scholarly journals Leveraging the mass balances of cellular metabolism to infer absolute concentrations from relative abundance metabolomics data

2021 ◽  
Author(s):  
Justin Y Lee ◽  
Mark P Styczynski
Keyword(s):  
Large Scale ◽  
A Priori ◽  
Kinetic Equations ◽  
Valuable Insight ◽  
Optimization Approach ◽  
Mass Balances ◽  
Response Factors ◽  
Metabolomics Data ◽  
Absolute Concentrations ◽  
Relative Abundances

Motivation: As the large-scale study of metabolites and a direct readout of a system's metabolic state, metabolomics has significant appeal as a source of information for many metabolic modeling platforms and other metabolic analysis tools. However, metabolomics data are typically reported in terms of relative abundances, which precluding use with tools where absolute concentrations are necessary. While chemical standards can be used to determine the absolute concentrations of metabolites, they are often time-consuming to run, expensive, or unavailable for many metabolites. A computational framework that can infer absolute concentrations without the use of chemical standards would be highly beneficial to the metabolomics community. Results: We have developed and characterized MetaboPAC, a computational strategy that leverages the mass balances of a system to infer absolute concentrations in metabolomics datasets. MetaboPAC uses a kinetic equations approach and an optimization approach to predict the most likely response factors that describe the relationship between absolute concentrations and their relative abundances. We determined that MetaboPAC performed significantly better than the other approaches assessed on noiseless data when at least 60% of kinetic equations are known a priori. Under the most realistic conditions (low sampling frequency, high noise data), MetaboPAC significantly outperformed other methods in the majority of cases when 100% of the kinetic equations were known. For metabolomics datasets extracted from systems that are well-studied and have partially known kinetic structures, MetaboPAC can provide valuable insight about their absolute concentration profiles.

scholarly journals Kernel weighted least square approach for imputing missing values of metabolomics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishith Kumar ◽  
Md. Aminul Hoque ◽  
Masahiro Sugimoto
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Missing Values ◽  
Kernel Weight ◽  
Least Square ◽  
Data Matrix ◽  
Data Imputation ◽  
Metabolomics Data ◽  
Missing Value ◽  
Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

scholarly journals A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

2021 ◽  
Author(s):  
Zahra Homayouni ◽  
Mir Saman Pishvaee ◽  
Hamed Jahani ◽  
Dmitry Ivanov
Keyword(s):  
Supply Chains ◽  
Large Scale ◽  
Programming Model ◽  
Supply Chain Design ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Economic Uncertainty ◽  
Cap And Trade ◽  
Sustainable Logistics ◽  
Goal Programming Model

AbstractAdoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by an increased complexity. Through the development and usage of a multi-choice goal programming model solved by an improved algorithm, this article investigates sustainability strategies for carbon regulations mechanisms. We first propose a sustainable logistics model that considers assorted vehicle types and gas emissions involved with product transportation. We then construct a bi-objective model that minimizes total cost as the first objective function and follows environmental considerations in the second one. With our novel robust-heuristic optimization approach, we seek to support the decision-makers in comparison and selection of carbon emission policies in supply chains in complex settings with assorted vehicle types, demand and economic uncertainty. We deploy our model in a case-study to evaluate and analyse two carbon reduction policies, i.e., carbon-tax and cap-and-trade policies. The results demonstrate that our robust-heuristic methodology can efficiently deal with demand and economic uncertainty, especially in large-scale problems. Our findings suggest that governmental incentives for a cap-and-trade policy would be more effective for supply chains in lowering pollution by investing in cleaner technologies and adopting greener practices.

Curvature Variability Study for Small- and Large-Scale Linear Fresnel Solar Fields: A Step Toward Optimization

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
Sara Benyakhlef ◽  
Ahmed Al Mers ◽  
Ossama Merroun ◽  
Abdelfattah Bouatem ◽  
Hamid Ajdad ◽  
...  
Keyword(s):  
Cost Reduction ◽  
Large Scale ◽  
Small Scale ◽  
Optimization Approach ◽  
Market Penetration ◽  
Concentrated Solar Power ◽  
Sustainable Technologies ◽  
Low Efficiency ◽  
New Research ◽  
Variability Study

