text:
Volume 15 Supplement 11
Proceedings of the 11th Annual MCBIOS Conference
Haystack, a web-based tool for metabolomics research
Stephen C Grace1,2,
Stephen Embry2 &
Heng Luo2
BMC Bioinformatics volume 15, Article number: S12 (2014) Cite this article
Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species.
To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins.
Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization options and supports non-biased differential profiling studies through a unique and flexible binning function that provides an alternative to conventional peak deconvolution analysis methods.
Untargeted metabolomics has become an increasingly powerful tool to investigate biological systems [1–3]. This approach typically employs gas or liquid chromatography combined with mass spectrometry or nuclear magnetic resonance to survey the metabolome and identify features associated with the genotype and/or biological state of the organism [4, 5]. Multivariate statistical analysis is then used to model classes and identify important metabolites [6, 7].
Similar to other omics disciplines, untargeted metabolomics requires analysis of large multidimensional datasets containing many independent variables [8]. Liquid chromatography-mass spectrometry (LCMS) is the most common analytical platform for these types of studies since it provides the highest sensitivity and broadest coverage of the metabolome [9, 10]. LCMS data contain a wealth of information about a sample since metabolites can be diagnosed by both retention time and mass over charge (m/z) properties. However, mining LCMS data for important features represents a major bottleneck in metabolomics research. Metabolites that could identify and classify different phenotypes or conditions can easily be missed if they occur in relatively low abundance. Given the enormous range and variability associated with metabolomic data, identifying a distinctive but weak signal from the data pool presents a formidable challenge [11]. Manual identification of these metabolites can be a cumbersome and error-prone task when dealing with large metabolomic studies that involve multiple files and groups.
Efficient and streamlined metabolomics experiments require that data processing be automated with computational tools. A number of server and software applications exist for analysis of metabolomic LCMS data that can help with data pre-processing, visualization, feature detection, and statistical analysis [12–18]. However, there is still a need for flexible web-based custom data visualization and processing tools for researchers in the life and health sciences.
In this paper we introduce Haystack, a versatile web-based tool for processing and analysis of LCMS-based metabolomics data. Haystack features traditional LCMS data processing options along with a unique and fast bin analysis for group classification and possible biomarker identification. It has an intuitive graphical interface and does not require technical expertise in command line programming languages such as R. Raw data files can be uploaded in one of several common formats, including mzData, mzML, mzXML and NetCDF through Haystack's browser interface [10, 19]. Haystack can perform a variety of tasks on full-scan LCMS data, including automatic display of the total ion chromatogram (TIC) and base peak chromatogram (BPC), and user-specified display of time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Haystack produces output files of every process in the common .csv format that can be used for further statistical analysis or customized graphing using numerous software/server applications.
The core function of Haystack is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Each bin has a specific mass range with the value of the total intensity of all ions within that range. Bin results are displayed visually as a histogram and can be downloaded in .csv format for further analysis. Binning is a common form of preprocessing for data sets with high dimensionality and is particularly well suited for mass spectrometry data [20, 21]. Binning can streamline and simplify the analysis of LCMS-based metabolomic data by reducing its dimensionality [21]. Another advantage of binning is that mass peaks are not tied to retention time indices, and thus represent an unbiased data matrix of all masses in the sample. This allows the user to extract quantitative information from LCMS chromatograms without requiring initial peak deconvolution, a process that is often hindered by poor chromatographic resolution and weak signals. However, chromatographic peaks can be recovered in Haystack as latent variables in EIC plots.
In this paper we demonstrate Haystack's main features and show how it can be used to process LCMS data to identify important features and determine group assignment with comparable accuracy to peak detection methods using the popular XCMS tool.
Haystack is a web-based server located at http://binf-app.host.ualr.edu/haystack/. It uses the scripting languages Perl and R and a website interface powered by PHP. Data storage and user information are handled by the MySQL database. Raw LCMS data saved in several common file formats including mzData, mzML, mzXML and netCDF are uploaded directly through Haystack's browser interface. The initial file parsing and TIC and BPC outputs are created by the XCMS R package. The graphical outputs, EIC, and bin analysis are produced by XCMS and MetaboAnalyst R packages [14, 16]. A Perl mission control system was implemented to dynamically optimize server resources and performance. All results can be downloaded via comma separated (.csv) files. Sigmaplot 11.0 (Systat Software) was used to generate customized graphs from output files.
Tomato plants (Solanum lycopersicon) were grown for 6 weeks in a growth chamber under fluorescent lights at either 100 (low light, LL) or 700 (high light, HL) µmol m-2 s-1 photosynthetically active radiation (PAR). The cultivar used was the h igh p igment-2 dark green (hp-2dg) mutant strain. For LCMS analysis three independent samples from four plants per treatment were taken for a total of 12 biological replicates for each treatment group. One LL sample was lost during preparation and so the LL dataset consists of 11 samples. Leaf tissue samples (1.2 cm2) were frozen in in liquid nitrogen and freeze dried. Samples were extracted in 80% methanol with glass bead homogenization followed by sonication and filtration. Extracts were dried and concentrated 10-fold prior to analysis.
LCMS analysis
Samples were analyzed by LCMS using an Agilent 1100 HPLC / MSD-VI Ion Trap mass spectrometer with an electrospray ionization (ESI) source. A 250 × 4.6 mm C18 column (Alltech Prevail™) was used with a linear mobile phase gradient consisting of water/0.1% formic acid and methanol. The mass spectrometer was operated in negative ion mode with a dry gas temperature of 350 °C, nebulizer pressure 40 psi, and flow rate 10 liters min-1. The trap parameters were set at a scan range of 100-1500 m/z; accumulation time 100 ms, target mass 300 m/z, compound stability 50%, and trap drive 50%. Mass spectra were collected in profile mode. Chromatogram files were saved in netCDF format from DataAnalysis for LC/MSD Trap Version 5.2 (Agilent Technologies).
Peaks were extracted from total ion chromatograms either manually or with the R-based version of XCMS [14]. Manual peak extraction was performed with the Agilent DataAnalysis LC/MSD Trap Software package, v. 5.2. A list of 69 ion peaks common to all samples was obtained by manually scanning total ion chromatograms of representative samples. Extracted ion chromatograms were generated and peak areas were obtained by integration. Both procedures produced a peak table used for subsequent statistical analysis.
Peak data from manual and XCMS peak processing and bin intensity data from Haystack were analyzed using the web-based statistical package MetaboAnalyst 2.0 (http://www.metaboanalyst.ca) [16, 22]. Pre-processing steps included filtering, sum normalization, log(2) transformation, and autoscaling. Univariate (fold-change, t-tests, volcano plots) and multivariate (HCA, PCA, PLS-DA) analyses were performed on all data sets.
Haystack is a web-based server designed to store, display, extract, process, and export LCMS-based metabolomic data. It also can process GCMS data. It has an intuitive browser interface and requires no command line operations, only the ability to use a typical webpage form. Users must register to set up an account. All user files are confidential.
Figure 1 shows a summary workflow of data processing steps in Haystack. Raw LCMS data can be saved in several common export formats including netCDF, mzData, mzML, and mzXML. Files are then uploaded into a user database directly through Haystack's browser interface. Files are stored and organized within projects and can be added or deleted as desired. Visualization and processing operations are arranged as tabs within a project. Graphical outputs are displayed as .png files that can be viewed in the browser or saved. Haystack displays the TIC and BPC plots automatically (Figure 2). In many applications involving LCMS data, it is often desirable to display the BPC plot since it contains less baseline noise than the TIC plot. The output of these processes are stored as .csv files within a project.
Workflow of LCMS data processing with Haystack.
Representative TIC and BPC plots of LCMS data from a tomato leaf sample.
Users can generate EICs over any specified mass range by clicking on the 'Generate EIC' tab and entering the starting and ending masses. Users can also generate time-resolved averaged mass spectra by clicking on the 'Generate Mass Spectra' tab and entering the starting and ending times. Haystack displays the EIC and MS plots and provides a .csv export option so data can be imported into a spreadsheet program such as Excel. Figure 3 provides an example of three EICs for the mass ranges 743-744, 707-708, and 608-610 and their corresponding mass spectra. The peaks at 11.46, 16.74, and 25.89 min were subsequently identified as the flavonoids caffeoylglucaric acid, 3-O-caffeoylquinic acid (chlorogenic acid), and quercetin-3-O-rutinoside (rutin), respectively. The mass specta of caffeoylglucaric acid and chlorogenic acid show the monoisotopic ions (m/z 371, 353) as well as a dimeric species [2M-1] (m/z 743, 707) whereas the mass spectrum of rutin shows only the monoisotopic ion (m/z 609). Users can generate as many EICs and mass spectra as desired, which allows easy comparison of multiple EICs from different samples. If a single file bin analysis is performed, the EICs for all bins are automatically generated and can be downloaded as a zip file, thus providing another data pool that can be mined for peak features. However, it is important to note that a small bin size will result in a large number of EIC files.
Representative EICs and mass spectra from a tomato leaf sample. EICs were generated in Haystack for mass ranges 743-744 (A), 707-708 (B), and 608-610 (C). Mass spectra are shown for time ranges 11.4-11.5 min (D), 16.7-16.8 min (E), and 25.8-25.9 min (F).
