We also rely on the Monaco-editor webpack plugin to register specific languages and configure web workers correctly in our project. When I try to build our project with webpack, I suddenly started seeing this error:

The .ttf is a font file packaged within the monaco-editor sources and I found a relevant issue for this. I followed the guidance in the docs to add rules for packaging up .ttf files correctly by modifying our webpack config:

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I also tried adding .ttf to the extensions configuration for webpack. I still seem to be hitting the same error. I think this might be due to our indirect dependency on MonacoEditor through the EditorPackage as I highlighted above.

The error that I saw was getting emitted from the file-loader plugin. With the webpack 4 upgrade, there were some breaking changes for loaders highlighted in the Incompatible Loaders section of this issue.

With the tools available to me, I can do real-time syntax highlighting and compilation of AutoStep tests in-browser, using WebAssembly to run my .NET library that does a lot of the heavy lifting, and the Monaco editor to provide the actual text editor behaviour. You can do some really cool stuff when you combine the power of .NET with a web-based user interface.

Below you can see a little demo GIF of how the editor control looks right now. You can see real-time syntax highlighting and test compilation as you type, with syntax errors being presented by the editor.

The first task was to get Monaco working as a Blazor component. I knew that I would need at least some Javascript code to function as the Interop layer, so rather than put that code in my main Blazor Client project (AutoStep.Editor.Client), I decided to put all the Monaco behaviour in a new Razor Component Library (AutoStep.Monaco), which I can use from my main project.

One nice feature of Blazor is that if you put your static files in the wwwroot folder of a Razor Component project, when you reference your Component project from your main Blazor App project, you can reference those static resources in your HTML, just by using the special _content path:

For people unfamiliar with it, syntax highlighting code usually involves tokenising a given line of code, which uses a lexer to go through a block of text and produce a set of tokens that give the position of named language constructs, like keywords, variables, strings, etc. The editor then knows which colours to apply to different parts of a line of text.

The problem with using Monarch in my situation is that the tokenisation would not be context-sensitive. By that, I mean that the tokenisation can only work off the content of the file it is highlighting, and cannot base the set of returned tokens on anything else.

In my situation, I want to highlight the Given/When/Then lines of a test a different colour if there is no backing step to call; in addition, I only know which part of a step is an argument (in red) based on which step it binds against.

Luckily, Monaco lets you define a manual token provider, using the setTokensProvider method. By implementing the Monaco-defined interface languages.TokensProvider, we can run our own custom code when Monaco needs to re-tokenise a line.

I showed you the TypeScript implementation of that interface earlier, when we were looking at how to call a .NET object from Javascript. All that the AutoStepTokenProvider TypeScript class does is call into an object in our Blazor .NET code, the AutoStepTokenizer, to handle the actual tokenisation.

To achieve the required tokenisation performance, I added Line Tokenisation support in the core AutoStep library, which is effectively a special-cased fast path through the normal compilation and linking process.

Once the AutoStep Core library returns the set of tokens for a line, I need to convert those tokens into TextMate scopes. Scopes are effectively names for the different tokens you can get, and Monaco can style each scope differently.

If you run the profiler in Chrome DevTools, you can see the activity happening on the background thread that calls into the WebAssembly system, and get an idea of how long your code is spending in the .NET world.

You can integrate the Monaco Editor with Kusto Query Language support (monaco-kusto) into your app. Integrating monaco-kusto into your app offers you an editing experience such as completion, colorization, refactoring, renaming, and go-to-definition. It requires you to build a solution for authentication, query execution, result display, and schema exploration. It offers you full flexibility to fashion the user experience that fits your needs.

The following steps describe how to set up your app to use monaco-kusto using webpack. The default entry point for a project is the src/index.js file and the default configuration file is the src/webpack.config.js file. The following steps assume that you're using the default webpack project setup to bundle your app.

Create a schema object that contains the database schema. For more information, see the clusterType interface in the src/schema.ts file. The following example shows a schema object that contains a single database and a single table:

PrEST regions (Agaton C et al. (2003); Lindskog M et al. (2005)) are first amplified with RT-PCR from total RNA template pools with specific oligonucleotide primers for each PrEST. Amplicons are automatically processed with solid phase restriction, and ligated into the plasmid vector pAff8c (Larsson M et al. (2000)) where the human gene fragment is fused to a histidine tag and albumin binding protein (His6ABP). After transformation into E. coli Rosetta(DE3), inserts are verified by DNA sequencing to omit clones with mutations and approved clones are single cell streaked. Plasmids are collected from all purified clones for deposition in the clone library and glycerol stocks are prepared and used as starting material for protein production.

