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The goal of checkpoint is to solve the problem of package reproducibility in R. Specifically, checkpoint allows you to install packages as they existed on CRAN on a specific snapshot date as if you had a CRAN time machine. To achieve reproducibility, the checkpoint() function installs the packages required or called by your project and scripts to a local library exactly as they existed at the specified point in time. Only those packages are available to your project, thereby avoiding any package updates that came later and may have altered your results. In this way, anyone using checkpoint's checkpoint() can ensure the reproducibility of your scripts or projects at any time. To create the snapshot archives, once a day (at midnight UTC) Microsoft refreshes the Austria CRAN mirror on the "Microsoft R Archived Network" server (). Immediately after completion of the rsync mirror process, the process takes a snapshot, thus creating the archive. Snapshot archives exist starting from 2014-09-17.

Checkpoint proteins, such as PD-L1 on tumor cells and PD-1 on T cells, help keep immune responses in check. The binding of PD-L1 to PD-1 keeps T cells from killing tumor cells in the body (left panel). Blocking the binding of PD-L1 to PD-1 with an immune checkpoint inhibitor (anti-PD-L1 or anti-PD-1) allows the T cells to kill tumor cells (right panel).

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One such drug acts against a checkpoint protein called CTLA-4. Other immune checkpoint inhibitors act against a checkpoint protein called PD-1 or its partner protein PD-L1. Some tumors turn down the T cell response by producing lots of PD-L1.

Immune checkpoint inhibitors can cause side effects that affect people in different ways. The side effects you may have and how they make you feel will depend on how healthy you are before treatment, your type of cancer, how advanced it is, the type of immune checkpoint inhibitor you are receiving, and the dose.

A Checkpoint uses its configuration to determine what data to Validate against which Expectation Suite(s), and what actions to perform on the Validation Results - these validations and Actions are executed by calling a Checkpoint's run method (analogous to calling validate with a single Batch). Checkpoint configurations are very flexible. At one end of the spectrum, you can specify a complete configuration in a Checkpoint's YAML file, and simply call my_checkpoint.run(). At the other end, you can specify a minimal configuration in the YAML file and provide missing keys as kwargs when calling run.

CheckpointResult objects include many convenience methods (e.g. list_data_asset_names) that make working with Checkpoint results easier. You can learn more about these methods in the documentation for class: great_expectations.checkpoint.types.checkpoint_result.CheckpointResult.

The Reproducible R Toolkit provides an R function checkpoint, which ensures that all of the necessary R packages are installed with the correct version. This makes it easy to reproduce your results at a later date or on another system, and makes it easier to share your code with the confidence that others will get the same results you did.

To achieve reproducibility, daily snapshots of CRAN are stored on our checkpoint server. At midnight UTC each day, we refresh our mirror of CRAN is refreshed. When the rsync process is complete, the checkpoint server takes and stores a snapshot of the CRAN mirror as it was at that very moment. These daily snapshots can then be accessed on the MRAN website or using the checkpoint package, which installs and consistently use these packages just as they existed at midnight UTC on a specified snapshot date. Daily snapshots are available as far back as 2014-09-17. For more information, visit the checkpoint server GitHub site.

The goal of the checkpoint package is to solve the problem of package reproducibility in R. Since packages get updated on CRAN all the time, it can be difficult to recreate an environment where all your packages are consistent with some earlier state. To solve this issue, checkpoint allows you to install packages locally as they existed on a specific date from the corresponding snapshot (stored on the checkpoint server) and it configures your R session to use only these packages. Together, the checkpoint package and the checkpoint server act as a CRAN time machine so that anyone using checkpoint() can ensure the reproducibility of their scripts or projects at any time.

Finally, the checkpoint function serves as a unified interface to create_checkpoint and use_checkpoint. It looks for a pre-existing checkpoint directory, and if not found, creates it with create_checkpoint. It then calls use_checkpoint to put the checkpoint into use.

