Summary:

Parallelising sequential applications is usually a very hard job, due to many different ways in which an application can be parallelised and a large number of programming models (each with its own advantages and disadvantages) that can be used. In this paper, we describe a method to semi-automatically generate and evaluate different parallelisations of the same application, allowing programmers to find the best parallelisation without significant manual reengineering of the code. We describe a novel, high-level domain-specific language, Refactoring Pattern Language (RPL), that is used to represent the parallel structure of an application and to capture its extra-functional properties (such as service time). We then describe a set of RPL rewrite rules that can be used to generate alternative, but semantically equivalent, parallel structures (parallelisations) of the same application. We also describe the RPL Shell that can be used to evaluate these parallelisations, in terms of the desired extra-functional properties. Finally, we describe a set of C++ refactorings, targeting OpenMP, Intel TBB and FastFlow parallel programming models, that semi-automatically apply the desired parallelisation to the application's source code, therefore giving a parallel version of the code. We demonstrate how the RPL and the refactoring rules can be used to derive efficient parallelisations of two realistic C++ use cases (Image Convolution and Ant Colony Optimisation).

Summary:

An open source iOS framework for GPU-based image and video processing

Summary: [1.1.0] - 2020-06-23 Added

• #112 - Per-amplicon coverage plot
• #124 - Intersect variants across callers
• #118 Updated GitHub Actions AWS workflow for small and full size tests.
• nf-core/tools#616 - Updated GitHub Actions to build Docker image and push to Docker Hub
• Parameters:
  • --min_mapped_reads to circumvent failures for samples with low number of mapped reads
  • --varscan2_strand_filter to toggle the default Varscan 2 strand filter
  • --skip_mosdepth - skip genome-wide and amplicon coverage plot generation from mosdepth output
  • --amplicon_left_suffix - to provide left primer suffix used in name field of --amplicon_bed
  • --amplicon_right_suffix - to provide right primer suffix used in name field of --amplicon_bed
  • Unify parameter specification with COG-UK pipeline:
    • --min_allele_freq - minimum allele frequency threshold for calling variants
    • --mpileup_depth - SAMTools mpileup max per-file depth
    • --ivar_exclude_reads renamed to --ivar_trim_noprimer
    • --ivar_trim_min_len - minimum length of read to retain after primer trimming
    • --ivar_trim_min_qual - minimum quality threshold for sliding window to pass
    • --ivar_trim_window_width - width of sliding window

Removed

• --skip_qc parameter

Dependencies

• Add mosdepth 0.2.6
• Add bioconductor-complexheatmap 2.2.0
• Add bioconductor-biostrings 2.54.0
• Add r-optparse 1.6.6
• Add r-tidyr 1.1.0
• Add r-tidyverse 1.3.0
• Add r-ggplot2 3.3.1
• Add r-reshape2 1.4.4
• Add r-viridis 0.5.1
• Update sra-tools 2.10.3 -> 2.10.7
• Update bowtie2 2.3.5.1 -> 2.4.1
• Update picard 2.22.8 -> 2.23.0
• Update minia 3.2.3 -> 3.2.4
• Update plasmidid 1.5.2 -> 1.6.3

Summary: This dataset includes particulate organic matter (POM) concentrations for carbon, nitrogen, and phosphorus. Data are from samples collected from NOAA Ship R/V Ronald H. Brown (cruise EXPOCODE: 33RO20180423), acting under the auspices of the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP), I07N GO-SHIP/CO2 Repeat Hydrography Cruise in 2018. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/879076

Summary: POM concentrations for carbon, nitrogen, phosphorus, and chemical oxygen from GO-SHIP Line P18 Legs 1 and 2 in 2016 and 2017. These data will be published in Moreno et al. (in press). For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/816347

Summary:

[1.0.0] - 2020-06-01

Initial release of nf-core/viralrecon, created with the nf-core template.

This pipeline is a re-implementation of the SARS_Cov2_consensus-nf and SARS_Cov2_assembly-nf pipelines initially developed by Sarai Varona and Sara Monzon from BU-ISCIII. Porting both of these pipelines to nf-core was an international collaboration between numerous contributors and developers, led by Harshil Patel from the The Bioinformatics & Biostatistics Group at The Francis Crick Institute, London. We appreciated the need to have a portable, reproducible and scalable pipeline for the analysis of COVID-19 sequencing samples and so the Avengers Assembled!

