ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies

ADAP-GC is an automated computational pipeline for untargeted, GC/MS-based metabolomics studies. It takes raw mass spectrometry data as input and carries out a sequence of data processing steps including construction of extracted ion chromatograms, detection of chromatographic peak features, deconvolution of coeluting compounds, and alignment of compounds across samples. Despite the increased accuracy from the original version to version 2.0 in terms of extracting metabolite information for identification and quantitation, ADAP-GC 2.0 requires appropriate specification of a number of parameters and has difficulty in extracting information on compounds that are in low concentration. To overcome these two limitations, ADAP-GC 3.0 was developed to improve both the robustness and sensitivity of compound detection. In this paper, we report how these goals were achieved and compare ADAP-GC 3.0 against three other software tools including ChromaTOF, AnalyzerPro, and AMDIS that are widely used in the metabolomics community

Evaluations of different imputation methods using labeled approaches.

<p>Pearson's correlation between log-transformed p-values of student’s t-tests on FFA dataset (upper left) and BA dataset (upper right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross). PLS-Procrustes sum of squared errors on FFA dataset (lower left) and BA dataset (lower right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross).</p

ligand;catalysts;polymers;application from Synthesis of block copolymer with <i>cis</i>-1,4-polybutadiene and isotactic-rich polystyrene using α-diimine nickel catalysts

Synthesis and characterization of ligand;XPS spectra of Ni-diimine-Ⅳ catalyst;Structural characterization of PS-b-PB via α-diimine nickel catalysts;Electrochemical performance data of PS-b-PB coatin

Comparisons of imputed values and original values on one variable.

<p>Scatter plots of imputed values (X-axis) and original values (Y-axis) on one example missing variable while non-missing elements represented as blue dots and missing elements as red dots based on four imputation methods: HM (upper left), QRILC (upper right), kNN-TN (lower left), and GSimp (lower right). Rug plots show the distributions of imputed values and original values.</p

Evaluations of different imputation methods using labeled approaches.

<p>Pearson's correlation between log-transformed p-values of student’s t-tests on FFA dataset (upper left) and BA dataset (upper right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross). PLS-Procrustes sum of squared errors on FFA dataset (lower left) and BA dataset (lower right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross).</p

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies

<div><p>Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: <a href="https://github.com/WandeRum/GSimp" target="_blank">https://github.com/WandeRum/GSimp</a>.</p></div

Sequentially parameters updating in GSimp.

<p>The first 500 iterations out of a total of 2000 (100×20) iterations using GSimp where <i>ŷ</i>, <i>ỹ</i> and <i>σ</i> represent fitted value, sample value and standard deviation correspondingly.</p

Evaluations of different imputation methods using TPR for various <i>p</i>-cutoffs on simulation dataset.

<p><i>TPR</i> along with different numbers of missing variables based on three imputation methods: QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross) among different p-cutoff = 0.05 (left panel), and 0.01 (right panel).</p

Photoelectroenzymatic Oxyfunctionalization on Flavin-Hybridized Carbon Nanotube Electrode Platform

Peroxygenases are very promising catalysts for oxyfunctionalization reactions. Their practical applicability, however, is hampered by their sensitivity against the oxidant (H₂O₂), therefore necessitating in situ generation of H₂O₂. Here, we report a photoelectrochemical approach to provide peroxygenases with suitable amounts of H₂O₂ while reducing the electrochemical overpotential needed for the reduction of molecular oxygen to H₂O₂. When tethered on single-walled carbon nanotubes (SWNTs) under illumination, flavins allowed for a marked anodic shift of the oxygen reduction potential in comparison to pristine SWNT and/or nonilluminated electrodes. This flavin-SWNT-based photoelectrochemical platform enabled peroxygenases-catalyzed, selective hydroxylation reactions

Image3_Alteration of the gut microbiota after surgery in preterm infants with necrotizing enterocolitis.jpeg

PurposeTo investigate the dynamic changes in the intestinal microbiota in preterm infants with necrotizing enterocolitis (NEC) before and after treatment via a prospective case-control study.MethodsPreterm infants with NEC and preterm infants with similar age and weight (control group) were enrolled in this study. They were divided into NEC_Onset (diagnosis time), NEC_Refeed (refeed time), NEC_FullEn (full enteral nutrition time), Control_Onset, and Control_FullEn groups according to the time of the fecal material collected. Except for basic clinical information, fecal specimens of the infants were obtained as well at indicated times for 16S rRNA gene sequencing. All infants were followed up after discharge from the NICU, and the growth data of the corrected age of 12 months were acquired from the electronic outpatient system and telephonic interviews.ResultsA total of 13 infants with NEC and 15 control infants were enrolled. A gut microbiota analysis showed that the Shannon and Simpson indices were lower in the NEC_FullEn group than in the Control_FullEn group (p ConclusionEven after reaching the full enteral nutrition period, alpha diversity in infants with NEC who underwent surgery was lower than that in the control group infants. It may take more time to reestablish the normal gut flora of NEC infants after surgery. The pathways of the synthesis and degradation of ketone bodies and sphingolipid metabolism might be related to the pathogenesis of NEC and physical development after the occurrence of NEC.</p