scholarly journals MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities

2021 ◽  
Author(s):  
Huaxu Yu ◽  
Tao Huan
Keyword(s):  
Principal Component ◽  
Fold Change ◽  
Superior Performance ◽  
Normalization Factor ◽  
High Quality ◽  
Metabolomics Data ◽  
Maximal Density ◽  
Normalization Methods ◽  
Quality Features ◽  
Signal Intensities

Sample normalization is a critical step in metabolomics to remove differences in total sample amount or concentration of metabolites between biological samples. Here, we present MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected by mass spectrometry (MS)-based platforms. The most important design of MAFFIN is the calculation of normalization factor using maximal density fold change (MDFC) value computed by a kernel density-based approach. MDFC is more accurate than traditional median FC-based normalization, especially when the numbers of up- and down-regulated metabolic features are different. In addition, we showcase two essential steps that are overlooked by conventional normalization methods, and incorporated them into MAFFIN. First, instead of using all detected metabolic features, MAFFIN automatically extracts and uses only the high-quality features to calculate FCs and determine the normalization factor. In particular, multiple orthogonal criteria are proposed to pick up the high-quality features. Second, to guarantee the accuracy of the FCs, the MS signal intensities of the high-quality features are corrected using serial quality control (QC) samples. Using simulated data and urine metabolomics datasets, we demonstrated the critical need of high-quality feature selection, MS signal correction, and MDFC. We also show the superior performance of MAFFIN over other commonly used post-acquisition sample normalization methods. Finally, a biological application on a human saliva metabolomics study shows that MAFFIN provides robust sample normalization, leading to better data separation in principal component analysis (PCA) and the identification of more significantly altered metabolic features.

scholarly journals Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Michiel Bongaerts ◽  
Ramon Bonte ◽  
Serwet Demirdas ◽  
Edwin H. Jacobs ◽  
Esmee Oussoren ◽  
...  
Keyword(s):  
Regression Model ◽  
Inborn Errors Of Metabolism ◽  
Untargeted Metabolomics ◽  
Detection Accuracy ◽  
Inborn Errors ◽  
Mixed Effect ◽  
Metabolomics Data ◽  
Normalization Methods ◽  
Metabolite Concentrations ◽  
Reference Samples

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

scholarly journals PCDRN: Progressive Cascade Deep Residual Network for Pansharpening

2020 ◽  
Vol 12 (4) ◽  
pp. 676 ◽  
Author(s):  
Yong Yang ◽  
Wei Tu ◽  
Shuying Huang ◽  
Hangyuan Lu
Keyword(s):  
High Resolution ◽  
Loss Function ◽  
High Frequency ◽  
Experimental Results ◽  
Superior Performance ◽  
Residual Network ◽  
High Quality ◽  
Convolution Approach ◽  
Visual Assessments ◽  
Coarse To Fine

Pansharpening is the process of fusing a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image. In the process of pansharpening, the LRMS image is often directly upsampled by a scale of 4, which may result in the loss of high-frequency details in the fused high-resolution multispectral (HRMS) image. To solve this problem, we put forward a novel progressive cascade deep residual network (PCDRN) with two residual subnetworks for pansharpening. The network adjusts the size of an MS image to the size of a PAN image twice and gradually fuses the LRMS image with the PAN image in a coarse-to-fine manner. To prevent an overly-smooth phenomenon and achieve high-quality fusion results, a multitask loss function is defined to train our network. Furthermore, to eliminate checkerboard artifacts in the fusion results, we employ a resize-convolution approach instead of transposed convolution for upsampling LRMS images. Experimental results on the Pléiades and WorldView-3 datasets prove that PCDRN exhibits superior performance compared to other popular pansharpening methods in terms of quantitative and visual assessments.

scholarly journals Experimental validation of new self-voltage balanced 9L-ANPC inverter for photovoltaic applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Jagabar Sathik ◽  
Dhafer J. Almakhles ◽  
N. Sandeep ◽  
Marif Daula Siddique
Keyword(s):  
Output Voltage ◽  
Pulse Width Modulation ◽  
Superior Performance ◽  
Modulation Scheme ◽  
Switching Frequency ◽  
Switched Capacitor ◽  
Renewable Energy Resources ◽  
High Quality ◽  
Multilevel Inverters ◽  
Blocking Voltage

