Enhanced biodegradation of glyphosate by Chlorella sorokiniana engineered with exogenous purple acid phosphatase.

Organophosphate pesticides, particularly glyphosate, persist in aquatic environments due to widespread agricultural usage, posing substantial environmental and health risks. This study explores the bioremediation potential of genetically engineered Chlorella sorokiniana, expressing purple acid phosphatase (PAP) from Phaeodactylum tricornutum, for glyphosate biodegradation. The engineered strain (OE line) demonstrated complete glyphosate biodegradation at concentrations below 10 ppm within 4-6 days, surpassing the wild type (WT).

Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring.

Addressing the challenge of mapping hyperlocal air pollution in areas without local monitoring, we evaluated unsupervised transfer learning-based land-use regression (LUR) models developed using mobile monitoring data from other cities: CORrelation ALignment (Coral) and its inverse distance-weighted modification (IDW_Coral). These models mitigated domain shifts and transferred patterns learned from mobile air quality monitoring campaigns in Copenhagen and Rotterdam to estimate annual average air pollution levels in Amsterdam (50m road segments) without involving any Amsterdam measurements in model development. For nitrogen dioxide (NO), IDW_Coral outperformed Copenhagen and Rotterdam LUR models directly applied to Amsterdam, achieving MAE (4.

LUR modeling of long-term average hourly concentrations of NO using hyperlocal mobile monitoring data.

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO) concentrations for each hour of the day.

Integrating large-scale stationary and local mobile measurements to estimate hyperlocal long-term air pollution using transfer learning methods.

Mobile air quality measurements are collected typically for several seconds per road segment and in specific timeslots (e.g., working hours).

Hyperlocal variation of nitrogen dioxide, black carbon, and ultrafine particles measured with Google Street View cars in Amsterdam and Copenhagen.

Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 - March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables.

A Knowledge Transfer Approach to Map Long-Term Concentrations of Hyperlocal Air Pollution from Short-Term Mobile Measurements.

Mobile measurements are increasingly used to develop spatially explicit (hyperlocal) air quality maps using land-use regression (LUR) models. The prevailing design of mobile monitoring campaigns results in the collection of short-term, on-road air pollution measurements during daytime on weekdays. We hypothesize that LUR models trained with such mobile measurements are not optimized for estimating long-term average residential air pollution concentrations.

Mixed-Effects Modeling Framework for Amsterdam and Copenhagen for Outdoor NO Concentrations Using Measurements Sampled with Google Street View Cars.

High-resolution air quality (AQ) maps based on street-by-street measurements have become possible through large-scale mobile measurement campaigns. Such campaigns have produced data-only maps and have been used to produce empirical models [i.e.

Environmental risk factors of type 2 diabetes-an exposome approach.

Type 2 diabetes is one of the major chronic diseases accounting for a substantial proportion of disease burden in Western countries. The majority of the burden of type 2 diabetes is attributed to environmental risks and modifiable risk factors such as lifestyle. The environment we live in, and changes to it, can thus contribute substantially to the prevention of type 2 diabetes at a population level.

Exosomes: A New Pathway for Cancer Drug Resistance.

Exosomes are extracellular vesicles (EVs) that are secreted into body fluids by multiple cell types and are enriched in bioactive molecules, although their exact contents depend on the cells of origin. Studies have shown that exosomes in the tumor microenvironment affect tumor growth, metastasis and drug resistance by mediating intercellular communication and the transport of specific molecules, although their exact mechanisms of action need to be investigated further. In this review, we have summarized current knowledge on the relationship between tumor drug resistance and exosomes, and have discussed the potential applications of exosomes as diagnostic biomarkers and therapeutic targets.