Fraction Of Peaks Research Articles

Noise Filtering Algorithm Using Gaussian Mixture Models for High-Resolution Mass Spectra of Natural Organic Matter.

High-resolution mass spectra of natural organic matter (NOM) contain a large number of noise signals. These signals interfere with the correct molecular composition estimation during nontargeted analysis because formula-assignment programs find empirical formulas for such peaks as well. Previously proposed noise filtering methods that utilize the profile of the intensity distribution of mass spectrum peaks rely on a histogram to calculate the intensity threshold value. However, the histogram profile can vary depending on the user settings. In addition, these algorithms are not automated, so they are handled manually. To overcome the mentioned drawbacks, we propose a new algorithm for noise filtering in mass spectra. This filter is based on Gaussian Mixture Models (GMMs), a machine learning method to find the intensity threshold value. The algorithm is completely data-driven and eliminates the need to work with a histogram. It has no customizable parameters and automatically determines the noise level for each individual mass spectrum. The algorithm performance was tested on mass spectra of natural organic matter obtained by averaging a different number of microscans (transients), and the results were compared with other noise filters proposed in the literature. Finally, the effect of this noise filtering approach on the fraction of peaks with assigned formulas was investigated. It was shown that there is always an increase in the identification rate, but the magnitude of the effect changes with the number of microscans averaged. The increase can be as high as 15%.

Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography–high resolution mass spectrometry

Controlling and minimising background contamination is crucial for maintaining a high quality of samples in human biomonitoring targeting organic chemicals. We assessed the contamination of three previous types and one newly introduced medical-grade type of sample tubes used for storing human body fluids at the German Environmental Specimen Bank. Aqueous extracts from these tubes were analysed by non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) before and after a dedicated cleaning procedure. After peak detection using MZmine, Bayesian hypothesis testing was used to group peaks into those originating either from instrumental and laboratory background contamination, or actual tube contaminants, based on if their peak height was reduced, increased or not affected by the cleaning procedure. For all four tube types 80–90% of the 2475 peaks (1549 in positive and 926 in negative mode) were assigned to laboratory/instrumental background, which we have to consider as potential sample tube contaminants. Among the tube contaminants, results suggest a considerable difference in the contaminant peak inventory and the absolute level of contamination among the different sample tube types. The cleaning procedure did not affect the largest fraction of peaks (50–70%). For the medical grade tubes, the removal of contaminants by the cleaning procedure was strongest compared to the previous tubes, but in all cases a small fraction increased in intensity after cleaning, probably due to a release of oligomers or additives. The identified laboratory background contaminants were mainly semi-volatile polymer additives such as phthalates and phosphate esters. A few compounds could be assigned solely as tube-specific contaminants, such as N,N-dibutylformamide and several constituents of the oligomeric light stabiliser Tinuvin-622. A cleaning procedure before use is an effective way to standardise the used sample tubes and minimises the background contamination, and therefore increases sample quality and therewith analytical results.

Motif models proposing independent and interdependent impacts of nucleotides are related to high and low affinity transcription factor binding sites in Arabidopsis

Position weight matrix (PWM) is the traditional motif model representing the transcription factor (TF) binding sites. It proposes that the positions contribute independently to TFs binding affinity, although this hypothesis does not fit the data perfectly. This explains why PWM hits are missing in a substantial fraction of ChIP-seq peaks. To study various modes of the direct binding of plant TFs, we compiled the benchmark collection of 111 ChIP-seq datasets for Arabidopsis thaliana, and applied the traditional PWM, and two alternative motif models BaMM and SiteGA, proposing the dependencies of the positions. The variation in the stringency of the recognition thresholds for the models proposed that the hits of PWM, BaMM, and SiteGA models are associated with the sites of high/medium, any, and low affinity, respectively. At the medium recognition threshold, about 60% of ChIP-seq peaks contain PWM hits consisting of conserved core consensuses, while BaMM and SiteGA provide hits for an additional 15% of peaks in which a weaker core consensus is compensated through intra-motif dependencies. The presence/absence of these dependencies in the motifs of alternative/traditional models was confirmed by the dependency logo DepLogo visualizing the position-wise partitioning of the alignments of predicted sites. We exemplify the detailed analysis of ChIP-seq profiles for plant TFs CCA1, MYC2, and SEP3. Gene ontology (GO) enrichment analysis revealed that among the three motif models, the SiteGA had the highest portions of genes with the significantly enriched GO terms among all predicted genes. We showed that both alternative motif models provide for traditional PWM greater extensions in predicted sites for TFs MYC2/SEP3 with condition/tissue specific functions, compared to those for TF CCA1 with housekeeping functions. Overall, the combined application of standard and alternative motif models is beneficial to detect various modes of the direct TF-DNA interactions in the maximal portion of ChIP-seq loci.

Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics

Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS, which uses machine learning based on a convoluted neural network to reduce the number and fraction of false peaks. NeatMS comes with a pre-trained model representing expert knowledge in the differentiation of true chemical signal from noise. Furthermore, it provides all necessary functions to easily train new models or improve existing ones by transfer learning. Thus, the tool improves peak curation and contributes to the robust and scalable analysis of large-scale experiments. We show how to integrate it into different liquid chromatography–mass spectrometry (LC-MS) analysis workflows, quantify its performance, and compare it to various other approaches. NeatMS software is available as open source on github under permissive MIT license and is also provided as easy-to-install PyPi and Bioconda packages.

Application of alternative de novo motif recognition models for analysis of structural heterogeneity of transcription factor binding sites: a case study of FOXA2 binding sites.

The most popular model for the search of ChIP-seq data for transcription factor binding sites (TFBS) is the positional weight matrix (PWM). However, this model does not take into account dependencies between nucleotide occurrences in different site positions. Currently, two recently proposed models, BaMM and InMoDe, can do as much. However, application of these models was usually limited only to comparing their recognition accuracies with that of PWMs, while none of the analyses of the co-prediction and relative positioning of hits of different models in peaks has yet been performed. To close this gap, we propose the pipeline called MultiDeNA. This pipeline includes stages of model training, assessing their recognition accuracy, scanning ChIP-seq peaks and their classification based on scan results. We applied our pipeline to 22 ChIP-seq datasets of TF FOXA2 and considered PWM, dinucleotide PWM (diPWM), BaMM and InMoDe models. The combination of these four models allowed a significant increase in the fraction of recognized peaks compared to that for the sole PWM model: the increase was 26.3 %. The BaMM model provided the main contribution to the recognition of sites. Although the major fraction of predicted peaks contained TFBS of different models with coincided positions, the medians of the fraction of peaks containing the predictions of sole models were 1.08, 0.49, 4.15 and 1.73 % for PWM, diPWM, BaMM and InMoDe, respectively. Thus, FOXA2 BSs were not fully described by only a sole model, which indicates theirs heterogeneity. We assume that the BaMM model is the most successful in describing the structure of the FOXA2 BS in ChIP-seq datasets under study.

Temperature features of non-radiative energy transfer in hybrid associates of CdS/TGA quantum dots with methylene blue molecules

The paper presents the study results of non-radiative resonance energy transfer of electronic excitation from colloidal CdS quantum dots passivated with thioglycolic acid (CdS/TGA QDs) with an average size of 2.9 nm to methylene blue (MB+) molecules in temperature ranging from 300 to 80 K. In an aqueous MB+ and colloidal CdS/TGA QDs mixture, the increase in (MB+)2 dimers fraction and shift of the absorption band peaks of monomers (MB+) from 662 to 676 nm and dimers (MB+)2 from 606 to 590 nm were found. It is concluded that the hybrid associates of CdS/TGA+MB+ and CdS/TGA+(MB+)2 are formed. Luminescence quenching at CdS/TGA QDs band and MB+ luminescence enhancement under increase in MB+ molecules concentration were observed. A decrease in average lifetime of CdS/TGA QDs luminescence from 25 to 4 ns was found for MB+ concentration of 42 μ M. The Stern–Volmer constant, determined from the data on luminescence quenching, is 0.36 ∗ 106 M− 1. It is concluded that there is non-radiative resonance energy transfer (FRET) from the luminescence center of CdS/TGA quantum dots to MB+ molecule. Decreasing in temperature value to 80 K led to an increase in the luminescence intensity by 12 times. At the same time, the average lifetime of luminescence for pure CdS/TGA QDs increases from 25 to 72 ns. The Stern–Volmer constant increased to 0.64 ∗ 106 M− 1. It is concluded that its increase is due to an increase in average lifetime of CdS/TGA QD luminescence. In the framework of FRET theory, the rate constant of non-radiative energy transfer was estimated for an even distribution of CdS/TGA quantum dots and MB molecules in solution. Its is much smaller (6.7 ∗ 104 s− 1) than the experimentally determined value (2.5 ∗ 108 s− 1). This fact confirms the conclusion about the formation of CdS/TGA+MB hybrid system with effective FRET.

