A general procedure for finding potentially erroneous entries in the database of retention indices.

BACKGROUND: The NIST retention index database is one the most widely used sources of retention indices. In both untargeted analysis and machine learning studies filtering for potential errors is rather lacking or nonexistent. According to our estimates about 80% of the compounds from both NIST 17 and NIST 20 retention index databases have only one RI value per stationary phase, which makes searching for erroneous values with statistical methods impossible. Manual inspection is also impractical because the database contains more than 300 000 entries. RESULTS: We suggest a two-step procedure to find potentially erroneous retention indices based on machine learning. The first step is to use five predictive models to obtain predicted retention index values for the whole database. The second one is to compare these predicted values against the experimental ones. We consider a retention index erroneous if its accuracy (the difference between predicted and experimental value) is in the bottom 5% for each of the five models simultaneously. Using this method, we were able to detect 2093 outlier entries for standard and semi-standard non-polar stationary phases in the NIST 17 retention index database, 566 of those were corrected or removed by the developers in the NIST 20. SIGNIFICANCE: This is a novel approach to find potentially erroneous entries in a large-scale database with mostly unique entries, which can be applied not only to retention indices. The procedure can help filter and report mishandled data to improve the quality of the dataset for machine learning applications and experimental use.

How searching against multiple libraries can lead to biased results in GC/MS-based metabolomics.

RATIONALE: Databases of electron ionization mass spectra are often used in GC/MS-based untargeted metabolomics analysis. The results of the library search depend on several factors, such as the size and quality of the database, and the library search algorithm. We found out that the list of considered m/z values is another important parameter. Unfortunately, this information is not usually specified by software developers and it is hidden from the end user. METHODS: We created synthetic data sets and figured out how several popular software products (AMDIS, ChromaTOF, MS Search, and Xcalibur) select the list of m/z values for the library search. Moreover, we considered data sets of real mass spectra (presented in both the NIST and FiehnLib libraries) and compared the library search results obtained within different software products. All programs under consideration use the NIST MS Search binaries to perform the library search using the Identity algorithm. RESULTS: We found that AMDIS and ChromaTOF can give biased library search results under particular conditions. In untargeted metabolomics, this can happen when NIST and FiehnLib libraries are used simultaneously, the scan range of the instrument is less than 85, and the correct answer is present only in the FiehnLib library. CONCLUSIONS: The main reason for biased results is that the information about the scan range is not stored in the metadata of library records. As a result, in the case of AMDIS and ChromaTOF software, some unrecorded peaks are considered as missing during the library search, the respective compound is penalized, and the correct answer falls outside the top five or even top 10 hits. At the same time, the default algorithm for selecting the list of considered m/z values implemented in MS Search is free from such unexpected behavior.

Multivariate Prognostic Model for Predicting the Outcome of Critically Ill Patients Using the Aromatic Metabolites Detected by Gas Chromatography-Mass Spectrometry.

A number of aromatic metabolites of tyrosine and phenylalanine have been investigated as new perspective markers of infectious complications in the critically ill patients of intensive care units (ICUs). The goal of our research was to build a multivariate model for predicting the outcome of critically ill patients regardless of the main pathology on the day of admission to the ICU. Eight aromatic metabolites were detected in serum using gas chromatography-mass spectrometry. The samples were obtained from the critically ill patients (n = 79), including survivors (n = 44) and non-survivors (n = 35), and healthy volunteers (n = 52). The concentrations of aromatic metabolites were statistically different in the critically ill patients and healthy volunteers. A univariate model for predicting the outcome of the critically ill patients was based on 3-(4-hydroxyphenyl)lactic acid (p-HPhLA). Two multivariate classification models were built based on aromatic metabolites using SIMCA method. The predictive models were compared with the clinical APACHE II scale using ROC analysis. For all of the predictive models the areas under the ROC curve were close to one. The aromatic metabolites (one or a number of them) can be used in clinical practice for the prognosis of the outcome of critically ill patients on the day of admission to the ICU.

A general procedure for rounding m/z values in low-resolution mass spectra.

