Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study

Researchers have suggested that more attractive women will show stronger preferences for masculine men because such women are better placed to offset the potential costs of choosing a masculine mate. However, evidence for correlations between measures of women’s own attractiveness and preferences for masculine men is mixed. Moreover, the samples used to test this hypothesis are typically relatively small. Consequently, we conducted two large-scale studies that investigated possible associations between women’s preferences for facial masculinity and their own attractiveness as assessed from third-party ratings of their facial attractiveness (Study 1, N = 454, laboratory study) and self-rated attractiveness (Study 2, N = 8972, online study). Own attractiveness was positively correlated with preferences for masculine men in Study 2 (self-rated attractiveness), but not Study 1 (third-party ratings of facial attractiveness). This pattern of results is consistent with the proposal that women’s beliefs about their own attractiveness, rather than their physical condition per se, underpins attractiveness-contingent masculinity preferences.

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BARDEX: A large-scale laboratory study of gravel barrier dynamics

Coastal Engineering ◽

10.1016/j.coastaleng.2012.02.007 ◽

2012 ◽

Vol 63 ◽

pp. 1-2 ◽

Cited By ~ 2

Author(s):

Richard J.S. Whitehouse

Keyword(s):

Laboratory Study ◽

Large Scale

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Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline

Metabolites ◽

10.3390/metabo11090568 ◽

2021 ◽

Vol 11 (9) ◽

pp. 568

Author(s):

Brechtje Hoegen ◽

Alan Zammit ◽

Albert Gerritsen ◽

Udo F. H. Engelke ◽

Steven Castelein ◽

...

Keyword(s):

Data Analysis ◽

Clinical Application ◽

Inborn Errors Of Metabolism ◽

Large Scale ◽

Clinical Diagnostics ◽

Untargeted Metabolomics ◽

Computational Pipeline ◽

Inborn Errors ◽

Metabolomics Data ◽

Metabolic Fingerprint

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.

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“A novel paradigm for optimal mass feature peak picking in large scale LC-MS datasets using the ‘isopair’: isoLock, autoCredential and anovAlign”

10.1101/2021.12.05.471237 ◽

2021 ◽

Author(s):

Allen Hubbard ◽

Louis Connelly ◽

Shrikaar Kambhampati ◽

Brad Evans ◽

Ivan Baxter

Keyword(s):

Retention Time ◽

Large Scale ◽

Time Windows ◽

Untargeted Metabolomics ◽

Liquid Chromatography Mass Spectrometry ◽

Computational Tools ◽

Mass Spectrometers ◽

New Era ◽

Clinical Dataset ◽

Carbon Labeling

AbstractUntargeted metabolomics enables direct quantification of metabolites without apriori knowledge of their identity. Liquid chromatography mass spectrometry (LC-MS), a popular method to implement untargeted metabolomics, identifies metabolites via combined mass/charge (m/z) and retention time as mass features. Improvements in the sensitivity of mass spectrometers has increased the complexity of data produced, leading to computational obstacles. One outstanding challenge is calling metabolite mass feature peaks rapidly and accurately in large LC-MS datasets (dozens to thousands of samples) in the presence of measurement and other noise. While existing algorithms are useful, they have limitations that become pronounced at scale and lead to false positive metabolite predictions as well as signal dropouts. To overcome some of these shortcomings, biochemists have developed hybrid computational and carbon labeling techniques, such as credentialing. Credentialing can validate metabolite signals, but is laborious and its applicability is limited. We have developed a suite of three computational tools to overcome the challenges of unreliable algorithms and inefficient validation protocols: isolock, autoCredential and anovAlign. Isolock uses isopairs, or metabolite-istopologue pairs, to calculate and correct for mass drift noise across LC-MS runs. autoCredential leverages statistical features of LC-MS data to amplify naturally present 13C isotopologues and validate metabolites through isopairs. This obviates the need to artificially introduce carbon labeling. anovAlign, an anova-derived algorithm, is used to align retention time windows across samples to accurately delineate retention time windows for mass features. Using a large published clinical dataset as well as a plant dataset with biological replicates across time, genotype and treatment, we demonstrate that this suite of tools is more sensitive and reproducible than both an open source metabolomics pipelines, XCMS, and the commercial software progenesis QI. This software suite opens a new era for enhanced accuracy and increased throughput for untargeted metabolomics.

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Biochar as a Stimulator for Germination Capacity in Seeds of Virginia Mallow (Sida hermaphrodita (L.) Rusby)

Applied Sciences ◽

10.3390/app9163213 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3213 ◽

Cited By ~ 5

Author(s):

Bogdan Saletnik ◽

Marcin Bajcar ◽

Grzegorz Zaguła ◽

Aneta Saletnik ◽

Maria Tarapatskyy ◽

...

