alankarmisra's Stars
pandas-dev/pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
janpaepke/ScrollMagic
The javascript library for magical scroll interactions.
altstoreio/AltStore
AltStore is an alternative app store for non-jailbroken iOS devices.
karpathy/micrograd
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
x-hw/amazing-qr
💮 amazing QRCode generator in Python (supporting animated gif) - Python amazing 二维码生成器（支持 gif 动态图片二维码）
kristoferjoseph/flexboxgrid
Grid based on CSS3 flexbox
mamaral/Onboard
An iOS framework to easily create a beautiful and engaging onboarding experience with only a few lines of code.
alexcasalboni/aws-lambda-power-tuning
AWS Lambda Power Tuning is an open-source tool that can help you visualize and fine-tune the memory/power configuration of Lambda functions. It runs in your own AWS account - powered by AWS Step Functions - and it supports three optimization strategies: cost, speed, and balanced.
AppPear/ChartView
ChartView made in SwiftUI
greyhatguy007/Machine-Learning-Specialization-Coursera
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
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.
0xced/XCDYouTubeKit
YouTube video player for iOS, tvOS and macOS
benkeen/generatedata
A powerful, feature-rich, random test data generator.
dynamoose/dynamoose
Dynamoose is a modeling tool for Amazon's DynamoDB
TanguyAladenise/BBBadgeBarButtonItem
A BarButtonItem with a badge on top.
ktamas77/firebase-php
Firebase PHP Client
kou-yeung/WebGLInput
IME for Unity WebGL
yannickl/Reactions
Fully customizable Facebook reactions like control
clarkie/dynogels
DynamoDB data mapper for node.js. Originally forked from https://github.com/ryanfitz/vogels
Oak-Harbor-Kits/Starter-KitV3
This is the latest version of my starter kit that I will be using from now on to build all my websites.
ClusterDuck-Protocol/ClusterDuck-Protocol
Firmware for an ad-hoc mesh network of Internet-of-Things devices based on LoRa (Long Range radio) that can be deployed quickly and at low cost.
alankarmisra/SwiftSignatureView
A lightweight, fast and customizable option for capturing fluid, variable-stroke-width signatures within your app.
marinavillaschi/ML-AndrewNg
Notebooks from the Machine Learning Specialization
ksm26/LangChain-Chat-with-Your-Data
Explore LangChain and build powerful chatbots that interact with your own data. Gain insights into document loading, splitting, retrieval, question answering, and more.
sbstjn/serverless-dynamodb-autoscaling
Serverless Plugin for Amazon DynamoDB Auto Scaling configuration.
nbertagnolli/counsel-chat
This repository holds the code for working with data from counselchat.com
labstreaminglayer/LSL4Unity
A integration approach of the LabStreamingLayer Framework for Unity3D
lamini-ai/docs-to-qa
AbdullahZN/dynamo-node
DynamoDB mapper
chRyNaN/Reactions
Android UI mimicking the look and feel of Facebook's Reactions