Development of an Enhanced Total Ion Current Chromatogram Algorithm to Improve Untargeted Peak Detection.

Accurate analyte peak detection from the background noise is a fundamental step in data analysis. Often, this is initially performed on the total ion current chromatogram (TIC), which is the summed signal from all mass spectral channels. Despite the detection of many of the most abundant peaks within a chromatogram, a large fraction of peaks remains undetected in the standard TIC due to their signal being below the limit of detection. To find peaks obscured by background noise, an untargeted peak detection method termed the "enhanced TIC algorithm" was developed for comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). The reported algorithm utilizes the entire mass spectral dimension to find regions of analytical signal above a threshold while zeroing the background noise. The resulting chromatographic data is summed together to create the enhanced TIC. The utility of the enhanced TIC algorithm is demonstrated using serial dilutions from a 10 parts-per-thousand (ppth) test mixture. For the chromatograms collected at 1 and 10 parts-per-million (ppm), the enhanced TIC algorithm recovered 62% and 93%, respectively, of the original peaks observed in the 10 ppth mixture, while the standard TIC recovered only 0% and 45%, respectively. The improvement in signal enhancement was also shown on a separation of a yeast cell metabolite extract, where the enhanced TIC found 33-64% more peaks than the standard TIC. Chromatographic simulations with increasing levels of background noise were also conducted to compare the enhanced and standard TICs in the context of statistical overlap theory (SOT). Simulated chromatograms with lower signal-to-noise were more accurately modeled by the SOT after enhanced TIC processing compared to those processed by the standard TIC. The enhanced TIC method demonstrates an immense benefit in peak discovery to improve data analysis efforts.

Stochastic gravitational waves associated with primordial black holes formed during an early matter era

An early matter-dominated (eMD) era may have preceded the standard radiation-dominated era. During an eMD era, primordial black holes (PBHs) can be formed due to the gravitational collapse of a fraction of high-density peaks. If their formation probability is sufficiently high, PBHs formed during an eMD era could play interesting cosmological roles, such as accounting for part of or entire dark matter, and/or causing PBH mergers which might have been or would be detected by ongoing and future gravitational-wave detectors. In addition to PBHs, gravitationally collapsed, virialized objects can also be formed during an eMD era. We discuss gravitational waves associated with the formation of such collapsed structures when PBH formation probability is relatively high during an eMD era, and show that such GWs can be observable in future.

Rapid deconvolution of low-resolution time-of-flight data using Bayesian inference.

The deconvolution of low-resolution time-of-flight data has numerous advantages, including the ability to extract additional information from the experimental data. We augment the well-known Lucy-Richardson deconvolution algorithm using various Bayesian prior distributions and show that a prior of second-differences of the signal outperforms the standard Lucy-Richardson algorithm, accelerating the rate of convergence by more than a factor of four, while preserving the peak amplitude ratios of a similar fraction of the total peaks. A novel stopping criterion and boosting mechanism are implemented to ensure that these methods converge to a similar final entropy and local minima are avoided. Improvement by a factor of two in mass resolution allows more accurate quantification of the spectra. The general method is demonstrated in this paper through the deconvolution of fragmentation peaks of the 2,5-dihydroxybenzoic acid matrix and the benzyltriphenylphosphonium thermometer ion, following femtosecond ultraviolet laser desorption.

Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection.

DNaseI footprinting is an established assay for identifying transcription factor (TF)–DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF–DNA interactions. DNase-seq footprints were absent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF–DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.

Quality control for building libraries from electrospray ionization tandem mass spectra.

