-wise peak alignment (RSPA) method, which leads to further improvement of the positional noise (RSD = 0.06). These results were validated using diverse selection of metabolites which lead to an overall improvement in positional noise using the suggested protocol. In summary, we provide an improved workflow for urine metabolite biomarker discovery to achieve higher data quality for better pathophysiological understanding of human diseases. Graphical abstract Citrate peaks in the range 2.75-2.5ppm from datasets with different sample preparation protocols and with/without in silico alignment. A Citrate peaks with standard phosphate buffering and without in silico alignment. B citrate peaks with standard phosphate buffering and with in silico alignment. C citrate peak with additional potassium fluoride and standard phosphate buffering without in silico alignment. D citrate peaks with additional potassium fluoride and standard phosphate buffering with in silico alignment. Below the respective spectrum are displayed the percent relative standard deviation (RSD) of the respective citrate peaks. This is a measure of the positional noise of peaks within a (1)H NMR analysis. It can be seen that D performs the best in reducing positional noise of citrate peaks. E-H STOCSY analysis of correlating spectral features with the driver peak at 2.675ppm (see red arrow) to identify structural correlations. As a, b, c, and d are known to be structurally correlated, STOCSY analysis should reveal r (2) = 1 if data is perfectly aligned and can therefore be used as a measure of peak alignment. E Strong positional noise does not allow identifying the c and d peaks of the AB system to be correlated. F, G Neither in silico alignment or KF addition alone can completely improve the alignment and therefore increase the correlations. H Highly improved alignment by combining both KF addition and in silico alignment reduces positional noise and elucidates all four citrate peaks to be strongly correlated.

An improved pseudotargeted metabolomics approach using multiple ion monitoring with time-staggered ion lists based on ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry

Pseudotargeted metabolomics is a novel strategy integrating the advantages of both untargeted and targeted methods. The conventional pseudotargeted metabolomics required two MS instruments, i.e., ultra-high performance liquid chromatography/quadrupole-time- of-flight mass spectrometry (UHPLC/Q-TOF MS) and UHPLC/triple quadrupole mass spectrometry (UHPLC/QQQ-MS), which makes method transformation inevitable. Furthermore, the picking of ion pairs from thousands of candidates and the swapping of the data between two instruments are the most labor-intensive steps, which greatly limit its application in metabolomic analysis. In the present study, we proposed an improved pseudotargeted metabolomics method that could be achieved on an UHPLC/Q-TOF/MS instrument operated in the multiple ion monitoring (MIM) mode with time-staggered ion lists (tsMIM). Full scan-based untargeted analysis was applied to extract the target ions. After peak alignment and ion fusion, a stepwise ion picking procedure was used to generate the ion lists for subsequent single MIM and tsMIM. The UHPLC/Q-TOF tsMIM MS-based pseudotargeted approach exhibited better repeatability and a wider linear range than the UHPLC/Q-TOF MS-based untargeted metabolomics method. Compared to the single MIM mode, the tsMIM significantly increased the coverage of the metabolites detected. The newly developed method was successfully applied to discover plasma biomarkers for alcohol-induced liver injury in mice, which indicated its practicability and great potential in future metabolomics studies.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/−)ESI/MS and RPLC/UV data: Application on green tea extracts

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/−ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA.

