Thermal, water, and land cover factors led to contrasting urban and rural vegetation resilience to extreme hot months.

With continuing global warming and urbanization, it is increasingly important to understand the resilience of urban vegetation to extreme high temperatures, but few studies have examined urban vegetation at large scale or both concurrent and delayed responses. In this study, we performed an urban-rural comparison using the Enhanced Vegetation Index and months that exceed the historical 90th percentile in mean temperature (referred to as "hot months") across 85 major cities in the contiguous United States. We found that hot months initially enhanced vegetation greenness but could cause a decline afterwards, especially for persistent (≥4 months) and intense (≥+2 °C) episodes in summer.

Epidemiology of healthcare-associated Pseudomonas aeruginosa in intensive care units: are sink drains to blame?

Background: Pseudomonas aeruginosa (PA) is a common cause of healthcare-associated infection (PA-HAI) in the intensive care unit (ICU).

Aim: To describe the epidemiology of PA-HAI in ICUs in Ontario, Canada, and to identify episodes of sink-to-patient PA transmission.

Methods: This was a prospective cohort study of patients in six ICUs from 2018 to 2019, with retrieval of PA clinical isolates, and PA-screening of antimicrobial-resistant organism surveillance rectal swabs, and of sink drain, air, and faucet samples.

Embracing fine-root system complexity in terrestrial ecosystem modeling.

Projecting the dynamics and functioning of the biosphere requires a holistic consideration of whole-ecosystem processes. However, biases toward leaf, canopy, and soil modeling since the 1970s have constantly left fine-root systems being rudimentarily treated. As accelerated empirical advances in the last two decades establish clearly functional differentiation conferred by the hierarchical structure of fine-root orders and associations with mycorrhizal fungi, a need emerges to embrace this complexity to bridge the data-model gap in still extremely uncertain models.