Synthesis of bent [4]phenylene (cyclobuta[1,2-a:3,4-b′]bisbiphenylene) and structure of a bis(trimethylsilyl) derivative: the last [4]phenylene isomerElectronic supplementary information (ESI) available: selected bond distances and angles for 9, spectral and analytical information. See http://www.rsc.org/suppdata/cc/b1/b109789j/

2002 ◽  
pp. 278-279 ◽  
Author(s):  
Dennis T.-Y. Bong ◽  
Lionel Gentric ◽  
Daniel Holmes ◽  
Adam J. Matzger ◽  
Frank Scherhag ◽  
...  
Keyword(s):  
Supplementary Information ◽  
Trimethylsilyl Derivative ◽  
Analytical Information

FEATURES OF DESIGNING BIKE PATHS IN ST.PETERSBURG BASED ON ANALYSIS OF EXPERIENCE OF THE BALTIC REGION

2020 ◽  
Vol 4 (26) ◽  
pp. 122-130
Author(s):  
A. A. Dobrovolskaya ◽  
Keyword(s):  
Traffic Volume ◽  
Design Methods ◽  
Baltic Region ◽  
Analytical Information ◽  
The World ◽  
Lane Width ◽  
The Baltic ◽  
Specific Section

The article deals with statistics on the development of Bicycle roads in Russia and in the world, as well as design methods for a specific section of the connection of Bicycle routes in St. Petersburg. The article discusses the experience of using and entering bike paths based on the experience of Finland, as well as the types of bike paths and infrastructure features for metropolises. A model for creating a bike path by partially narrowing the roadway, graphical functions, and analytical information are provided. Practical examples of changing the infrastructure for bike paths are given. Keywords: bike path, traffic volume, design the roadway, lane width.

scholarly journals ccNetViz: a WebGL-based JavaScript library for visualization of large networks

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  
Keyword(s):  
Systems Biology ◽  
Complex Networks ◽  
Open Source ◽  
High Speed ◽  
A Priori ◽  
Supplementary Information ◽  
Network Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs

2020 ◽  
Vol 36 (9) ◽  
pp. 2690-2696
Author(s):  
Jarkko Toivonen ◽  
Pratyush K Das ◽  
Jussi Taipale ◽  
Esko Ukkonen
Keyword(s):  
Markov Models ◽  
Expectation Maximization Algorithm ◽  
Software Tool ◽  
Specific Weight ◽  
Training Data ◽  
Supplementary Information ◽  
Markov Modeling ◽  
Binding Motifs ◽  
The Difference ◽  
Probability Matrices

Abstract Motivation Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average. Availability and implementation Software implementation is available from https://github.com/jttoivon/moder2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Returning to dialysis after kidney allograft failure: the experience of the Italian Registry of Paediatric Chronic Dialysis

2021 ◽  
Author(s):  
Edoardo La Porta ◽  
Ester Conversano ◽  
Daniela Zugna ◽  
Roberta Camilla ◽  
Raffaella Labbadia ◽  
...  
Keyword(s):  
Kidney Transplantation ◽  
Chronic Dialysis ◽  
Supplementary Information ◽  
Dialysis Modality ◽  
Specific Population ◽  
Kidney Allograft ◽  
Allograft Failure ◽  
Younger Age ◽  
Complicated Course ◽  
Significant Mortality

Abstract Background The need for dialysis after kidney allograft failure (DAGF) is among the top five reasons for dialysis initiation, making this an important topic in clinical nephrology. However, data are scarce on dialysis choice after transplantation and clinical outcomes for DAGF in children. Methods Patients receiving chronic dialysis < 18 years were recorded from January 1991 to January 2019 by the Italian Registry of Pediatric Chronic Dialysis (IRPCD). We investigated factors influencing choice of dialysis modality, patient outcome in terms of mortality, switching dialysis modality, and kidney transplantation. Results Among 118 patients receiving DAGF, 41 (35%) were treated with peritoneal dialysis (PD), and 77 (65%) with haemodialysis (HD). Significant predictors for treatment with PD were younger age at dialysis start (OR 0.85 per year increase [95%CI 0.72–1.00]) and PD use before kidney transplantation (OR 8.20 [95%CI 1.82–37.01]). Patients entering DAGF in more recent eras (OR 0.87 per year increase [95%CI 0.80–0.94]) and with more than one dialysis modality before kidney transplantation (OR 0.56 for being treated with PD [0.12–2.59]) were more likely to be initiated on HD. As compared to patients on HD, those treated with PD exhibited increased but non-significant mortality risk (HR 2.15 [95%CI 0.54–8.6]; p = 0.28) and higher prevalence of dialysis-related complications during DAGF (p = 0.002) Conclusions Patients entering DAGF in more recent years are more likely to be initiated on HD. In this specific population of children, use of PD seems associated with a more complicated course. Graphical abstract A higher resolution version of the Graphical abstract is available as Supplementary information

scholarly journals How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 741
Author(s):  
Yuseok Ban ◽  
Kyungjae Lee
Keyword(s):  
High Precision ◽  
Recommender System ◽  
Visual Information ◽  
Experimental Results ◽  
Supplementary Information ◽  
Digital Platform ◽  
Different Types ◽  
Types Of Information

Many researchers have suggested improving the retention of a user in the digital platform using a recommender system. Recent studies show that there are many potential ways to assist users to find interesting items, other than high-precision rating predictions. In this paper, we study how the diverse types of information suggested to a user can influence their behavior. The types have been divided into visual information, evaluative information, categorial information, and narrational information. Based on our experimental results, we analyze how different types of supplementary information affect the performance of a recommender in terms of encouraging users to click more items or spend more time in the digital platform.

TIGER: inferring DNA replication timing from whole-genome sequence data

2021 ◽  
Author(s):  
Amnon Koren ◽  
Dashiell J Massey ◽  
Alexa N Bracci
Keyword(s):  
Dna Replication ◽  
Genome Sequence ◽  
Genomic Dna ◽  
Sequence Data ◽  
Replication Timing ◽  
Whole Genome Sequence ◽  
Supplementary Information ◽  
Whole Genome ◽  
Genome Sequence Data ◽  
Dna Replication Timing

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online

ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

2021 ◽  
Author(s):  
Irzam Sarfraz ◽  
Muhammad Asif ◽  
Joshua D Campbell
Keyword(s):  
Single Cell ◽  
R Package ◽  
Poor Quality ◽  
Data Matrix ◽  
Supplementary Information ◽  
Data Provenance ◽  
Rna Seq ◽  
Efficient Management ◽  
The Matrix ◽  
The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

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