Dataset for "Development and Application of Liquid Chromatographic Retention Time Indices in HRMS-Based Suspect and Nontarget Screening". This dataset is associated with the following publication: Aalizadeh, R., N. Alygizakis, E. Schymanski, M. Krauss, T. Schulze, M. Ibanez, A. McEachran, A. Chao, A. Williams, P. Gago-Ferrero, A. Covaci, C. Moschet, T. Young, J. Hollender, J. Slobodnik, and N. Thomaidis. Development and Application of Liquid Chromatographic Retention Time Indices in HRMS-Based Suspect and Nontarget Screening. Analytical Chemistry. American Chemical Society, Washington, DC, USA, 93(33): 11601-11611, (2021).

A suspect screening analysis method is presented to rapidly characterize chemicals in 100 consumer products -- whether they be formulations (shampoos, paints), articles (upholsteries, shower curtains), or foods (cereals) – and therefore supports broader efforts to prioritize chemicals based on potential human health risks. A two-dimensional gas chromatography-time of flight/mass spectrometry method was used to screen for chemicals in selected products. Analysis yielded 4270 unique chemical signatures across the products, with 1602 signatures tentatively identified using the National Institute of Standards and Technology 2008 spectral database. Chemical standards confirmed the presence of 119 compounds. Of the 1602 chemicals, 1404 were not present in a public database of known consumer product chemicals. This dataset is associated with the following publication: Phillips, K., A. Yau, K. Favela, K. Isaacs, A. McEachran, C. Grulke, A. Richard, A. Williams, J. Sobus, R. Thomas, and J. Wambaugh. Suspect Screening Analysis of Chemicals in Consumer Products. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 52(5): 3125-3135, (2018).

대전광역시 농산물 잔류농약검사 현황에 대한 데이터로 공영도매시장 현장검사소 농산물 잔류농약검사(2024년 4월말) 검사건수 및 부적합내역을 제공합니다.

Liquid-chromatography-mass spectrometry (LC-MS) raw data sets from various instruments delivered in their native instrument format. 31 files in all. 7.5 GB data.

This data includes metabolite predictions for in vitro inactive chemicals, predictions of those metabolite's estrogen receptor binding activity, in vitro and in silico information regarding parent compound binding activities, linking of metabolite structures and routes to parent compounds, and estimates of binding activity obtained from literature when possible. This dataset is associated with the following publication: Leonard, J., C. Stevens, K. Mansouri, D. Chang, H. Pudukodu, S. Smith, and C. Tan. A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 6: 71-83, (2018).

The Distributed Structure-Searchable Toxicity (DSSTox) Database Network provides a public forum for search and publishing downloadable, structure-searchable, standardized chemical structure files associated with toxicity data.

We screened 44 chemicals in MCF7 cells in concentration response and generated HTTr data using the TempO-Seq hWTv1 assay. First, we provide an outline of the quality of the HTTr data based on a set of QC metrics we developed for this platform. Second, we evaluate the reproducibility and mechanistic accuracy of the HTTr platform using inter-plate analysis of reference samples and comparison of reference chemical treatment effects with CMAP signatures, respectively. Third, we summarize the concentration-dependent HTTr responses for all 44 chemicals to stratify them in terms of their overall effect on the transcriptome. Fourth, we present a new gene signature based concentration-response analysis that provides potency estimates for perturbation of cellular biology (ie, BPACs). This dataset is associated with the following publication: Harrill, J., L. Everett, D. Haggard, T. Sheffield, J. Bundy, C. Willis, R. Thomas, I. Shah, and R. Judson. High-Throughput Transcriptomics Platform for Screening Environmental Chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 181(1): 68-89, (2021).

한국화학연구원 보유 연구장비 목록으로 한글장비명, 영문장비명, 모델, 제작사, 취득금액, 문의처, 활용범위, 공동활용 정보 등을 포함

Dataset includes the SRA accessions of the sequencing data used to benchmark the MetaCompare 2.0 pipeline as well as tables of bacterial taxa and antibiotic resistance genes used to perform the risk assessments. A link to the GitHub page where the pipeline source code can be found is also provided. This dataset is associated with the following publication: Rumi, M., M. Oh, B. Davis, C. Brown, J. Adeesh, P. Vikesland, A. Pruden, and L. Zhang. MetaCompare 2.0: Differential ranking of ecological and human health resistome risks. FEMS Microbiology Ecology. Oxford University Press, OXFORD, UK, 100(12): fiae155, (2024).