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deepmind / alphafold Public

header

This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document.

We also provide an implementation of AlphaFold-Multimer. This represents a work in progress and AlphaFold-Multimer isn't expected to be as stable as our monomer AlphaFold system. Read the guide for how to upgrade and update code.

Any publication that discloses findings arising from using this source code or the model parameters should cite the AlphaFold paper and, if applicable, the AlphaFold-Multimer paper.

Please also refer to the Supplementary Information for a detailed description of the method.

You can use a slightly simplified version of AlphaFold with this Colab notebook or community-supported versions (see below).

CASP14 predictions

First time setup

The following steps are required in order to run AlphaFold:

  1. Install Docker.

  2. Download genetic databases (see below).

  3. Download model parameters (see below).

  4. Check that AlphaFold will be able to use a GPU by running:

    docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

    The output of this command should show a list of your GPUs. If it doesn't, check if you followed all steps correctly when setting up the NVIDIA Container Toolkit or take a look at the following NVIDIA Docker issue.

If you wish to run AlphaFold using Singularity (a common containerization platform on HPC systems) we recommend using some of the third party Singularity setups as linked in https://github.com/deepmind/alphafold/issues/10 or https://github.com/deepmind/alphafold/issues/24.

Genetic databases

This step requires to be installed on your machine.

AlphaFold needs multiple genetic (sequence) databases to run:

We provide a script that can be used to download and set up all of these databases:

  • Default:

    scripts/download_all_data.sh <DOWNLOAD_DIR>

    will download the full databases.

  • With :

    scripts/download_all_data.sh <DOWNLOAD_DIR> reduced_dbs

    will download a reduced version of the databases to be used with the database preset.

Note: The download directory should not be a subdirectory in the AlphaFold repository directory. If it is, the Docker build will be slow as the large databases will be copied during the image creation.

We don't provide exactly the database versions used in CASP14 – see the note on reproducibility. Some of the databases are mirrored for speed, see mirrored databases.

Note: The total download size for the full databases is around 415 GB and the total size when unzipped is 2.2 TB. Please make sure you have a large enough hard drive space, bandwidth and time to download. We recommend using an SSD for better genetic search performance.

The script will also download the model parameter files. Once the script has finished, you should have the following directory structure:

is only downloaded if you download the full databases, and is only downloaded if you download the reduced databases.

Model parameters

While the AlphaFold code is licensed under the Apache 2.0 License, the AlphaFold parameters are made available for non-commercial use only under the terms of the CC BY-NC 4.0 license. Please see the Disclaimer below for more detail.

The AlphaFold parameters are available from https://storage.googleapis.com/alphafold/alphafold_params_2021-10-27.tar, and are downloaded as part of the script. This script will download parameters for:

  • 5 models which were used during CASP14, and were extensively validated for structure prediction quality (see Jumper et al. 2021, Suppl. Methods 1.12 for details).
  • 5 pTM models, which were fine-tuned to produce pTM (predicted TM-score) and (PAE) predicted aligned error values alongside their structure predictions (see Jumper et al. 2021, Suppl. Methods 1.9.7 for details).
  • 5 AlphaFold-Multimer models that produce pTM and PAE values alongside their structure predictions.

Updating existing AlphaFold installation to include AlphaFold-Multimers

If you have AlphaFold v2.0.0 or v2.0.1 you can either reinstall AlphaFold fully from scratch (remove everything and run the setup from scratch) or you can do an incremental update that will be significantly faster but will require a bit more work. Make sure you follow these steps in the exact order they are listed below:

  1. Update the code.
    • Go to the directory with the cloned AlphaFold repository and run to get all code updates.
  2. Download the UniProt and PDB seqres databases.
    • Run .
    • Remove . It is needed to have PDB SeqRes and PDB from exactly the same date. Failure to do this step will result in potential errors when searching for templates when running AlphaFold-Multimer.
    • Run .
    • Run .
  3. Update the model parameters.
    • Remove the old model parameters in .
    • Download new model parameters using .
  4. Follow Running AlphaFold.

