Competence to restart meiosis of oocytes from different follicle sizes obtained from slaughterhouse ovaries

Se evaluó la capacidad de los ovocitos de folículos según su tamaño para reiniciar la meiosis. Los folículos se midieron y clasificaron como Grupo 1 (G1 <4 mm), Grupo 2 (G2 4-8 mm) y Grupo 3 (G3 >8 mm). La aspiración se realizó por grupo con una aguja 21G conectada a una bomba de vacío, con una presión de 65 mmHg. Los complejos cúmulos de ovocitos (COC) recuperados se clasificaron como adecuados y no adecuados para la producción de embriones in vitro, de acuerdo con las características del cúmulo y del citoplasma. Los COC elegibles e inadecuados se valoraron con la prueba de azul brillante de Cresilo (BCB) y se clasificaron en BCB+ y BCB-. La maduración in vitro (IVM) se llevó a cabo en microgotas, incubadas en una cámara de CO2 al 5%, 38.5 °C y 90% de humedad durante 24 horas. La progresión meiótica se determinó por extrusión del corpúsculo polar mediante epifluorescencia bajo un microscopio invertido. La morfometría de ovocitos se estableció mediante una cámara de alta definición (Excelis AU-600-HD) y un software (AmScope v.3.7). El porcentaje de recuperación de ovocitos fue mayor de 63%. Los folículos de G2 proporcionaron un mayor porcentaje de COC elegibles (65.7%), donde el 59% de este grupo se clasificó como BCB+. Los ovocitos aptos de G1 y G2 reanudaron la meiosis en un 75%. Además, se observó que los ovocitos después de IVM redujeron su diámetro. Se concluye que los ovocitos de folículos entre 4-8 mm (G2) proporcionan un mayor porcentaje de COC maduros; sin embargo, el 50% de folículos <4 mm (G1) son una fuente prometedora de ovocitos viables, por lo que deben usarse para la producción de embriones in vitro.The competence of oocytes according to follicle size to restart meiosis was evaluated. The follicles were measured and classified as Group 1 (G1 <4 mm), Group 2 (G2 4-8 mm) and Group 3 (G3 >8 mm). The aspiration was performed by group with a 21G needle connected to a vacuum pump, with a pressure of 65 mmHg. The complex oocyte clusters (COCs) recovered were classified as suitable and unsuitable to produce embryos in vitro, according to the characteristics of the cumulus and the cytoplasm. Eligible and unsuitable COCs were assessed with the Brilliant Cresyl Blue (BCB) and were classified into BCB+ and BCB-. In vitro maturation (IVM) was carried out in microdroplets, incubated in a 5% CO2 chamber, 38.5 °C and 90% humidity for 24 hours. The meiotic progression was determined by extrusion of the polar corpuscle by epifluorescence under an inverted microscope. Oocyte morphometry was established using a high definition camera (Excelis AU-600-HD) and software (AmScope v.3.7). The recovery percentage of oocytes was greater than 63%. The G2 follicles provided a higher percentage of eligible COCs (65.7%), where 59% of this group was classified as BCB+. The fit oocytes of G1 and G2 resumed meiosis by 75%. In addition, it was observed that oocytes after IVM reduced their diameter. It is concluded that follicle oocytes between 4-8 mm (G2) provide a higher percentage of mature COCs; however, 50% of follicles <4 mm (G1) are a promising source of viable oocytes, so they should be used for in vitro embryo production

Measurement of the cosmic ray energy spectrum using hybrid events of the Pierre Auger Observatory

The energy spectrum of ultra-high energy cosmic rays above 10(18)eV is measured using the hybrid events collected by the Pierre Auger Observatory between November 2005 and September 2010. The large exposure of the Observatory allows the measurement of the main features of the energy spectrum with high statistics. Full Monte Carlo simulations of the extensive air showers (based on the CORSIKA code) and of the hybrid detector response are adopted here as an independent cross check of the standard analysis (Phys. Lett. B 685, 239 (2010)). The dependence on mass composition and other systematic uncertainties are discussed in detail and, in the full Monte Carlo approach, a region of confidence for flux measurements is defined when all the uncertainties are taken into account. An update is also reported of the energy spectrum obtained by combining the hybrid spectrum and that measured using the surface detector array

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.