Supplementary Document Jaiswal et al. v1 (protocols.io.74ihque)

protocols.io ◽  
2019 ◽  
Author(s):  
SATISH JAISWAL ◽  
Satish Jaiswal ◽  
Shao Yang ◽  
Chi Hung ◽  
Wei Kuang ◽  
...  
Keyword(s):  
Supplementary Document

scholarly journals The 1-way on-line coupled atmospheric chemistry model system MECO(n) – Part 1: Description of the limited-area atmospheric chemistry model COSMO/MESSy

2012 ◽  
Vol 5 (1) ◽  
pp. 87-110 ◽  
Author(s):  
A. Kerkweg ◽  
P. Jöckel
Keyword(s):  
Boundary Conditions ◽  
Atmospheric Chemistry ◽  
General Circulation ◽  
Weather Prediction ◽  
Model System ◽  
Small Scale ◽  
Limited Area ◽  
Tracer Transport ◽  
Chemistry Research ◽  
Supplementary Document

Abstract. The numerical weather prediction model of the Consortium for Small Scale Modelling (COSMO), maintained by the German weather service (DWD), is connected with the Modular Earth Submodel System (MESSy). This effort is undertaken in preparation of a new, limited-area atmospheric chemistry model. Limited-area models require lateral boundary conditions for all prognostic variables. Therefore the quality of a regional chemistry model is expected to improve, if boundary conditions for the chemical constituents are provided by the driving model in consistence with the meteorological boundary conditions. The new developed model is as consistent as possible, with respect to atmospheric chemistry and related processes, with a previously developed global atmospheric chemistry general circulation model: the ECHAM/MESSy Atmospheric Chemistry (EMAC) model. The combined system constitutes a new research tool, bridging the global to the meso-γ scale for atmospheric chemistry research. MESSy provides the infrastructure and includes, among others, the process and diagnostic submodels for atmospheric chemistry simulations. Furthermore, MESSy is highly flexible allowing model setups with tailor made complexity, depending on the scientific question. Here, the connection of the MESSy infrastructure to the COSMO model is documented and also the code changes required for the generalisation of regular MESSy submodels. Moreover, previously published prototype submodels for simplified tracer studies are generalised to be plugged-in and used in the global and the limited-area model. They are used to evaluate the TRACER interface implementation in the new COSMO/MESSy model system and the tracer transport characteristics, an important prerequisite for future atmospheric chemistry applications. A supplementary document with further details on the technical implementation of the MESSy interface into COSMO with a complete list of modifications to the COSMO code is provided.

scholarly journals A Sensitivity-Based Three-Phase Weather-Dependent Power Flow Algorithm for Networks with Local Controllers—Supplementary

2020 ◽  
Author(s):  
Evangelos Pompodakis ◽  
Arif Ahmed ◽  
Minas Alexiadis
Keyword(s):  
Reaction Time ◽  
Time Delays ◽  
Power Flow ◽  
Distribution Systems ◽  
Distribution Networks ◽  
Switching Sequence ◽  
Three Phase ◽  
Supplementary Document ◽  
Flow Algorithm ◽  
Weather Variations

This supplementary document is part of the original 2-Part manuscript titled “A SensitivityBased Three-Phase Weather-Dependent Power Flow Algorithm for Networks with Local Controllers.” This research focuses on proposing a novel sensitivity-based three-phase weather-dependent power flow algorithm for distribution networks with local voltage controllers (LVCs). The proposed algorithm has four distinct characteristics: a) it considers the three-phase unbalanced nature of distribution systems, b) the operating state of LVCs is calculated using sensitivity parameters, which accelerates the convergence speed of the algorithm, c) it considers the precise switching sequence of LVCs based on their reaction time delays, and d) the nonlinear influence of weather variations in the power flow is also taken into consideration. In this supplementary document, the relevant derivations of the sensitivity parameters are presented to complement the original 2-Part manuscript.

scholarly journals Rapid Reconstruction of Time-varying Gene Regulatory Networks

2018 ◽  
Author(s):  
Saptarshi Pyne ◽  
Alok Ranjan Kumar ◽  
Ashish Anand
Keyword(s):  
Gene Regulatory Networks ◽  
False Positive ◽  
Regulatory Networks ◽  
Source Code ◽  
Main Memory ◽  
Structural Constraints ◽  
Time Varying ◽  
True Positive ◽  
Supplementary Document ◽  
Gene Regulatory

Abstract—Rapid advancements in high-throughput technologies has resulted in genome-scale time series datasets. Uncovering the temporal sequence of gene regulatory events, in the form of time-varying gene regulatory networks (GRNs), demands computationally fast, accurate and scalable algorithms. The existing algorithms can be divided into two categories: ones that are time-intensive and hence unscalable; others that impose structural constraints to become scalable. In this paper, a novel algorithm, namely ‘an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators’ (TGS), is proposed. TGS is time-efficient and does not impose any structural constraints. Moreover, it provides such flexibility and time-efficiency, without losing its accuracy. TGS consistently outperforms the state-of-the-art algorithms in true positive detection, on three benchmark synthetic datasets. However, TGS does not perform as well in false positive rejection. To mitigate this issue, TGS+ is proposed. TGS+ demonstrates competitive false positive rejection power, while maintaining the superior speed and true positive detection power of TGS. Nevertheless, main memory requirements of both TGS variants grow exponentially with the number of genes, which they tackle by restricting the maximum number of regulators for each gene. Relaxing this restriction remains a challenge as the actual number of regulators is not known a priori.ReproducibilityThe datasets and results can be found at: https://github.com/aaiitg-grp/TGS. This manuscript is currently under review. As soon as it is accepted, the source code will be made available at the same link. There are mentions of a ‘supplementary document’ throughout the text. The supplementary document will also be made available after acceptance of the manuscript. If you wish to be notified when the supplementary document and source code are available, kindly send an email to [email protected] with subject line ‘TGS Source Code: Request for Notification’. The email body can be kept blank.

scholarly journals A Graph Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

2020 ◽  
Vol 34 (01) ◽  
pp. 719-726
Author(s):  
Ziqi Ke ◽  
Haris Vikalo
Keyword(s):  
Dna Sequencing ◽  
Single Individual ◽  
Sequencing Data ◽  
Genomic Component ◽  
Source Codes ◽  
Sequencing Errors ◽  
Supplementary Document ◽  
Viral Communities ◽  
High Throughput Dna Sequencing ◽  
Sequencing Platforms

Reconstructing components of a genomic mixture from data obtained by means of DNA sequencing is a challenging problem encountered in a variety of applications including single individual haplotyping and studies of viral communities. High-throughput DNA sequencing platforms oversample mixture components to provide massive amounts of reads whose relative positions can be determined by mapping the reads to a known reference genome; assembly of the components, however, requires discovery of the reads' origin – an NP-hard problem that the existing methods struggle to solve with the required level of accuracy. In this paper, we present a learning framework based on a graph auto-encoder designed to exploit structural properties of sequencing data. The algorithm is a neural network which essentially trains to ignore sequencing errors and infers the posterior probabilities of the origin of sequencing reads. Mixture components are then reconstructed by finding consensus of the reads determined to originate from the same genomic component. Results on realistic synthetic as well as experimental data demonstrate that the proposed framework reliably assembles haplotypes and reconstructs viral communities, often significantly outperforming state-of-the-art techniques. Source codes, datasets and supplementary document are available at https://github.com/WuLoli/GAEseq.

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