The bin feature of Haystack parses full-scan LCMS data into uniform bins over any specified mass range. To perform a bin analysis, users simply click on the 'Bin Analysis' tab and enter the bin size. There is also an option to perform a group bin analysis on all files within a project. Haystack returns the total intensity for each bin and displays the results graphically as a histogram. Bin results and can also be downloaded as a .csv file for easy import into a spreadsheet program for display, sorting, and statistical analysis. Figure 4 shows an example of a data set that was analyzed with six different bin sizes (50, 20, 10, 5, 2, 1). The total number of bins (N) is given by the formula:
Bin histograms of LCMS data generated by Haystack for a tomato leaf sample with bin sizes of 50, 20, 10, 5, 2, and 1.
N = mass range / bin size
It can be seen that there is very little increase in resolution below a bin size of 2. Therefore, we used a bin size of 2 for all subsequent analyses.
Bin results can be used to explore properties of the data, identify important features, and model group assignment. Binning generates a set of interval, non-zero variables in the mass dimension that can be analyzed by univariate (fold change, ANOVA) as well as multivariate (PCA, HCA, PLS-DA) statistical tests. This provides a faster method for processing LCMS data for single or multiple group analysis compared to peak deconvolution methods.
To demonstrate this idea we analyzed metabolite profiles from tomato plants grown under either high light (HL) or low light (LL) conditions (see Methods for details). Samples were prepared from 12 HL samples and 11 LL samples and investigated by LCMS over a mass range of m/z 100-1500. Figure 5 shows a representative TIC and bin histogram from each group. Clear differences in the intensity of several peaks can be observed in the chromatograms of the two samples. However, the bin results provide a quantitative "fingerprint" that can be analyzed by chemometric methods to reveal important and often more subtle features. Sorting bin results provides a snapshot of major features. For example, the mass range 608-610 had the highest intensity in the HL sample, whereas the mass range 370-372 had the highest intensity in the LL sample. EICs of these bins identified a peak at 25.8 minutes corresponding to rutin for mass range 608-610 and a series of peaks between 8.8 and 11.5 minutes corresponding to isomers of caffeoylglucaric acid for mass range 370-372 (Figure 3). The masses of other high intensity bins are indicated in Figure 5B.
Comparison of TICs (A) and bin histograms (B) from representative LL and HL samples. A bin size of 2 was used in this example. High intensity mass bins are indicated.
To test the functionality of Haystack to model class assignment and identify discriminatory features, we used principal component analysis (PCA) to compare the results of bin analysis with manual and automated peak detection methods. For manual peak detection we generated EICs for 69 unique ion peaks in HL and LL samples and integrated the corresponding peaks using Agilent's DataAnalysis Version 5.2 software. For automated peak processing we used the command line version of the open source software XCMS to generate 664 peaks using a peak deconvolution procedure with a default bin size of 0.1. All data sets used in this study are available in Additional file 1.
For each data processing method, we imported the results into MetaboAnalyst 2.0 for statistical analysis [22]. Since, as with many types of chemometric data, binned LCMS data contains a significant amount of baseline noise, removing constant or very weak variables generally improves the analysis [23, 24]. Using the interquartile range filter option, MetaboAnalyst removed 25% of the variables (176 out of 700), most of which were at the high and low ends of the mass range and contained mostly baseline signal.
The data were normalized prior to MVA by dividing each variable by the total bin intensity for the sample (sum normalization) to correct for differences in tissue mass between HL and LL samples. The data then were log transformed and autoscaled to improve normality and satisfy equal variance to make features more comparable. The current version of Haystack uses these pre-processing steps as default options for PCA.
PCA score plots for the three data processing methods are shown in Figure 6. Each method clearly separated the HL and LL groups along the first PCA axis. There was more scatter observed in the Haystack data as compared with the two peak detection methods, which may be explained by greater variance in binned data since all masses are included in the data set. However, the overall pattern in PCA score plots was similar in all three methods, suggesting that binned data can be used to model group assignment with comparable accuracy to peak detection methods despite a large number of neutral variables.
PCA score plots of LCMS data from LL and HL samples processed using Haystack (A), XCMS (B), or manual extraction (C). The number of variables included in the analysis is indicated above each figure.
Multivariate data can also be used to identify discriminatory features. We applied hierarchical cluster analysis (HCA) to each of the three data processing methods to evaluate patterns in the data, and the results were visualized by heatmaps (Figure 7). All three methods produced similar patterns, with HL and LL samples clearly resolved into separate clusters as expected from PCA. Variables were also separated into two major clusters corresponding to the two sample groups. One interesting discrepancy we observed was a greater fraction of variables shown as upregulated in LL samples in the Haystack data set as compared to the two peak detection methods. Although we cannot fully explain this observation, we postulate that the sum normalization pre-processing step may have caused a higher percentage of neutral variables to appear as upregulated in LL samples in the Haystack data set as compared to the other two methods.
Hierarchical cluster analysis of data from Haystack (A), XCMS (B), and manual extraction (C) methods. Pearson's correlation for measure of similarity and Ward's linkage algorithm for clustering were used to generate heatmaps in MetaboAnalyst 2.0.
To further explore the utility of binned data to identify important features, we examined the top 30 PCA loadings in HL and LL groups for each data processing method. We determined retention times from the EICs of significant bins to compare bin results with peak detection results. Peaks were tentatively identified wherever possible based on results of two recent surveys of the tomato fruit metabolome [25, 26] as well as MS/MS analysis of selected ions (data not shown). Features were classified as either upregulated or downregulated in HL samples based on normalized mean data. A subset of 19 features were identified as highly discriminatory in at least two of the data processing methods. Figure 8 shows a graphic comparison of the results. The EICs from a representative LL and HL sample for the mass bins in Haystack of each of the corresponding features in Figure 8 is available in Additional file 2.
Comparison of significant features identified by PCA loadings in Haystack, XCMS, and manual extraction. Features were identified as either downregulated or upregulated in HL samples. Features that were not present in the top 30 PCA loadings are listed as Not Identified.
Although there was good agreement between the three methods, five of the 19 features fell outside of the top 30 PCA loadings in the Haystack analysis. Inspection of the corresponding EICs showed that these bins contained additional peaks to the feature of interest. For example, both manual and XCMS processing methods identified a peak at 25.2 min with m/z 725.6 that was strongly upregulated in HL samples and was annotated as kaempferol rutinoside pentoside based on published reports [25, 26]. The corresponding bin for the mass range 724-726 contained an additional prominent peak at 29.1 min that was present in both LL and HL samples. Consequently, the total bin intensity was not significantly different between the sample groups. Thus, bins containing multiple peaks may not be able to identify discriminatory features. However, both manual and XCMS processing methods also failed to identify several features that were successfully identified by Haystack. We conclude that binning was as effective as peak detection methods in detecting significant features.
Volcano plots provide another useful way to identify features that show large differences between groups. Figure 9 shows a volcano plot of bin results from HL and LL samples. Mass bins that show both large magnitude fold-changes and high statistical significance are indicated on the graph. Investigation of the EICs of significant bins were used to identify peaks according to their retention times and mass spectra. We should note that some significant bins may represent the same metabolite feature. For example, mass bins 354-356 and 706-708 both contained a prominent extracted ion peak at 16.7 min that was identified as chlorogenic acid (Figure 3). As stated above, such highly correlated bins are most likely due to common ions in the mass spectra of individual metabolites. Raw bin data cannot distinguish ions that belong to the same metabolite. However, we observed that automated peak detection methods also produced a large number of highly correlated redundant variables.
Volcano plot of bin results comparing HL and LL samples. Green symbols indicate mass bins that were significantly downregulated while red symbols indicate mass bins that were significantly upregulated in HL samples (p<1e-10). Significant bins that were upregulated or downregulated by at least 4-fold are indicated.
Figure 10 shows an example of how bin analysis can be used to pinpoint significant features. Mass bin m/z 208-210 was revealed by volcano plot analysis to be highly represented in LL samples, and this was confirmed by box plot analysis of bin intensity data (Figure 10C). The corresponding overlaid EICs revealed a prominent peak at 3.6 minutes in LL samples which was barely detectable in HL samples (Figure 10A). This peak was identified a D-glucaric acid based on its early elution time and a monoisotopic mass of 209 for the deprotonated ion. D-glucaric acid and its hydroxycinnamate ester caffeoylglucaric acid have been reported in leaves of tomato and Cestrum euanthes, a related solanaceous species [27–29]. To our knowledge this is the first report that D-glucaric acid may be a biomarker of LL growth conditions in plants.
Example of biomarker identification in LL and HL samples. Top Panel: EICs of mass bins m/z 208-210 (A) and m/z 592-594 (B). Bottom Panel: Box plots of bin intensities for mass bins m/z 208-210 (C) and m/z 592-594 (D). The chemical structures and molecular formulae are shown for the annotated peaks corresponding to D-glucaric acid (A) and kaempferol 3-O-rutinoside (B).
