Advancing Open Science

Organic micropollutants in agricultural soils pose significant ecological and health risks. This study conducted the first large-scale, integrated non-targeted screening and targeted analysis across China’s major food-producing regions. Using high-resolution mass spectrometry, 498 micropollutants were identified, including pesticides, industrial chemicals, pharmaceuticals, personal care products, food additives, natural products, and emerging contaminants. Spatial analysis revealed strong correlations in pesticide detections between Henan and Hebei, as well as between Hebei and Shandong, indicating pronounced regional similarities in pesticide occurrence patterns. Concentrations of 50 quantified micropollutants showed clear spatial variability, which was associated with precipitation, water use, and agricultural output, reflecting climate–agriculture–socioeconomic synergies. Greenhouse soils accumulated higher micropollutant levels than open fields, driven by intensive agrochemical inputs, plastic-film confinement, and reduced phototransformation. Co-occurrence patterns indicated similar pathways for personal care products, industrial chemicals, and pesticides, whereas natural products and pharmaceuticals showed lower levels of co-occurrence due to crop-specific exudates, fertilization, and rainfall-driven leaching. Among cropping systems, orchard soils had the highest micropollutant accumulation, followed by paddy and vegetable soils, consistent with frequent pesticide use and minimal tillage. Risk quotients indicated moderate-to-high ecological risks at over half of the sites. These results reveal complex soil pollution patterns and highlight the need for dynamic inventories and spatially differentiated, crop- and system-specific mitigation strategies.

Active biopolymer-based packaging incorporating phytochemicals offers promising sustainable alternatives for reducing postharvest losses and extending food shelf life. This study aimed to advance natural food packaging by (i) developing and characterizing natural deep eutectic solvents (NADES) using choline chloride combined with citric acid (CC-CA), glucose (CC-G), and urea (CC-U); (ii) obtaining bioactive extracts from Uxi bark and Jambolan leaves using these NADES; (iii) formulating babassu mesocarp-based coatings enriched with CC-CA extracts; and (iv) evaluating their application on cherry tomatoes. CC-U exhibited the lowest density (1.152 ± 0.037 g cm⁻³), while CC-G demonstrated the highest viscosity (18.375 ± 0.430 mPa s), and CC-CA presented the lowest polarity parameter (E_NR) value (44.6 ± 0.1 kcal mol⁻¹). Extracts obtained with CC-CA (YU-CA and JL-CA) showed high extraction efficiency, strong antioxidant activity (DPPH inhibition > 95%), and antimicrobial activity, particularly against Pseudomonas aeruginosa. Although the coatings exhibited lower bioactivity than the extracts, they effectively reduced weight loss, maintained firmness, and preserved the microbiological quality of tomatoes for up to 9 days. Sensory analysis of bruschetta prepared with coated tomatoes indicated high acceptance (>80%). Babassu mesocarp-based coatings enriched with Amazonian plant extracts emerge as an innovative active packaging strategy aligned with the 2030 Agenda.

Promoting demand-responsive transit (DRT) is crucial for developing sustainable and green transportation systems in urban areas, especially in light of decreasing transit ridership and increasingly varying demand. However, the effectiveness of such services hinges on their ability to efficiently match varying travel demand. This paper presents a data-driven framework for the joint optimization of customized bus routes and timetables, to enhance both service quality and operational sustainability. Our approach leverages large-scale taxi trip data to identify latent travel demand, applying a spatial–temporal clustering method to group trip requests and identify DRT stops by trip origin, destination, and direction. An adaptive large neighborhood search (ALNS) algorithm is improved to co-optimize passenger waiting times and bus operation costs, where an unbalanced penalty for early or late schedule deviations is developed to better reflect passengers’ discomfort. The framework’s performance is validated through a real-world case study, demonstrating its ability to generate efficient routes and schedules. The model manages to improve passenger experience and reduce operation costs. By creating a more appealing and efficient service, this model contributes directly to the goals of green transport in terms of reducing the total vehicle kilometers that are traveled, and demonstrating a viable, high-quality alternative to private car usage. This study offers a practical and robust tool for transit planners to design a next-generation DRT system that is both economically viable and environmentally sustainable.

Complications are frequent in cancer patients and contribute to adverse outcomes and higher healthcare costs, underscoring the need for earlier identification and prediction. This study evaluated the feasibility of using passively generated smartphone sensor data to explore early-warning signals of complications and symptom worsening during cancer treatment. A total of 108 patients were continuously monitored using accelerometer, GPS, and screen on/off data collected through the LAIMA application, while symptoms of depression, fatigue, and nausea were assessed every two weeks and complications were confirmed during clinic visits or emergency presentations. Smartphone data streams were aggregated into variables describing activity and sociability patterns. Machine learning models, including Decision Tree, Extreme Gradient Boosting, K-Nearest Neighbors, and Support Vector Machine, were used for complication prediction, and time-series models such as Autoregressive Integrated Moving Average, Holt–Winters, TBATS, Long Short-Term Memory neural network, and General Regression Neural Network were applied to identify early behavioral changes preceding symptom reports. In this exploratory analysis, the ensemble model demonstrated high sensitivity (89%) for identifying complication events. Smartphone-derived behavioral indicators enabled earlier detection of depression, fatigue, and vomiting by about nine days in a subset of patients. These findings demonstrate the feasibility of passive smartphone sensor data as exploratory early-warning signals, warranting validation in larger cohorts.