Dynamical features of GPS PWV variation associated with lightning activity

ABSTRACTWater vapour is one component that causes spatial and temporal fluctuations in the lower atmosphere, which, in turn, introduce time delays into the global positioning system (GPS) signal. These delays can be exploited to estimate the precipitable water vapour (PWV), which is beneficial for meteorological applications. Because the vertical transfer of warm air to the troposphere triggers instability and atmospheric charges, lightning phenomena can fundamentally affect the GPS signals through changes in water vapour. From this hypothesis, GPS-derived PWV data have been proposed as a tool for monitoring lightning activity. In this paper, the variation of PWV in days with lightning at four selected stations in Peninsular Malaysia during the past two inter-monsoon events in May and November 2009 was observed. To verify the response, a peak alignment method was proposed with regard to the lightning with more than 100 discharge events and lightning days from 07:00 to 20:00 LT. A total of 66 lightning series were assessed, and PWV was observed to increase by approximately 2.46 mm and decrease by 4.16 mm before and after the peak point, respectively, when compared to fair-weather conditions. Approximately 62% of the lightning start times were concentrated within a period of less than 1 h before or after the PWV reached a maximum peak, 24% were observed between 1–2 h, and 14% started after 2 h. This trend implies that the use of GPS PWV data was more consistent and robust for the detection of lightning activity compared to the use of surface temperature and relative humidity data.

Chemometrics applied to quality control and metabolomics for traditional Chinese medicines.

Traditional Chinese medicines (TCMs) bring a great challenge in quality control and evaluating the efficacy because of their complexity of chemical composition. Chemometric techniques provide a good opportunity for mining more useful chemical information from TCMs. Then, the application of chemometrics in the field of TCMs is spontaneous and necessary. This review focuses on the recent various important chemometrics tools for chromatographic fingerprinting, including peak alignment information features, baseline correction and applications of chemometrics in metabolomics and modernization of TCMs, including authentication and evaluation of the quality of TCMs, evaluating the efficacy of TCMs and essence of TCM syndrome. In the conclusions, the general trends and some recommendations for improving chromatographic metabolomics data analysis are provided.

NMR-based metabonomics for understanding the influence of dormant female genital tuberculosis on metabolism of the human endometrium.

Does investigation of metabolic perturbations in endometrial tissue of women with dormant genital tuberculosis (GTB) during the window of implantation (WOI) assist in improving the understanding of endometrial receptivity? In dormant GTB cases significant alterations in endometrial tissue metabolites occur, largely related to energy metabolism and amino acid biosynthesis in dormant GTB cases. As an intracellular pathogen, Mycobacterium tuberculosis strongly influences the metabolism of host cells causing metabolic dysregulation. It is also accepted that dormant GTB impairs the receptive status of the endometrium. Global metabolic profiling is useful for an understanding of disease progression and distinguishing between diseased and non-diseased groups. Endometrial tissue samples were collected from patients reporting at the tertiary infertility care center during the period September 2011-March 2013. Women having tested positive for GTB were considered as the study group (n = 24). Normal healthy women undergoing sterilization (n = 26) and unexplained infertile women with repeated IVF failure (n = 21) volunteered to participate as controls. Endometrial tissue samples were collected 6-10 days after confirmation of ovulation. PCR and BACTEC-460 culture were used for diagnosing GTB. Proton nuclear magnetic resonance (1H NMR) spectra of tissue were recorded using a 700 MHz Bruker Avance AV III spectrometer. Following phase and baseline correction of all NMR spectra by Bruker Topspin 2.1 software, spectral peak alignment of the data was performed. Multivariate analysis was applied to all spectra and individual metabolites identified and multiple correlation analysis was performed. Leucine, isoleucine, acetate, lactate, glutamate, glutamine, methionine, lysine, creatine, glycogen, glycine, proline and choline were found to be significantly increased (P < 0.05) in endometrial tissue of women with dormant GTB compared with unexplained infertile women with repeated implantation failure. Valine, citrate, succinate and aspartate were also observed to be significantly up-regulated (P < 0.01). Furthermore, a significant decrease in glucose (P < 0.05), threonine (P < 0.05), tyrosine (P < 0.01) and phenylalanine (P < 0.0001) was observed in women with dormant GTB. Pearson's correlation analysis between the expression of various endometrial receptivity markers and metabolites showed a significant negative correlation (-0.236 to -0.545, P < 0.05). Also, the metabolites were positively correlated with endometrial receptivity markers (0.207 to 0.618, P < 0.05). It is often difficult to diagnose dormant GTB because it tends to exist without any clinical signs or symptoms. In addition, the diagnosis of GTB by culture remains a challenge due to low detection rates and its paucibacillary nature. Testing for prostate-specific antigen or the Y chromosome in order to account for the possible influences of recent exposure to semen on endometrial metabolism would be important. The metabolic changes associated with the dormant tubercle infection are of potential relevance to clinicians for the treatment of dormant GTB-related infertility. Government of India, Indian Council of Medical Research. There are no conflicts of interest.

