Unusual mechanistic pathways. The novel chemistry of compounds with tris(trimethylsilyl)methyl or related ligands on siliconElectronic supplementary information (ESI) available: a complete list of the author's publications, with titles, on compounds with trisyl or related ligands on silicon (and for comparison, on germanium or tin) or species containing trisyl-type groups not on a metal or metalloid. See http://www.rsc.org/suppdata/dt/b1/b106509m/

2001 ◽  
pp. 3397-3406 ◽  
Author(s):  
Colin Eaborn
Keyword(s):  
Complete List ◽  
Supplementary Information ◽  
The Novel

scholarly journals SARS2020: an integrated platform for identification of novel coronavirus by a consensus sequence-function model

2020 ◽  
Author(s):  
Dachuan Zhang ◽  
Tong Zhang ◽  
Sheng Liu ◽  
Dandan Sun ◽  
Shaozhen Ding ◽  
...  
Keyword(s):  
Biological Function ◽  
Consensus Sequence ◽  
Rapid Identification ◽  
Data Driven ◽  
Supplementary Information ◽  
Respiratory Syndrome Virus ◽  
The Novel ◽  
Function Model ◽  
Catalytic Function ◽  
Novel Coronavirus

Abstract Motivation The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been conducted to identify viruses by the enzymes that they encode, and which are key to pathogen propagation. Results We built a comprehensive scientific resource, SARS2020, which integrates coronavirus-related research, genomic sequences and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the severe acute respiratory syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics. Availabilityand implementation SARS2020 is available at http://design.rxnfinder.org/sars2020/. Supplementary information Supplementary data are available at Bioinformatics online.

XXIX.—The First Chemical Society, the First Chemical Journal, and the Chemical Revolution (Part II)

1952 ◽  
Vol 63 (4) ◽  
pp. 385-400 ◽  
Author(s):  
James Kendall
Keyword(s):  
Special Interest ◽  
Presidential Address ◽  
Chemical Society ◽  
Complete List ◽  
Supplementary Information ◽  
Chemical Revolution ◽  
In The Beginning ◽  
Revolutionary Period

Since the delivery of my presidential address (1) in July I have assembled an amount of supplementary information regarding “the Chemical Society instituted in the beginning of the Year 1785”. This, together with a brief description of some other chemical societies of the revolutionary period, forms the basis of the present paper.First of all, it will be expedient to furnish a complete list of the dissertations read before the Society during 1785–86 and included in the first volume of its Proceedings, appending short comments with respect to the communicators or their topics when anything of special interest arises.

scholarly journals SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

2020 ◽  
Author(s):  
Stefan Schulze ◽  
Anne Oltmanns ◽  
Christian Fufezan ◽  
Julia Krägenbring ◽  
Michael Mormann ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Glycosylation ◽  
Supplementary Information ◽  
Cyanidioschyzon Merolae ◽  
Human Breast Milk ◽  
The Novel ◽  
Post Translational Modification ◽  
Cellular Functions ◽  
Domains Of Life

AbstractMotivationProtein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes.ResultsTherefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae.AvailabilityThe source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating [email protected] and [email protected] informationSupplementary data are available online.

scholarly journals Topology-based classification of tetrads and quadruplex structures

2019 ◽  
Vol 36 (4) ◽  
pp. 1129-1134 ◽  
Author(s):  
Mariusz Popenda ◽  
Joanna Miskiewicz ◽  
Joanna Sarzynska ◽  
Tomasz Zok ◽  
Marta Szachniuk
Keyword(s):  
Nucleic Acid ◽  
3D Structure ◽  
Data Bank ◽  
Supplementary Information ◽  
The Novel ◽  
Disease Treatment ◽  
Structure Topology ◽  
Secondary Structure Topology ◽  
Nucleic Acid Structures

Abstract Motivation Quadruplexes attract the attention of researchers from many fields of bio-science. Due to a specific structure, these tertiary motifs are involved in various biological processes. They are also promising therapeutic targets in many strategies of drug development, including anticancer and neurological disease treatment. The uniqueness and diversity of their forms cause that quadruplexes show great potential in novel biological applications. The existing approaches for quadruplex analysis are based on sequence or 3D structure features and address canonical motifs only. Results In our study, we analyzed tetrads and quadruplexes contained in nucleic acid molecules deposited in Protein Data Bank. Focusing on their secondary structure topology, we adjusted its graphical diagram and proposed new dot-bracket and arc representations. We defined the novel classification of these motifs. It can handle both canonical and non-canonical cases. Based on this new taxonomy, we implemented a method that automatically recognizes the types of tetrads and quadruplexes occurring as unimolecular structures. Finally, we conducted a statistical analysis of these motifs found in experimentally determined nucleic acid structures in relation to the new classification. Availability and implementation https://github.com/tzok/eltetrado/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology

2020 ◽  
Author(s):  
Daniel Domingo-Fernández ◽  
Shounak Baksi ◽  
Bruce Schultz ◽  
Yojana Gadiya ◽  
Reagon Karki ◽  
...  
Keyword(s):  
Web Application ◽  
Research Community ◽  
Supplementary Information ◽  
Knowledge Graph ◽  
The Novel ◽  
Knowledge Model ◽  
Global Response ◽  
Cause And Effect ◽  
Link Type ◽  
Novel Coronavirus

AbstractSummaryThe past few weeks have witnessed a worldwide mobilization of the research community in response to the novel coronavirus (COVID-19). This global response has led to a burst of publications on the pathophysiology of the virus, yet without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats.AvailabilityThe COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de.Contactalpha.tom.kodamullil@scai.fraunhofer.deSupplementary informationSupplementary data are available online.

