bryan-starbuck's Stars
openai/gym
A toolkit for developing and comparing reinforcement learning algorithms.
CSSEGISandData/COVID-19
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
GoogleContainerTools/distroless
🥑 Language focused docker images, minus the operating system.
dusty-nv/jetson-inference
Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
microsoft/azurelinux
Linux OS for Azure 1P services and edge appliances
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.
opendatacam/opendatacam
An open source tool to quantify the world
NVIDIA-AI-IOT/jetracer
An autonomous AI racecar using NVIDIA Jetson Nano
kartben/artificial-nose
Instructions, source code, and misc. resources needed for building a Tiny ML-powered artificial nose.
microsoft/PowerApps-Tooling
Tooling support for PowerApps language and .msapp files
ela-compil/BACnet
BACnet protocol library for .NET :satellite:
google-research/rl-reliability-metrics
The RL Reliability Metrics library provides a set of metrics for measuring the reliability of reinforcement learning (RL) algorithms, as well as statistical tools for comparing algorithms and for computing confidence intervals on these metrics.
kartben/lorawan-node-simulator
Simulation infrastructure for a LoRaWAN network (gatways and end devices) that's easy to configure and run from your CLI. Also available as a Docker container.
microsoft/azure-percept-advanced-development
Azure Percept DK advanced topics
OrleansContrib/Orleans.Redis
Redis support packages for Orleans
toolboc/IoTEdge-DevOps
A living repository of best practices and examples for developing AzureIoT Edge solutions doubly presented as a hands-on-lab.
1manprojects/one_Sgp4
C# SGP4 orbit prediction Library
Azure/meta-iotedge
Yocto layer for Azure IoT Edge
Azure-Samples/iot-edge-for-iiot
Learn how to use a hierarchy of Azure IoT Edge devices in a manufacturing environment to extract data from industrial assets and upload it to the Cloud while meeting the strict requirements of the Purdue network model.
emmanuel-bv/iotedge-iva-nano
Quickstart to deploy an Intelligent Video Analytics application running at the edge over multiple cameras and with custom AI models.
linklab-uva/f1tenth_gtc_tutorial
Instructor led training for F1/10 Autonomous Racing
veyalla/logspout-loganalytics
loganalytics provider for logspout
iModeljs-meets-AzureDT/civil-iot
bryan-starbuck/WeatherBalloon
Azure IoT Edge Weather Balloon project
sobhan-moosavi/DCRNN
This repo contains all the codes and sample files for the DCRNN paper
idavis/iot-edge-rabbitmq
Azure IoT Edge & RabbitMQ
madhurbehl/f1tenth_docker
michaelgorkow/SAS_ESP_GPU
Private repository to provide information about how to create your own SAS Event Stream Processing Docker container with GPU acceleration
michaelgorkow/SAS_ESP_JETSON_GPU
Private repository to provide information about how to create your own SAS Event Stream Processing on Edge Docker container with GPU acceleration for NVIDIA Jetson TX2 devices.