Keyword(s):

Laboratory Study ◽

Plant Material ◽

Large Scale ◽

Low Cost ◽

Control Sample ◽

Control Group ◽

Scale Production ◽

Germination Capacity ◽

Beneficial Effects ◽

Large Scale Production

This article presents the findings of a laboratory study investigating the stimulation and conditioning of seeds with biochar and the effects observed in the germination and emergence of Virginia mallow (Sida hermaphrodita (L.) Rusby) seedlings. The study shows that biochar, applied as a conditioner added to water in the process of seed hydration, improves their germination capacity. When the processed plant material was added to water at a rate of 5 g (approx. 1250 seeds) per 100 mL, the rate of germination increased to 45.3%, and was 23.3% higher when compared to the control group, and 7.3% higher than in the seeds hydrated without biochar. The beneficial effects of biochar application were also reflected in the increased mass of Virginia mallow seedlings. The mass of seedlings increased by 73.5% compared to the control sample and by 25.9% compared to the seeds hydrated without biochar. Given the low cost of charcoal applied during the hydro-conditioning process, the material can be recommended as a conditioner in large-scale production of Virginia mallow.

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Interactions of children and young adults using large-scale elastic displays

Avances en Interacción Humano-Computadora ◽

10.47756/aihc.y5i1.67 ◽

2020 ◽

pp. 54

Author(s):

Franceli L Cibrian ◽

Deysi Ortega ◽

Judith Ley ◽

Hugo Rodríguez ◽

Monica Tentori

Keyword(s):

Young Adults ◽

Early Development ◽

Pervasive Computing ◽

Laboratory Study ◽

Potential Application ◽

Large Scale ◽

Children With Autism ◽

Emerging Technology ◽

Open Questions ◽

Children And Young Adults

Elastic displays provide a unique and intuitive interaction and could be deployed at large-scale. As an emerging technology, open questions about the benefits large-scale elastic displays offer over rigid displays and their potential application to our everyday lives. In this paper, we present an overview of a 4-year project. First, we describe the development of a large-scale elastic display, called BendableSound. Second, we explain the results of a laboratory study, showing the elastic display has a better user and sensory experience than a rigid one. Third, we describe the results of two deployment studies showing how BendableSound could support the therapeutic practices of children with autism and the early development of toddlers. We close discussing open challenges to study the untapped potential of elastic displays in pervasive computing.

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Metabolic Reaction Network-based Recursive Metabolite Identification for Untargeted Metabolomics

10.1101/305201 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xiaotao Shen ◽

Xin Xiong ◽

Ruohong Wang ◽

Yandong Yin ◽

Yuping Cai ◽

...

Keyword(s):

Data Acquisition ◽

Biological Sample ◽

Large Scale ◽

Recursive Algorithm ◽

Reaction Network ◽

Metabolite Identification ◽

Untargeted Metabolomics ◽

Metabolic Reaction ◽

Link Type ◽

Functional Metabolomics

Metabolite identification is a long-standing challenge in untargeted metabolomics and a major hurdle for functional metabolomics studies. Here, we developed a metabolic reaction network-based recursive algorithm and webserver called MetDNA for the large-scale and unambiguous identification of metabolites (available at http://metdna.zhulab.cn). We showcased the versatility of our workflow using different instrument platforms, data acquisition methods, and biological sample types and demonstrated that over 2,000 metabolites could be identified from one experiment.

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Deep Learning assisted Peak Curation for large scale LC-MS Metabolomics

10.1101/2020.08.09.242727 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yoann Gloaguen ◽

Jennifer Kirwan ◽

Dieter Beule

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Open Source ◽

Large Scale ◽

Peak Detection ◽

Untargeted Metabolomics ◽

Peak List ◽

Ms Analysis ◽

Automated Methods

ABSTRACTAvailable automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS which uses machine learning to replace peak curation by human experts. We show how to integrate our open source module into different LC-MS analysis workflows and quantify its performance. NeatMS is designed to be suitable for large scale studies and improves the robustness of the final peak list.

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Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

Metabolites ◽

10.3390/metabo11050285 ◽

2021 ◽

Vol 11 (5) ◽

pp. 285

Author(s):

Kristina E. Haslauer ◽

Philippe Schmitt-Kopplin ◽

Silke S. Heinzmann

Keyword(s):

Nmr Spectroscopy ◽

Data Processing ◽

Large Scale ◽

Linear Regression Analysis ◽

Untargeted Metabolomics ◽

Metabolomics Data ◽

Non Linear ◽

Data Processing Tool ◽

Processing Optimization ◽

Spectral Libraries

Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine 1H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.

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