API changes between v2.0.0 and v2.1.0

We tried to keep the API as much backwards compatible as possible, but we had to change the following:

  • The now needs a argument as MSA sampling happens inside the Multimer model.
  • The flag in and was split into and .
  • The models to use are not specified using but rather using the flag. If you want to customize which models are used for each preset, you will have to modify the the dictionary in .
  • Setting the flag is now needed when using .

Running AlphaFold

The simplest way to run AlphaFold is using the provided Docker script. This was tested on Google Cloud with a machine using the with 12 vCPUs, 85 GB of RAM, a 100 GB boot disk, the databases on an additional 3 TB disk, and an A100 GPU.

  1. Clone this repository and into it.

    git clone https://github.com/deepmind/alphafold.git
  2. Build the Docker image:

    docker build -f docker/Dockerfile -t alphafold .
  3. Install the dependencies. Note: You may optionally wish to create a Python Virtual Environment to prevent conflicts with your system's Python environment.

    pip3 install -r docker/requirements.txt
  4. Run pointing to a FASTA file containing the protein sequence(s) for which you wish to predict the structure. If you are predicting the structure of a protein that is already in PDB and you wish to avoid using it as a template, then must be set to be before the release date of the structure. You must also provide the path to the directory containing the downloaded databases. For example, for the T1050 CASP14 target:

    python3 docker/run_docker.py \ --fasta_paths=T1050.fasta \ --max_template_date=2020-05-14 \ --data_dir=$DOWNLOAD_DIR

    By default, Alphafold will attempt to use all visible GPU devices. To use a subset, specify a comma-separated list of GPU UUID(s) or index(es) using the flag. See GPU enumeration for more details.

  5. You can control which AlphaFold model to run by adding the flag. We provide the following models:

    • monomer: This is the original model used at CASP14 with no ensembling.

    • monomer_casp14: This is the original model used at CASP14 with , matching our CASP14 configuration. This is largely provided for reproducibility as it is 8x more computationally expensive for limited accuracy gain (+0.1 average GDT gain on CASP14 domains).

    • monomer_ptm: This is the original CASP14 model fine tuned with the pTM head, providing a pairwise confidence measure. It is slightly less accurate than the normal monomer model.

    • multimer: This is the AlphaFold-Multimer model. To use this model, provide a multi-sequence FASTA file. In addition, the UniProt database should have been downloaded.

  6. You can control MSA speed/quality tradeoff by adding or to the run command. We provide the following presets:

    • reduced_dbs: This preset is optimized for speed and lower hardware requirements. It runs with a reduced version of the BFD database. It requires 8 CPU cores (vCPUs), 8 GB of RAM, and 600 GB of disk space.

    • full_dbs: This runs with all genetic databases used at CASP14.

    Running the command above with the model preset and the data preset would look like this:

    python3 docker/run_docker.py \ --fasta_paths=T1050.fasta \ --max_template_date=2020-05-14 \ --model_preset=monomer \ --db_preset=reduced_dbs \ --data_dir=$DOWNLOAD_DIR

Running AlphaFold-Multimer

All steps are the same as when running the monomer system, but you will have to

  • provide an input fasta with multiple sequences,
  • set ,
  • optionally set the flag with booleans that determine whether all input sequences in the given fasta file are prokaryotic. If that is not the case or the origin is unknown, set to for that fasta.

An example that folds a protein complex that is prokaryotic:

python3 docker/run_docker.py \ --fasta_paths=multimer.fasta \ --is_prokaryote_list=true \ --max_template_date=2020-05-14 \ --model_preset=multimer \ --data_dir=$DOWNLOAD_DIR

Examples

Below are examples on how to use AlphaFold in different scenarios.

