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The redox activity of natural organic matter (NOM) is crucial for contaminants transformation in soils. Soil micropores (<2.5 nm) have limited accessibility for microorganisms and large NOM molecules; therefore, insoluble organic pollutants and heavy metals trapped in these micropores are usually reached by low molecular weight fractions (LMWF) of NOM. However, the mechanism of spatial electron transfer via electron shuttle of LMWF remains unclear. In this study, we separated low molecular weight fractions (LMWF < 3500 Da and LMWF < 14,000 Da) of Leonardite humic acids (LHA) and measured its acceleration of microbial ferrihydrite reduction. The results show that LMWF, as an electron shuttle, significantly accelerates the reduction in Fe (III), among which 3500-LMWF is the main fraction contributing to the acceleration. Additionally, 3D-EEM shows that quinone content was positively correlated with reduction efficiency, supporting its role as the key functional group. Based on the accelerating experiments, we determined an electron shuttling critical distance of 117.2 nm for LMWF LHA. These findings establish LMWFs as effective natural electron shuttles, providing a theoretical basis for understanding pollutant dynamics in soil micropores.

In order to investigate the current status of soil heavy metal pollution, ecological risk, and risk sources in the black soil area of the Eastern Inner Mongolia Province, topsoil (0–20 cm) samples from farmland in the black soil area (N = 163) were collected to determine the contents of seven heavy metals. The levels of soil heavy metal pollution and ecological risk in the study area were evaluated by combining the geo-accumulation index, potential ecological risk index, and static environmental carrying capacity; the positive matrix factorization (PMF) model was used to identify the pollution sources and contributions of heavy metals in the soil and analyze the risk levels to adults and children. The soil was predominantly weakly acidic, with mean values of Cr, Ni, Cu, As, Cd, Pb, and Zn of 61.77, 26.77, 17.07, 12.11, 0.08, 12.61, and 85.71 mg·kg−1. The mean concentrations of heavy metals exceeded the background values, except for Pb, the mean concentration of which was lower than the soil background. Ni concentrations of 6.21% at the sampling sites exceeded the risk screening value for agricultural soils. The geo-accumulation index showed that Cr (55.15%) and As (54.00%) were mainly mild pollutants; the static environmental carrying capacity indicated that the soils were slightly polluted by Ni, As, and Zn; and the potential ecological risk indices of Cd, Ni, and As were at moderate levels. The PMF model analyzed three pollution sources: mixed agricultural practice–transportation sources (39.46%), mineral-related activity sources (27.01%), and pesticide–fertilizer agricultural practices (33.53%). The human health risk assessment indicated that 46.58% of sampling sites posed a carcinogenic risk to children, with Ni as the main carcinogenic element. In conclusion, the potential contamination of As, Cd, Ni, Cr, and Zn in the Eastern Inner Mongolia farmland black soil area should be further studied.

Accurate extraction of representative operating conditions is crucial for optimizing systems in renewable energy applications. This study proposes a novel framework that combines the Parzen window estimation method, ideal for nonparametric modeling of wind, solar, and load datasets, with a game theory-based time scale selection mechanism. The novelty of this work lies in integrating probabilistic density modeling with multi-indicator evaluation to derive realistic operational profiles. We first validate the superiority of the Parzen window approach over traditional Weibull and Beta distributions in estimating wind and solar probability density functions. In addition, we analyze the influence of key meteorological parameters such as wind direction, temperature, and solar irradiance on energy production. Using three evaluation metrics, the main result shows that a 3-day representative time scale offers optimal accuracy when determined through game theory methods. Validation with real-world data from Inner Mongolia confirms the robustness of the proposed method, yielding low errors in wind, solar, and load profiles. This study contributes a novel 3-day typical profile extraction method validated on real meteorological data, providing a data-driven foundation for optimizing energy storage systems under renewable uncertainty. This framework supports energy sustainability by ensuring realistic modeling under renewable intermittency.

Infancy is a critical window for the colonization of gut microbiome. However, xenobiotic impacts on gut microbiome development in early life remain poorly understood. Here, we recruit 146 mother-infant pairs and collect stool samples at 3, 6, and 12 months after delivery for amplicon sequencing ( N  = 353), metagenomics ( N  = 65), and metabolomics ( N  = 198). Trace elements in maternal hair samples ( N  = 119) affect diversity and composition of the infant gut microbiome. Shannon diversity in 3 month-old infants is correlated positively with selenium and negatively with copper, and relative abundance of Bifidobacterium increases under high exposure to aluminum and manganese. During the first year of life, infants and their paired mothers have distinct microbial diversity and composition, and their bacterial community structures gradually approach. here are 56 differential metabolites between the first and second visit and 515 differential metabolites between the second and third visit. The typical profile of antibiotic resistance genes (ARGs) significantly differs between infants and their mothers. High levels of copper and arsenic exposure may induce the enrichment of ARGs in the infant gut. Our findings highlight the dynamics of the gut microbiome, metabolites, and ARG profiles of mother-infant pairs after delivery, associated with prenatal exposure to trace elements. Here, using samples from 146 mother-infant pairs and multi-omics, the authors characterize the dynamics of the gut microbiome, metabolome, and antibiotic resistance gene profiles during the first year of life in association with prenatal exposure to trace elements.