Reducing levelized electricity costs of concentrated solar power (CSP) plants can be of great potential in accelerating the market penetration of these sustainable technologies. Linear Fresnel reflectors (LFRs) are one of these CSP technologies that may potentially contribute to such cost reduction. However, due to very little previous research, LFRs are considered as a low efficiency technology. In this type of solar collectors, there is a variety of design approaches when it comes to optimizing such systems. The present paper aims to tackle a new research axis based on variability study of heliostat curvature as an approach for optimizing small and large-scale LFRs. Numerical investigations based on a ray tracing model have demonstrated that LFR constructors should adopt a uniform curvature for small-scale LFRs and a variable curvature per row for large-scale LFRs. Better optical performances were obtained for LFRs regarding these adopted curvature types. An optimization approach based on the use of uniform heliostat curvature for small-scale LFRs has led to a system cost reduction by means of reducing its receiver surface and height.

scholarly journals Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs

2018 ◽  
Vol 19 (3) ◽  
Author(s):  
Khe Foon Hew ◽  
Chen Qiao ◽  
Ying Tang
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Online Courses ◽  
Subject Area ◽  
Online Course ◽  
Valuable Insight ◽  
Course Quality ◽  
Learning Classifier ◽  
Quality Rating ◽  
Open Courses

Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze 24,612 reflective sentences posted by 5,884 students, who participated in one or more of 18 highly rated MOOCs. Highly rated MOOCs were sampled because they exemplify good practices or teaching strategies. We selected highly rated MOOCs from Coursetalk, an open user-driven aggregator and discovery website that allows students to search and review various MOOCs. We defined a highly rated MOOC as a free online course that received an overall five-star course quality rating, and received at least 50 reviews from different learners within a specific subject area. We described six specific themes found across the entire data corpus: (a) structure and pace, (b) video, (c) instructor, (d) content and resources, (e) interaction and support, and (f) assignment and assessment. The findings of this study provide valuable insight into factors that students find engaging in large-scale open online courses.

scholarly journals The history of settlement and agrarian land use in a boreal forest in Värmland, Sweden, new evidence from pollen analysis

2021 ◽  
Author(s):  
Sigrún Dögg Eddudóttir ◽  
Eva Svensson ◽  
Stefan Nilsson ◽  
Anneli Ekblom ◽  
Karl-Johan Lindholm ◽  
...  
Keyword(s):  
Land Use ◽  
Pollen Analysis ◽  
Large Scale ◽  
Pollen Grains ◽  
Ice Age ◽  
Valuable Insight ◽  
Archaeological Survey ◽  
History Of ◽  
New Evidence ◽  
Insight Into

AbstractShielings are the historically known form of transhumance in Scandinavia, where livestock were moved from the farmstead to sites in the outlands for summer grazing. Pollen analysis has provided a valuable insight into the history of shielings. This paper presents a vegetation reconstruction and archaeological survey from the shieling Kårebolssätern in northern Värmland, western Sweden, a renovated shieling that is still operating today. The first evidence of human activities in the area near Kårebolssätern are Hordeum- and Cannabis-type pollen grains occurring from ca. 100 bc. Further signs of human impact are charcoal and sporadic occurrences of apophyte pollen from ca. ad 250 and pollen indicating opening of the canopy ca. ad 570, probably a result of modification of the forest for grazing. A decrease in land use is seen between ad 1000 and 1250, possibly in response to a shift in emphasis towards large scale commodity production in the outlands. Emphasis on bloomery iron production and pitfall hunting may have caused a shift from agrarian shieling activity. The clearest changes in the pollen assemblage indicating grazing and cultivation occur from the mid-thirteenth century, coinciding with wetter climate at the beginning of the Little Ice Age. The earliest occurrences of anthropochores in the record predate those of other shieling sites in Sweden. The pollen analysis reveals evidence of land use that predates the results of the archaeological survey. The study highlights how pollen analysis can reveal vegetation changes where early archaeological remains are obscure.

EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences

2021 ◽  
Author(s):  
Ting-Hsuan Wang ◽  
Cheng-Ching Huang ◽  
Jui-Hung Hung
Keyword(s):  
Open Source Software ◽  
Large Scale ◽  
A Priori ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Comparable Accuracy ◽  
Meta Analyses ◽  
Next Generation Sequencing Ngs ◽  
Adapter Trimming ◽  
Generation Sequencing

Abstract Motivation Cross-sample comparisons or large-scale meta-analyses based on the next generation sequencing (NGS) involve replicable and universal data preprocessing, including removing adapter fragments in contaminated reads (i.e. adapter trimming). While modern adapter trimmers require users to provide candidate adapter sequences for each sample, which are sometimes unavailable or falsely documented in the repositories (such as GEO or SRA), large-scale meta-analyses are therefore jeopardized by suboptimal adapter trimming. Results Here we introduce a set of fast and accurate adapter detection and trimming algorithms that entail no a priori adapter sequences. These algorithms were implemented in modern C++ with SIMD and multithreading to accelerate its speed. Our experiments and benchmarks show that the implementation (i.e. EARRINGS), without being given any hint of adapter sequences, can reach comparable accuracy and higher throughput than that of existing adapter trimmers. EARRINGS is particularly useful in meta-analyses of a large batch of datasets and can be incorporated in any sequence analysis pipelines in all scales. Availability and implementation EARRINGS is open-source software and is available at https://github.com/jhhung/EARRINGS. Supplementary information Supplementary data are available at Bioinformatics online.

A Novel Approach for Two-Stage UAV Path Planning in Pipeline Network Inspection

2020 ◽  
Author(s):  
Rui Qiu ◽  
Yongtu Liang
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Flight Path ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Two Stage ◽  
Calculation Time ◽  
Pipeline Network ◽  
Second Stage ◽  
Computational Performance

Abstract Currently, unmanned aerial vehicle (UAV) provides the possibility of comprehensive coverage and multi-dimensional visualization of pipeline monitoring. Encouraged by industry policy, research on UAV path planning in pipeline network inspection has emerged. The difficulties of this issue lie in strict operational requirements, variable flight missions, as well as unified optimization for UAV deployment and real-time path planning. Meanwhile, the intricate structure and large scale of the pipeline network further complicate this issue. At present, there is still room to improve the practicality and applicability of the mathematical model and solution strategy. Aiming at this problem, this paper proposes a novel two-stage optimization approach for UAV path planning in pipeline network inspection. The first stage is conventional pre-flight planning, where the requirement for optimality is higher than calculation time. Therefore, a mixed integer linear programming (MILP) model is established and solved by the commercial solver to obtain the optimal UAV number, take-off location and detailed flight path. The second stage is re-planning during the flight, taking into account frequent pipeline accidents (e.g. leaks and cracks). In this stage, the flight path must be timely rescheduled to identify specific hazardous locations. Thus, the requirement for calculation time is higher than optimality and the genetic algorithm is used for solution to satisfy the timeliness of decision-making. Finally, the proposed method is applied to the UAV inspection of a branched oil and gas transmission pipeline network with 36 nodes and the results are analyzed in detail in terms of computational performance. In the first stage, compared to manpower inspection, the total cost and time of UAV inspection is decreased by 54% and 56% respectively. In the second stage, it takes less than 1 minute to obtain a suboptimal solution, verifying the applicability and superiority of the method.

scholarly journals Metabolite discovery through global annotation of untargeted metabolomics data

2021 ◽  
Author(s):  
Li Chen ◽  
Wenyun Lu ◽  
Lin Wang ◽  
Xi Xing ◽  
Xin Teng ◽  
...  
Keyword(s):  
Global Optimization ◽  
High Resolution Mass Spectrometry ◽  
Mass Difference ◽  
Untargeted Metabolomics ◽  
Global Network ◽  
Optimization Approach ◽  
Metabolomics Data ◽  
Isotope Tracer ◽  
Powerful Approach ◽  
Fragmentation Patterns

AbstractA primary goal of metabolomics is to identify all biologically important metabolites. One powerful approach is liquid chromatography-high resolution mass spectrometry (LC-MS), yet most LC-MS peaks remain unidentified. Here, we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. We consider all experimentally observed ion peaks together, and assign annotations to all of them simultaneously so as to maximize a score that considers properties of peaks (known masses, retention times, MS/MS fragmentation patterns) as well network constraints that arise based on mass difference between peaks. Global optimization results in accurate peak assignment and trackable peak-peak relationships. Applying this approach to yeast and mouse data, we identify a half-dozen novel metabolites, including thiamine and taurine derivatives. Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to annotate untargeted metabolomics data, revealing novel metabolites.

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