Folding a monomer

Say we have a monomer with the sequence . The input fasta should be:

Then run the following command:

python3 docker/run_docker.py \ --fasta_paths=monomer.fasta \ --max_template_date=2021-11-01 \ --model_preset=monomer \ --data_dir=$DOWNLOAD_DIR

Folding a homomer

Say we have a homomer from a prokaryote with 3 copies of the same sequence . The input fasta should be:

Then run the following command:

python3 docker/run_docker.py \ --fasta_paths=homomer.fasta \ --is_prokaryote_list=true \ --max_template_date=2021-11-01 \ --model_preset=multimer \ --data_dir=$DOWNLOAD_DIR

Folding a heteromer

Say we have a heteromer A2B3 of unknown origin, i.e. with 2 copies of and 3 copies of . The input fasta should be:

Then run the following command:

python3 docker/run_docker.py \ --fasta_paths=heteromer.fasta \ --is_prokaryote_list=false \ --max_template_date=2021-11-01 \ --model_preset=multimer \ --data_dir=$DOWNLOAD_DIR

Folding multiple monomers one after another

Say we have a two monomers, and .

We can fold both sequentially by using the following command:

python3 docker/run_docker.py \ --fasta_paths=monomer1.fasta,monomer2.fasta \ --max_template_date=2021-11-01 \ --model_preset=monomer \ --data_dir=$DOWNLOAD_DIR

Folding multiple multimers one after another

Say we have a two multimers, and . Both are from a prokaryotic organism.

We can fold both sequentially by using the following command:

python3 docker/run_docker.py \ --fasta_paths=multimer1.fasta,multimer2.fasta \ --is_prokaryote_list=true,true \ --max_template_date=2021-11-01 \ --model_preset=multimer \ --data_dir=$DOWNLOAD_DIR

AlphaFold output

The outputs will be saved in a subdirectory of the directory provided via the flag of (defaults to ). The outputs include the computed MSAs, unrelaxed structures, relaxed structures, ranked structures, raw model outputs, prediction metadata, and section timings. The directory will have the following structure:

The contents of each output file are as follows:

  • – A file containing the input feature NumPy arrays used by the models to produce the structures.

  • – A PDB format text file containing the predicted structure, exactly as outputted by the model.

  • – A PDB format text file containing the predicted structure, after performing an Amber relaxation procedure on the unrelaxed structure prediction (see Jumper et al. 2021, Suppl. Methods 1.8.6 for details).

  • – A PDB format text file containing the relaxed predicted structures, after reordering by model confidence. Here should contain the prediction with the highest confidence, and the prediction with the lowest confidence. To rank model confidence, we use predicted LDDT (pLDDT) scores (see Jumper et al. 2021, Suppl. Methods 1.9.6 for details).

  • – A JSON format text file containing the pLDDT values used to perform the model ranking, and a mapping back to the original model names.

  • – A JSON format text file containing the times taken to run each section of the AlphaFold pipeline.

  • - A directory containing the files describing the various genetic tool hits that were used to construct the input MSA.

  • – A file containing a nested dictionary of the various NumPy arrays directly produced by the model. In addition to the output of the structure module, this includes auxiliary outputs such as:

    • Distograms ( contains a NumPy array of shape [N_res, N_res, N_bins] and contains the definition of the bins).
    • Per-residue pLDDT scores ( contains a NumPy array of shape [N_res] with the range of possible values from to , where means most confident). This can serve to identify sequence regions predicted with high confidence or as an overall per-target confidence score when averaged across residues.
    • Present only if using pTM models: predicted TM-score ( field contains a scalar). As a predictor of a global superposition metric, this score is designed to also assess whether the model is confident in the overall domain packing.
    • Present only if using pTM models: predicted pairwise aligned errors ( contains a NumPy array of shape [N_res, N_res] with the range of possible values from to , where means most confident). This can serve for a visualisation of domain packing confidence within the structure.

The pLDDT confidence measure is stored in the B-factor field of the output PDB files (although unlike a B-factor, higher pLDDT is better, so care must be taken when using for tasks such as molecular replacement).

This code has been tested to match mean top-1 accuracy on a CASP14 test set with pLDDT ranking over 5 model predictions (some CASP targets were run with earlier versions of AlphaFold and some had manual interventions; see our forthcoming publication for details). Some targets such as T1064 may also have high individual run variance over random seeds.