Summary Selenium (Se) is an essential trace element for humans and other animals, yet approximately one billion people worldwide suffer from Se deficiency. Rice is a staple food for over half of the world's population that is a major dietary source of Se. In paddy soils, rice roots mainly take up selenite. Se speciation analysis indicated that most of the selenite absorbed by rice is predominantly transformed into selenomethinone (SeMet) and retained in roots. However, the mechanism by which SeMet is transported in plants remains largely unknown. In this study, SeMet uptake was found to be an energy‐dependent symport process involving H+ transport, with neutral amino acids strongly inhibiting SeMet uptake. We further revealed that NRT1.1B, a member of rice peptide transporter (PTR) family which plays an important role in nitrate uptake and transport in rice, displays SeMet transport activity in yeast and Xenopus oocyte. The uptake rate of SeMet in the roots and its accumulation rate in the shoots of nrt1.1b mutant were significantly repressed. Conversely, the overexpression of NRT1.1B in rice significantly promoted SeMet translocation from roots to shoots, resulting in increased Se concentrations in shoots and rice grains. With vascular‐specific expression of NRT1.1B, the grain Se concentration was 1.83‐fold higher than that of wild type. These results strongly demonstrate that NRT1.1B holds great potential for the improvement of Se concentrations in grains by facilitating SeMet translocation, and the findings provide novel insight into breeding of Se‐enriched rice varieties.

Bioelectrochemical systems (BESs) are an innovative technology for the efficient degradation of antibiotics. Shewanella oneidensis (S. oneidensis) MR-1 plays a pivotal role in degrading sulfamethoxazole (SMX) in BESs. Our study investigated the effect of BES conditions on SMX degradation, focusing on microbial activity. The results revealed that BESs operating with a 0.05 M electrolyte concentration and 2 mA/cm2 current density outperformed electrolysis cells (ECs). Additionally, higher electrolyte concentrations and elevated current density reduced SMX degradation efficiency. The presence of nutrients had minimal effect on the growth of S. oneidensis MR-1 in BESs; it indicates that S. oneidensis MR-1 can degrade SMX without nutrients in a short period of time. We also highlighted the significance of mass transfer between the cathode and anode. Limiting mass transfer at a 10 cm electrode distance enhanced S. oneidensis MR-1 activity and BES performance. In summary, this study reveals the complex interaction of factors affecting the efficiency of BES degradation of antibiotics and provides support for environmental pollution control.

The workload of breast cancer pathological diagnosis is very heavy. The purpose of this study is to establish a nomogram model based on pathological images to predict the benign and malignant nature of breast diseases and to validate its predictive performance. In retrospect, a total of 2,723 H&E-stained pathological images were collected from 1,474 patients at Qingdao Central Hospital between 2019 and 2022. The dataset consisted of 509 benign tumor images (adenosis and fibroadenoma) and 2,214 malignant tumor images (infiltrating ductal carcinoma). The images were divided into a training set (1,907) and a validation set (816). Python3.7 was used to extract the values of the R channel, G channel, B channel, and one-dimensional information entropy from all images. Multivariable logistic regression was used to select variables and establish the breast tissue pathological image prediction model. The R channel value, B channel value, and one-dimensional information entropy of the images were identified as independent predictive factors for the classification of benign and malignant pathological images (P < 0.05). The area under the curve (AUC) of the nomogram model in the training set was 0.889 (95% CI: 0.869, 0.909), and the AUC in the validation set was 0.838 (95% CI: 0.7980.877). The calibration curve results showed that the calibration curve of this nomogram model was close to the ideal curve. The decision curve results indicated that the predictive model curve had a high value for auxiliary diagnosis. The nomogram model for the prediction of benign and malignant breast diseases based on pathological images demonstrates good predictive performance. This model can assist in the diagnosis of breast tissue pathological images.

Soil is an essential part of the critical zone, and soil-microbe-plant system serves as a key link among lithosphere, biosphere, atmosphere and hydrosphere. As one of the habitats with the richest biodiversity, soil plays a critical role in element biogeo- chemistry on the earth surface （weathered crust）. Here we review the soil biological processes that are relevant to mineral weathering, element cycling, and transformation, with an emphasis on rock weathering mediated by soil microbes, plant root and the rhizosphere.

AbstractNuclear grade zirconium and hafnium are the key to promoting the development of nuclear industry. We designed and synthesized a new diphosphonic acid extractant N,N-n-octylamine di(methylene phenylphosphinic acid) (OADMPPA) with excellent extraction ability and priority for extraction of zirconium. This paper focused on the changes in extraction mechanisms and composition of complexes. With increasing concentration of HCl, cation exchange mechanism gradually changed into solvation mechanism, and complexes converted in the order of ZrOA, Zr(HA)2Cl2 and ZrCl4·H2A. Compared with bis(2,4,4-trimethylpentyl)phosphinic acid (Cyanex272), OADMPPA could increase the extraction rates of zirconium and hafnium by 7.2 and 12.6 times.Graphic abstract