Bacterial community assembly and antibiotic resistance genes in soils exposed to antibiotics at environmentally relevant concentrations.

Understanding how bacterial community assembly and antibiotic resistance genes (ARGs) respond to antibiotic exposure is essential to deciphering the ecological risk of anthropogenic antibiotic pollution in soils. In this study, three loam soils with different land management (unmanured golf course, dairy-manured pasture, and swine-manured cornfield) were spiked with a mixture of 11 antibiotics at the initial concentration of 100 and 1000 μg kg for each antibiotic and incubated over 132 days, mimicking a scenario of pulse disturbance and recovery in soils, with unspiked soil samples as the control treatment. The Infer Community Assembly Mechanisms by Phylogenetic-bin-based null model (iCAMP) analysis demonstrated that drift and dispersal limitation contributed to 57%-65% and 16%-25%, and homogeneous selection 12%-16% of soil bacterial community assembly.

Assessing the role of the gut microbiome at the interface between environmental chemical exposures and human health: Current knowledge and challenges.

The explosion of microbiome research over the past decade has shed light on the various ways that external factors interact with the human microbiome to drive health and disease. Each individual is exposed to more than 300 environmental chemicals every day. Accumulating evidence indicates that the microbiome is involved in the early response to environmental toxicants and biologically mediates their adverse effects on human health.

Economic evaluation of first-line nivolumab plus cabozantinib for advanced renal cell carcinoma in China.

Background: In the Checkmate9ER trial, first-line treatment with nivolumab combined with cabozantinib (NI + CA) has shown efficacy for advanced renal cell carcinoma. This study aims to evaluate the impact of the health and economic outcomes of NI + CA in China.

Methods: Clinical efficacy data were derived from pivotal phase III CheckMate 9ER trial.

In Utero Exposure to Caffeine and Acetaminophen, the Gut Microbiome, and Neurodevelopmental Outcomes: A Prospective Birth Cohort Study.

Pregnant individuals are exposed to acetaminophen and caffeine, but it is unknown how these exposures interact with the developing gut microbiome. We aimed to determine whether acetaminophen and/or caffeine relate to the childhood gut microbiome and whether features of the gut microbiome alter the relationship between acetaminophen/caffeine and neurodevelopment. Forty-nine and 85 participants provided meconium and stool samples at 6-7, respectively, for exposure and microbiome assessment.

Predicting pesticide dissipation half-life intervals in plants with machine learning models.

Pesticide dissipation half-life in plants is an important factor to assessing environmental fate of pesticides and establishing pre-harvest intervals critical to good agriculture practices. However, empirically measured pesticide dissipation half-lives are highly variable and the accurate prediction with models is challenging. This study utilized a dataset of pesticide dissipation half-lives containing 1363 datapoints, 311 pesticides, 10 plant types, and 4 plant component classes.

Predicting chemical ecotoxicity by learning latent space chemical representations.

In silico prediction of chemical ecotoxicity (HC) represents an important complement to improve in vivo and in vitro toxicological assessment of manufactured chemicals. Recent application of machine learning models to predict chemical HC yields variable prediction performance that depends on effectively learning chemical representations from high-dimension data. To improve HC prediction performance, we developed an autoencoder model by learning latent space chemical embeddings.

Associations of Childhood and Perinatal Blood Metals with Children's Gut Microbiomes in a Canadian Gestation Cohort.

Background: The gut microbiome is important in modulating health in childhood. Metal exposures affect multiple health outcomes, but their ability to modify bacterial communities in children is poorly understood.

Objectives: We assessed the associations of childhood and perinatal blood metal levels with childhood gut microbiome diversity, structure, species, gene family-inferred species, and potential pathway alterations.

Direct Prediction of Bioaccumulation of Organic Contaminants in Plant Roots from Soils with Machine Learning Models Based on Molecular Structures.

Root concentration factor (RCF) is an important characterization parameter to describe accumulation of organic contaminants in plants from soils in life cycle impact assessment (LCIA) and phytoremediation potential assessment. However, building robust predictive models remains challenging due to the complex interactions among chemical-soil-plant root systems. Here we developed end-to-end machine learning models to devolve the complex molecular structure relationship with RCF by training on a unified RCF data set with 341 data points covering 72 chemicals.

Predicting crop root concentration factors of organic contaminants with machine learning models.

Accurate prediction of uptake and accumulation of organic contaminants by crops from soils is essential to assessing human exposure via the food chain. However, traditional empirical or mechanistic models frequently show variable performance due to complex interactions among contaminants, soils, and plants. Thus, in this study different machine learning algorithms were compared and applied to predict root concentration factors (RCFs) based on a dataset comprising 57 chemicals and 11 crops, followed by comparison with a traditional linear regression model as the benchmark.

Assessing Consumer Buy and Pay Preferences for Labeled Food Products with Statistical and Machine Learning Methods.

Abstract: Food labeling is one approach to encourage safe, healthy, and sustainable dietary practices. Consumer buy and pay preferences for specially labeled food products (e.g.

Bacterial community assembly and antibiotic resistance genes in the lettuce-soil system upon antibiotic exposure.

Bacteria and antibiotic resistance genes (ARGs) in vegetables may influence human gut microbiome and ultimately human health. However, little is known about how vegetable microbiomes and ARGs respond to exposure of anthropogenic antibiotics from crop irrigation water. This study investigated bacterial community assembly and ARG profiles in lettuce (Lactuca sativa) shoots and roots, rhizosphere soil, and bulk soil irrigated with antibiotics-containing water, using 16S rRNA amplicon sequencing and high throughput real-time qPCR, respectively.

Comparing root concentration factors of antibiotics for lettuce (Lactuca sativa) measured in rhizosphere and bulk soils.

Plant uptake of antibiotics raises serious food safety concerns. Measurements and predictions of antibiotic uptake by plants are often based on root concentration factors (RCF) determined using antibiotic concentrations in bulk soil (RCF) rather than in rhizosphere soil (RCF) where root uptake actually occurs. This study investigated the fate and transport of nine antibiotics in the continuum of bulk soil, rhizosphere soil, roots and shoots of lettuce (Lactuca sativa) under soil-surface irrigation.

Pharmaceutical exposure changed antibiotic resistance genes and bacterial communities in soil-surface- and overhead-irrigated greenhouse lettuce.

New classes of emerging contaminants such as pharmaceuticals, antibiotic resistant bacteria (ARB), and antibiotic resistance genes (ARGs) have received increasing attention due to rapid increases of their abundance in agroecosystems. As food consumption is a direct exposure pathway of pharmaceuticals, ARB, and ARGs to humans, it is important to understand changes of bacterial communities and ARG profiles in food crops produced with contaminated soils and waters. This study examined the level and type of ARGs and bacterial community composition in soil, and lettuce shoots and roots under soil-surface or overhead irrigation with pharmaceuticals-contaminated water, using high throughput qPCR and 16S rRNA amplicon sequencing techniques, respectively.

Analysis of transcriptome in hickory (Carya cathayensis), and uncover the dynamics in the hormonal signaling pathway during graft process.

Background: Hickory (Carya cathayensis), a woody plant with high nutritional and economic value, is widely planted in China. Due to its long juvenile phase, grafting is a useful technique for large-scale cultivation of hickory. To reveal the molecular mechanism during the graft process, we sequenced the transcriptomes of graft union in hickory.