Pipeline summary

1. Download samples via SRA, ENA or GEO ids (ENA FTP, parallel-fastq-dump; if required)
2. Merge re-sequenced FastQ files (cat; if required)
3. Read QC (FastQC)
4. Adapter trimming (fastp)
5. Variant calling
   1. Read alignment (Bowtie 2)
   2. Sort and index alignments (SAMtools)
   3. Primer sequence removal (iVar; amplicon data only)
   4. Duplicate read marking (picard; removal optional)
   5. Alignment-level QC (picard, SAMtools)
   6. Choice of multiple variant calling and consensus sequence generation routes (VarScan 2, BCFTools, BEDTools || iVar variants and consensus || BCFTools, BEDTools)
      • Variant annotation (SnpEff, SnpSift)
      • Consensus assessment report (QUAST)
6. De novo assembly
   1. Primer trimming (Cutadapt; amplicon data only)
   2. Removal of host reads (Kraken 2)
   3. Choice of multiple assembly tools (SPAdes || metaSPAdes || Unicycler || minia)
      • Blast to reference genome (blastn)
      • Contiguate assembly (ABACAS)
      • Assembly report (PlasmidID)
      • Assembly assessment report (QUAST)
      • Call variants relative to reference (Minimap2, seqwish, vg, Bandage)
      • Variant annotation (SnpEff, SnpSift)
7. Present QC and visualisation for raw read, alignment, assembly and variant calling results (MultiQC)

Summary:

The codes provided here enable users to validate, explore, describe, and visualise the CCCSL Dataset.

Summary:

There are no code updates with this release. This release has been made to enable DOI integration with ZENODO.

Summary:

International audience Facing the ever-growing demand for data storage will most probably require a new paradigm. Nanoscale magnetic skyrmions are anticipated to solve this issue as they are arguably the smallest spin textures in magnetic thin films in nature. We designed cobalt-based multilayered thin films where the cobalt layer is sandwiched between two heavy metals providing additive interfacial Dzyaloshinskii-Moriya interactions, which reach a value close to 2 mJ m-2 in the case of the Ir|Co|Pt asymmetric multilayers. Using a magnetization-sensitive scanning x-ray transmission microscopy technique, we imaged small magnetic domains at very low field in these multilayers. The study of their behavior in perpendicular magnetic field allows us to conclude that they are actually magnetic skyrmions stabilized by the large Dzyaloshinskii-Moriya interaction. This discovery of stable sub-100 nm individual skyrmions at room temperature in a technologically relevant material opens the way for device applications in a near future. on

Summary:

Release of codes and data to reproduce results and visualizations of the article.

Summary:

FeatureViewer is a BioJS component that lays out, maps, orients, and renders position-based annotations for protein sequences. SimpleFeatureViewer and DasProteinFeatureViewer are built upon the FeatureViewer. For more information visit https://github.com/biojs/biojs

Summary: This dataset includes particulate organic matter (POM) concentrations for carbon, nitrogen, and phosphorus. Data are from samples collected from NOAA Ship R/V Ronald H. Brown (cruise EXPOCODE: 33RO20200321), acting under the auspices of the Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP), A13.5 GO-SHIP/CO2 Repeat Hydrography Cruise in 2020. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/868908

Summary: Physical indicators of winter climate variability (coastal upwelling, sea level, precipitation) influenced by the winter North Pacific High. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/686578

Summary: Monthly Regional Cumulative Upwelling Index (Ekman transport) for California and Benguela Ecosystems from 1979-2014/ For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/674979

Summary: Path analysis, run in Stata v. 11.1, for direct/indirect effects of upwelling on seabirds; data were collected at Dassen and Robben Islands, Malgas Island and in Lamberts Bay, South Africa. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/679946

Summary: [2.3.1] - 2022-02-15 Enhancements & fixes

• [#277] - Misuse of rstrip in make_variants_long_table.py script

Software dependencies Dependency Old version New version mosdepth 0.3.2 0.3.3 pangolin 3.1.19 3.1.20

Summary:

This post refers to an old set of outdated information. 

The system extracting those links was the precursor of the current web domain ranking in https://varocarbas.com/domain_ranking/

 

CLARIFICATION: this has nothing to do with marketing, SEO or paid anything. The sole purpose of this system now and ever has been to show my software development skills and related issues (e.g., big system running on very restricted hardware). All the displayed information is meant to show the actual reality as objectively as possible. 

There is only one way to be part of the aforementioned ranking or to get a better/worse position in it: the system (crawling bots, ranking algorithms, correction subsystems, etc.) voluntarily deciding so.

Summary:

High quality backlinks to instagram.com. The retrieval process was completed on 21-Nov-2014.

 

Note that the bots in varocarbas.com (Project 1 - Stage 2) are collecting a maximum of 1000 high-quality backlinks (e.g., "site.com/backlink" rather than "site.com/this/that/backlink") for each domain.

Summary:

High quality backlinks to facebook.com. The retrieval process was completed on 08-Nov-2014.

 

Note that the bots in varocarbas.com (Project 1 - Stage 2) are collecting a maximum of 1000 high-quality backlinks (e.g., "site.com/backlink" rather than "site.com/this/that/backlink") for each domain.