It was revealed that nominal mass spectra extracted from the same NetCDF file using different gas chromatography/mass spectrometry (GC/MS) software products are not identical. This phenomenon is caused by differences in algorithms used for rounding floating-point m/z values to integers. It was found that all programs under consideration (AMDIS, ChemStation, ChromaTOF, MS Search, OpenChrom) use different procedures. It is necessary to know how fractional parts of accurate m/z values of ions are distributed to determine which algorithm yields more robust results. We estimated the respective distribution using two databases (PubChem and NIST). As a result, we came up with a procedure that minimizes the influence of random errors on rounding to integer m/z values. The procedure we suggest is to sum intensities of all floating-point m/z values in a bin [MZ - 0.38; MZ + 0.62] and assign MZ as a nominal m/z value, where MZ is an integer m/z value.

Preparation of W-C-Co Composite Micropowder with Spherical Shaped Particles Using Plasma Technologies.

The possibility of obtaining composite micropowders of the W-C-Co system with a spherical particle shape having a submicron/nanoscale internal structure was experimentally confirmed. In the course of work carried out, W-C-Co system nanopowders with the average particle size of approximately 50 nm were produced by plasma-chemical synthesis. This method resulted in the uniform distribution of W, Co and C among the nanoparticles of the powder in the nanometer scale range. Dense microgranules with an average size of 40 microns were obtained from the nanopowders by spray drying. The spherical micropowders with an average particle size of 20 microns were received as a result of plasma treatment of 25.36 microns microgranule fraction. The spherical particles obtained in the experiments had a predominantly dense microstructure and had no internal cavities. The influence of plasma treatment process parameters on dispersity, phase, and chemical composition of spherical micropowders and powder particles microstructure has been established.

Structure and Properties of Heat-Resistant Alloys NiAl-Cr-Co-X (X = La, Mo, Zr, Ta, Re) and Fabrication of Powders for Additive Manufacturing.

The NiAl-Cr-Co-X alloys were produced by centrifugal self-propagating high-temperature synthesis (SHS) casting. The effects of dopants X = La, Mo, Zr, Ta, and Re on combustion, as well as the phase composition, structure, and properties of the resulting cast alloys, have been studied. The greatest improvement in overall properties was achieved when the alloys were co-doped with 15% Mo and 1.5% Re. By forming a ductile matrix, molybdenum enhanced strength characteristics up to the values σucs = 1604 ± 80 MPa, σys = 1520 ± 80 MPa, and εpd = 0.79%, while annealing at T = 1250 ℃ and t = 180 min improved strength characteristics to the following level: σucs = 1800 ± 80 MPa, σys = 1670 ± 80 MPa, and εpd = 1.58%. Rhenium modified the structure of the alloy and further improved its properties. The mechanical properties of the NiAl, ZrNi5, Ni0.92Ta0.08, (Al,Ta)Ni3, and Al(Re,Ni)3 phases were determined by nanoindentation. The three-level hierarchical structure of the NiAl-Cr-Co+15%Mo alloy was identified. The optimal plasma treatment regime was identified, and narrow-fraction powders (fraction 8-27 µm) characterized by 95% degree of spheroidization and the content of nanosized fraction <5% were obtained.

Urine S-Adenosylmethionine are Related to Degree of Renal Insufficiency in Patients with Chronic Kidney Disease.

OBJECTIVE: To determine whether urine S-adenosylmethionine (SAM) might be an indicator of chronic kidney disease (CKD). METHODS: We investigated urine levels of SAM and related metabolites (S-adenosylhomocysteine and homocysteine cysteine) in 62 patients (average age, 65.9 years) with CKD (stages II-V). RESULTS: Patients with stages III-V CKD stages have significantly decreased urine levels and SAM/S-adenosylhomocysteine ratio and also cysteine/homocysteine ratio in blood plasma (P <.05), compared with patients with stage II CKD. Urine SAM levels allowed us to distinguish patients with mildly decreased kidney function from those with moderate to severe renal impairment (AUC, 0.791; sensitivity, 85%; specificity, 78.6%). CONCLUSIONS: Our study results demonstrate that urine SAM is a potent biomarker for monitoring renal function decline at early CKD stages. Urine SAM testing confers an additional advantage to healthcare professionals in that it is noninvasive.

Predicting the absence of an unknown compound in a mass spectral database.

