Download Mineable Miner Zip PORTABLE
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Download Mineable Miner zip
Miner Mike Saves the Day! or Ground Support... It's Important! exudes both humor and wisdom. Filmed in six different underground locations, the video teaches the necessity and use of ground supports. The video uses actual hard rock miners to teach the videos' protagonists (Jeff and Jason) about this important feature. This video was the winner of the NIOSH 2001 Alice Hamilton Award for Excellence in Educational Materials.
Effective Ethereum mining speed is higher by 3-5% because of a completely different miner code - much less invalid and outdated shares, higher GPU load, optimized OpenCL code, optimized assembler kernels.
You can use minerstat for Windows on personal computer, but we don't recommend it. We recommend mining on dedicated rigs and computers with a mining-prepared environment which is separated from the personal and sensitive data.
Like most cryptocurrencies, Zcash relies on miners to add transactions to the blockchain. While all nodes in the Zcash network contribute to verifying the validity of transactions, the miners take on the heavy lifting to secure the network.
To secure the network, Zcash uses a proof-of-work mining algorithm. Proof-of-work means that miners compete against each other using processing power to produce a new block on the chain. The first miner that succeeds completing the proper computation for each block is awarded with a network standard block reward and any fees for any transactions they add to that block.
As part of the Heartwood network upgrade, Zcash miners and mining pools have the option to mine directly to a shielded coinbase. Shielded coinbase allows Zcash users to have completely shielded ZEC from its creation. In addition to increased privacy, a considerable benefit to shielded coinbase is that miners have the option of getting their mining rewards issued immediately into a z-address. Reach out to your mining pool and encourage them to adopt shielded coinbase.
The ARCHS4 three-dimensional viewer also supports manual lasso selection of samples through a snipping tool. The colors used to highlight selected samples can be changed by the user. The genes view of ARCHS4 provides the same manual selection feature as with the samples view. Selected gene lists can be downloaded directly from ARCHS4, or submitted for gene list enrichment analysis with Enrichr28,29. Additionally, individual genes can be queried to locate an ARCHS4-dedicated gene-landing page. These single gene landing pages contain predicted biological functions based on correlations with genes assigned to GO categories; predicted upstream transcription factors based on correlation with identified targets as determined by ChIP-seq data from the ChEA and ENCODE gene set libraries; predicted knockout mouse phenotypes based on annotated MGI mammalian phenotypes; predicted human phenotypes based on co-expression correlation with genes that have assigned human phenotypes in the Human Phenotype Ontology30; predicted upstream protein kinases based on known kinase-substrates from KEA; and membership in pathways based on co-expression with pathways from KEGG. The single gene landing pages also list the top 100 most co-expressed genes for each individual gene. Additionally, for 53 distinct tissues and 67 cell lines, expression levels are visualized for each gene. These are visualized as two hierarchical trees with tissues and cells grouped by system and organ.
In addition, ARCHS4 processed data can be accessed via the ARCHS4 Chrome extension, which is freely available from the Chrome Web Store. The Chrome extension detects GEO series landing pages and then inserts a Series Matrix File (SMF) for download for each series that has been processed by the ARCHS4 pipeline. Each SMF contains read counts for all available samples in the series. The sample expression is also visualized as a heatmap using the Clustergrammer plugin31. Clustergrammer loads JSON files containing the z-score normalized gene expression of the top 500 most variable genes across the series and embeds the interactive heatmap directly into the GEO series landing page.
Multiple efforts attempted to uniformly reprocess large collections of RNA-seq data16,17,18,19,20. Table 1 provides an overview comparison of several resources with respect to size, cost per sample, and other attributes. The total sample size and cost for data processing are visualized in Fig. 4. Even though ARCHS4 contains more than double the number of RNA-seq samples than other resources, the estimated costs compared with Recount and Toil Recompute is an order of magnitude lower with $1745, $44,785, and $25,910, respectively. All approaches rely on the use of either private or public high-performance computer clusters. Toil Recompute16 was applied to re-compute the transcript level counts of 19,931 RNA-seq samples. The UCSC pipeline architecture was run on an Amazon Web Services (AWS) cluster and averaged $1.30 per sample. The data set contains 11,194 samples from TCGA, 8002 from GTEx, and 734 additional samples. The processed data is made available through a web interface called the Xena browser. Expression Atlas17 provides processed RNA-seq and microarray gene expression data for multiple species. Expression data is processed by a pipeline named iRAP18. The total number of assays in Expression Atlas is 118,209 from 3035 experiments. From these, only 565 are RNA-seq. All data is reported at the gene level and is accessible as a bulk zip download.
