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7226ctrl

controller for 722.6 automatic transmission

a-multi-layer-and-multi-ensembled-stock-trader-using-deep-learning-and-deep-reinforcement-learning

adabelief-optimizer

Repository for NeurIPS 2020 Spotlight "AdaBelief Optimizer: Adapting stepsizes by the belief in observed gradients"

adtk

A Python toolkit for rule-based/unsupervised anomaly detection in time series

advancedml

Reading list for the Advanced Machine Learning Course

ai-notebooks

Some ipython notebooks implementing AI algorithms

aialpha

Use unsupervised and supervised learning to predict stocks

alfa

Source code for NeurIPS 2020 paper "Meta-Learning with Adaptive Hyperparameters"

alpha-vantage-cookbook

JavaScript examples to help you with accessing market data from https://www.alphavantage.co/

alpha_vantage

A python wrapper for Alpha Vantage API for financial data.

alphafold2

To eventually become an unofficial Pytorch implementation of Alphafold2, as details of the architecture get released

amm-arbitrageur

An arbitrage bot between Uniswap AMMs

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.

assistentepessoal

Assistente pessoal virtual desenvolvida com Python 🤖

augmented-random-search-

The repository includes the Augmented Random Search algorithm implemented from scratch in Python. This AI algorithm as released on March 2018 research paper is a faster and more efficient than other reinforcement algorihtms.

awesome-automl-papers

A curated list of automated machine learning papers, articles, tutorials, slides and projects

awesome-crypto-trackers

A curated list of crypto project trackers and analytics dashboards.

awesome-crypto-trading

An awesome curated list of resources, software and tools for crypto traders.

awesome-deep-neuroevolution

A collection of Deep Neuroevolution resources or evolutionary algorithms applying in Deep Learning (constantly updating)

awesome-deep-trading

List of awesome resources for machine learning-based algorithmic trading

awesome-financial-nlp

Researches for Natural Language Processing for Financial Domain

awesome-gpt3

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

awesome-singing-voice-synthesis-and-singing-voice-conversion

A paper and project list about the cutting edge Speech Synthesis, Text-to-Speech (TTS), Singing Voice Synthesis (SVS), Voice Conversion (VC), Singing Voice Conversion (SVC), and related interesting works (such as Music Synthesis, Automatic Music Transcription, Automatic MOS Prediction, SSL-based ASR...etc).

awesome-systematic-trading

A curated list of insanely awesome libraries, packages and resources for systematic trading. Crypto, Stock, Futures, Options, CFDs, FX, and more | 量化交易 | 量化投资

awesome-tensorlayer

TensorLayer - A curated list of dedicated resources

awesome-ts-anomaly-detection

List of tools & datasets for anomaly detection on time-series data.

awful-ai

😈Awful AI is a curated list to track current scary usages of AI - hoping to raise awareness

background-matting

Background Matting: The World is Your Green Screen

backtesting.py

:mag_right: :chart_with_upwards_trend: :snake: :moneybag: Backtest trading strategies in Python.