A large number of known metabolites were found to be upregulated in HL plants, primarily flavonoids and other phenolic compounds. For example, volcano plot analysis and PCA loadings revealed mass bin m/z 592-594 to be a highly significant feature of HL samples. The EIC of this bin showed a prominent peak at 28.3 min in HL samples but a very weak signal in LL samples (Figure 10B), and this was confirmed by box plot analysis of bin intensity data (Figure 10D). This peak was subsequently identified as the flavonoid kaempferol 3-O-rutinoside based on MS/MS analysis which produced a daughter ion with m/z 286 corresponding to kaempferol aglycone and a neutral loss fragment of 308 corresponding the to disaccharide rutinose [25, 26]. Kaempferol glycosides have previously been observed to be induced by HL in leaves of tomato and bilberry and therefore may represent a biomarker of HL growth conditions in higher plants [30–32].
A significant challenge in untargeted metabolomics is extracting quantitative information from multidimensional LCMS datasets that provides an accurate, comprehensive, and unbiased representation of the metabolome. This is particularly true in plant metabolomics due to the large number of metabolites and their inherent chemical diversity. Plants typically contain hundreds to thousands of unique metabolites that may be present over a large concentration range spanning several orders of magnitude [4, 33, 34].
In this paper we present Haystack, a new easy to use online platform for metabolomics research. Haystack is a versatile web-based tool that can be used to visualize, parse, and extract information from LCMS datasets rapidly and efficiently. Raw full-scan LCMS data files in one of several common export formats (mzData, mzML, mzXML, NetCDF) can be easily uploaded with a few simple mouse clicks. Users can create projects for multiple file analysis. Haystack's server can store and process a large number of data files, which is often necessary for metabolomics studies.
Haystack automatically generates and displays the total ion chromatogram (TIC) and base peak chromatogram (BPC) when a file is uploaded. Users can search for target masses and obtain extracted ion chromatograms (EICs) with time and intensity values for any mass range. Time-resolved mass spectra can also be generated. A graphical output of all operations is provided, along with a .csv file that the user can export for further analysis or customized graphing. These basic features can be used to extract information from LCMS datasets without requiring technical expertise in command line software tools.
The core function of Haystack is a flexible binning procedure that converts full-scan LCMS data into user-defined mass bins. The resulting histogram provides a graphical representation of the data akin to a "barcode" that uniquely identifies a sample. Sorting by intensity identifies the most abundant masses in a sample. Weak or highly redundant variables can be removed by filtering. When comparing multiple samples, binning creates a set of common interval variables that can be analyzed by chemometric methods such as PCA. A group bin analysis will automatically generate a PCA scores plot, volcano plot, and dendrogram for a two group comparison. For further analysis, group bin results can be imported into a variety of statistical tools. We have found the online tool MetaboAnalyst 2.0 to be particularly useful for analysis of binned mass data since it provides a range of data normalization options as well as a full suite of statistical processing functions [16, 22].
The current version of Haystack does not include automated peak detection. However, the EICs of significant bins can be analyzed "by eye" or using signal-to-noise algorithms to identify important peaks. EICs can be generated for any bin or mass range to identify peaks of interest. Unlike traditional MS metabolic fingerprinting methods, which only contain data in the mass dimension, the chromatographic peaks in binned LCMS data are retained as latent variables, so no information is lost during the analysis.
We show that bin analysis can be used as a fast and practical alternative to peak detection methods to streamline and simplify the analysis of LCMS-based metabolomic data. Bin results are less sensitive to weak signals, baseline noise, retention time shifts, and poor chromatographic resolution than traditional peak detection methods, and they do not require complex filtering, smoothing, or alignment pre-processing steps. Mass bins provide an unbiased data matrix of all ions in a sample since mass distributions not tied to specific retention time indices. Unlike peak detection methods, the bin analysis implemented in HayStack has the advantage of differentiating the samples without requiring retention time alignment.
LCMS data processing methods
Two common approaches for processing LCMS data are manual and automated peak detection [10, 24]. Manual methods generally involve identifying peaks in the TIC and producing extracted ion chromatograms (EICs) for the most abundant mass in each peak. Peaks in the EICs are then integrated, usually within the software environment of the instrument, to produce an intensity value for each mass at a given retention time.
An unavoidable drawback with manual peak extraction is that only the most well-defined peaks of relatively abundant metabolites are generally detected, since minor metabolite peaks often cannot be distinguished from baseline noise. This will result in undersampling the data and so significant features may be missed. A second problem arises from the fact that manual extraction within the software environment of most LCMS instruments is time consuming and not feasible for the sample sizes generally required for robust statistical analysis.
The second and more widely used method of LCMS data processing is automated peak detection. This usually involves several pre-processing steps, such as baseline correction, noise reduction, and retention time alignment, followed by peak deconvolution [24]. Automated detection attempts to resolve the TIC into individual component peaks by removing extraneous ion peaks from extracted mass spectra. This approach has been shown to work very well for GCMS data using peak deconvolution tools such as AMDIS combined with data extraction and quantification tools such as MET-IDEA [13]. However, a variety of factors including greater retention time variation, poor chromatographic resolution, isotopic mass spectra, and baseline noise can all create problems with automatic peak detection with LCMS data.
Despite these limitations, a number of software tools are available for processing, visualization, and analysis of LCMS data [12, 17, 24, 35]. A popular and widely used platform for metabolomics data analysis is XCMS [14]. Originally released as a command line tool based in R, XCMS recently has become available in a web-based version known as XCMS Online [18]. Both versions perform automated peak detection on raw full-scan LCMS data using nonlinear retention time alignment, peak detection, and peak matching using a second derivative Gaussian filter. XCMS Online generates and displays EICs, box plots, and mass spectra for all significant features, along with fold-change data and PCA results. An interactive cloud plot feature has been added that offers a range of visualization options. The command line version of XCMS provides a greater range of options for data processing and peak analysis. However, it requires considerably more technical expertise than XCMS Online.
Haystack provides a more flexible data management system than XCMS Online. Single files can be uploaded and processed individually or in zip format for bulk file uploads in Haystack. XCMS Online limits the basic user to a total workspace of 2 Gb, while Haystack has an individual file limit size of 20 Mb but does not limit total number of files for a user. Both Haystack and XCMS Online allow stored datasets to be edited, but HayStacks offers greater flexibility in manipulating stored datasets. Haystack allows the user to create projects in which files can be uploaded. In a project the user can label files with group names and conduct a group bin analysis. There is even a total files list that allows group analysis to be conducted across projects, thus eliminating the need to upload the same files into another group. XCMS Online does not allow files between datasets to be exchanged to form new groups when conducting a new analysis.
In this study we compared metabolite profiles of plants grown under low and high light conditions to generate a data set for differential profiling. Our goal was to test whether Haystack's bin results could accurately predict group assignment and identify discriminatory features in a multivariate analysis. To do this we compared Haystack data with manual and XCMS automated peak processing using the same sample dataset. All three methods gave similar PCA score plots and heatmap patterns, and identified similar important variables by ANOVA, volcano plots and PCA loadings. However, we also observed some notable discrepancies in feature detection between the three methods. Overall, the results appear to validate Haystack's binning function as a means to diagnose both classes and variables in multidimensional LCMS data.
We note that XCMS produced a high percentage (~15%) of missing values in the peak table due to weak or poorly resolved chromatographic peaks. This can be a problem for modeling multivariate data using covariance methods such as PCA [36]. We avoided this problem in manual analysis by deliberately excluding peaks that were not present in at least half the samples of each group. Similarly, all variables in Haystack's bin analysis had a positive value, thus avoiding the missing value problem here as well. Bins that contain mainly baseline noise would be expected to have a weak influence on the covariance matrix, and these were largely removed by filtering during the data pre-processing steps in MetaboAnalyst.
We also observed that XCMS oversampled a large number of peaks in the TIC. Oversampling results in metabolite peaks in the TIC being represented multiple times in the peak table as highly correlated redundant variables. This gives an inflated estimate of the number of unique metabolite peaks in a sample. Oversampling was caused mainly by the default small bin size of 0.1 used in the initial data processing steps in XCMS. Thus, certain aspects of XCMS results should be viewed with caution.
Although binning is one of the critical steps in the automated processing of LCMS data, defining a proper bin size is not a straightforward task [21, 37]. Forcing a fixed bin size that is either too large or too small has several disadvantages in peak deconvolution methods. A larger bin size may result in poor resolution of the peaks, while a smaller bin size may split a peak which carries a risk of oversampling the data. In both cases, the outcome is a poorly resolved peak and a less confident integration result. Some authors have proposed variable bin sizes or overlapping bins [14]. One of the problems with applying these methods is the lack of information in the raw data to estimate the parameters for setting these values.
Haystack allows the user to set the bin size. This makes it easy to test different bin sizes to determine an optimal number of variables for a particular dataset, which will depend on the biological complexity of the sample as well as the resolution of the mass spectrometer. Peak deconvolution is not required in Haystack since bin intensity values are not coupled to specific retention times. However, peak information can be recovered by analysis of the time domain of relevant bins. It is inevitable that some bins will contain multiple peaks, since the average number of unique ion peaks increases with bin size.
