georghildebrand
Data Engineer and Data Scientist. Focus on ML infrastructure to put models efficiently into production.
EU
georghildebrand's Stars
hyperledger/fabric
Hyperledger Fabric is an enterprise-grade permissioned distributed ledger framework for developing solutions and applications. Its modular and versatile design satisfies a broad range of industry use cases. It offers a unique approach to consensus that enables performance at scale while preserving privacy.
kubernetes-sigs/kustomize
Customization of kubernetes YAML configurations
bufbuild/buf
The best way of working with Protocol Buffers.
jigish/slate
A window management application (replacement for Divvy/SizeUp/ShiftIt)
project-chip/connectedhomeip
Matter (formerly Project CHIP) creates more connections between more objects, simplifying development for manufacturers and increasing compatibility for consumers, guided by the Connectivity Standards Alliance.
git-ecosystem/git-credential-manager
Secure, cross-platform Git credential storage with authentication to GitHub, Azure Repos, and other popular Git hosting services.
fikovnik/ShiftIt
Managing windows size and position in OSX
pre-commit/pre-commit-hooks
Some out-of-the-box hooks for pre-commit
jakehilborn/displayplacer
macOS command line utility to configure multi-display resolutions and arrangements. Essentially XRandR for macOS.
NAalytics/Assemblies-of-putative-SARS-CoV2-spike-encoding-mRNA-sequences-for-vaccines-BNT-162b2-and-mRNA-1273
RNA vaccines have become a key tool in moving forward through the challenges raised both in the current pandemic and in numerous other public health and medical challenges. With the rollout of vaccines for COVID-19, these synthetic mRNAs have become broadly distributed RNA species in numerous human populations. Despite their ubiquity, sequences are not always available for such RNAs. Standard methods facilitate such sequencing. In this note, we provide experimental sequence information for the RNA components of the initial Moderna (https://pubmed.ncbi.nlm.nih.gov/32756549/) and Pfizer/BioNTech (https://pubmed.ncbi.nlm.nih.gov/33301246/) COVID-19 vaccines, allowing a working assembly of the former and a confirmation of previously reported sequence information for the latter RNA. Sharing of sequence information for broadly used therapeutics has the benefit of allowing any researchers or clinicians using sequencing approaches to rapidly identify such sequences as therapeutic-derived rather than host or infectious in origin. For this work, RNAs were obtained as discards from the small portions of vaccine doses that remained in vials after immunization; such portions would have been required to be otherwise discarded and were analyzed under FDA authorization for research use. To obtain the small amounts of RNA needed for characterization, vaccine remnants were phenol-chloroform extracted using TRIzol Reagent (Invitrogen), with intactness assessed by Agilent 2100 Bioanalyzer before and after extraction. Although our analysis mainly focused on RNAs obtained as soon as possible following discard, we also analyzed samples which had been refrigerated (~4 ℃) for up to 42 days with and without the addition of EDTA. Interestingly a substantial fraction of the RNA remained intact in these preparations. We note that the formulation of the vaccines includes numerous key chemical components which are quite possibly unstable under these conditions-- so these data certainly do not suggest that the vaccine as a biological agent is stable. But it is of interest that chemical stability of RNA itself is not sufficient to preclude eventual development of vaccines with a much less involved cold-chain storage and transportation. For further analysis, the initial RNAs were fragmented by heating to 94℃, primed with a random hexamer-tailed adaptor, amplified through a template-switch protocol (Takara SMARTerer Stranded RNA-seq kit), and sequenced using a MiSeq instrument (Illumina) with paired end 78-per end sequencing. As a reference material in specific assays, we included RNA of known concentration and sequence (from bacteriophage MS2). From these data, we obtained partial information on strandedness and a set of segments that could be used for assembly. This was particularly useful for the Moderna vaccine, for which the original vaccine RNA sequence was not available at the time our study was carried out. Contigs encoding full-length spikes were assembled from the Moderna and Pfizer datasets. The Pfizer/BioNTech data [Figure 1] verified the reported