Application of the JULES-crop model and agrometeorological indicators for forecasting off-season maize yield in Brazil.

Zea mays L is a crucial crop for Brazil, ranking second in terms of production and sixth in terms of exports. In Brazil, the second season, or off-season, accounts for 80 % of the overall maize output, which primarily occurs after the soybean main season. A maize yield forecast model for the off-season was developed and implemented throughout Brazilian territory due to its importance to the country's economy and food security. The model was built using multiple linear regressions that connected outputs simulated from a land surface model used in large-scale analysis for agriculture (JULES-crop), to agrometeorological indicators. The application of the developed model occurred every 10 days from the sowing until the maturation. A comparison of the forecasting model was verified with the official off-season maize yields for the years 2003-2016. Agrometeorological indicators during the reproductive phase accounted for 60 % of the interannual variability in maize production. When outputs simulated by JULES-crop were included, the forecasting model achieved Nash-Sutcliffe modeling efficiency (EF) of 0.77 in the maturation and EF = 0.72 in the filling-grain stage, suggesting that this approach can generate useful predictions for final maize yield beginning on the 80th day of the cycle. Outputs of JULES crop enhanced modeling performance during the vegetative stage, reducing the standard deviation error in prediction from 0.59 to 0.49 Mg ha-1.

Holistic rockfall risk assessment in high mountain areas affected by seismic activity: Application to the Uspallata valley, Central Andes, Chile.

Over large regions exposed to natural disasters, cascading effects resulting from complex or concatenated natural processes may represent a large portion of total risk. Populated high-mountain environments are a major concern, and methods for large-scale quantitative risk analyses are urgently required to improve risk mitigation. This article presents a comprehensive quantitative rockfall risk assessment over a large archetypal valley of the Andean mountains, in Central Chile, which integrates a wide spectrum of elements at risk. Risk is expressed as an expected damage both in monetary terms and casualties, at different scales relevant for decision making. Notably, total rockfall risk is divided into its main drivers, which allows quantifying seismically induced rockfall risk. For this purpose, the local seismic hazard is quantified and the yield acceleration, that is, acceleration required to initiate rockfall, is determined at the regional scale. The probability of failure is thereafter derived in terms of annual frequency of rockfall initiation and integrated in the quantitative risk assessment (QRA) process. Our results show the significant role of seismic activity as the triggering mechanism of rockfalls, and highlight elements at risk that have a major contribution to the total risk. Eventually a sensitivity analysis is conducted to (i) assess the robustness of obtained risk estimates to the data and modeling choices and (ii) identify the most influential assumptions. Our approach evidences the feasibility of large-scale QRAs in sensitive environments and opens perspectives for refining QRAs in similar territories significantly affected by cascading effects and multihazards.

A One-Health environmental risk assessment of contaminants of emerging concern in London's waterways throughout the SARS-CoV-2 pandemic.

The SARS-CoV-2 pandemic had huge impacts on global urban populations, activity and health, yet little is known about attendant consequences for urban river ecosystems. We detected significant changes in occurrence and risks from contaminants of emerging concern (CECs) in waterways across Greater London (UK) during the pandemic. We were able to rapidly identify and monitor large numbers of CECs in n = 390 samples across 2019-2021 using novel direct-injection liquid chromatography-mass spectrometry methods for scalable targeted analysis, suspect screening and prioritisation of CEC risks. A total of 10,029 measured environmental concentrations (MECs) were obtained for 66 unique CECs. Pharmaceutical MECs decreased during lockdown in 2020 in the R. Thames (p ≤ 0.001), but then increased significantly in 2021 (p ≤ 0.01). For the tributary rivers, the R. Lee, Beverley Brook, R. Wandle and R. Hogsmill were the most impacted, primarily via wastewater treatment plant effluent and combined sewer overflows. In the R. Hogsmill in particular, pharmaceutical MEC trends were generally correlated with NHS prescription statistics, likely reflecting limited wastewater dilution. Suspect screening of âˆ¼ 1,200 compounds tentatively identified 25 additional CECs at the five most impacted sites, including metabolites such as O-desmethylvenlafaxine, an EU Watch List compound. Lastly, risk quotients (RQs) ≥ 0.1 were calculated for 21 compounds across the whole Greater London freshwater catchment, of which seven were of medium risk (RQ ≥ 1.0) and three were in the high-risk category (RQ ≥ 10), including imidacloprid (RQ = 19.6), azithromycin (15.7) and diclofenac (10.5). This is the largest spatiotemporal dataset of its kind for any major capital city globally and the first for Greater London, representing âˆ¼ 16 % of the population of England, and delivering a foundational One-Health case study in the third largest city in Europe across a global pandemic.

Large-Scale Analysis of Fitness Cost of tet(X4)-Positive Plasmids in Escherichia coli.

Tigecycline is one of important antimicrobial agents for the treatment of infections caused by multidrug-resistant (MDR) Gram-negative bacteria. However, the emergence and prevalence of plasmid-mediated tigecycline resistance gene tet(X4) are threatening human and animal health. Fitness cost elicited by resistance plasmids is a key factor affecting the maintenance and transmission of antibiotic resistance genes (ARGs) in the host. A comparative analysis of the fitness cost of different types of tet(X4)-positive plasmids is helpful to understand and predict the prevalence of dominant plasmids. In this study, we performed a large-scale analysis of fitness cost of tet(X4)-positive plasmids origin from clinical isolates. These plasmids were successfully electroporated into a reference strain Escherichia coli TOP10, and a series of transformants carrying the tet(X) gene were obtained. The effects of tet(X4)-positive plasmids on the growth rate, plasmid stability, relative fitness, biofilm formation, and virulence in a Galleria mellonella model were evaluated. Consequently, we found that these plasmids resulted in varying degrees of fitness cost on TOP10, including delayed bacterial growth and attenuated virulence. Out of these plasmids, tet(X4)-harboring IncFII plasmids showed the lowest fitness cost on the host. Furthermore, by means of experimental evolution in the presence of commonly used drugs in clinic, the fitness cost of tet(X4)-positive plasmids was substantially alleviated, accompanied by increased plasmid stability. Collectively, our data reveal the differential fitness cost caused by different types of tet(X4)-positive plasmids and suggest that the wide use of tetracycline antibiotics may promote the evolution of plasmids.