Pesticide Fate and Transport throughout Unsaturated Zones in Five Agricultural Settings, USA

Pesticide transport through the unsaturated zone is a function of chemical and soil characteristics, application, and water recharge rate. The fate and transport of 82 pesticides and degradates were investigated at five different agricultural sites. Atrazine and metolachlor, as well as several of the degradates of atrazine, metolachlor, acetochlor, and alachlor, were frequently detected in soil water during the 2004 growing season, and degradates were generally more abundant than parent compounds. Metolachlor and atrazine were applied at a Nebraska site the same year as sampling, and focused recharge coupled with the short time since application resulted in their movement in the unsaturated zone 9 m below the surface. At other sites where the herbicides were applied 1 to 2 yr before sampling, only degradates were found in soil water. Transformations of herbicides were evident with depth and during the 4-mo sampling time and reflected the faster degradation of metolachlor oxanilic acid and persistence of metolachor ethanesulfonic acid. Th e fraction of metolachlor ethanesulfonic acid relative to metolachlor and metolachlor oxanilic acid increased from 0.3 to \u3e0.9 at a site in Maryland where the unsaturated zone was 5 m deep and from 0.3 to 0.5 at the shallowest depth. The flux of pesticide degradates from the deepest sites to the shallow ground water was greatest (3.0–4.9 μmol m−2 yr−1) where upland recharge or focused flow moved the most water through the unsaturated zone. Flux estimates based on estimated recharge rates and measured concentrations were in agreement with fluxes estimated using an unsaturated-zone computer model (LEACHM)

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024