scholarly journals Multi-attribute Glycan Identification and FDR Control for Glycoproteomics

2021 ◽  
Author(s):  
Daniel A. Polasky ◽  
Daniel J Geiszler ◽  
Fengchao Yu ◽  
Alexey I Nesvizhskii
Keyword(s):  
Large Scale ◽  
New Method ◽  
Mass Spectrometry Data ◽  
Peptide Sequence ◽  
Analysis Tool ◽  
Sources Of Information ◽  
Multiple Sources ◽  
False Discovery ◽  
False Discovery Rate Estimation ◽  
Major Bottleneck

Rapidly improving methods for glycoproteomics have enabled increasingly large-scale analyses of complex glycopeptide samples, but annotating the resulting mass spectrometry data with high confidence remains a major bottleneck. We recently introduced a fast and sensitive glycoproteomics search method in our MSFragger search engine, which reports glycopeptides as a combination of a peptide sequence and the mass of the attached glycan. In samples with complex glycosylation patterns, converting this mass to a specific glycan composition is not straightforward, however, as many glycans have similar or identical masses. Here, we have developed a new method for determining the glycan composition of N-linked glycopeptides fragmented by collision or hybrid activation that uses multiple sources of information from the spectrum, including observed glycan B- (oxonium) and Y-type ions and mass and precursor monoisotopic selection errors to discriminate between possible glycan candidates. Combined with false discovery rate estimation for the glycan assignment, we show this method is capable of specifically and sensitively identifying glycans in complex glycopeptide analyses and effectively controls the rate of false glycan assignments. The new method has been incorporated into the PTM-Shepherd modification analysis tool to work directly with the MSFragger glyco search in the FragPipe graphical user interface, providing a complete computational pipeline for annotation of N-glycopeptide spectra with FDR control of both peptide and glycan components that is both sensitive and robust against false identifications.

Improving scheduling, benchmarking and forecasting to boost irrigation productivity

2020 ◽  
Author(s):  
Andrew Western ◽  
Danlu Guo ◽  
Arash Parehkar ◽  
Zitian Gao ◽  
Dongryeol Ryu ◽  
...  
Keyword(s):  
Water Conservation ◽  
Large Scale ◽  
Limited Resource ◽  
Irrigation Scheduling ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Project Outcomes ◽  
Crop Types ◽  
Irrigation Practices

<p>Irrigation water is an expensive and limited resource, and optimized water use is beneficial to saving water while boosting productivity. This project aims to develop integrated irrigation scheduling, benchmarking and forecasting capabilities to inform optimal irrigation practices and the suitable tools and information required for this. To achieve this, we designed a three-year project which combines simulations and field-scale monitoring. One aspect of this project is to develop a comprehensive uncertainty framework to better understand the uncertainty in scheduling, which is informed by soil water models, along with multiple sources of information such as soil, crop, weather and field management. Besides, we are also conducting large-scale benchmarking study to identify better irrigation practices across multiple farms, fields, crop types and seasons. The project outcomes will be integrated with our partner, Rubicon’s water ordering portal and adopted by most Australian irrigation farmers, with significant long-term benefits expected in agricultural production and water conservation. </p>

scholarly journals Reliable identification of protein-protein interactions by crosslinking mass spectrometry

2020 ◽  
Author(s):  
Swantje Lenz ◽  
Ludwig R. Sinn ◽  
Francis J. O’Reilly ◽  
Lutz Fischer ◽  
Fritz Wegner ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Large Scale ◽  
Protein Complexes ◽  
Estimation Procedure ◽  
Scale Analysis ◽  
Protein Protein Interactions ◽  
False Discovery ◽  
Large Scale Analysis ◽  
False Discovery Rate Estimation

Crosslinking mass spectrometry is widening its scope from structural analyzes of purified multi-protein complexes towards systems-wide analyzes of protein-protein interactions. Assessing the error in these large datasets is currently a challenge. Using a controlled large-scale analysis of Escherichia coli cell lysate, we demonstrate a reliable false-discovery rate estimation procedure for protein-protein interactions identified by crosslinking mass spectrometry.

Large-Scale Computational Modeling for Environmental Impact Assessment

2011 ◽  
pp. 1-17
Author(s):  
Ting Yu ◽  
Manfred Lenzen ◽  
Christopher Dey
Keyword(s):  
Computational Modeling ◽  
Large Scale ◽  
Data Retrieval ◽  
Input Output ◽  
Multiple Sources ◽  
Matrix Estimation ◽  
Large Size ◽  
Major Bottleneck ◽  
Parallel Supercomputer ◽  
Estimation System