IMet-Q: A User-Friendly Tool for Label-Free Metabolomics Quantitation Using Dynamic Peak-Width Determination.

Efficient and accurate quantitation of metabolites from LC-MS data has become an important topic. Here we present an automated tool, called iMet-Q (intelligent Metabolomic Quantitation), for label-free metabolomics quantitation from high-throughput MS1 data. By performing peak detection and peak alignment, iMet-Q provides a summary of quantitation results and reports ion abundance at both replicate level and sample level. Furthermore, it gives the charge states and isotope ratios of detected metabolite peaks to facilitate metabolite identification. An in-house standard mixture and a public Arabidopsis metabolome data set were analyzed by iMet-Q. Three public quantitation tools, including XCMS, MetAlign, and MZmine 2, were used for performance comparison. From the mixture data set, seven standard metabolites were detected by the four quantitation tools, for which iMet-Q had a smaller quantitation error of 12% in both profile and centroid data sets. Our tool also correctly determined the charge states of seven standard metabolites. By searching the mass values for those standard metabolites against Human Metabolome Database, we obtained a total of 183 metabolite candidates. With the isotope ratios calculated by iMet-Q, 49% (89 out of 183) metabolite candidates were filtered out. From the public Arabidopsis data set reported with two internal standards and 167 elucidated metabolites, iMet-Q detected all of the peaks corresponding to the internal standards and 167 metabolites. Meanwhile, our tool had small abundance variation (≤0.19) when quantifying the two internal standards and had higher abundance correlation (≥0.92) when quantifying the 167 metabolites. iMet-Q provides user-friendly interfaces and is publicly available for download at http://ms.iis.sinica.edu.tw/comics/Software_iMet-Q.html.

MetTailor: dynamic block summary and intensity normalization for robust analysis of mass spectrometry data in metabolomics.

Accurate cross-sample peak alignment and reliable intensity normalization is a critical step for robust quantitative analysis in untargetted metabolomics since tandem mass spectrometry (MS/MS) is rarely used for compound identification. Therefore shortcomings in the data processing steps can easily introduce false positives due to misalignments and erroneous normalization adjustments in large sample studies. In this work, we developed a software package MetTailor featuring two novel data preprocessing steps to remedy drawbacks in the existing processing tools. First, we propose a novel dynamic block summarization (DBS) method for correcting misalignments from peak alignment algorithms, which alleviates missing data problem due to misalignments. For the purpose of verifying correct re-alignments, we propose to use the cross-sample consistency in isotopic intensity ratios as a quality metric. Second, we developed a flexible intensity normalization procedure that adjusts normalizing factors against the temporal variations in total ion chromatogram (TIC) along the chromatographic retention time (RT). We first evaluated the DBS algorithm using a curated metabolomics dataset, illustrating that the algorithm identifies misaligned peaks and correctly realigns them with good sensitivity. We next demonstrated the DBS algorithm and the RT-based normalization procedure in a large-scale dataset featuring >100 sera samples in primary Dengue infection study. Although the initial alignment was successful for the majority of peaks, the DBS algorithm still corrected ∼7000 misaligned peaks in this data and many recovered peaks showed consistent isotopic patterns with the peaks they were realigned to. In addition, the RT-based normalization algorithm efficiently removed visible local variations in TIC along the RT, without sacrificing the sensitivity of detecting differentially expressed metabolites. The R package MetTailor is freely available at the SourceForge website http://mettailor.sourceforge.net/. hyung_won_choi@nuhs.edu.sg Supplementary data are available at Bioinformatics online.