Electrospray ionization (ESI) tandem mass spectrometry coupled with liquid chromatography is a routine technique for identifying and quantifying compounds in complex mixtures. The identification step can be aided by matching acquired tandem mass spectra (MS(2)) against reference library spectra as is routine for electron ionization (EI) spectra from gas chromatography/mass spectrometry (GC/MS). However, unlike the latter spectra, ESI MS(2) spectra are likely to originate from various precursor ions for a given target molecule and may be acquired at varying energies and resolutions and have characteristic noise signatures, requiring processing methods very different from EI to obtain complete and high quality reference spectra for individual analytes. This paper presents procedures developed for creating a tandem mass spectral library that addresses these factors. Library building begins by acquiring MS(2) spectra for all major MS(1) peaks in an infusion run, followed by assigning MS(2) spectra to clusters and creating a consensus spectrum for each. Intensity-based constraints for cluster membership were developed, as well as peak testing to recognize and eliminate suspect peaks and reduce noise. Consensus spectra were then examined by a human evaluator using a number of criteria, including a fraction of annotated peaks and consistency of spectra for a given ion at different energies. These methods have been developed and used to build a library from >9000 compounds, yielding 230,000 spectra.

REDUCING SYSTEMATIC ERROR IN WEAK LENSING CLUSTER SURVEYS

Weak lensing provides an important route toward collecting samples of clusters of galaxies selected by mass. Subtle systematic errors in image reduction can compromise the power of this technique. We use the B-mode signal to quantify this systematic error and to test methods for reducing this error. We show that two procedures are efficient in suppressing systematic error in the B-mode: (1) refinement of the mosaic CCD warping procedure to conform to absolute celestial coordinates and (2) truncation of the smoothing procedure on a scale of 10$^{\prime}$. Application of these procedures reduces the systematic error to 20% of its original amplitude. We provide an analytic expression for the distribution of the highest peaks in noise maps that can be used to estimate the fraction of false peaks in the weak lensing $\kappa$-S/N maps as a function of the detection threshold. Based on this analysis we select a threshold S/N = 4.56 for identifying an uncontaminated set of weak lensing peaks in two test fields covering a total area of $\sim 3$deg$^2$. Taken together these fields contain seven peaks above the threshold. Among these, six are probable systems of galaxies and one is a superposition. We confirm the reliability of these peaks with dense redshift surveys, x-ray and imaging observations. The systematic error reduction procedures we apply are general and can be applied to future large-area weak lensing surveys. Our high peak analysis suggests that with a S/N threshold of 4.5, there should be only 2.7 spurious weak lensing peaks even in an area of 1000 deg$^2$ where we expect $\sim$ 2000 peaks based on our Subaru fields.

MASK EFFECTS ON COSMOLOGICAL STUDIES WITH WEAK-LENSING PEAK STATISTICS

In this paper, we analyze in detail with numerical simulations how the mask effect can influence the weak lensing peak statistics reconstructed from the shear measurement of background galaxies. It is found that high peak fractions are systematically enhanced due to masks, the larger the masked area, the higher the enhancement. In the case with about $13\%$ of the total masked area, the fraction of peaks with SNR $\nu\ge 3$ is $\sim 11\%$ in comparison with $\sim 7\%$ of the mask-free case in our considered cosmological model. This can induce a large bias on cosmological studies with weak lensing peak statistics. Even for a survey area of $9\hbox{ deg}^2$, the bias in $(\Omega_m, \sigma_8)$ is already close to $3\sigma$. It is noted that most of the affected peaks are close to the masked regions. Therefore excluding peaks in those regions can reduce the bias but at the expense of loosing usable survey areas. Further investigations find that the enhancement of high peaks number can be largely attributed to higher noise led by the fewer number of galaxies usable in the reconstruction. Based on Fan et al. (2010), we develop a model in which we exclude only those large masks with radius larger than $3\arcmin. For the remained part, we treat the areas close to and away from the masked regions separately with different noise levels. It is shown that this two-noise-level model can account for the mask effect on peak statistics very well and the cosmological bias is significantly reduced.

Prioritization of putative metabolite identifications in LC‐MS/MS experiments using a computational pipeline

One of the major bottle-necks in current LC-MS-based metabolomic investigations is metabolite identification. An often-used approach is to first look up metabolites from databases through peak mass, followed by verification of the obtained putative identifications using MS/MS data. However, the mass-based search may provide inappropriate putative identifications when the observed peak is from isotopes, fragments, or adducts. In addition, a large fraction of peaks is often left with multiple putative identifications. To differentiate these putative identifications, manual verification of metabolites through comparison between biological samples and authentic compounds is necessary. However, such experiments are laborious, especially when multiple putative identifications are encountered. It is desirable to use computational approaches to obtain more reliable putative identifications and prioritize them before performing experimental verification of the metabolites. In this article, a computational pipeline is proposed to assist metabolite identification with improved metabolome coverage and prioritization capability. Multiple publicly available software tools and databases, along with in-house developed algorithms, are utilized to fully exploit the information acquired from LC-MS/MS experiments. The pipeline is successfully applied to identify metabolites on the basis of LC-MS as well as MS/MS data. Using accurate masses, retention time values, MS/MS spectra, and metabolic pathways/networks, more appropriate putative identifications are retrieved and prioritized to guide subsequent metabolite verification experiments.