AbstractMultilevel inverters play an important role in extracting the power from renewable energy resources and delivering the output voltage with high quality to the load. This paper proposes a new single-stage switched capacitor nine-level inverter, which comprises an improved T-type inverter, auxiliary switch, and switched cell unit. The proposed topology effectively reduces the DC-link capacitor voltage and exhibits superior performance over recently switched-capacitor inverter topologies in terms of the number of power components and blocking voltage of the switches. A level-shifted multilevel pulse width modulation scheme with a modified triangular carrier wave is implemented to produce a high-quality stepped output voltage waveform with low switching frequency. The proposed nine-level inverter’s effectiveness, driven by the recommended modulation technique, is experimentally verified under varying load conditions. The power loss and efficiency for the proposed nine-level inverter are thoroughly discussed with different loads.

scholarly journals The recognition and modelling of a backbone and its deformity

2014 ◽  
Vol 19 (1) ◽  
pp. 55-66
Author(s):  
Ramūnas Markauskas ◽  
Algimantas Juozapavičius ◽  
Kęstutis Saniukas ◽  
Giedrius Bernotavičius
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Idiopathic Scoliosis ◽  
Input Data ◽  
Thoracic Curve ◽  
Line Centre ◽  
High Quality ◽  
Radiographic Images ◽  
Initial Curvature ◽  
Curvature Detection ◽  
Quality Features

In this article the authors present a method for the backbone recognition and modelling. The process of recognition combines some classical techniques (Hough transformation, GVF snakes) with some new (authors present a method for initial curvature detection, which they call the Falling Ball method). The result enables us to identify high-quality features of the spine and to detect the major deformities of backbone: the intercrestal line, centre sacral vertical line, C7 plumbline; as well as angles: proximal thoracic curve, main thoracic curve, thoracolumbar/lumbar. These features are used for measure in adolescent idiopathic scoliosis, especially in the case of treatment. Input data are just radiographic images, meet in everyday practice.

scholarly journals Differences in the metabolite profiles of tender leaves of wheat, barley, rye and Triticale based on LC-MS

2020 ◽  
Author(s):  
Piyi Xing ◽  
Zhenqiao Song ◽  
Xingfeng Li
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Principal Component ◽  
Liquid Chromatography Mass Spectrometry ◽  
Metabolomics Data ◽  
Chromatography Mass Spectrometry ◽  
Metabolite Profiles ◽  
Positive Mode ◽  
Sugar Derivatives ◽  
Important Basis

AbstractWheatgrass has emerged as a functional food source in recent years, but the detailed metabolomics basis for its health benefits remains poorly understood. In this study, liquid chromatography-mass spectrometry (LC-MS) analysis were used to study the metabolic profiling of seedlings from wheat, barley, rye and triticale, which revealed 1800 features in positive mode and 4303 features in negative mode. Principal component analysis (PCA) showed clear differences between species, and 164 differentially expressed metabolites (DEMs) were detected, including amino acids, organic acids, lipids, fatty acids, nucleic acids, flavonoids, amines, polyamines, vitamins, sugar derivatives and others. Unique metabolites in each species were identified. This study provides a glimpse into the metabolomics profiles of wheat and its wild relatives, which may form an important basis for nutrition, health and other parameters.Practical ApplicationThis manuscript present liquid chromatography-mass spectrometry (LC-MS) results of young sprouts of common wheat and its relatives. Our results may help to better understand the natural variation due to the genotype before metabolomics data are considered for application to wheatgrass and can provide a basis (assessment) for its potential pharmaceutical and nutritional value.

scholarly journals Detecting selection in low-coverage high-throughput sequencing data using principal component analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jonas Meisner ◽  
Anders Albrechtsen ◽  
Kristian Hanghøj
Keyword(s):  
Principal Component Analysis ◽  
High Throughput ◽  
East Asian ◽  
Principal Component ◽  
Component Analysis ◽  
Human Populations ◽  
Population Genetic Study ◽  
Sequencing Data ◽  
High Quality ◽  
Low Coverage

Abstract Background Identification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data. Materials and methods We have extended two principal component analysis based selection statistics to genotype likelihood data and applied them to low-coverage sequencing data from the 1000 Genomes Project for populations with European and East Asian ancestry to detect signals of selection in samples with continuous population structure. Results Here, we present two selections statistics which we have implemented in the framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes. Conclusion We show that selection scans of low-coverage sequencing data of populations with similar ancestry perform on par with that obtained from high quality genotype data. Moreover, we demonstrate that outperform selection statistics obtained from called genotypes from low-coverage sequencing data without the need for ad-hoc filtering.

scholarly journals Heterosis in crosses among white grain maize populations with high quality protein

2006 ◽  
Vol 41 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Márcio Costa Rodrigues ◽  
Lázaro José Chaves ◽  
Cleso Antônio Patto Pacheco
Keyword(s):  
Grain Yield ◽  
Agronomic Traits ◽  
Least Square ◽  
Superior Performance ◽  
Normal Equation ◽  
High Quality ◽  
Maize Populations ◽  
High Quality Protein ◽  
Female Flowering ◽  
White Grain