scholarly journals Domain-adversarial multi-task framework for novel therapeutic property prediction of compounds

2020 ◽  
Vol 36 (9) ◽  
pp. 2848-2855 ◽  
Author(s):  
Lingwei Xie ◽  
Song He ◽  
Zhongnan Zhang ◽  
Kunhui Lin ◽  
Xiaochen Bo ◽  
...  
Keyword(s):  
Small Molecule ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Supplementary Information ◽  
The Novel ◽  
Linear Dependency ◽  
Non Linear ◽  
Therapeutic Properties ◽  
Novel Therapeutic ◽  
Task Framework

Abstract Motivation With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides significant opportunities to improve pharmaceutical research and development. One important application is the purpose prediction of small-molecule compounds with the objective of specifying the therapeutic properties of extensive purpose-unknown compounds and repurposing the novel therapeutic properties of FDA-approved drugs. Such a problem is extremely challenging because compound attributes include heterogeneous data with various feature patterns, such as drug fingerprints, drug physicochemical properties and drug perturbation gene expressions. Moreover, there is a complex non-linear dependency among heterogeneous data. In this study, we propose a novel domain-adversarial multi-task framework for integrating shared knowledge from multiple domains. The framework first uses an adversarial strategy to learn target representations and then models non-linear dependency among several domains. Results Experiments on two real-world datasets illustrate that our approach achieves an obvious improvement over competitive baselines. The novel therapeutic properties of purpose-unknown compounds that we predicted have been widely reported or brought to clinics. Furthermore, our framework can integrate various attributes beyond the three domains examined herein and can be applied in industry for screening significant numbers of small-molecule drug candidates. Availability and implementation The source code and datasets are available at https://github.com/JohnnyY8/DAMT-Model. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Index suffix–prefix overlaps by (w, k)-minimizer to generate long contigs for reads compression

2018 ◽  
Vol 35 (12) ◽  
pp. 2066-2074 ◽  
Author(s):  
Yuansheng Liu ◽  
Zuguo Yu ◽  
Marcel E Dinger ◽  
Jinyan Li
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Reference Sequence ◽  
Supplementary Information ◽  
The Novel ◽  
Rna Seq ◽  
File Size ◽  
Sequencing Technologies ◽  
Efficient Storage ◽  
Merging Process

Abstract Motivation Advanced high-throughput sequencing technologies have produced massive amount of reads data, and algorithms have been specially designed to contract the size of these datasets for efficient storage and transmission. Reordering reads with regard to their positions in de novo assembled contigs or in explicit reference sequences has been proven to be one of the most effective reads compression approach. As there is usually no good prior knowledge about the reference sequence, current focus is on the novel construction of de novo assembled contigs. Results We introduce a new de novo compression algorithm named minicom. This algorithm uses large k-minimizers to index the reads and subgroup those that have the same minimizer. Within each subgroup, a contig is constructed. Then some pairs of the contigs derived from the subgroups are merged into longer contigs according to a (w, k)-minimizer-indexed suffix–prefix overlap similarity between two contigs. This merging process is repeated after the longer contigs are formed until no pair of contigs can be merged. We compare the performance of minicom with two reference-based methods and four de novo methods on 18 datasets (13 RNA-seq datasets and 5 whole genome sequencing datasets). In the compression of single-end reads, minicom obtained the smallest file size for 22 of 34 cases with significant improvement. In the compression of paired-end reads, minicom achieved 20–80% compression gain over the best state-of-the-art algorithm. Our method also achieved a 10% size reduction of compressed files in comparison with the best algorithm under the reads-order preserving mode. These excellent performances are mainly attributed to the exploit of the redundancy of the repetitive substrings in the long contigs. Availability and implementation https://github.com/yuansliu/minicom Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Predictive values, uncertainty, and interpretation of serology tests for the novel coronavirus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Naomi C. Brownstein ◽  
Yian Ann Chen
Keyword(s):  
Disease Burden ◽  
False Negative ◽  
False Negative Rate ◽  
Supplementary Information ◽  
The Novel ◽  
Positive Serology ◽  
Predictive Values ◽  
Infected People ◽  
Novel Coronavirus ◽  
Different Levels

AbstractAntibodies testing in the coronavirus era is frequently promoted, but the underlying statistics behind their validation has come under more scrutiny in recent weeks. We provide calculations, interpretations, and plots of positive and negative predictive values under a variety of scenarios. Prevalence, sensitivity, and specificity are estimated within ranges of values from researchers and antibodies manufacturers. Illustrative examples are highlighted, and interactive plots are provided in the Supplementary Information. Implications are discussed for society overall and across diverse locations with different levels of disease burden. Specifically, the proportion of positive serology tests that are false can differ drastically from up to 3%–88% for people from different places with different proportions of infected people in the populations while the false negative rate is typically under 10%.

scholarly journals SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

2020 ◽  
Author(s):  
Stefan Schulze ◽  
Anne Oltmanns ◽  
Christian Fufezan ◽  
Julia Krägenbring ◽  
Michael Mormann ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Glycosylation ◽  
Supplementary Information ◽  
Cyanidioschyzon Merolae ◽  
Human Breast Milk ◽  
The Novel ◽  
Post Translational Modification ◽  
Cellular Functions ◽  
Domains Of Life

Abstract Motivation Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. Results Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae. Availability and implementation The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

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