Urinary metabolic alterations associated with occupational exposure to metals and polycyclic aromatic hydrocarbons based on non-target metabolomics.

Long-term occupational exposure to metals and organics have been reported to be under great health risks. However, limited data are available on the molecular mechanism between combined exposure to metals and polycyclic aromatic hydrocarbons (PAHs) and harmful health effects. In present work, non-target metabolomics study was conducted based on urine samples from nonferrous metal smelting workers (n = 207), surrounding residents (n = 180), and the control residents (n = 187) by using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS).

Air-sea exchange of PAHs in the Taiwan Strait: Seasonal dynamics and regulation mechanisms revealed by machine learning approach.

In this study, to understand the seasonal dynamics of air-sea exchange and its regulation mechanisms, we investigated polycyclic aromatic hydrocarbons (PAHs) at the air-sea interface in the western Taiwan Strait in combination with measurements and machine learning (ML) predictions. For 3-ring PAHs and most of 4- to 6-ring, volatilization and deposition fluxes were observed, respectively. Seasonal variations in air-sea exchange flux suggest the influence of monsoon transitions.

Internal exposure risk based on urinary metabolites of PAHs of occupation and non-occupation populations around a non-ferrous metal smelting plant.

The emission of various metals from non-ferrous metal smelting activities is well known. However, relative investigations on potential occupational exposure of organic pollutants are still limited. Herein, total of 619 human urine samples were collected from workers engaged in smelting activities and residents living near and/or far from the smelting sites, and ten mono-hydroxylated metabolites of polycyclic aromatic hydrocarbons (OH-PAHs) in human urine were determined.

Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature.

High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping.

Quantitative profiling of N-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing.

There are no comprehensive methods to identify N-methyladenosine (mA) at single-base resolution for every single transcript, which is necessary for the estimation of mA abundance. We develop a new pipeline called Nanom6A for the identification and quantification of mA modification at single-base resolution using Nanopore direct RNA sequencing based on an XGBoost model. We validate our method using methylated RNA immunoprecipitation sequencing (MeRIP-Seq) and mA-sensitive RNA-endoribonuclease-facilitated sequencing (m6A-REF-seq), confirming high accuracy.