Substantiation of the Expediency to Use Iodine-enriched Soya Flour in the Production of Bread for Special Dietary Consumption

We have studied the possibility of using iodine-enriched soy flour in the process of making bread for people suffering from iodine deficiency, diabetes and celiac disease. The organoleptic, physical-and-chemical, and microbiological indicators have been investigated, as well as the content of toxic elements and iodine content in the developed soy flour. The rationally permissible formulation ratios have been proven experimentally. The quality indicators confirmed the possibility of using enriched soy flour in the process of making bread for special dietary consumption.The conducted complex of studies provides recommendations for technologists for production of bread with special dietary properties. That will make it possible to expand a range and to fill the market with products, which are in short supply now. A lack of the mentioned products is about 15 % of the total production of bakery products. We established that the iodine content is 50 μg per 100 g in the developed soy flour. The developed flour complies with the regulatory and technical documentation for food soy flour in terms of quality and safety. The rational dosage of the developed soy flour to green buckwheat flour is 10 % in new bread formulations. It will be rational to replace 15 % of buckwheat flour with 10 % of the developed soy flour and 5 % of carrot or beet powder in products with vegetable powders.The bread developed according to new formulations complies with DSTU 4588 for "Bakery products for special dietary consumption" in terms of organoleptic and physical-and-chemical parameters. The content of organically bound iodine is 48.9; 49.4; 50.0 mcg per 100 g 72 hours after baking in the bread made by the new formulations.Our study has made it possible to state that bread that is made according to the new formulations satisfies 1/3 % of the daily need for iodin

A global metagenomic map of urban microbiomes and antimicrobial resistance

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.