chapering

10PL

10 papers that all PhD students in programming languages ought to know, for some value of 10

aamo

AAMO: Another Android Malware Obfuscator

abc-voting

awesome-directed-fuzzing

A curated list of awesome directed fuzzing research papers

chaperingold.github.io

personal web site

DataAnalysis

Various data analysis utilities, including those for DTI skeleton extraction, region coloring, and study result statistics

DTIStudy

The entire experiment environment for a study of seeding resolution on 3D visualizations of human-brain DTI

LinuxFlaw

This repo records all the vulnerabilities of linux software I have reproduced in my local workspace

PyVolRender

Volume rendering of DTI models atop VTK using Python

sourcegraph

Code search and intelligence, self-hosted and scalable

chapering/awesome-directed-fuzzing

A curated list of awesome directed fuzzing research papers

chapering/chaperingold.github.io

personal web site

chapering/abc-voting

chapering/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.

chapering/awesome-chatgpt-prompts

This repo includes ChatGPT prompt curation to use ChatGPT better.

chapering/awesome-explainable-graph-reasoning

A collection of research papers and software related to explainability in graph machine learning.

chapering/awesome-tips

chapering/biblatex-publist

BibLaTeX bibliography support for publication lists

chapering/chapering.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

chapering/CodeXGLUE

CodeXGLUE

chapering/CSrankings

A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.

chapering/DeepSpeedExamples

Example models using DeepSpeed

chapering/elia

A snappy, keyboard-centric terminal user interface for interacting with large language models. Chat with ChatGPT, Claude, Llama 3, Phi 3, Mistral, Gemma and more.

chapering/gpt-llm-trainer

chapering/InShape

An interactive 3D tube visualization with shape-based ROI selection

chapering/instructor

structured outputs for llms

chapering/Jackalope

Binary, coverage-guided fuzzer for Windows and macOS

chapering/machine-learning-cheat-sheet

Classical equations and diagrams in machine learning

chapering/ml4se

A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering

chapering/mlc-llm

Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.

chapering/Multiple-language-project

chapering/oss-fuzz

OSS-Fuzz - continuous fuzzing for open source software.

chapering/oss-fuzz-gen

LLM powered fuzzing via OSS-Fuzz.

chapering/paper-reading

深度学习论文精读

chapering/Project_CodeNet

This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX

chapering/regexgen

Generate regular expressions that match a set of strings

chapering/regexp-examples

Generate strings that match a given regular expression

chapering/syzkaller

syzkaller is an unsupervised coverage-guided kernel fuzzer

chapering/the-algorithm

Source code for Twitter's Recommendation Algorithm

chapering/WASI

WebAssembly System Interface