If you do not have an actual project open, this will usually expand to your R user directory (~/ on Unix/Linux and MacOS, or C:\Users\\Documents on Windows). For most people, this means that the function will scan through all the projects they have on their machine, which can lead to checkpointing a very large number of packages. Because of this, you should ensure that you are not in your user directory when you run checkpoint. A mitigating factor is that this should happen only once, as long as the checkpoint directory remains intact.

checkpoint will automatically add the rmarkdown package as a dependency if it finds any Rmarkdown-based files (those with extension .Rmd, .Rpres or .Rhtml) in your project. This allows you to continue working with such documents after checkpointing.

The functions delete_checkpoint() and delete_all_checkpoints() allow you to remove checkpoint directories that are no longer required. They check that the checkpoint(s) in question are not actually in use before deleting.

Each time create_checkpoint() is run, it saves a series of json files in the main checkpoint directory. These are outputs from the pkgdepends package, which checkpoint uses to perform the actual package installation, and can help you debug any problems that may occur.

When you run this script, checkpoint will create the checkpoint by scanning your project for packages, and then downloading them from the MRAN snapshot for 2020-01-01. As this happens, you should see a number of messages appear in your R window that tell how the installation is proceeding. It then sets the library search path and CRAN mirror for your session, to point to the local checkpoint directory and MRAN snapshot respectively.

A checkpoint is a point in the write-ahead log sequence at which all data files have been updated to reflect the information in the log. All data files will be flushed to disk. Refer to Section 30.5 for more details about what happens during a checkpoint.

The CHECKPOINT command forces an immediate checkpoint when the command is issued, without waiting for a regular checkpoint scheduled by the system (controlled by the settings in Section 20.5.2). CHECKPOINT is not intended for use during normal operation.

Drugs that block checkpoint proteins are called checkpoint inhibitors. They stop the proteins on the cancer cells from pushing the stop button. This turns the immune system back on and the T cells are able to find and attack the cancer cells.

The release of negative regulators of immune activation (immune checkpoints) that limit antitumor responses has resulted in unprecedented rates of long-lasting tumor responses in patients with a variety of cancers. This can be achieved by antibodies blocking the cytotoxic T lymphocyte-associated protein 4 (CTLA-4) or the programmed cell death 1 (PD-1) pathway, either alone or in combination. The main premise for inducing an immune response is the preexistence of antitumor T cells that were limited by specific immune checkpoints. Most patients who have tumor responses maintain long-lasting disease control, yet one-third of patients relapse. Mechanisms of acquired resistance are currently poorly understood, but evidence points to alterations that converge on the antigen presentation and interferon- signaling pathways. New-generation combinatorial therapies may overcome resistance mechanisms to immune checkpoint therapy.

For performance reasons, the Database Engine performs modifications to database pages in memory, in the buffer cache, and doesn't write these pages to disk after every change. Rather, the Database Engine periodically issues a checkpoint on each database. A checkpoint writes the current in-memory modified pages (known as dirty pages) and transaction log information from memory to disk, and also records the information in the transaction log.

The -k SQL Server advanced setup option enables a database administrator to throttle checkpoint I/O behavior based on the throughput of the I/O subsystem for some types of checkpoints. The -k setup option applies to automatic checkpoints and any otherwise unthrottled manual and internal checkpoints.

For automatic, manual, and internal checkpoints, only modifications made after the latest checkpoint need to be rolled forward during database recovery. This reduces the time required to recover a database.

An automatic checkpoint occurs each time the number of log records reaches the number the Database Engine estimates it can process during the time specified in the recovery interval server configuration option. For more information, see Configure the recovery interval Server Configuration Option.

In every database without a user-defined target recovery time, the Database Engine generates automatic checkpoints. The frequency depends on the recovery interval advanced server configuration option, which specifies the maximum time that a given server instance should use to recover a database during a system restart. The Database Engine estimates the maximum number of log records it can process within the recovery interval. When a database using automatic checkpoints reaches this maximum number of log records, the Database Engine issues a checkpoint on the database.

The time interval between automatic checkpoints can be highly variable. A database with a substantial transaction workload will have more frequent checkpoints than a database used primarily for read-only operations. Under the simple recovery model, an automatic checkpoint is also queued if the log becomes 70 percent full. 2351a5e196