Inferencing many proteins

The provided inference script is optimized for predicting the structure of a single protein, and it will compile the neural network to be specialized to exactly the size of the sequence, MSA, and templates. For large proteins, the compile time is a negligible fraction of the runtime, but it may become more significant for small proteins or if the multi-sequence alignments are already precomputed. In the bulk inference case, it may make sense to use our function to pad the inputs to a uniform size, thereby reducing the number of compilations required.

We do not provide a bulk inference script, but it should be straightforward to develop on top of the method with a parallel system for precomputing multi-sequence alignments. Alternatively, this script can be run repeatedly with only moderate overhead.

Note on CASP14 reproducibility

AlphaFold's output for a small number of proteins has high inter-run variance, and may be affected by changes in the input data. The CASP14 target T1064 is a notable example; the large number of SARS-CoV-2-related sequences recently deposited changes its MSA significantly. This variability is somewhat mitigated by the model selection process; running 5 models and taking the most confident.

To reproduce the results of our CASP14 system as closely as possible you must use the same database versions we used in CASP. These may not match the default versions downloaded by our scripts.

For genetics:

For templates:

An alternative for templates is to use the latest PDB and PDB70, but pass the flag , which restricts templates only to structures that were available at the start of CASP14.

Citing this work

If you use the code or data in this package, please cite:

@Article{AlphaFold2021, author = {Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and {\v{Z}}{\'\i}dek, Augustin and Potapenko, Anna and Bridgland, Alex and Meyer, Clemens and Kohl, Simon A A and Ballard, Andrew J and Cowie, Andrew and Romera-Paredes, Bernardino and Nikolov, Stanislav and Jain, Rishub and Adler, Jonas and Back, Trevor and Petersen, Stig and Reiman, David and Clancy, Ellen and Zielinski, Michal and Steinegger, Martin and Pacholska, Michalina and Berghammer, Tamas and Bodenstein, Sebastian and Silver, David and Vinyals, Oriol and Senior, Andrew W and Kavukcuoglu, Koray and Kohli, Pushmeet and Hassabis, Demis}, journal = {Nature}, title = {Highly accurate protein structure prediction with {AlphaFold}}, year = {2021}, volume = {596}, number = {7873}, pages = {583--589}, doi = {10.1038/s41586-021-03819-2} }

In addition, if you use the AlphaFold-Multimer mode, please cite:

@article {AlphaFold-Multimer2021, author = {Evans, Richard and O{\textquoteright}Neill, Michael and Pritzel, Alexander and Antropova, Natasha and Senior, Andrew and Green, Tim and {\v{Z}}{\'\i}dek, Augustin and Bates, Russ and Blackwell, Sam and Yim, Jason and Ronneberger, Olaf and Bodenstein, Sebastian and Zielinski, Michal and Bridgland, Alex and Potapenko, Anna and Cowie, Andrew and Tunyasuvunakool, Kathryn and Jain, Rishub and Clancy, Ellen and Kohli, Pushmeet and Jumper, John and Hassabis, Demis}, journal = {bioRxiv} title = {Protein complex prediction with AlphaFold-Multimer}, year = {2021}, elocation-id = {2021.10.04.463034}, doi = {10.1101/2021.10.04.463034}, URL = {https://www.biorxiv.org/content/early/2021/10/04/2021.10.04.463034}, eprint = {https://www.biorxiv.org/content/early/2021/10/04/2021.10.04.463034.full.pdf}, }

Community contributions

Colab notebooks provided by the community (please note that these notebooks may vary from our full AlphaFold system and we did not validate their accuracy):

Acknowledgements

AlphaFold communicates with and/or references the following separate libraries and packages:

We thank all their contributors and maintainers!

License and Disclaimer

This is not an officially supported Google product.

Copyright 2021 DeepMind Technologies Limited.

AlphaFold Code License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Model Parameters License

The AlphaFold parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode

Third-party software

Use of the third-party software, libraries or code referred to in the Acknowledgements section above may be governed by separate terms and conditions or license provisions. Your use of the third-party software, libraries or code is subject to any such terms and you should check that you can comply with any applicable restrictions or terms and conditions before use.

