Multifaceted Interplay among Social Dominance, Body Condition, Appetitive and Consummatory Sexual Behaviors, and Semen Quality in Dorper Rams during Out-Of-Season and Transition Periods

Dorper rams (n = 24) were evaluated during the sexual resting season to determine their social rank (SR), either high (HSR) or low (LSR), under intensive management conditions in northern Mexico (25° N). Aggressive behaviors were quantified during male-to-male interactions, and appetitive and consummatory sexual behaviors during male-to-female interactions. Morphometric, live weight (LW), and body condition score (BCS) were recorded. During the early reproductive season, male-to-female behaviors were newly itemized simultaneously by seminal quality and quantity sampling. Finally, the dependent variables of the hemogram components were also quantified. Neither LW (61.25 ± 2.4 kg) nor morphometric variables differed between SR groups. However, BCS (2.25 vs. 2.66 u), sexual behaviors (i.e., approaches: 59.6 vs. 21.73 n, mating with ejaculation: 77.7 vs. 42.86 %, latency to ejaculation: 16.6 vs. 143.07 s), ejaculate volume (0.57 vs. 0.23 mL), and hemogram components favored the HSR rams (p 50% of the LSR rams failed to display any sexual activity. HSR rams displayed a greater number of threatening behaviors, managing to displace LSR rams when exposed to estrus ewes during the male sexual resting season; more sexual behaviors; and an increased seminal volume in a non-live weight-dependent fashion

Connectedness between Intensive and Extensive Ruminant Production Systems: Using Dairy Cow Feed Leftovers to Generate Out-of-Season Bio-Economic Indices in Goats

Founded on a circular economy perspective, the possible effect of targeted supplementation with leftover feed from dairy cows (i.e., intensive system) upon the productive economic performance of crossbred–rangeland goats (i.e., extensive system) in northern arid Mexico was assessed. Multiparous goats (n = 38) with similar body condition score (BCS) and body weight (BW) were randomly assigned during the deep anestrus season (i.e., March–April, 25° N) into two groups: (1) the control-non-supplemented group (CONT; n = 19; BCS: 1.76 ± 0.06; BW: 44.3 ± 2.5 kg) and (2) the supplemented group (SUPL; n = 19; BCS: 1.76 ± 0.07; BW: 43.7 ± 1.8 kg). While the SUPL group received 400 g goat d−1 of dairy cow feed leftovers prior to grazing, both groups went daily to the rangeland (i.e., ≈8 h). The study considered an experimental period of 36 d with an experimental breeding of 11 d (d0–d10). Previously, on days −20, −10, −1 preceding the male-to-female interaction, the anovulatory status of goats was confirmed through ultrasonographic scanning. Prior to mating, the males were separated from goats and treated for a period of 3 weeks (i.e., every 3rd d) with testosterone (i.e., 50 mg i.m.). The response variables evaluated considered goats induced to estrus (GIE, %), goats ovulating (GO, %), ovulation rate (OR, units), pregnancy rate-1 (PRd36, %), pregnancy rate-2 (PRd50, %), embryo mortality-d50 (EMO, %), potential kidding index-d50 (PKId50,%), kid weight at birth simples (KWBS, kg), potential litter efficiency at birth (PLEB, kg), and potential litter efficiency at weaning (i.e., d21 post kidding), either expressed as kg head−1 (PLEW1) or USD head−1 (PLEW2). Although no differences (p > 0.05) occurred for GIE and PRd50, increases in the phenotypic expression of OR (1.42 vs. 0.73), PRd36 (68.4 vs. 36.8), EMO (23.0 vs. 0), PKId50 (74.7 vs. 26.8), and KWBS (4.1 vs. 3.3) occurred (p p p > 0.05) regarding BW, BCS, and serum glucose concentrations between experimental groups. Furthermore, applying the main research outcomes from this specific study toward the large-scale goat production system in the Comarca Lagunera—one of the largest dairy goat production hubs in The Americas—denoted promising expectations, either from an economic or productive–reproductive standpoint. Certainly, goat producers from the region would increase their potential annual income just from the sale of kids by close to 250%; that is from MUSD 1.1 to 3.9. This result should reduce food insecurity and economic stress, as well as enhance the livelihoods of the goat keepers and their families

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.