Input-output table plays a central role in the Economic Input-Output Life Cycle Assessment (EIO-LCA) method. This chapter presents an integrated and distributed computational modeling system capable of estimating and updating large-size input-output tables. The complexity of national economy leads to extremely large-size models to represent every detail of an economy. In order to construct the table reflecting the underlying industry structure faithfully, multiple sources of data are integrated and analyzed together. The major bottleneck of matrix estimation is the lack of memory allocation. In order to include more memory, this unique distributed matrix estimation system runs over a parallel supercomputer to enable it to estimate a matrix with the size of more than 1,000-by-1,000 with relatively high accuracy. This system is the first distributed matrix estimation package for such a large-size economic matrix. This chapter presents a comprehensive example of facilitating this estimation process by integrating a series of components with the purposes of data retrieval, data integration, distributed machine learning, and quality checking.

scholarly journals Target-Decoy-Based False Discovery Rate Estimation for Large-Scale Metabolite Identification

2018 ◽  
Vol 17 (7) ◽  
pp. 2328-2334 ◽  
Author(s):  
Xusheng Wang ◽  
Drew R. Jones ◽  
Timothy I. Shaw ◽  
Ji-Hoon Cho ◽  
Yuanyuan Wang ◽  
...  
Keyword(s):  
False Discovery Rate ◽  
Large Scale ◽  
Metabolite Identification ◽  
Rate Estimation ◽  
False Discovery ◽  
False Discovery Rate Estimation

Supporting the Development of Clinical Reasoning of Preprofessional Novices in Dysphagia Management

2019 ◽  
Vol 40 (03) ◽  
pp. 151-161 ◽  
Author(s):  
Sebastian Doeltgen ◽  
Stacie Attrill ◽  
Joanne Murray
Keyword(s):  
Clinical Reasoning ◽  
Clinical Decision Making ◽  
Management Practice ◽  
Clinical Decision ◽  
Assessment Process ◽  
Evidence Based ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Implicit Processing ◽  
Reasoning Skills

AbstractProficient clinical reasoning is a critical skill in high-quality, evidence-based management of swallowing impairment (dysphagia). Clinical reasoning in this area of practice is a cognitively complex process, as it requires synthesis of multiple sources of information that are generated during a thorough, evidence-based assessment process and which are moderated by the patient's individual situations, including their social and demographic circumstances, comorbidities, or other health concerns. A growing body of health and medical literature demonstrates that clinical reasoning skills develop with increasing exposure to clinical cases and that the approaches to clinical reasoning differ between novices and experts. It appears that it is not the amount of knowledge held, but the way it is used, that distinguishes a novice from an experienced clinician. In this article, we review the roles of explicit and implicit processing as well as illness scripts in clinical decision making across the continuum of medical expertise and discuss how they relate to the clinical management of swallowing impairment. We also reflect on how this literature may inform educational curricula that support SLP students in developing preclinical reasoning skills that facilitate their transition to early clinical practice. Specifically, we discuss the role of case-based curricula to assist students to develop a meta-cognitive awareness of the different approaches to clinical reasoning, their own capabilities and preferences, and how and when to apply these in dysphagia management practice.

scholarly journals PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohammadreza Yaghoobi ◽  
Krzysztof S. Stopka ◽  
Aaditya Lakshmanan ◽  
Veera Sundararaghavan ◽  
John E. Allison ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Metals And Alloys ◽  
Analysis Tool ◽  
Computational Framework ◽  
Crystal Plasticity Finite Element ◽  
Polycrystalline Metals ◽  
Simulation Based ◽  
Open Source Framework

AbstractThe PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

scholarly journals Accessible Routes Integrating Data from Multiple Sources

2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Miguel R. Luaces ◽  
Jesús A. Fisteus ◽  
Luis Sánchez-Fernández ◽  
Mario Munoz-Organero ◽  
Jesús Balado ◽  
...  
Keyword(s):  
Information System ◽  
Data Model ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Heterogeneous Data Sources ◽  
Different Types ◽  
Software Sensors ◽  
The City

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

scholarly journals PEDRERA. Positive Energy District Renovation Model for Large Scale Actions

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2833
Author(s):  
Paolo Civiero ◽  
Jordi Pascual ◽  
Joaquim Arcas Abella ◽  
Ander Bilbao Figuero ◽  
Jaume Salom
Keyword(s):  
Simulation Model ◽  
Performance Indicators ◽  
Large Scale ◽  
Key Performance Indicators ◽  
Positive Energy ◽  
Design Phase ◽  
Multiple Sources ◽  
Reliable Prediction ◽  
Sensitive Analysis ◽  
Web Platform

In this paper, we provide a view of the ongoing PEDRERA project, whose main scope is to design a district simulation model able to set and analyze a reliable prediction of potential business scenarios on large scale retrofitting actions, and to evaluate the overall co-benefits resulting from the renovation process of a cluster of buildings. According to this purpose and to a Positive Energy Districts (PEDs) approach, the model combines systemized data—at both building and district scale—from multiple sources and domains. A sensitive analysis of 200 scenarios provided a quick perception on how results will change once inputs are defined, and how attended results will answer to stakeholders’ requirements. In order to enable a clever input analysis and to appraise wide-ranging ranks of Key Performance Indicators (KPIs) suited to each stakeholder and design phase targets, the model is currently under the implementation in the urbanZEB tool’s web platform.

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