Aquarium Biogeography and Succession of Microbial Communities in Built Aquatic Environments

<p>There has been extensive research conducted to characterize the microbial communities of natural ecosystems and environments. However, much less is known about the communities of microbes associated with the “built environment”--our cars, schools, offices,water pipes, etc. Aquariums are habitats that bridge the natural and built environments and were the focus of this work. In this study, we aim to better understand the biogeography and succession of the microbial communities inhabiting a pair of newly established tanks in the UC Davis Biological Sciences Teaching Laboratory.We utilize 16S rRNA PCR surveys, a culture-independent, DNA-based sequencing method to answer two questions: 1) how does the microbial composition of newly established aquarium systems change over time? 2) how do the environmental conditions (temperature, salinity, pH, oxygen and nutrient concentrations) correlate with these changes? We collected ~500 samples and daily water chemistry data from two new coral ponds to map the succession of microbes over a period of two months.</p

Global Impact of the COVID-19 Pandemic on Stroke Volumes and Cerebrovascular Events: One-Year Follow-up.

BACKGROUND AND OBJECTIVES Declines in stroke admission, intravenous thrombolysis, and mechanical thrombectomy volumes were reported during the first wave of the COVID-19 pandemic. There is a paucity of data on the longer-term effect of the pandemic on stroke volumes over the course of a year and through the second wave of the pandemic. We sought to measure the impact of the COVID-19 pandemic on the volumes of stroke admissions, intracranial hemorrhage (ICH), intravenous thrombolysis (IVT), and mechanical thrombectomy over a one-year period at the onset of the pandemic (March 1, 2020, to February 28, 2021) compared with the immediately preceding year (March 1, 2019, to February 29, 2020). METHODS We conducted a longitudinal retrospective study across 6 continents, 56 countries, and 275 stroke centers. We collected volume data for COVID-19 admissions and 4 stroke metrics: ischemic stroke admissions, ICH admissions, intravenous thrombolysis treatments, and mechanical thrombectomy procedures. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases. RESULTS There were 148,895 stroke admissions in the one-year immediately before compared to 138,453 admissions during the one-year pandemic, representing a 7% decline (95% confidence interval [95% CI 7.1, 6.9]; p<0.0001). ICH volumes declined from 29,585 to 28,156 (4.8%, [5.1, 4.6]; p<0.0001) and IVT volume from 24,584 to 23,077 (6.1%, [6.4, 5.8]; p<0.0001). Larger declines were observed at high volume compared to low volume centers (all p<0.0001). There was no significant change in mechanical thrombectomy volumes (0.7%, [0.6,0.9]; p=0.49). Stroke was diagnosed in 1.3% [1.31,1.38] of 406,792 COVID-19 hospitalizations. SARS-CoV-2 infection was present in 2.9% ([2.82,2.97], 5,656/195,539) of all stroke hospitalizations. DISCUSSION There was a global decline and shift to lower volume centers of stroke admission volumes, ICH volumes, and IVT volumes during the 1st year of the COVID-19 pandemic compared to the prior year. Mechanical thrombectomy volumes were preserved. These results suggest preservation in the stroke care of higher severity of disease through the first pandemic year. TRIAL REGISTRATION INFORMATION This study is registered under NCT04934020

Global Impact of COVID-19 on Stroke Care and IV Thrombolysis

Objective To measure the global impact of COVID-19 pandemic on volumes of IV thrombolysis (IVT), IVT transfers, and stroke hospitalizations over 4 months at the height of the pandemic (March 1 to June 30, 2020) compared with 2 control 4-month periods. Methods. We conducted a cross-sectional, observational, retrospective study across 6 continents, 70 countries, and 457 stroke centers. Diagnoses were identified by their ICD-10 codes or classifications in stroke databases. Results. There were 91,373 stroke admissions in the 4 months immediately before compared to 80,894 admissions during the pandemic months, representing an 11.5% (95% confidence interval [CI] −11.7 to −11.3, p \u3c 0.0001) decline. There were 13,334 IVT therapies in the 4 months preceding compared to 11,570 procedures during the pandemic, representing a 13.2% (95% CI −13.8 to −12.7, p \u3c 0.0001) drop. Interfacility IVT transfers decreased from 1,337 to 1,178, or an 11.9% decrease (95% CI −13.7 to −10.3, p = 0.001). Recovery of stroke hospitalization volume (9.5%, 95% CI 9.2–9.8, p \u3c 0.0001) was noted over the 2 later (May, June) vs the 2 earlier (March, April) pandemic months. There was a 1.48% stroke rate across 119,967 COVID-19 hospitalizations. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was noted in 3.3% (1,722/52,026) of all stroke admissions. Conclusions. The COVID-19 pandemic was associated with a global decline in the volume of stroke hospitalizations, IVT, and interfacility IVT transfers. Primary stroke centers and centers with higher COVID-19 inpatient volumes experienced steeper declines. Recovery of stroke hospitalization was noted in the later pandemic months

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.