The Recount project19 performed sequence alignment with a pipeline termed Rail-RNA. The reported cost per sample is $0.72. The data in Recount contains 9662 samples from GTEx, 11,350 from TCGA, and 50,000 human SRA samples. The data is available as bulk download or through an R package. Expression is reported at the gene and transcript levels. The RNAseqDB20 also contains all GTEx and TCGA processed samples. The alignment for RNAseqDB was performed on an internal cluster and no cost analysis is available. The data is provided as bulk download files with FPKM normalized transcripts. The data is deposited in a GitHub repository. In contrast with ARCHS4, the reported cost for all similar efforts is about two orders of magnitude more expensive for processing a sample. Cost per sample is a critical factor in processing RNA-seq data because of the rapid growth in data production. Compute cost for ARCHS4 is almost negligible compared to the cost of comparable efforts. The number of samples already available from the ARCHS4 resource is by far the largest collection of processed RNA-seq to date, and the low-cost pipeline enables a rolling update as more samples become available. In contrast with other resources, ARCHS4 provides multiple methods for data accessibility. While Recount and Expression Atlas support programmatic access through R packages, only the Expression Atlas supports enrichment analysis on signatures derived from the expression profiles. A unique feature of ARCHS4 is the real-time data and metadata querying support that allows the identification and selection of relevant subsets of samples.
The ARCHS4 resource of processed RNA-seq data is created by systematically processing publicly available raw FASTQ samples from GEO/SRA. This resource can facilitate rapid progress of retrospective post hoc focal and global analyses. The ARCHS4 data processing pipeline employs a modular Dockerized software infrastructure that can align RNA-seq samples at an average cost of less than a cent (US $0.01). To our knowledge, this is an improvement of more than an order of magnitude over previously published solutions. The automation of the pipeline enables constant updating of the data repository by regular inclusion of newly published gene expression samples. The pipeline is open source and available on GitHub so it can be continually enhanced and adopted by the community for other projects. The pipeline uses Kallisto as the main alignment algorithm that was demonstrated through an unbiased benchmark to perform as well as, or even better than, another leading aligner, STAR. We compared the ARCHS4 co-expression data with co-expression data we created from other existing gene expression resources, namely GTEx and CCLE, and demonstrated how co-expression data from ARCHS4 is more effective in predicting biological functions and protein interactions. This could be because the data from ARCHS4 is more diverse. The fact that the data within ARCHS4 is from many sources has its disadvantages. These include batch effects and quality control inconsistencies. Standard batch effect removal methods are not applicable to the entire ARCHS4 data but may be useful for improving the analysis of segments of ARCHS4 data. The ARCHS4 web application and Chrome extension enable users to access and query the ARCHS4 data through both metadata and data searches. For data-driven queries, the unique JL dimensionality reduction method is implemented to maintain pairwise distances and correlations between samples even after reducing the number of dimensions by two orders of magnitude. Reducing the data to a lower dimension facilitates data-driven searches that return results instantly. The gene expression data provided by ARCHS4 is freely accessible for download in the compact HDF5 file format allowing programmatic access. The HDF5 files contain all available metadata information about all samples, but such metadata can be improved by having it follow community standards such as linking it to established identifiers and biological ontologies. The ARCHS4 three-dimensional data viewer lets users gain intuition about the global space of gene expression data from human and mouse at the sample and gene levels. The interface supports interactive data exploration through manual sample selection and highlighting of samples from tissues and cell lines. With the available data, we constructed comprehensive gene landing pages containing information about predicted gene function and PPI, co-expression with other genes, and average expression across cell lines and tissues. For a variety of tissues and cell lines, gene expression distributions are calculated for each gene. Such data can complement tissue and cell line expression resources such as BioGPS35 and GTEx14 as well as resources that provide accumulated knowledge about genes and proteins such as GeneCards36, the Harmonizome37, and the NCBI gene database38. Overall, the ARCHS4 resource contains comprehensive processed mRNA expression data that can further enable biological discovery toward better understanding of the inner-workings of mammalian cells. 041b061a72