It should be emphasized that Haystack is not meant to replace conventional peak analysis. An important goal of a metabolomics experiment is to identify all distinct chemical entities in a biological sample, which are best represented by fully resolved and annotated peaks in an LCMS chromatogram. Since binned LCMS data can only provide intensities for specific mass ranges, subsequent data processing is crucial to identify specific ion peaks. Tools such as XCMS can perform global deconvolution of LCMS datasets through peak alignment, filtering, smoothing, etc., and XCMS Online version makes this type of data analysis more accessible for the average user. Table 1 provides a comparison of features in Haystack and XCMS Online. While the latter is an extremely useful tool, we believe that Haystack provides an alternative platform with several unique advantages, particularly the ability to process and visualize raw full-scan LCMS data rapidly and efficiently, and more importantly its unique bin analysis for identifying mass ranges of possible interesting features. Haystack may be considered as the first step in the data analysis pipeline to obtain a snapshot of the data, where XCMS Online may provide a more comprehensive analysis of the data based on peak deconvolution. A version of Haystack that incorporates peak detection is currently under development.
Table 1 Comparison of Haystack and XCMS Online features.
A major bottleneck in non-targeted metabolomics studies is processing the large amounts of data produced by most metabolomics experiments to obtain useful biological information. In this article we present Haystack, a novel web-based discovery tool that offers a range of data visualization options for LCMS-based metabolomics data and supports non-biased differential profiling studies through a unique and flexible binning function. While other software tools incorporate binning into the initial stages of data processing, only Haystack allows users to export and manipulate raw binned data for statistical analysis. We show that by applying standard chemometric analysis methods, binned data can be used to model class assignment and elucidate significant features without the need for peak deconvolution. The main advantage of this approach is that it is faster and less prone to chromatographic errors than conventional peak extraction methods. Furthermore, average users can upload and process their LCMS data through Haystack's simple web interface without having technical expertise in command line programming languages and software tools. With additional features being engineered such as peak integration and more data processing options, Haystack will become a valuable tool in the workflow of metabolomics research.
Availability and requirements
Project name: Haystack
Project home page: http://binf-app.host.ualr.edu/haystack
Operating system(s): Platform independent
Programming language: PHP, R, Perl, MySQL
Other requirements: Internet browser
BPC:
Base Peak Chromatogram
CSV:
Comma Separated Value
EIC:
Extracted Ion Chromatogram
ESI:
Electrospray Ionization
HCA:
Hierarchical Cluster Analysis
LCMS:
Liquid Chromatography Mass Spectrometry
MVA:
PCA:
PLS-DA:
Partial Least Squared-Discriminant Analysis
TIC:
Total Ion Chromatogram.
Vorst O, de Vos CHR, Lommen A, Staps RV, Visser RGF, Bino RJ, Hall RD: A non-directed approach to the differential analysis of multiple LC-MS-derived metabolic profiles. Metabolomics. 2005, 1: 169-180. 10.1007/s11306-005-4432-7.
Wikoff WR, Gangoiti JA, Barshop BA, Siuzdak G: Metabolomics identifies perturbations in human disorders of propionate metabolism. Clin Chem. 2007, 53: 2169-2176. 10.1373/clinchem.2007.089011.
Ruiz-Aracama A, Peijnenburg A, Kleinjans J, Jennen D, van Delft J, Hellfrisch C, Lommen A: An untargeted multi-technique metabolomics approach to studying intracellular metabolites of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin. BMC Genomics. 2011, 12: 251-10.1186/1471-2164-12-251.
Fiehn O: Metabolomics: the link between genotypes and phenotypes. Plant Mol Biol. 2002, 48: 155-171. 10.1023/A:1013713905833.
Zhang J, Bowers J, Liu L, Wei S, Nagana Gowda GA, Hammoud Z, Raftery D: Esophageal cancer metabolite biomarkers detected by LC-MS and NMR Methods. PLoS One. 2012, 7: e30181-10.1371/journal.pone.0030181.
Januar HI, Marraskuranto E, Patantis G, Chasanah E: LCMS metabolomic analysis of environmental stressor impacts on the metabolite diversity in Nephthea spp. Chron Young Sci. 2012, 3: 57-62. 10.4103/2229-5186.94319.
Dettmer K, Aronov PA, Hammock BD: Mass spectrometry-based metabolomics. Mass Spectrom Rev. 2007, 26: 51-78. 10.1002/mas.20108.
Fushiki T, Fujisawa H, Eguchi S: Identification of biomarkers from mass spectrometry data using a "common" peak approach. BMC Bioinformatics. 2006, 7: 358-10.1186/1471-2105-7-358.
Want EJ, Nordstrom A, Morita H, Siuzdak G: From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics. J Proteome Res. 2007, 6: 459-468. 10.1021/pr060505+.
Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M: Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis. Curr Bioinformatics. 2012, 7: 96-108. 10.2174/157489312799304431.
Melamud E, Vastag L, Rabinowitz JD: Metabolomic analysis and visualization engine for LC-MS data. Anal Chem. 2010, 82: 9818-26. 10.1021/ac1021166.
Katajamaa M, Oresic M: Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics. 2005, 6: 179-10.1186/1471-2105-6-179.
Broeckling CD, Reddy IR, Duran AL, Zhao X, Sumner LW: MET-IDEA: Data extraction tool for mass spectrometry-based metabolomics. Anal Chem. 2006, 78: 4334-4341. 10.1021/ac0521596.
Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G: XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006, 78: 779-787. 10.1021/ac051437y.
Lommen A: MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data pre-processing. Anal Chem. 2009, 81: 3079-3086. 10.1021/ac900036d.
Xia J, Wishart DS: Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat Protocol. 2011, 6: 743-760. 10.1038/nprot.2011.319.
Fructuoso S, Sevilla A, Bernal C, Lozano AB, Iborra JL, Cánovas M: EasyLCMS: an asynchronous web application for the automated quantification of LC-MS data. BMC Res Notes. 2012, 5: 428-10.1186/1756-0500-5-428.
Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G: XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem. 2012, 84: 5035-5039. 10.1021/ac300698c.
Deutsch EW: File formats commonly used in mass spectrometry proteomics. Mol Cell Proteomics. 2012, 11 (12): 1612-1621. 10.1074/mcp.R112.019695.
Baran R, Kochi H, Saito N, Suematsu M, Soga T, Nishioka T, Robert M, Tomita M: MathDAMP: a package for differential analysis of metabolite profiles. BMC Bioinformatics. 2006, 7: 530-10.1186/1471-2105-7-530.
Krishnan S, Vogels JT, Coulier L, Bas RC, Hendriks MW, Hankemeier T, Thissen U: Instrument and process independent binning and baseline correction methods for liquid chromatography-high resolution-mass spectrometry deconvolution. Anal Chim Acta. 2012, 740: 12-19.
Xia J, Mandal R, Sinelnikov I, Broadhurst D, Wishart DS: MetaboAnalyst 2.0 - a comprehensive server for metabolomic data analysis. Nucl Acids Res. 2012, 40: W127-33. 10.1093/nar/gks374.
Scholz M, Selbig J: Visualization and analysis of molecular data. Meth Mol Biol v358: Metabolomics Methods and Protocols. Edited by: W Weckwerth. 2007, 87-104.
Katajamaa M, Oresic M: Data processing for mass spectrometry-based metabolomics. J Chromatogr A. 2007, 1158: 318-328. 10.1016/j.chroma.2007.04.021.
Moco S, Bino RJ, Vorst O, Verhoeven HA, de Groot J, van Beek TA, Vervoort J, de Vos CHR: A liquid chromatography-mass spectrometry-based metabolome database for tomato. Plant Physiol. 2006, 141: 1205-1218. 10.1104/pp.106.078428.
Gomez-Romero M, Segura-Carretero A, Fernandez-Guitierrez A: Metabolite profiling and quantification of phenolic compounds in methanol extracts of tomato fruit. Phytochem. 2010, 71: 1848-1864. 10.1016/j.phytochem.2010.08.002.
Elliger CA, Lundin RE, Haddon WF: Caffeoyl esters of glucaric acid in Lycopersicon esculentum leaves. Phytochem. 1981, 20: 1133-1134. 10.1016/0031-9422(81)83044-0.
Nagels L, Van Dongen W, Parmentier F: Cestric acid, a caffeic acid ester from Cestrum euanthes. Phytochem. 1982, 21: 743-746. 10.1016/0031-9422(82)83179-8.
Strack D, Gross W, Wray V, Grotjahn L: Enzymic synthesis of caffeoylglucaric acid from chlorogenic acid and glucaric acid by a protein preparation from tomato cotyledons. Plant Physiol. 1987, 83: 475-478. 10.1104/pp.83.3.475.
Wilkens RT, Spoerke JM, Stamp NE: Differential responses of growth and to soluble phenolics of tomato to resource availability. Ecology. 1996, 77: 247-258. 10.2307/2265674.
Jaakola L, Maatta-Riihenin K, Karenlampi S, Hohtola A: Activation of flavonoid biosynthesis by solar radiation in bilberry (Vaccinium myrtillus L.) leaves. Planta. 2004, 218: 721-728. 10.1007/s00425-003-1161-x.
Olsen KM, Slimestad R, Lea US, Brede C, Løvdal T, Ruoff P: Temperature and nitrogen effects on regulators and products of the flavonoid pathway: experimental and kinetic model studies. Plant Cell Environ. 2009, 32: 286-299. 10.1111/j.1365-3040.2008.01920.x.