The objective of this work was to investigate heterosis and its components in 16 white grain maize populations presenting high quality protein. These populations were divided according to grain type in order to establish different heterosis groups. The crosses were carried out according to a partial diallel cross design among flint and dent populations. Seven agronomic traits were evaluated in three environments while four leaf diseases and incidence of corn stunt were evaluated in one. Least square procedure was applied to the normal equation X'Xbeta = X'Y, to estimate the model effects and their respective sum of squares. Among the heterosis components, in diallel analysis, significance for average heterosis in grain yield, number of days to female flowering and to all evaluated diseases was detected. Specific heterosis was significant for days to female flowering and resistance to Puccinia polysora. Results concerned to grain yield trait indicate that populations with superior performance in dent group, no matter what flint population group is used in crosses, tend to generate superior intervarietal hybrids. In decreasing order of preference, the dent type populations CMS 476, ZQP/B 103 and ZQP/B 101 and the flint type CMS 461, CMS 460, ZQP/B 104 and ZQP/B 102 are recommended to form composites.

Wood anatomy of historic French violin bows made of Pernambuco wood

IAWA Journal ◽  
2020 ◽  
Vol 41 (3) ◽  
pp. 320-332
Author(s):  
Tahysa Mota Macedo ◽  
Cecília Gonçalves Costa ◽  
Haroldo Cavalcante de Lima ◽  
Claudia Franca Barros
Keyword(s):  
Wood Anatomy ◽  
Principal Component ◽  
Rio Grande ◽  
Lower Percentage ◽  
High Quality ◽  
String Instruments ◽  
Axial Parenchyma ◽  
Anatomical Features ◽  
19Th And 20Th Century ◽  
Brazilian Northeast

Abstract Paubrasilia echinata is recognized as the best wood in the manufacture of high-quality bows for string instruments. The wood anatomy of five historic French violin bows of the 19th and 20th century made of Pernambuco wood were investigated in order to reveal the wood anatomic features of these historical bows, to determine which P. echinata morphotype (arruda, café or laranja) was used in their manufacture and to identify the state of origin of the wood. Five bow samples were compared to 33 P. echinata specimens from the Brazilian states of Rio de Janeiro, Bahia, Paraíba and Rio Grande do Norte. The wood anatomical features were compared by means of principal component analysis, which revealed the type of axial parenchyma and percentage of tissue to be the most important to sort specimens. The best wood anatomical features previously described for high-quality bows were corroborated here and the bows in general showed similar wood anatomical features. Based on wood anatomy we found that the violin bows were most similar to the samples from the arruda morphotype derived from the States of Paraíba and Rio Grande do Norte by presenting scanty, unilateral and vasicentric axial parenchyma without confluences forming bands, higher percentage of fibres and lower percentage of axial parenchyma. We can therefore suggest that the historical French violin bows studied here were all made of the arruda morphotype from the Brazilian Northeast region helping explain the preference of the French explorers for this region.

scholarly journals An Efficient Automatic Midsagittal Plane Extraction in Brain MRI

2018 ◽  
Vol 8 (11) ◽  
pp. 2203 ◽  
Author(s):  
Hafiz Rehman ◽  
Sungon Lee
Keyword(s):  
Brain Mri ◽  
Principal Component ◽  
Bilateral Symmetry ◽  
Magnetic Resonance Images ◽  
Superior Performance ◽  
Computationally Efficient ◽  
Midsagittal Plane ◽  
Accuracy And Precision ◽  
Principal Axes ◽  
Plane Extraction

In this paper, a fully automatic and computationally efficient midsagittal plane (MSP) extraction technique in brain magnetic resonance images (MRIs) has been proposed. Automatic detection of MSP in neuroimages can significantly aid in registration of medical images, asymmetric analysis, and alignment or tilt correction (recenter and reorientation) in brain MRIs. The parameters of MSP are estimated in two steps. In the first step, symmetric features and principal component analysis (PCA)-based technique is used to vertically align the bilateral symmetric axis of the brain. In the second step, PCA is used to achieve a set of parallel lines (principal axes) from the selected two-dimensional (2-D) elliptical slices of brain MRIs, followed by a plane fitting using orthogonal regression. The developed algorithm has been tested on 157 real T1-weighted brain MRI datasets including 14 cases from the patients with brain tumors. The presented algorithm is compared with a state-of-the-art approach based on bilateral symmetry maximization. Experimental results revealed that the proposed algorithm is fast (<1.04 s per MRI volume) and exhibits superior performance in terms of accuracy and precision (a mean z-distance of 0.336 voxels and a mean angle difference of 0.06).

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