DISCOVERY OF GAMMA-RAY PULSATIONS FROM THE TRANSITIONAL REDBACK PSR J1227-4853

The 1.69 ms spin period of PSR J1227-4853 was recently discovered in radio observations of the low-mass X-ray binary XSS J12270-4859 following the announcement of a possible transition to a rotation-powered millisecond pulsar state, inferred from decreases in optical, X-ray, and gamma-ray flux from the source. We report the detection of significant (5$\sigma$) gamma-ray pulsations after the transition, at the known spin period, using ~1 year of data from the Large Area Telescope on board the Fermi Gamma-ray Space Telescope. The gamma-ray light curve of PSR J1227-4853 can be fit by one broad peak, which occurs at nearly the same phase as the main peak in the 1.4 GHz radio profile. The partial alignment of light-curve peaks in different wavebands suggests that at least some of the radio emission may originate at high altitude in the pulsar magnetosphere, in extended regions co-located with the gamma-ray emission site. We folded the LAT data at the orbital period, both pre- and post-transition, but find no evidence for significant modulation of the gamma-ray flux. Analysis of the gamma-ray flux over the mission suggests an approximate transition time of 2012 November 30. Continued study of the pulsed emission and monitoring of PSR J1227-4853, and other known redback systems, for subsequent flux changes will increase our knowledge of the pulsar emission mechanism and transitioning systems.

Cluster analysis of commercial samples of Bauhinia spp. using HPLC-UV/PDA and MCR-ALS/PCA without peak alignment procedure.

Bauhinia forficata Link. is recognised by the Brazilian Health Ministry as a treatment of hypoglycemia and diabetes. Analytical methods are useful to assess the plant identity due the similarities found in plants from Bauhinia spp. HPLC-UV/PDA in combination with chemometric tools is an alternative widely used and suitable for authentication of plant material, however, the shifts of retention times for similar compounds in different samples is a problem. To perform comparisons between the authentic medicinal plant (Bauhinia forficata Link.) and samples commercially available in drugstores claiming to be "Bauhinia spp. to treat diabetes" and to evaluate the performance of multivariate curve resolution - alternating least squares (MCR-ALS) associated to principal component analysis (PCA) when compared to pure PCA. HPLC-UV/PDA data obtained from extracts of leaves were evaluated employing a combination of MCR-ALS and PCA, which allowed the use of the full chromatographic and spectrometric information without the need of peak alignment procedures. The use of MCR-ALS/PCA showed better results than the conventional PCA using only one wavelength. Only two of nine commercial samples presented characteristics similar to the authentic Bauhinia forficata spp., considering the full HPLC-UV/PDA data. The combination of MCR-ALS and PCA is very useful when applied to a group of samples where a general alignment procedure could not be applied due to the different chromatographic profiles. This work also demonstrates the need of more strict control from the health authorities regarding herbal products available on the market.

A Comparison of One-Dimensional and Comprehensive Two-Dimensional Gas Chromatography for Decomposition Odour Profiling Using Inter-Year Replicate Field Trials

Decomposition odour analysis involves the chemical profiling of volatile organic compounds produced by decomposing remains. This is important for areas of forensic science that rely on the detection of decomposition odour such as insect attraction to carrion, positive alerts of cadaver dogs to decomposing remains, and the development of field instrumentation for search and recovery procedures. Traditionally decomposition odour analysis has been performed using gas chromatography–quadrupole mass spectrometry (GC–qMS); however, the use of comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC×GC–TOFMS) is rapidly becoming more prevalent. The objective of this study was to compare GC–qMS and GC×GC–TOFMS for decomposition odour profiling based on inter-year replicate field studies using decomposing porcine remains. The increased peak capacity, sensitivity and selectivity afforded by GC×GC–TOFMS allowed peak co-elutions, chromatographic artefacts, and dynamic range to be more easily addressed and managed. Furthermore, the software associated with GC×GC–TOFMS provided several additional benefits including improved peak alignment between samples and increased consistency of reported results, overall allowing for additional statistical tests to be applied following data processing. Future GC–qMS results could be improved by implementing some of these software-associated procedures, potentially reducing the magnitude of variation observed between GC–qMS and GC×GC–TOFMS studies. One-dimensional GC analysis may also benefit substantially from coupling with TOFMS detection to provide an indirect increase in peak capacity using deconvolution. However, the wealth of information gained by using GC×GC–TOFMS in decomposition odour profiling is undoubtedly an asset in this field of research.