Roessner U, Luedemann A, Brust D, Fiehn O, Linke T, Willmitzer L, Fernie AR: Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell. 2001, 13: 11-29. 10.1105/tpc.13.1.11.
Sumner LW, Mendes P, Dixon RA: Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochem. 2003, 62: 817-836. 10.1016/S0031-9422(02)00708-2.
Danielsson R, Bylund D, Markides KE: Matched filtering with background suppression for improved quality of base peak chromatograms and mass spectra in liquid chromatography-mass spectrometry. Anal Chim Acta. 2002, 454: 167-184. 10.1016/S0003-2670(01)01574-4.
Stacklies W, Redestig H, Scholz M, Walther D, Selbig J: pcaMethods: a bioconductor package, providing PCA methods for incomplete data. Bioinformatics. 2007, 23: 1164-1167. 10.1093/bioinformatics/btm069.
Bino RJ, Hall RD, Fiehn O, Kopka J, Saito K, Draper J, Nikolau BJ, Mendes P, Roessner-Tunali U, Beale MH, Trethewey RN, Lange BM, Wurtele ES, Sumner LW: Potential of metabolomics as a functional genomics tool. Trends Plant Sci. 2004, 9: 418-25. 10.1016/j.tplants.2004.07.004.
Support for this project was provided by the Arkansas INBRE program, supported by grant funding from the National Institutes of Health (NIH) National Institute of General Medical Sciences (NIGMS) (P20 GM103429) (formerly P20RR016460), the Arkansas Center for Plant Powered Production, supported by grant funding from the NSF EPSCoR Arkansas Asset Initiative (EPS-1003970), and NSF Award 0421422. The authors would like to thank Chen Chen and Senetibeb Gebre for help with LCMS sample analysis.
Funding for this publication was provided the National Institutes of Health (P20 GM103429) and the National Science Foundation (EPS-1003970).
This article has been published as part of BMC Bioinformatics Volume 15 Supplement 11, 2014: Proceedings of the 11th Annual MCBIOS Conference. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S11.
Biology Department, University of Arkansas at Little Rock, 2801 South University Ave., Little Rock, AR, 72204, USA
Stephen C Grace
University of Arkansas for Medical Sciences Bioinformatics Graduate Program, University of Arkansas at Little Rock, Little Rock, AR, 72204, USA
Stephen C Grace, Stephen Embry & Heng Luo
Stephen Embry
Heng Luo
Correspondence to Stephen C Grace.
SCG conducted the experiments, performed the analysis and drafted the manuscript. SE and HL created the server, designed and implemented the coding for Haystack, participated in the design of the study and helped to draft the manuscript. All authors read and approved the final manuscript.
12859_2014_6659_MOESM1_ESM.xlsx
Additional file 1: Supplemental information. Excel file that contains data for 23 samples that were analyzed by the three data processing methods used in this paper. (XLSX 392 KB)
12859_2014_6659_MOESM2_ESM.pdf
Additional file 2: Supplemental information. PDF file that contains EIC plots for the 19 mass bins shown in figure 8 from a representative LL and HL sample. (PDF 713 KB)
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Grace, S.C., Embry, S. & Luo, H. Haystack, a web-based tool for metabolomics research. BMC Bioinformatics 15, S12 (2014). https://doi.org/10.1186/1471-2105-15-S11-S12
DOI: https://doi.org/10.1186/1471-2105-15-S11-S12
Principal Component Analysis Score Plot
Untargeted Metabolomics
Principal Component Analysis Loading
Peak Detection Method


Lily & Ally / Flower Ballerina Musical Jewelry Box, with Melody of "Waltz of the Flowers / the Nutcracker"
Search hundreds of online stores that deliver to California, Texas, New York, Florida, Illinois, Pennsylvania, Ohio, Michigan, Virginia, Washington, Indiana, Arizona, Missouri, Colorado, Maryland, Wisconsin, Minnesota, Colorado, Alabama, Louisiana, Oregon, Nevada, Idaho, Nebraska, Delaware, Montana, Vermont, Wyoming, and all over United States and find the best offers for Fiberboard Case deals. Only on TopPriceChecker you can find the lowest Fiberboard Case prices available, learn about the latest discounts and compare discount deals. Here you can browse through the photo gallery, find out all about its features and specs and read the product reviews and user comments from our community.
Compare prices online and buy from USA's leading merchants. You'll find a wide range of products listed on TopPriceChecher.com including Tea Length Hem, Jeep Wagoneer Transfer Case, 3m Utility Hooks, and Daisy Lounge Pants. Find, compare and buy!


people, other then how to survive. Read this essay to learn about the human being, a rational and social partner in environmental action! We must use as much environmentally friendly technology as we can in our world. Through out the article there are many example of how nurture overcomes nature. (d) Creating public my daily day mother essay awareness about the benefits and implications of environment.
Below is an essay on "Impact of Technology Progress to Our. Society and, environment ". Read this essay to learn about the human being, a rational and social partner in environmental.
Man and environment are inter-related. Watering lawn every day Only water every third day. (Nebel and Wright, 2000,. The Relationship between Heredity and Environment. To sustain and increase agricultural growth: Without damaging environment. The technology has been developed and is available to create commuter vehicles that produce very few harmful emissions and have a very minimal impact on non-renewable resources. Home, environment, title description author Name author Email. Waste Waste, trashing all waste produced Recycle glass, paper, plastic aluminum; build a compost heap Changing car oil Take to approved site to dispose Old cans of paint Take to approved site to dispose Air Air Air conditioning units Check yearly for proper operation Heaters. Public opinion, government intervention, and manufacturers seem to have the largest influence in determining how the earths resources are used. Soil Soil, un-landscaped lawn Landscape; add bushes on property line to avoid erosion; use terracing.


SEATTLE – The Coast Guard Cutter Polar Sea sits in dry-dock completely stripped of its bright red color while crew members and contractors conduct maintenance here July 11. The Polar Sea went into dry-dock at Todd's Shipyard in early June 2007 and is not scheduled to leave until late August or early September. Highlights of the Polar Sea's shipyard availability include sandblasting and complete repainting of the hull and superstructure, major engine overhauls on all six diesel engines and three main gas turbine engines, removal of all three propeller blades and hubs, complete shut-down and replacement of the ship's sewage and water piping systems and a total overhaul of the ship's galley.


text:
Streaming plans
Download store
Web Player My account
See all article in Hi-Fi
Testing Ground
Hi-Fi >
Testing Ground >
THX Onyx Test: a remarkable USB mini DAC with a powerful headphone output
By Alban Amouroux | March 22, 2022
Mini USB DAC amplifiers have been multiplying in recent years. Their primary purpose is simple: to improve the original headphone output of smartphones. This is not that difficult to achieve, as mobile phone manufacturers do not really have the quest for audiophile sound quality at the top of their to-do list. With the THX Onyx, the world-famous THX brand offers a compact, high-performance solution for amplifying music on headphones, both on the move and in the office.
THX, three letters recognised throughout the world for their link with surround sound in the cinema, later became available to home cinemas. THX is involved in the sound signature of many home audio products. The company also developed processing and enhancement technologies for HiFi (THX AAA) and 3D sound (THX Spatial Audio). Recently, THX decided to demonstrate its know-how to headphone users with a mini USB DAC amplifier called the THX Onyx.
In the case of the Onyx, it is high-resolution HiFi quality audio that is targeted thanks to the THX AAA amplifier circuit. This allows the brand to develop the recognition of its technologies in a field in which it was not necessarily expected. However, many HiFi equipment manufacturers have already integrated the AAA circuit into their headphone amplifiers or even directly into the headphones. This is the case with Razer, known for its many gaming products, from laptops and headsets to ergonomic seats and game controllers. A close partner of THX, Razer sells the Onyx DAC on its own website.
● Price : €209.99
● Type : USB DAC amp
● D/A Conversion : ESS ES9281PRO
● Amplification : THX AAA-78
● Impedance Range : 22-1.000 Ohms
● Output Impedance : 0.25 Ohm
● Dynamic Range : 118 dB
● Distorsion (THD+N, 1 kHz) : -110 dB
● Power Output Per Channel (22 Ω, <1 % THD+N) : 180 mW
● Input : USB-C + including USB-A to USB-C adapter
● Output : Microphone compatible stereo mini-jack (non-Apple)
● Dimensions : 208 x 12 x 7 mm
● Weight : 18g
Overview of the THX Onyx
The portable mini-DAC category is a recent development. Up to now, components could not be miniaturised enough to integrate a digital/analogue converter and a pre-amplifier/amplifier section into a unit scarcely larger than a USB stick. Now it's possible, using real quality components. The THX Onyx uses the ESS9281PRO chip. It is mainly found in USB DACs because of its specific characteristics such as its minimal size and low power consumption. Another special feature is that this chip can decode MQA and DSD, the two other Hi-Res audio formats apart from PCM. Its compatibility is therefore universal.
It will not have escaped your attention that THX features in the product name. This certification originated in the cinema world and has gradually been developed in other sound related fields. Home cinema of course, and, more recently, headphone amplifiers. This product has THX AAA technology, for Achromatic Audio Amplifier, built into its product. It delivers the most faithful sound possible by extending the dynamic range. Thanks to this technology, the THX Onyx delivers 180 mW per channel at 22 ohms to provide the power needed for the most demanding headphones. Even some sedentary format HiFi devices do not go this far in terms of pure power.