Only a small subset of known organic compounds (amenable for gas chromatography/mass spectrometry) is present in the largest mass spectral databases (such as NIST or Wiley). Nevertheless, library search algorithms available in the market are not able to predict the absence of a compound in the database. In the present work, we have tried to implement such prediction by means of supervised classification. Training and validation set contained 1500 and 750 compounds, respectively. Two prediction sets (containing 750 and about 3000 mass spectra) were considered. The easiest-to-use models were built with only one input variable: match factor of the best candidate or InLib factor (both parameters were calculated within MS Search (NIST) software). Multivariate classification models were built by partial least squares discriminant analysis (PLS-DA); match factors of top n candidates were used as input variables. PLS-DA was found to be the most effective approach. The prediction efficiency strongly depended on the 'uniqueness' of mass spectra presented in the test set. PLS-DA model was able to correctly predict the absence of a compound in the database in 29.9% for prediction set #1 and in 74.4% for prediction set #2 (only 1.3% and 2.5% of compounds actually presented in the database were wrongly classified).

Systematic Study of the Behavior of Different Metal and Metal-Containing Particles under the Microwave Irradiation and Transformation of Nanoscale and Microscale Morphology.

In recent years, the application of microwave (MW) irradiation has played an increasingly important role in the synthesis and development of high performance nanoscale catalytic systems. However, the interaction of microwave irradiation with solid catalytic materials and nanosized structures remains a poorly studied topic. In this paper we carried out a systematic study of changes in morphology under the influence of microwave irradiation on nanoscale particles of various metals and composite particles, including oxides, carbides, and neat metal systems. All systems were studied in the native solid form without a solvent added. Intensive absorption of microwave radiation was observed for many samples, which in turn resulted in strong heating of the samples and changes in their chemical structure and morphology. A comparison of two very popular catalytic materials-metal particles (M) and supported metal on carbon (M/C) systems-revealed a principal difference in their behavior under microwave irradiation. The presence of carbon support influences the heating mechanism; the interaction of substances with the support during the heating is largely determined by heat transfer from the carbon. Etching of the carbon surface, involving the formation of trenches and pits on the surface of the carbon support, were observed for various types of the investigated nanoparticles.

Spectral skewing in gas chromatography-mass spectrometry: Misconceptions and realities.

In the field of gas chromatography-mass spectrometry, linear quadrupole instruments remain the most popular. Quadrupole mass spectrometers are scanning instruments. It means that ions with different m/z values pass through the quadrupole sequentially. When analyte concentration changes during scan period, recorded mass spectrum is distorted. This phenomenon is called spectral skewing. There are two misconceptions associated with the spectral skewing: (1) the spectral skewing can affect the results of library search; (2) bilinear chemometric methods are often applied to skewed (distorted) GC/MS data to perform automatic data processing. We attempt to clarify these misconceptions. We can conclude that even strong distortion of mass spectrum (caused by the spectral skewing) practically does not change efficiency of automatic search against mass spectral database. On the other hand, even minor distortion of data (caused by the spectral skewing) can significantly distort results of automatic data processing (especially in the case of minor components). Interactive Excel files (presented in Supplementary material) illustrate our findings.

Evaluation of mass spectral library search algorithms implemented in commercial software.

Performance of several library search algorithms (against EI mass spectral databases) implemented in commercial software products ( acd/specdb, chemstation, gc/ms solution and ms search) was estimated. Test set contained 1000 mass spectra, which were randomly selected from NIST'08 (RepLib) mass spectral database. It was shown that composite (also known as identity) algorithm implemented in ms search (NIST) software gives statistically the best results: the correct compound occupied the first position in the list of possible candidates in 81% of cases; the correct compound was within the list of top ten candidates in 98% of cases. It was found that use of presearch option can lead to rejection of the correct answer from the list of possible candidates (therefore presearch option should not be used, if possible). Overall performance of library search algorithms was estimated using receiver operating characteristic curves.

Distinguishing by principal component analysis of o-xylene, m-xylene, p-xylene and ethylbenzene using electron ionization mass spectrometry.

The possibility of distinguishing between o-, m-, p-xylene and ethylbenzene on the basis of only their 70 eV electron ionization (EI) mass spectra has been investigated. These four isomers were distinguished by principal component analysis (PCA) of respective EI mass spectra (recorded under identical conditions). Considered mass spectra contained either eleven or five m/z values, which had intensity greater than 5% or 10% of base peak, respectively.