sequence for that vaccine (https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/), while the Moderna sequence [Figure 2] could not be checked against a published reference. RNA preparations lacking dsRNA are desirable in generating vaccine formulations as these will minimize an otherwise dramatic biological (and nonspecific) response that vertebrates have to double stranded character in RNA (https://www.nature.com/articles/nrd.2017.243). In the sequence data that we analyzed, we found that the vast majority of reads were from the expected sense strand. In addition, the minority of antisense reads appeared different from sense reads in lacking the characteristic extensions expected from the template switching protocol. Examining only the reads with an evident template switch (as an indicator for strand-of-origin), we observed that both vaccines overwhelmingly yielded sense reads (>99.99%). Independent sequencing assays and other experimental measurements are ongoing and will be needed to determine whether this template-switched sense read fraction in the SmarterSeq protocol indeed represents the actual dsRNA content in the original material. This work provides an initial assessment of two RNAs that are now a part of the human ecosystem and that are likely to appear in numerous other high throughput RNA-seq studies in which a fraction of the individuals may have previously been vaccinated. ProtoAcknowledgements: Thanks to our colleagues for help and suggestions (Nimit Jain, Emily Greenwald, Lamia Wahba, William Wang, Amisha Kumar, Sameer Sundrani, David Lipman, Bijoyita Roy). Figure 1: Spike-encoding contig assembled from BioNTech/Pfizer BNT-162b2 vaccine. Although the full coding region is included, the nature of the methodology used for sequencing and assembly is such that the assembled contig could lack some sequence from the ends of the RNA. Within the assembled sequence, this hypothetical sequence shows a perfect match to the corresponding sequence from documents available online derived from manufacturer communications with the World Health Organization [as reported by https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/]. The 5’ end for the assembly matches the start site noted in these documents, while the read-based assembly lacks an interrupted polyA tail (A30(GCATATGACT)A70) that is expected to be present in the mRNA.
antonbabenko/pre-commit-terraform
pre-commit git hooks to take care of Terraform configurations 🇺🇦
awslabs/dgl-ke
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
Azure/Azure-Functions
IFCjs/web-ifc-viewer
Graphics engine and toolkit for client applications.
aws/graph-notebook
Library extending Jupyter notebooks to integrate with Apache TinkerPop, openCypher, and RDF SPARQL.
stormcat24/protodep
Collect necessary .proto files (Protocol Buffers IDL) and manage dependencies
OSC/ondemand
Supercomputing. Seamlessly. Open, Interactive HPC Via the Web
smoke-trees/Voice-synthesis
This repository is an implementation of Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis (SV2TTS) with a vocoder that works in real-time. SV2TTS is a three-stage deep learning framework that allows to create a numerical representation of a voice from a few seconds of audio, and to use it to condition a text-to-speech model trained to generalize to new voices.
ClearcodeHQ/mirakuru
Mirakuru - a python library that starts your subprocess and waits for a clear indication, that it's running (process orchestrator)
spacemakerai/unified-design-tech-core-beliefs
Core beliefs, our guiding principles for time to value; speed, quality, reliability and agility
jbittel/base32-crockford
A Python implementation of Douglas Crockford's base32 encoding scheme
djdefi/gitavscan
Git Anti-Virus Scan Action - Detect trojans, viruses, malware & other malicious threats.
rupeshtech/k8s-grpc-dotntecore
This repository contains step-by-step guide to create test and deploy grpc server in .net cre 3.0,. It also includes sample application and yaml file
SuLab/sparql_to_pandas
Example for accessing SPARQL endpoints in Python with Pandas
OSC/ood_core
Open OnDemand core library
inveniosoftware/base32-lib
Library to generate, encode and decode random base32 strings.
simplegeo/zbase32
An alternate base32 encoder (not RFC 3548 compliant)
gtfierro/brick-ifc-convert
Converting IFC models into Brick models
QTimort/binary-gRPC
Implementation of a binary payload communication system using java-gRPC framework and Google protobuff.
ohze/zbase32-commons-codec
pimp commons-codec adding zbase32 class