This USB DAC has a really small format. It ends in a USB-C plug about ten centimetres away from the electronics housing. They are joined by a flexible cable allowing the THX Onyx to be easily folded. What is more, when the cable is folded, the two ends are held together by a magnetic clasp thus taking up even less space. If your equipment doesn't have a USB-C socket, THX provides a compact adapter to USB-A.
On the electronics side, the DAC ends in a 3.5mm mini-jack output. As we have seen, its power will be sufficient for most headphones. This output is not balanced. If this feature is important to you, you should look at one of the competing models. In the output end, it delivers up to a maximum 2v, meaning that this DAC can be used upstream of an amplifier or a pair of active speakers. For those who favour minimalism in all things, you can imagine a simplified quality HiFi system: a smartphone, the THX Onyx DAC and amplified speakers.
Installing the THX Onyx
The 3.5mm headphone jack is still very common on computers, tablets and smartphones. Although in the latter case, it is disappearing as the USB-C has become more generally used. However a USB-C to mini-jack dongle is sometimes provided by smartphone manufacturers. But the output is usually of inferior quality. This is not really so bad, as the current trend is towards Bluetooth headsets. Although very convenient, Bluetooth is not the best way to have direct access to very high-quality audio.
The USB DAC allows you to receive digital sound and convert it to analogue. This THX Onyx is compatible with all devices capable of delivering sound through a USB port. This is the case for desktop and laptop computers, tablets and smartphones. The USB DAC runs on Windows and macOS. If you connect a headset to the THX Onyx, the microphone function is not supported under macOS. If you are using an iPhone, you will need an intermediate adapter, from the proprietary Apple Lightning plug to USB-A. Apart from these two exceptions, you should have no doubt that the THX Onyx works with everything.
Once connected, the THX USB DAC remains in sleep mode. Only one of its micro LEDs is lit in blue. It will not appear in the list of devices. To activate it, you must connect headphones or a HiFi amplifier to the mini-jack output. The DAC then makes itself known on the device being used. It will appear as THX Onyx USB Amplifier.
When music is played, the three micro LEDs light up blue if the source is CD quality. In high-resolution PCM, the LEDs change to yellow. DSD is indicated by the colour red, while MQA is identified by pink. This makes it possible to check the audio quality at a glance. THX's website has available for download a comprehensive user guide covering all scenarios, according to the hardware, music service or software used. The site also states that the THX Onyx is compatible with Qobuz Studio on all devices. For our test, we connected the USB DAC to a Sony Xperia Android smartphone and Beyerdynamic Amiron Home headphones, all via the Qobuz app.
We started by listening to Makaya McCraven and his last album, Deciphering the Message. The THX Onyx treated us to a wide soundstage in which the different instruments of this octet are distinctly isolated from each other, with a very noticeable depth and distance effect. With analogue headphone output, this sense of depth is lost. The sound also seems less alive, more closed in, more compacted. With the THX Onyx, the bass offers more depth, the notes are sustained longer. There's no doubt about it, the contribution of this mini USB DAC is significant. This was confirmed listening to the track My Girl, one of the Royal Philharmonic Orchestra's covers of Motown classics. Everything is smooth with this DAC. Micro-information appears with that little reverberation effect that indicates there is a world behind the instruments. But above all, voices are clearly discernible from the orchestral mass. Compared to the original headphone output, the midrange is better defined with the THX, and the snapping of fingers sounds more natural.
The ambient details behind Juliette Armanet's voice are fine and precise when listening to Brûler le Feu. Her voice is separated from the piano, the latter is lower down the scale while the voice is higher. The finesse in the upper midrange is remarkable without becoming aggressive. Compared to the basic headphone output, the result offered by the THX Onyx is like removing a veil from the music. We moved on to the electro of Darius on his track Rise. The round, percussive bass that forms the backbone of the piece gives its full measure without ever sagging. Controlled, it is even explosive when it is alone at the beginning and end of a piece. The airy aesthetic is perfectly rendered with the lead vocal extending from ear to ear. Keyboard gimmicks are reproduced outside the head to provide a pleasant fullness.
Pros :
Compact and Ultra Light
USB-C and USB-A
Compatible with PC/Mac/Smartphone/Tablet
Midrange/Treble Finesse
Open Sound Feeling
A little more expensive than the competition
THX rarely markets electronic products under its own brand, however, with the Onyx USB DAC, the company has pulled off a major coup. This small USB DAC is invaluable for the sound quality it delivers. Everything is well thought out, there is absolutely nothing to criticise. The technical choices tilt the balance in its favour. The excellent separation makes it easy to enjoy all types of music. The latter feature bears no comparison to the basic headphone output of a smartphone, tablet or computer. If you own a quality headset, once you have experienced the THX Onyx you'll never again settle for the original outputs. It will quickly become inseparable from your favourite headset. The price may seem high compared to the competition offering equivalent models, but the compact size and the improved quality will more than compensate for this difference.
Links: , Rise, Brûler le Feu
Section: Testing Ground
Qobuz is hiring
The Qobuz Blog
Qobuz Society
Streaming subscription plans
Qobuz Duo
Qobuz Family
Give Qobuz
Discover the high quality sound
© 2023 XANDRIE SA – 45 rue de Delizy, 93692 Pantin CEDEX, France
Legal noticesCGU & T&CData protectionCookies

redpajama_set_name: RedPajamaCommonCrawl


Moments is Tim's seventh CD, released in October 2015. The album was nominated for Classical Album of the Year by solopiano.com.
Galaxy is Tim's sixth CD, released in May 2015. This album includes all of the tracks from Tim's 2014-2015 track-a-month campaign. Galaxy was named Best Instrumental/Piano Album in June 2015 by The Akademia Music Awards, and it is currently nominated by One World Music for Best Solo Piano Album of 2015.
Storm is Tim's fifth CD, released in April 2014. This album includes all of the tracks from Tim's 2013-2014 track-a-month campaign. Storm was named Best Solo Piano Album of 2014 by One World Music, and it was nominated by Enlightened Piano Radio for Album of the Year.
In an era where individual tracks are the norm, Storm was composed as a single project that's meant to be experienced from the first track to the last, while also providing tracks that are memorable enough to stand alone. Download the CD liner notes in PDF, or visit the store to buy the CD and sheet music.
Opus Four is Tim's fourth CD, released in 2013. Opus Four received nominations from SoloPiano.com (2013 Album of the Year in the Classical/Modern Classical category) and two from Hollywood Music in Media: Best Contemporary Instrumental Song (A Moment of Joy) and Best Classical piece (Adagio Sostenuto).
This album includes all of the tracks from Tim's 2012 track-a-month campaign. Unlike Tim's other CDs, where almost every track had a specific dedication or inspiration, Opus Four's song titles are drawn from inspiration or the emotion evoked by the music itself. Tracks are written about family (birth of a daughter, a father battling cancer), written about the moment of the track's inspiration (A Moment of Joy, Winter Dreams), or simply written as music for the sake of music (Waltz in F Minor, Adagio Sostenuto, Persian Rhapsody). As with all Tim's CDs, there is a meditation piece to calm your mood, and Opus Four also includes a closing track, Chasing Glory, that will knock your socks off.
There is a range of pieces from contemplative to energetic, from classical to contemporary - you're sure to love it! Download the CD liner notes in PDF, or visit the store to buy the CD and sheet music.
Influence is Tim's third CD, originally released in 2009 and remastered in 2014. This album includes tracks dedicated to people and places that have inspired his music. Tracks are written for a former teacher, his wife, church congregations, his parents, and even a dog. Two tracks are for his favorite composer, Antonin Dvorak, using familiar themes of Dvorak's music. There is a range of pieces from contemplative to energetic, from classical to contemporary - there are sure to be a number of enjoyable pieces for the solo piano listener! Download the CD liner notes in PDF, or visit the store to buy the CD and sheet music.
Biography is Tim's debut CD. The CD includes 12 tracks: 11 original compositions and an original arrangement of Karl Jenkins' Palladio, the orchestal score commonly heard in diamond commercials. Biography has received numerous positive reviews, and was ranked in the Top-3 albums on PayPlay.com for over two years. Called "a very impressive debut... very highly recommended" by MainlyPiano.com, and a "fantastic CD... receives my highest recommendation" by Piano-Heaven, this is a great album for any solo piano music lover! Download the CD liner notes in PDF, or visit the store to buy the CD and sheet music.
Christmas, Tim's second album, includes 11 original arragements of traditional Christmas pieces. Click here to order!


Dr Menezes talks to ecancertv at the UICC World Cancer Congress 2014 about the benefits of exercise for women with breast cancer. As well as enhancing quality of life by reducing fatigue and depression, exercise has also been found to moderate serum concentration in the tumour microenvironment, reducing tumour inflammation and progression.
Exercise produces a lot of benefits in women with breast cancer, including psychological effects about depression, fatigue and an overall conception of quality of life. But now we have observed the effects, the specific effects, of exercise in inflammatory biomarkers going beyond the phenomena of quality of life and addressing the molecular mechanics that exercise can produce benefits for women with breast cancer.