Fast parametric time warping of peak lists.

Alignment of peaks across samples is a difficult but unavoidable step in the data analysis for all analytical techniques containing a separation step like chromatography. Important application examples are the fields of metabolomics and proteomics. Parametric time warping (PTW) has already shown to be very useful in these fields because of the highly restricted form of the warping functions, avoiding overfitting. Here, we describe a new formulation of PTW, working on peak-picked features rather than on complete profiles. Not only does this allow for a much more smooth integration in existing pipelines, it also speeds up the (already among the fastest) algorithm by orders of magnitude. Using two publicly available datasets we show the potential of the new approach. The first set is a LC-DAD dataset of grape samples, and the second an LC-MS dataset of apple extracts. Parametric time warping of peak lists is implemented in the ptw package, version 1.9.1 and onwards, available from Github (https://github.com/rwehrens/ptw) and CRAN (http://cran.r-project.org). The package also contains a vignette, providing more theoretical details and scripts to reproduce the results below. ron.wehrens@wur.nl.

Multiple reaction monitoring-ion pair finder: a systematic approach to transform nontargeted mode to pseudotargeted mode for metabolomics study based on liquid chromatography-mass spectrometry.

Pseudotargeted metabolic profiling is a novel strategy combining the advantages of both targeted and untargeted methods. The strategy obtains metabolites and their product ions from quadrupole time-of-flight (Q-TOF) MS by information-dependent acquisition (IDA) and then picks targeted ion pairs and measures them on a triple-quadrupole MS by multiple reaction monitoring (MRM). The picking of ion pairs from thousands of candidates is the most time-consuming step of the pseudotargeted strategy. Herein, a systematic and automated approach and software (MRM-Ion Pair Finder) were developed to acquire characteristic MRM ion pairs by precursor ions alignment, MS(2) spectrum extraction and reduction, characteristic product ion selection, and ion fusion. To test the reliability of the approach, a mixture of 15 metabolite standards was first analyzed; the representative ion pairs were correctly picked out. Then, pooled serum samples were further studied, and the results were confirmed by the manual selection. Finally, a comparison with a commercial peak alignment software was performed, and a good characteristic ion coverage of metabolites was obtained. As a proof of concept, the proposed approach was applied to a metabolomics study of liver cancer; 854 metabolite ion pairs were defined in the positive ion mode from serum. Our approach provides a high throughput method which is reliable to acquire MRM ion pairs for pseudotargeted metabolomics with improved metabolite coverage and facilitate more reliable biomarkers discoveries.

Broad focus declaratives in Veneto-Spanish bilinguals: Peak alignment and language contact

Abstract This paper examines peak alignment in Veneto-Spanish bilinguals in the small community of Chipilo, Mexico. We have two goals: First, to provide a description of the peak alignment patterns present in bilingual Chipilo Spanish. As Chipilo Spanish is in contact with a northern Italian variety (Veneto), we hypothesize that changes in peak alignment from monolingual norms, specifically regarding early peak alignment, may be due to transfer from Veneto. Second, we seek to compare the present data, based on controlled speech, to the results of a previous study on semi-spontaneous speech in Chipilo Spanish, contributing to the literature that compares methodologies in intonation research. Our results show that bilinguals demonstrate early peaks in controlled speech, although to a lesser extent than in semi-spontaneous speech. We attribute this to contact with Veneto and a strong sense of ethnolinguistic identity that leads speakers to maintain features of a Chipileño variety of Spanish.