Mirrored Databases

The following databases have been mirrored by DeepMind, and are available with reference to the following:

  • BFD (unmodified), by Steinegger M. and Söding J., available under a Creative Commons Attribution-ShareAlike 4.0 International License.

  • BFD (modified), by Steinegger M. and Söding J., modified by DeepMind, available under a Creative Commons Attribution-ShareAlike 4.0 International License. See the Methods section of the AlphaFold proteome paper for details.

  • Uniclust30: v2018_08 (unmodified), by Mirdita M. et al., available under a Creative Commons Attribution-ShareAlike 4.0 International License.

  • MGnify: v2018_12 (unmodified), by Mitchell AL et al., available free of all copyright restrictions and made fully and freely available for both non-commercial and commercial use under CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.

Источник: https://github.com/deepmind/alphafold

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Science Research Software

Ansoft HFSS & Maxwell 3D; Ansys Electromagnetic Desktop
SRSC will provide the software client to install on your computer. It runs checking the ITS License Server. You can also run it on Yale HPC clusters.
Yale’s license allows 275  EM and 275 HFSS design and post-processing jobs to run simultaneously.
HFSS 11 - 2016, Maxwell 12 - 2016
Windows, Linux
Individual License for Faculty Members: $100
Lab subscription (up to 12 computers):  $500
Finite Elements Electromagnetic Simulator
ChemDraw Professional
Yale Software Library
Version 15
Mac and Windows
Free
Chemical Structure Drawing Suite
LabView
Yale Software Library
2015, earlier versions and additional modules and toolkits Sonnet Suite Pro Activation key available by contacting SRSC
Windows 32 and 64 bit, Mac and Unix
Individual Faculty Member: $50/year
Lab or Workgroup: $250/year
Academic Department: $1000/year
Students are not eligible for this license.
Laboratory virtual instrumentation engineering workbench
Multisim and Uniboard (NI Circuit Design Kit)
SRSC provides software client to install on your computer. It runs checking SRSC License Server.
Yale’s license allows 100 network seats to run simultaneously.
2013 (Win XP and later)
2014 (Win 7 and later).
Individual copy: $50. Students are not eligible for this license (they can run it on computers in Yale Morse Teaching Center).
Electronic schematic capture, simulation and printed circuit layout program
Mathematica
Individual Copies are available at the Yale Software Library
You can also run it on Yale HPC clusters. 
Faculty can request home use copies.
SRSC provides also license to Wolfram Alpha Pro and WebMathematica
7.X - 10.X
Mac, Windows and Linux
Individual Faculty Member: $50/year
Lab or Workgroup: $250/year
Academic Department: $1000/year
Students are eligible for free fully functional copy.
Mathematical software
Matlab
Yale Software Library
You can also run it on the Yale HPC clusters
2015b, earlier versions available, please contact us.
Mac, Windows, Linux
Individual Faculty Member: $75/year
Lab or Workgroup: $375/year
Academic Department: $1000/year
Students are eligible for the free student edition.
Mathematical computing and high-level technical computing language
Microwave Office (NI AWRD Design Environment)
SRSC provides software client to install on your computer. It runs checking ITS License Server.  
SRSC can provide you with certified training materials.
AWR Analyst MP is available per request
MWO 2007 -2012
Windows
Free
Software environment for microwave design
Origin
Yale Software Library
2016
Windows
Individual License for Faculty Member: No Cost
Lab or Workgroup: $125
Academic Department: $750
Scientific Visualization Package
SolidWorks
Educational Version: SRSC will provide software client to install on your computer. It runs checking the ITS License Server.
University Research Version: Schedule an appointment to run it on dedicated computer located in SRSC Lab.
SRSC can also provide HSMWORKS - The Malware Hunter Pro 1.125.0.723 Crack + License Key Free 2021 solution for Solidworks
Students can inquire about the free Student Engineering Kit from Digital camera control Software Library to be used on your own computer
2015 Educational
2016 University Research
Windows.  
The most current versions of this software does not run on Windows XP.  Previous versions can be provided for this purpose.
Educational Version Individual License for Faculty Members and Researchers: $50
University Research Version: Free
CAD Software
Sonnet
SRSC will provide the software client to install on your computer. It runs checking the SRSC License Server.
SRSC can also provide you with certified training materials.
v. 12 – 16
Windows
Current version are not compatible with XP
Free
3D Planar High-Frequency Electromagnetic Software
COMSOL Multiphysics
SRSC will provide the software client to install on your computer. It runs checking the SRSC License Server.
SRSC maintains 3 network licenses for the main computational engine and 1 seat for almost all classical physics modules. Availability is based on a first come first run basis.
SRSC can run large problems on our workstation with 192 GB of RAM and on Yale HPC clusters.
3.5a, 4.x, 5.x
Mac, Windows, Linux
Free
3-D finite element package to solve coupled problems in physics and engineering
Fluent – Ansys Academic CFD
SRSC will provide software client to install Sonnet Suite Pro Activation key your computer. It runs checking the SRSC License Server.
Yale’s license includes 5 research seats and 50 teaching seats (fully functional but limited by the number of nodes)
We can run it on HPC clusters (without distributing solvers)
v. 14 – 17 Windows, Linux
Free
Computational Fluid Dynamics
JMol and JSMol
JSpecView
SRSC provides assistance with programming and setup
Current Version Available
Mac, Windows and Linux
Free
OS-independent Java and JavaScript based tool for molecule and spectra visualization.
ChemAxon Marvin and JChem
SRSC will provide software client to install on your computer.
SRSC can assist with design and porting of chemical databases, similarity and substructure searches, clustering
Current Version Available
Mac, Windows, Linux
Free
Chemical Informatics Software
Molecule Drawing
PyMol
SRSC will provide software client to install on your computer.
SRSC can provide right version of Python
1.5.02, requires Python 2.7
Windows
Free
Molecular Sonnet Suite Pro Activation key System written in Python
Magma
SRSC will provide software client to install on your computer.
SRSC will provide databases and 3rd party tools
Version 2.2
Linux, Windows, Mac
Free
Computational Algebra Package from University of Sydney, Australia
Источник: https://research.computing.yale.edu/services/science-research-software-core/science-research-software