The benefits are specifically focussed on tumour progression and how the exercise can modulate the tumour microenvironment to reduce the concentration of inflammatory markers that are molecular that produce in the signalling pathway leading to tumour growth. We undertook a meta-analysis which is a novel mode of analysis where you can pull several results obtained from randomised controlled trials. In this sense we have alligned nearly ten studies involving women with breast cancer after successful treatment and then we pooled these effects in an analysis synthesis.
So how many patients are in the meta-analysis and how many trials?
Trials were nine, nine trials, and the sample size was 349 women.
No, as a whole. There specifically were 200 women in an experimental group and then the other women were allocated to control groups, specifically in conventional care or non-exercise.
So what's the difference in the outcomes?
The differences that we found were that exercise can specifically modulate the serum concentrations of inflammatory markers including IL-6 which is one of the most important biomarkers related to tumour progression and systemic inflammation, it's a reactive protein; IL-2, which is also linked to immunoregulatory effects in cancer patients, and also we found statistically significant differences for IL-10. The results are expected to be published next month; now we are working in preparing the last version of the manuscript.
We hope that this result will encourage the scientific sector to gain insight in their prescription and recommendation of physical activity going beyond these phenomena that it may show what is worthy about quality of life, now addressing specifically the tumour behaviour and tumour microenvironment and how exercise can stop or can contract tumour progression.


text:
<?xml version="1.0" encoding="UTF-8"?>
<problems>
  <problem>
    <file>Test.java</file>
    <line>17</line>
    <description>Method is never used as a member of this interface, but only as a member of the implementation class(es). The project will stay compilable if the method is removed from the interface.</description>
  </problem>
  <problem>
    <file>Test.java</file>
    <line>17</line>
    <description>Parameter 'i' is not used in any implementation</description>
  </problem>
</problems>

redpajama_set_name: RedPajamaGithub


Brilliant Face Makeup Techniques Every Makeup Artist Should Know!
The world of fashion is endless, and so are the tricks. Keeping yourselves updated on these techniques can make you one of the best makeup artists and can help you bag name and fame quickly in a vastly growing industry like this. If you are pondering over on the techniques of achieving those looks on your client taking a walk through this write-up is necessary.
Knowing the face shape is one of the prerequisites even before attempting to perform a full face makeup. Well, understanding the face shapes can help you in using the right kind of techniques at ease. Mentioned below are the methods of makeup to be used based on the face shapes!
There are four main types of face shapes, and it is mandatory to seek assistance from Fabulive makeup tutorials and courses to determine the type of face you are dealing with.
The first and the foremost thing to do are to measure the face and note down the measurements quickly on a piece of paper. The shapes of the face can be determined and by measuring the forehead, cheekbones, and jaws at their widest points. Along with this, it is also required to take measurements from ear to ear and brow to chin as this can help us understand the ratio easily.
People with oval face shape are blessed as this is considered to be the ideal face shape. While every makeup and hairdos including the white face makeup go well with this kind, it is best to know the best techniques to be used to achieve some lovely looks.
Using a foundation closest to your complexion can do a lot of tricks else, it can make your face look oblong. Do not get carried away with the whims of using every foundation that you get just because you have an ideal face.
People with oval face shape can carry themselves well with any eye makeup hence; there is no restriction in this area. However, using the creamy eyeshadows and a pair of false eyelashes can add more to the already existing charm.
Try not going heavy with bronzers on your cheekbones please! Either with lighter or louder shades of bronzer, you must go easy with them to get a balanced look because the cheekbones are the prominent areas of oval face shape.
Are you dying to allure people with your bountiful lips then, you must try lighter shades of lipsticks with some gloss. Glossy effects look extremely well on oval face shapes.
The measurements across the face would be equal hence, people with this face shape would have sharp features and require more contouring to get an ideal face.
Reduce those sharp edges on the corner of the face by the upward blending techniques using darker shades of foundation or a bronzer and follow the same procedure on the sides of the forehead as well.
People with square face shape should know how to apply makeup on face and especially to draw the attention on cheekbones. Using light blushes with proper highlighting techniques can make you look just perfect.
Jawlines are incredibly sharp in square face shapes hence, drawing attention to lips by making it look full and luscious does the trick.
People having a heart face would have a sharp chin and would have prominent cheekbones, and to make it an ideal face shape, the idea is to reduce the width of the forehead by using proper contouring and highlighting methods.
Make sure to use some concealer along with the foundation to add some extra prominence to those cheekbones. This can easily distract the width of the forehead by creating a balanced look.
Yes, you heard us right! Shape your brows round and neat and this again balances the area around the forehead, and with little dramatic eyes, you are ready to step out with the utmost confidence.
Making the chin, appear wide has to be your goal, and this can be easily achieved by using bold lipsticks or more related products.
One of the most feminine shapes with a lot of soft features is the round face shape and accentuating the cheekbones becomes vital to creating a balanced look.
This adds much beauty to those round faces. Using the deep and dark colors can help in accentuating the eyes and makes a face look attractive.
Use the right bronzer and start contouring the cheekbones. You need first to feel the cheekbones and then start working on the hollow part by pulling it in the upward direction for a perfect look.
Matt finishing lipsticks can bring out the best looks. Using the same color lip liner and choosing either a dark red or dark plum color can make you steal the show.
These are some of the simple, yet useful makeup tips which would help you understand the art of makeup in a much better way. As the goal is to make a face an ideal one performing these steps and knowing how to do face makeup appropriately can help you make look gorgeous!
Though these are some of the basic face shapes there are also people with a combination of these faces and tricks to bring the best out for such face types can also be experimented by blending techniques as given below. Try these and let us know about your experience, we are hungry for feedback!


The Angels and Masters together with countless groups of ordinary spirit helpers are our guardians, keepers and nursemaids in the world of light. Under the command of the Great Father/Mother they are constantly watching over our progress, ever ready to assist whenever one of us is in need of it. Their helping hands, invisible to earthly eyes, are always reaching out to us. All we have to do is ask for their help. As soon as one of us has done this, that person begins to learn how to follow their guidance and walk with them.
The Great Mother's way of teaching us, Her beloved children of the Earth, the gift of self-mastery has always been through allowing each one of us to learn from their own experiences. The road of spiritual mastery starts with taking charge of every part of our being and ends with freely and willingly practising self-mastery and self-discipline. And when after the lessons of Saturn in Capricorn and the tenth house, we arrive in the next sign and house, Aquarius and the eleventh, Saturn has by no means left us. The planets energies and the Angels in charge of them stand guard at the sign's gateway to ensure that none of us can come anywhere near the Aquarian spiritual freedom until we truly have become masters of every aspect of our nature.
High on the list of things to be shed from our consciousness are the false beliefs, prejudices and superstitions that still abound in our world. Each one of them is waiting to be replaced by our own truth, i.e. that which the living God within tells us is true, good and right for us, now – even though it may not be for anyone else. As pioneers of a new age we have to be prepared that our truth may not yet match anyone else's beliefs. Those we come into contact with may not yet understand what we have to give.


text:
More / Book of the Month / God's Not Dead: A Light in Darkness (2018) Michael Mason - Movie Review
God's Not Dead: A Light in Darkness (2018) Michael Mason - Movie Review
April 10, 2018	Posted in 2018 / Pastor Ted Giese / Movie Review / issuesetc.org / Radio Interview
God's Not Dead: A Light in Darkness (2018) Directed by: Michael Mason, Writer: Michael Mason Stars: David A.R. White, John Corbett, Shane Harper, Ted McGinley, Jennifer Taylor, Benjamin A. Onyango, Samantha Boscarino, Mike C. Manning, Gregory Alan Williams, Run Time: 105 min Rated: PG (Canada), PG (MPAA) for thematic elements including some violence and suggestive material.
Listen here for audio of radio interviews about films from a Christian perspective with Pastors Ted Giese and Todd Wilken on IssuesEtc.org where Christianity meets culture.
Improved but could be better
In this instalment of the God's Not Dead franchise, first-time director Michael Mason takes over from Harold Cronk who directed the first two films and inevitably the new outing is different. God's Not Dead: A Light in Darkness picks up where God's Not Dead 2 left off with Rev. Dave Hill (David A.R. White) arrested and jailed for not providing copies of his sermons to local authorities. However, this is not what the film is about. The old storyline is quickly dispatched and replaced with the story of Rev. Hill trying to stop a university from annexing St. James Church which is situated on its state-run campus. This opportunistic play by the administration of Hadleigh University, originally founded by the congregation it is trying to remove, follows an arson fire suffered by the church during the controversy swirling around Rev. Hill and his sermons.
Hill's friend, Rev. Jude (Benjamin A. Onyango), who in the previous films was a visiting African missionary and in this film is the new co-pastor at St. James, dies in the fire. The tragic fire with its fatality and the university's betrayal sends Rev. Hill's faith and trust in the Lord into a tailspin.
The film's B-plot revolves around a young student, Keaton (Samantha Boscarino), who is questioning her faith and struggling in her relationship with her lapsed-Christian boyfriend Adam (Mike C. Manning) the accidental church arsonist with a guilty conscience. Add to this Rev. Hill's estranged brother and lawyer Pearce Hill (John Corbett) coming to help with the lawsuit; a reluctant university president, Thomas Ellsworth (Ted McGinley), drawn into Hill's feelings of betrayal by the university administration while also being a Hill's close friend; plus a possible romance for Hill with soup kitchen operator Meg Harvey (Jennifer Taylor) who "likes bad boys" and for good measure Josh Wheaton (Shane Harper), the lead from the first film, is now a student-turned-youth-pastor and what do you have? A soap opera!
