10 research outputs found

    A pH Monitoring Algorithm for Orifice Plate Culture Medium

    No full text
    Recently, there has been renewed interest in cell therapy, which plays a key role in the clinical research of genetic diseases, advanced blood disease, and other diseases. It shows considerable clinical application value and is known as “the new pillar of future medicine”. Automatic cell culture and operation technology is the key to ensuring scale, standardization, and stability between batches of therapeutic cells. The pH of the cell culture medium is vital for cell growth. Most cells are suitable for growth at pH 7.2~7.4. A pH of cell culture medium lower than 6.8 or higher than 7.6 is harmful to cells, and cells will degenerate or even die. At present, the monitoring method of cell culture medium pH of automatic cell culture equipment is mainly a visual observation method, which can not accurately or quickly reflect changes in the cell culture medium. To address the issue of monitoring of cell culture fluid pH for automated cell culture equipment and the inability to employ invasive sensors to measure pH during well plate culture, a pH monitoring method for orifice plate culture medium algorithm based on HSV (hue, saturation, value) model is proposed by studying the changes of cell culture medium in the process of cell culture. The research presented here reveals the laws of cell culture fluid pH change and its color moment, and the intelligent monitoring of cell culture fluid pH was successfully achieved. The problem of non-destructive monitoring of the pH of cell culture fluids in well plates is also addressed

    A global metagenomic map of urban microbiomes and antimicrobial resistance

    No full text
    We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.