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ToolsOnAir Idm crack 2019 - Free Activators Sonnet Technologies' xMac™ mini Server for Latest 'TV Station in a Mac' Solution

The just:Broadcast Suite is an integrated suite of QuickTime®-based software tools that enable broadcast professionals Sonnet Suite Pro Activation key create a complete TV production facility in a Mac mini computer. Sonnet's xMac mini Server enables A/V professionals to rack-mount a Mac mini where it can be easily cabled and connected to two onboard PCIe slots via integrated Thunderbolt-to-PCIe expansion.

For the new solution's hardware platform, ToolsOnAir used six 2.6 GHz Mac minis, each one housed in its own xMac mini Server, and each configured with a Blackmagic Design DeckLink Quad PCIe capture and playback card and a Sonnet Presto Gigabit Ethernet PCIe card. The system also includes ToolsOnAir's flow:rage A/V optimized network-attached storage (NAS) system. Software components include ToolsOnAir's live:cut, a centralized interface for ingest, metadata, live logging, media management, and workflow automation, and the just:in multicamera ingest system.

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"In the previous generation of this solution, we needed a 5U rackable Mac Pro — and even then, the system only provided four HD ingest channels. But thanks to the compact 1U form factor of the xMac mini Server enclosure, we've been able to quintuple the workload within the same space," said Manuel Grund, product Sonnet Suite Pro Activation key of ToolsOnAir´s just:in ingest solution. "When we tested the xMac mini Server, we knew it would provide the ideal solution in terms of reliability, ease of deployment, and suitability for a 24/7/365 operation. We highly recommend the xMac mini Server product line, and Sonnet's solutions will be our first choice for deployment in current and future projects."