Genome-wide analysis of hepatic LRH-1 reveals a promoter binding preference and suggests a role in regulating genes of lipid metabolism in concert with FXR

BackgroundIn a previous genome-wide analysis of FXR binding to hepatic chromatin, we noticed that an extra nuclear receptor (NR) half-site was co-enriched close to the FXR binding IR-1 elements and we provided limited support that the monomeric LRH-1 receptor that binds to NR half-sites might function together with FXR to activate gene expression.ResultsTo analyze the global pattern for LRH-1 binding and to determine whether it might associate with FXR on a whole genome-wide scale, we analyzed LRH-1 binding to the entire hepatic genome using a non-biased genome-wide ChIP-seq approach. We identified over 10,600 LRH-1 binding sites in hepatic chromatin and over 20% were located within 2 kb of the 5' end of a known mouse gene. Additionally, the results demonstrate that a significant fraction of the genome sites occupied by LRH-1 are located close to FXR binding sites revealed in our earlier study. A Gene ontology analysis revealed that genes preferentially enriched in the LRH-1/FXR overlapping gene set are related to lipid metabolism. These results demonstrate that LRH-1 recruits FXR to lipid metabolic genes. A significant fraction of FXR binding peaks also contain a nuclear receptor half-site that does not bind LRH-1 suggesting that additional monomeric nuclear receptors such as RORs and NR4As family members may also target FXR to other pathway selective genes related to other areas of metabolism such as glucose metabolism where FXR has also been shown to play an important role.ConclusionThese results document an important role for LRH-1 in hepatic metabolism through acting predominantly at proximal promoter sites and working in concert with additional nuclear receptors that bind to neighboring sites

Relationship between selectivity and average resolution in comprehensive two-dimensional separations with spectroscopic detection

The average value of the multivariate selectivity ( SEL) of randomly positioned peaks in a multi-component separation is shown to equal the average fraction of peaks that are singlets, as predicted by statistical-overlap theory (SOT). This equality is the basis for proposing a useful metric, specifically the average minimum resolution of nearest-neighbor peaks, for the performance of comprehensive two-dimensional (2D) separations. Furthermore this metric was computed both without ancillary spectroscopic information and with the assistance of such help, specifically multi-wavelength UV–vis spectra, acquired during the separation. Separations are simulated with randomly positioned peaks over wide ranges of total number of peaks, first- and second-dimension peak capacity, dimensionless first-dimension sampling time, and spectral diversity. The specific version of the general multivariate selectivity concept that is used here – identified as SEL – gives the relative precision of quantification when using the PARAFAC (parallel factor analysis) method, a popular curve resolution algorithm. The SEL values of all peaks were calculated, averaged, and compared to the predictions of SOT. In the absence of auxiliary spectral data, the SEL-based average minimum resolution required to separate two peaks in a 2D separation is 0.256, compared to resolution of 0.5 if no chemometric assistance is available. This was found to be valid over a wide range of conditions and is essentially independent of peak crowding. With the assistance of the spectral data, the requisite minimum resolution substantially improves, that is, it decreases, especially when peak crowding is severe. The requisite minimum resolution decreases even further, up to a limit, as the spectral diversity is increased. In contrast, the SEL-based average under-sampling correction factor is virtually independent of the presence of the additional spectral data, and additionally is about the same as calculated with SOT from the average number of maxima in closely analogous simulations. The use of selectivity greatly increases the fraction of peaks that are singlets, relative to the number of singlet maxima, especially when spectral assistance is added. The insensitivity of the under-sampling correction factor to either the use of selectivity or added spectral data simplifies optimization of the corrected peak capacity in on-line comprehensive 2D separations.

