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. Results of interpretable ML approach (XGBoost) indicated that volatilization of 3-ring PAHs was significantly controlled by dissolved PAH concentrations (contributed 24.0 %), and the gaseous deposition of 4- to 6-ring PAHs was related to more contaminated air masses originating from North China during the northeast monsoon. Henry's law constant emerged as a secondary factor, influencing the intensity of air-sea exchange, particularly for low molecular weight PAHs. Among environmental parameters, notably high wind speed emerges as the primary factor and biological pump's depletion of PAHs in surface seawater amplifies the gaseous deposition process. The distinct dynamics of exchanges at the air-water interface for PAHs in the western TWS can be attributed to variations in primary emission intensities, biological activity, and the inconsistent pathways of long-range atmospheric transport, particularly within the context of the monsoon transition.

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. The median levels of Σ10OH-PAHs in smelting workers (25.6 ng/mL) were significantly higher (p < 0.01) than that of surrounding residents (9.00 ng/mL) and rural residents as the control (8.17 ng/mL), indicating an increase in occupational PAH exposure in non-ferrous metal smelting activities. The composition profiles of OH-PAH congeners were similar in three groups, in which naphthalene metabolites accounted for 76-82% of the total. The effects of smoking, drinking, gender, BMI, and occupational categories on urinary OH-PAHs were considered. The partial correlation analysis showed an insignificant effect of non-ferrous metal smelting activities on PAH exposure for surrounding residents. In the health risk assessments, almost all smelting workers had cancer risks exceeded the acceptable level of 10-6. This study provides a reference to occupational PAH exposure and reinforce the necessary of health monitoring among smelting workers.

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. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.

Quantitative profiling of N6-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing.

There are no comprehensive methods to identify N6-methyladenosine (m6A) at single-base resolution for every single transcript, which is necessary for the estimation of m6A abundance. We develop a new pipeline called Nanom6A for the identification and quantification of m6A 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 m6A-sensitive RNA-endoribonuclease-facilitated sequencing (m6A-REF-seq), confirming high accuracy. Using this method, we provide a transcriptome-wide quantification of m6A modification in stem-differentiating xylem and reveal that different alternative polyadenylation (APA) usage shows a different ratio of m6A.