The new ToolsOnAir "TV Station in a Mac" is able to capture up to 24 SD or 12 HD channels in ProRes HQ within only 6U of rack space. Each of the six Mac minis ingests up to two HD channels via the Blackmagic DeckLink card. The content is then stored in the flow:rage NAS system via the Gigabit Ethernet connection provided by the Sonnet Presto card. The Mac mini's internal Ethernet interface provides a 10Gb connection to an external Mac, where the operator can access the just:in and live:cut graphical user interfaces for remote control of the application. The system enables automated export of camera angles into a multiclip XML sequence that is readable by Apple Final Cut Pro® X and Adobe® Premiere® Pro, with automatic launch of the selected nonlinear editor upon export.

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"ToolsOnAir's new implementation is exactly the leading-edge live production ingest and playout solution for which our Sonnet Suite Pro Activation key mini Server was designed. The system offers many advantages from both practical and technological perspectives by leveraging the Mac mini's small footprint and lower cost to provide support for 12 HD channels in only 6U of rack space," said Greg LaPorte, vice president of sales and marketing, Sonnet Technologies. "As an added benefit, ToolsOnAir has built Sonnet Suite Pro Activation key system using off-the-shelf hardware components, which radically reduces downtime and makes the exchange of defective parts a simple task in case of a Sonnet Suite Pro Activation key failure."

ToolsOnAir provides powerful, intuitive software-based applications designed to create a professional, fully functional broadcast production facility using Mac OS® X and low-cost, Sonnet Suite Pro Activation key hardware components. The ToolsOnAir just: suite is designed with live broadcast production in mind, integrating video, audio, and graphics along with automation, asset management, and multiple format capabilities. Full QuickTime®, Final Cut®, AVID, and Adobe® Premiere® Pro compatibility eliminates time-intensive file conversion, and a powerful, intuitive GUI streamlines the workflow from ingest to playout.

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Sonnet Technologies Inc. is a leading provider of storage systems, Thunderbolt™ technology PCIe® expansion products, interface cards, and media readers for professional users in the audio, video, and broadcast industries. Sonnet's Thunderbolt expansion products enable the use of pro audio I/O and DSP cards, pro video capture and transcoding cards, interface cards, and other high-performance PCIe cards with mini, portable, and all-in-one computers. The company's product line features a range of high-performance, cost-effective, and reliable portable, desktop, and rackmount RAID storage solutions. For more than 25 years Sonnet has pioneered and brought to market numerous innovative and award-winning products that enhance the performance and connectivity of Mac®, Windows®, and other industry-standard computers.

Источник: https://www.live-production.tv/news/products/toolsonair-chooses-sonnet-technologies-xmac%E2%84%A2-mini-server-latest-tv-station-mac
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ACS delivers high performance state-of-the-art RF and microwave circuit design and simulation software for the Electronic Design Automation (EDA) marketplace.  In addition to circuit simulation Sonnet Suite Pro Activation key analysis, LINC2 Pro provides schematic capture and an integrated design environment that includes a suite of synthesis tools for automating the design of circuits from audio and HF (high frequency) to microwave and beyond.  Physical models for stripline and microstrip transmission line extend the accuracy to millimeter waves.  And now LINC2 has recently been upgraded to include a new set of circuit simulation models for coplanar waveguide circuit design. 

A wide range of applications are supported, from analog sub-circuit design to RF circuit design for complex wireless communications systems.  ACS offers circuit design, synthesis, simulation and optimization programs for the advanced engineering professional and first time CAE (Computer Aided Engineering) user alike.

LINC2 from ACS offers a high degree of connectivity to the leading RF/microwave CAD/CAE, EDA and EM simulation software.  LINC2 directly interfaces to leading RF and Microwave design suites, allowing it to be used stand-alone or by leveraging its capabilities with those of other major packages.  Schematics and circuit designs created in LINC2 can be automatically exported to AWR's Microwave Office and Sonnet Software's Sonnet EM simulation software.  Click on the links below to learn more:

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ACS is an AWR Alliance Partner

 

ACS is a Sonnet EDA Software Partner

The LINC2/Sonnet interface uses Sonnet Suite Pro Activation key single button click to automatically transfer the LINC2 layout geometry into Sonnet for world-class EM analysis.

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Sonnet and Sonnet-Lite are trademarks of Sonnet Software, Inc.
Windows and Windows XP are trademarks of Microsoft corporation.
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