Hydrograph separation and precipitation source identification using stable water isotopes and conductivity: River Ganga at Himalayan foothills

AbstractThe observed retreat of several Himalayan glaciers and snow packs is a cause of concern for the huge population in southern Asia that is dependent on the glacial‐fed rivers emanating from Himalayas. There is considerable uncertainty about how cryospheric recession in the Himalayan region will respond to climate change, and how the water resource availability will be affected. As a first step towards quantifying the contribution of glacier‐melt water, hydrograph separation of River Ganga at Rishikesh into its constituent components, namely (i) surface runoff, (ii) glacial ice‐melt and (iii) groundwater discharge has been done in this paper. A three‐component mixing model has been employed using the values of δ18O and electrical conductivity (EC) of the river water, and its constituents, to estimate the time‐varying relative fraction of each component. The relative fraction of the surface runoff peaks (70–90%) during winter, due to the near‐zero contribution of glacial ice‐melt, essentially represents the melting of surface snow from the catchment. The contribution of glacial ice‐melt to the stream discharge peaks during summer and monsoon reaches a maximum value of ∼40% with an average of 32%. The fraction of groundwater discharge varies within a narrow range (15 ± 5%) throughout the year. On the basis of the variation in the d‐excess values of river water, it is also suggested that the snow‐melt and ice‐melt component has a significant fraction derived from winter precipitation with moisture source from mid‐latitude westerlies (also known as western disturbances). Copyright © 2010 John Wiley & Sons, Ltd.

High-resolution mass spectrometry analysis of secondary organic aerosol generated by ozonolysis of isoprene

The chemical composition of secondary organic aerosol (SOA) generated from the ozonolysis of isoprene (C 5H 8) in the presence of an OH scavenger was examined using high-resolution electrospray ionization mass spectrometry (ESI-MS) in the mass range m/ z = 50–1000. The chemical composition of SOA is complex, with more than 1000 assigned peaks observed in the positive and negative ion mode spectra. Only a small fraction of peaks correspond to known products of isoprene oxidation, such as pyruvic acid, glycolic acid, methylglyoxal, etc. The absolute majority of the detected peaks correspond to highly oxidized oligomeric constituents of SOA, with an average O:C molar ratio of 0.6. The corresponding organic mass (OM) to organic oxygen (OO) ratio is 2.4. Approximately 8% of oxygen atoms in SOA are in the form of peroxides, as quantified with an iodide test. Double bond equivalency (DBE) factors, representing the sum of all double bonds and rings, increase by 1 for every 1–2 additional carbon atoms in the molecule. The number of unoxidized C C double bonds is estimated to be less than 10%; the remaining DBE is due to C O carbonyl groups. Kendrick analysis suggests that the prevalent oligomer building blocks are small carbonyls with a C 1–C 2 skeleton. Formaldehyde (CH 2O) is identified as the most common repetitive building block in the observed oligomeric compounds.

Substrate bias dependence of Raman spectra for TiN films deposited by filtered cathodic vacuum arc

Raman spectroscopy was used to characterize TiN films deposited by using an off-plane double bend filtered cathodic vacuum arc technique. The influence of substrate bias on the Raman spectra was systematically studied. Four peaks at 235, 320, 440, and 570 cm−1, related to transverse acoustic (TA), longitudinal acoustic (LA), second-order acoustic (2A), and transverse optical (TO) modes of TiN, respectively, were observed in the Raman spectra of TiN films. The intensity of all four peaks and the area fraction as well as the full width at half maximum (FWHM) of the TO peak increase drastically with increasing substrate bias, reaching a maximum at −100 V, and then decrease greatly. However, the area fraction of TA, LA, and 2A peaks, the FWHM of TA and 2A peaks, as well as the frequency of all four peaks decrease rapidly with increasing substrate bias to −100 V, and then increase greatly. At a bias above −200 V, only a slight change in the Raman spectra of TiN films were observed. The change in the N/Ti ratio is the main reason for the evolution in the Raman spectra of TiN films with increasing substrate bias. The internal stress and the crystal size play only a minor role in the Raman spectra of TiN films in the present study.

Two-dimensional excitations of superfluid [formula omitted] confined in 72% porosity xerogel

We present neutron inelastic-scattering measurements of the low-energy excitation spectrum of superfluid 4 He confined in porous xerogel glass. In addition to the bulk-like excitation, we also observe a broad peak centred at energies of less than that of the bulk-like peak. The dependence on filling fraction of both peaks has been investigated and we find that the second broad peak exhibits behaviour that is consistent with that of an excitation confined to the high-density liquid layers near the pore walls, i.e. with that of a two-dimensional excitation.