All proteins are expressed as His6ABP fusions in E. coli shake flask cultures upon induction with IPTG. A fully automated protein purification system has been developed to allow for purifications of up to 60 cell lysates at a time. One-step purification is enabled by the hexahistidine affinity tag and metal affinity chromatography (IMAC) and performed under denaturing conditions. After evaluation of protein concentration and purity, the molecular weight of the PrEST proteins is determined by mass spectrometry as a final quality control. The purified proteins are then used to prepare antigens and affinity columns with PrEST-ligands. In addition, affinity resin with His6ABP-ligand is also produced.

The Human Protein Atlas contains images of histological sections from normal and cancer tissues obtained by immunohistochemistry. Antibodies are labeled with DAB (3,3'-diaminobenzidine) and the resulting brown staining indicates where an antibody has bound to its corresponding antigen. The section is furthermore counterstained with hematoxylin to enable visualization of microscopical features. Tissue microarrays are used to show antibody staining in samples from 144 individuals corresponding to 44 different normal tissue types, and samples from 216 cancer patients corresponding to 20 different types of cancer (movie about tissue microarray production and immunohistochemical staining). Each sample is represented by 1 mm tissue cores, resulting in a total number of 576 images for each antibody. Normal tissues are represented by samples from three individuals each, one core per individual, except for endometrium, skin, soft tissue and stomach, which are represented by samples from six individuals each and parathyroid gland, which is represented by one sample. Protein expression is annotated in 76 different normal cell types present in these tissue samples. For cancer tissues, two cores are sampled from each individual and protein expression is annotated in tumor cells. A small fraction of the 576 images are missing for most antibodies due to technical issues. Specimens containing normal and cancer tissue have been collected and sampled from anonymized paraffin embedded material of surgical specimens, in accordance with approval from the local ethics committee. For selected proteins extended tissue profiling is performed in addition to standard tissue microarrays. Examined tissues include mouse brain, human lactating breast, eye, thymus and extended samples of adrenal gland, skin and brain.

Since specimens are derived from surgical material, normal is here defined as non-neoplastic and morphologically normal. It is not always possible to obtain fully normal tissues and thus several of the tissues denoted as normal will include alterations due to inflammation, degeneration and tissue remodeling. In rare tissues, hyperplasia or benign proliferations are included as exceptions. It should also be noted that within normal morphology there may exist interindividual differences and variations due to primary diseases, age, sex etc. Such differences may also affect protein expression and thereby immunohistochemical staining patterns. Samples from cancer are also derived from surgical material. Due to subgroups and heterogeneity of tumors within each cancer type, included cases represent a typical mix of specimens from surgical pathology. The inclusion of tumors is based on availability and representativity, however, an effort has been made to include high and low grade malignancies where such is applicable. In certain tumor groups, subtypes have been included, e.g. breast cancer includes both ductal and lobular cancer, lung cancer includes both squamous cell carcinoma and adenocarcinoma and liver cancer includes both hepatocellular and cholangiocellular carcinoma etc. Tumor heterogeneity and interindividual differences may be reflected in diverse expression of proteins resulting in variable immunohistochemical staining patterns.

In order to provide an overview of protein expression patterns, all images of tissues stained by immunohistochemistry are manually annotated by a specialist followed by verification by a second specialist. Annotation of each different normal and cancer tissue is performed using fixed guidelines for classification of immunohistochemical results. Each tissue is examined for representability, and subsequently immunoreactivity in the different cell types present in normal or cancer tissues was annotated. Basic annotation parameters include an evaluation of i) staining intensity (negative, weak, moderate or strong), ii) fraction of stained cells (75%) and iii) subcellular localization (nuclear and/or cytoplasmic/membranous). The manual annotation also provides two summarizing texts describing the staining pattern for each antibody in normal tissues and in cancer tissues.

The terminology and ontology used is compliant with standards used in pathology and medical science. SNOMED classification is used for assignment of topography and morphology. SNOMED classification also underlies the given original diagnosis from which normal as well as cancer samples were collected.

A histological dictionary used in the annotation is available as a PDF-document, containing images stained by immunohistochemistry using antibodies included in the Human Protein Atlas. The dictionary displays subtypes of cells distinguishable from each other and also shows specific expression patterns in different intracellular structures. Annotation dictionary: screen usage (15 MB), printing (95 MB). 152ee80cbc

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