Proteomic profiling of the retinas in a neonatal rat model of oxygen-induced retinopathy with a reproducible ion-current-based MS1 approach.

Investigation of the retina proteome during hypoxia-induced retinal neovascularization is valuable for understanding pathogenesis of retinopathy of prematurity (ROP). Here we employed a reproducible ion-current-based MS1 quantification approach (ICB) to explore the retinal proteomic changes in early stage of ROP in a rat model of oxygen-induced retinopathy (OIR). Retina proteins, which are rich in membrane proteins, were efficiently extracted by a detergent-cocktail and subjected to precipitation/on-pellet-digestion, followed by nano-LC-MS analysis on a 75-cm column with a 7-h gradient. The high reproducibility of sample preparation and chromatography separation enabled excellent peak alignment and contributed to the superior performance of ICB over parallel label-free approaches. In this study, sum-of-intensity with rejection was incorporated to determine the protein ratios. In total, 1325 unique protein groups were quantified from rat retinas (n = 4/group) with at least two distinct peptides at a protein FDR of 1%. Thirty-two significantly altered proteins were observed with confidence, and the elevated glial fibrillary acidic protein and decreased crystalline proteins in OIR retinas agree well with previous studies. Selected key alterations were further validated by Western blot analysis. Interestingly, Rab21/RhoA/ROCK2/moesin signaling pathway was found to be involved in retinal neovascularization of OIR. Moreover, highly elevated annexin A3, a potential angiogenic mediator, was observed in OIR retinas and may serve as a potential therapeutic target. In conclusion, reproducible ICB profiling enabled reliable discovery of many altered mediators and pathways in OIR retinas, thereby providing new insights into molecular mechanisms involved in pathogenesis of ROP.

Tonal patterns, associations, and alignment of peaks in regional French

The intonation of regional varieties of French has been under-studied and remains an important empirical question. In this paper, I present an analysis of two varieties of French that are geographically distant but historically related. The main purpose of the analysis was to examine the effect of the regional factor on the tonal patterns, associations, and timing of the stress group initial and final peaks. The results showed no dialect effect on tonal pattern inventory and distribution or on tonal association and alignment. However, in both dialects women produced less utterance-medial falls and significantly delayed the peaks, compared with men. Additionally, the analysis suggested that the group initial versus final peaks in French appear to differ in secondary associations, which allows us to extend this concept to express the contrasts between different types of high targets and to detail the French language intonational grammar. This work contributes thus to the prosodic grammar of French and to the description of its dialects by providing evidence that they share the same intonational grammar but that groups of speakers demonstrate differences pertaining to its surface realization.

MQTL.NMR: an integrated suite for genetic mapping of quantitative variations of (1)H NMR-based metabolic profiles.

High-throughput (1)H nuclear magnetic resonance (NMR) is an increasingly popular robust approach for qualitative and quantitative metabolic profiling, which can be used in conjunction with genomic techniques to discover novel genetic associations through metabotype quantitative trait locus (mQTL) mapping. There is therefore a crucial necessity to develop specialized tools for an accurate detection and unbiased interpretability of the genetically determined metabolic signals. Here we introduce and implement a combined chemoinformatic approach for objective and systematic analysis of untargeted (1)H NMR-based metabolic profiles in quantitative genetic contexts. The R/Bioconductor mQTL.NMR package was designed to (i) perform a series of preprocessing steps restoring spectral dependency in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accurate mQTL mapping in human cohorts as well as in rodent models, (iii) statistically enhance structural assignment of genetically determined metabolites, and (iv) illustrate results with a series of visualization tools. Built-in flexibility and implementation in the powerful R/Bioconductor framework allow key preprocessing steps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific problems. The mQTL.NMR package is freely available with its source code through the Comprehensive R/Bioconductor repository and its own website ( http://www.ican-institute.org/tools/ ). It represents a significant advance to facilitate untargeted metabolomic data processing and quantitative analysis and their genetic mapping.