Therapeutic antibodies have revolutionized hematology, offering targeted and effective treatments for both malignant and non-malignant diseases. In hematologic malignancies, anti-CD20, anti-CD19, anti-CD38, and anti–B-cell maturation antigen (BCMA) antibodies have markedly improved survival outcomes, whereas antibody–drug conjugates and bispecific antibodies continue to expand therapeutic possibilities. Besides cancer, complement inhibitors such as eculizumab, ravulizumab, and the recently approved crovalimab have redefined paroxysmal nocturnal hemoglobinuria and atypical hemolytic uremic syndrome management, and the bispecific antibody emicizumab has transformed prophylaxis in hemophilia A. Furthermore, novel antibody formats such as the trifunctional anti-CD38 × CD3 antibody (Tri-31C2) exhibit enhanced anti-myeloma activity compared to chimeric CD38 antibodies, underscoring the future potential of T-cell–redirecting designs. This review summarizes key developments in therapeutic antibodies for hematological disorders, their action mechanisms, and emerging strategies to further optimize their efficacy and safety.

Dust generation from wet-mix shotcrete (WMS) is a major source of aerosol pollutants in underground construction. However, research on aerosol pollutant control equipment during the WMS process is still scarce. To achieve effective control of aerosol pollution during WMS production, this study introduced and applied air curtain dust suppression technology. A multi-dimensional jet test platform was used to investigate the dust suppression effects of a direct air curtain, an inner ring wall-attached air curtain, and an outer ring wall-attached air curtain during WMS production. By analyzing the variation characteristics of the dust concentration curve, key characteristic points were determined, and the diffusion phase and sedimentation phase were demarcated. With the incorporation of a K-C air curtain, the range reduction rates for the diffusion and sedimentation phases reached 51.92% and 80.85%, respectively, with an aerosol control efficiency of 57.10%. Additionally, numerical simulation was conducted to investigate the flow field characteristics during WMS production. It was found that the radial velocity gradient of the entire flow field in the spatial coordinate system was reduced, with a maximum reduction rate of 57% at (Y-axis = 560 mm). Furthermore, the affected area of the vorticity in the main jet shear layer was significantly reduced.

Background: Early prediction of gestational diabetes mellitus (GDM) remains a major clinical challenge, and the current oral glucose tolerance test (OGTT) is time-consuming and inconvenient for clinical routine. This study aimed to develop a novel predictive model for postprandial hyperglycemia GDM (pp-GDM) and postprandial glucose elevation using fasting serological and metabolic profiles. Method: We used High-Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS) to analyze fasting plasma amino acid profiles at 24–28 weeks of gestation for 60 pp-GDM patients and 120 controls. Binary logistic regression model was constructed to identify potential biomarkers for pp-GDM prediction. Results: By incorporating amino acid indicators such as isoleucine, phenylalanine, threonine, and aspartate into the predictive model alongside traditional predictors (including BMI at sampling, fasting insulin, glycated hemoglobin, and uric acid), the overall predictive performance was significantly improved from 78.2% to 91.1%. A clinically practical nomogram for risk assessment was subsequently developed. Conclusions: This fasting metabolite-based model provides a reliable tool for early prediction of pp-GDM and postprandial hyperglycemia, which may reduce the need for OGTT and facilitate timely clinical decision making.

In recent years, the RRT* algorithm has been widely applied in industrial fields because of its asymptotic optimality. However, the traditional RRT* algorithm exhibits limitations in terms of convergence speed and quality of generated paths, and its path exploration capability in complex environments remains inadequate. To address these issues, this study proposes a self-adaptive bidirectional APF-RRT* (SA-Bi-APF-RRT*) algorithm. Specifically, a hierarchical node expansion mechanism is established, enabling dynamic adjustment of the new node expansion strategy. Furthermore, a bidirectional artificial potential field (APF) guidance strategy is introduced to enhance obstacle avoidance performance. An obstacle range density evaluation module, which autonomously adjusts APF parameters according to the density distribution of surrounding obstacles, is then incorporated. Additionally, the algorithm integrates a segmented greedy approach with Bézier curve fitting techniques to achieve simultaneous optimization of path length and smoothness, while ensuring path safety. Finally, the proposed algorithm is compared against RRT*, GB-RRT*, Bi-RRT*, APF-RRT*, and Bi-APF-RRT*, demonstrating superior adaptability and efficiency in environments characterized by low iteration counts and high obstacle density. Results indicate that the SA-Bi-APF-RRT* algorithm constitutes a promising optimization solution for USVs path planning tasks.