The key to understanding these films is that they are melodrama not drama. Lots of people like melodramas but not everyone. If a viewer goes into God's Not Dead: A Light in Darkness expecting a deep thoughtful storyline they will be very disappointed although not as disappointed as they would have been with the previous two films since this one is a little less over the top. More importantly, stories of Christian persecution and the melodrama genre don't go well together. Thankfully, there is at least one moment when director Michael Mason realizes this. As Hill is complains to a fellow pastor, Reverend Roland Dial (Gregory Alan Williams), that Christians need to get up and fight and not be pushovers Dial says to him "You can't fight hate with more hate," to which Hill responds that the persecution he's feeling is somehow special and that if Dial knew what Hill was going through he'd see it different. Dial points out "I'm a black pastor in the Deep South! I could build a church with all the bricks that have been thrown through my windows"– basically: you don't know from suffering and persecution, so be quiet.
Where God's Not Dead 2 (2016) focused on apologetics by including Lee Strobel, author of The Case for Christ (1998), and J. Warner Wallace, author of Cold-Case Christianity (2013), this film has set apologetics aside. The focus is on Christian doubt in the face of tragic circumstances and lapsed faith due to the bad behaviour of fellow Christians. While these are noble and good topics to investigate, is melodrama the right tool for the job?
Viewers faulted earlier God's Not Dead films for "preaching to the choir" and pretending to engage atheism in the public square with apologetics while creating strawmen and phoney two-dimensional atheistic villains to knock down. This time the audience is unapologetically Christians and, for the most part, the cheesy fake atheists are removed. A good question to ask then is, "If God's Not Dead: A Light in Darkness isn't about preaching Christian apologetics to its Christian audience what is it preaching?"
The young student Keaton, whose faith is on shaky ground, provides the film's thesis statement: "The whole world knows what the church is against but it's getting harder and harder to know what it's for." In her personal struggle she is upset because in her prayer life she "can't hear God speaking to her." All she hears is the noise from the fighting between secular ideas and the Church. By the end of the film, in an attempt to mediate the warring parties, Rev. Hill, who starts out with solid Christian conviction that, "Truth is a person and that person is Jesus," winds up preaching a generic Jesus-less kind of peace-hope-and- love-will-conquer-all 'gospel' with a call to "stop fighting and yelling at each other and just listen to each other; so everyone put down your signs."
While listening to people and having a real conversation is good and hopefully productive and, while displaying peace, hope and love to the people set against the Christian faith is also good, it needs be done in Christ Jesus and in such a way that Jesus is not an afterthought. So, where the film could have great strength and conviction in the end it fails.
One other odd thing is that St. James largely appears to be a church without a congregation. The pews are full on Sunday. They have a pastor, a youth pastor, and a kind of university ministry but there is no core group of members to roll up their sleeves and help amid tragedy or to provide support for their pastor in his struggle. Granted, this would further complicate the film's plot and add to its length, but it's peculiar to have a pastor trying to save a church for a largely imaginary or theoretical congregation. Also, there are no shut-in calls or hospital visits, the kind of things that still go on even if the church building has suffered fire damage. Yes, there's one church council meeting, but that floats by and is gone as fast as it's introduced. What all this speaks to is a sort of individualistic representation of the Christian faith. God's Not Dead: A Light in Darkness presents the Christian life as a collection of individuals not as a community. Part of Rev. Hill's character arch is moving from pride to humility and pride is very self-centred. However, in the end Hill is only a little bit humbler. He confronts his theology of glory but doesn't replace it with the theology of the cross of Christ's suffering; he leapfrogs over Jesus right back to a theology of glory. There is even an oddball moment when Meg, his love interest, takes him out to a wooded lake to show him where she lights a candle and prays for people … because getting into nature alone to pray is much more important than praying in a church with other Christians right? Again, this points to the film's emphasis on the individual.
There are some positive things to note. Scripture is quoted and even sometimes applied in good ways. For instance, when Rev. Hill is spiraling into the deepest pit of his despair and Keaton expresses her disappointment in him for his lack of faith, the youth pastor Wheaton comforts him by comparing Hill's doubt with John the Baptizer's doubt quoting Matthew 11:2–6, "Now when John heard in prison about the deeds of the Christ, he sent word by his disciples and said to Him, 'Are You the one who is to come, or shall we look for another?' And Jesus answered them, 'Go and tell John what you hear and see: the blind receive their sight and the lame walk, lepers are cleansed and the deaf hear, and the dead are raised up, and the poor have good news preached to them. And blessed is the one who is not offended by Me.'" There is more than doubt to John's imprisonment and sending his followers to Jesus, but it is a good example of applying a Scripture text to a character's legitimate concerns in a meaningful way. Also, in one of his sermons Rev. Dial paraphrases Psalm 30:5 "For His anger is but for a moment, and His favor is for a lifetime. Weeping may tarry for the night, but joy comes with the morning." Overall, the film is by no means devoid of talk of Jesus, Scripture quotes, or moments of genuine repentance and forgiveness.
Another positive element is the respectful treatment of nominal Christians, doubting Christians, and lapsed Christians which was not the case in previous God's Not Dead films. Their concerns and troubles are more true-to-life and this is a positive change. For example, Rev. Hill's lawyer brother Pearce, whose faith lapsed while he was in University studying law, confronts his brother saying that when his faith was on the rocks their family shunned him for his lack of faith and didn't help him in his time of need. When Hill asks Pearce why he's helping him try to win the case against the university and save St. James Church, Pearce replies, "I'm fighting for you because you're my little brother and I don't want to see you get pushed around." It's these kinds of natural human moments given to people struggling with their faith that makes this film better that earlier films.
Mason's God's Not Dead: A Light in Darkness is a vast improvement over Harold Cronk's God's Not Dead (2014) and God's Not Dead 2 (2016) but that doesn't mean it's a good film; it only means it's a better film. If the producers choose to continue the franchise they should continue down this new path and move further away from melodrama to drama. Trite, "It only takes a spark to keep a fire going," and "God is good – all the time" and "all the time – God is good" sentimentality needs to be further unpacked and investigated in a Christ-centred and cross-focused way. If they are going to embrace the idea of "preaching to the choir" they need to preach Christ and Him crucified. If they want to reach out to a world filled with doubt, pain, and even anger and hatred towards Christianity, they need to preach Christ and Him crucified and do it in a way that is winsome and engaging. They will not be well served if they continue to characterize Jesus as a social justice warrior or tell stories that avoid hard questions and real life spiritual pain and suffering.
One final thought; The Gospel of Mark records this conversation between Jesus and St. John, "John said to Him, "Teacher, we saw someone casting out demons in Your name, and we tried to stop him, because he was not following us." But Jesus said, "Do not stop him, for no one who does a mighty work in My name will be able soon afterward to speak evil of Me. For the one who is not against us is for us."" Making a movie is not casting out demons nor is it likely to be the kind of mighty work that Jesus is speaking of here. However Christian viewers uncomfortable with God's Not Dead: A Light in Darkness may be comforted in the fact that this film is evidence that the folks behind the God's Not Dead franchise are in fact listening to the criticisms and encouragements provided by their fellow Christians across the landscape of the Church. Pray that they continue to listen and stand firm on Christ resisting the temptations of the World while striving to be true to Him and that they would make their films in His name and not in the name of profit or for any other reason but Him.
Rev. Ted Giese is lead pastor of Mount Olive Lutheran Church, Regina, Saskatchewan, Canada; a contributor to The Canadian Lutheran, Reporter; and movie reviewer for the "Issues, Etc." radio program. Follow Pastor Giese on Twitter @RevTedGiese. Check out our Movie Review Index!


On Tuesday 21st November the new G'Vine Nouaison recipe was released.
Launching from three global bar temples including Nobu Shoreditch, Tiger Bar Paris and Atlas Bar Singapore, The Gin To My Tonic were kindly invited to the Nobu launch.
Much like it's sibling Floraison, Nouaison celebrates the unconventional by combining the very best French grapes. Whilst most gins are made with grain, G'Vine Gin is crafted with grape spirit and is infused with the exclusive vine flower. Nouaison meaning 'setting' represents the metamorphosis stage of its star ingredients, and this is certainly evident when you compare G'Vine's two creations. Nouaison is the missing piece to the G'Vine puzzle, whilst Floraison is floral and zesty, Nouaison is rich and intense with a distinguished 45% ABV.
In addition to its star ingredients Nouaison is combined with 14 botanicals including sandalwood, bergamot, prune, java pepper and vetitveria. These aromatic botanicals create a gin that is fragrant on the nose but also suitably flavoursome whether served as a Gin and Tonic, Negroni or in new creations such as a Divinius created by David Rios the 2013 world best bartender.
Master Distiller Jean-Sebastien Robicquet has literally gone against the grain with his gin creations but for all the right reasons.
The Gin To My Tonic Show. Liverpool. 7th-9th September 2018.