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Although cumulative N2O emissions are greater in the winter fallow season than in the rice-growing period, the mechanisms by which the emissions affect fallow paddy fields remain unclear. We aimed to identify N2O flux characteristics and illustrate how key nirS-, nirK- and nosZ-containing denitrifiers affect N2O emission levels in acidic fallow paddy soil. Five water-filled pore space (WFPS) levels were set at 25%, 50%, 75%, 100% and 125%, respectively. During the 48-h-long, high-flux incubation period, the N2O flux was the highest in soil samples with 75% WFPS, followed by those with 100% WFPS. The size of nirS-containing denitrifier community was more sensitive to the shifts in soil moisture and showed a stronger correlation with N2O flux than that of nirK-containing denitrifiers, whereas higher N2O concentrations induced an increase in the levels of nosZ-containing bacteria. After incubation for 48 h, nirK- and nosZ-denitrifying bacterial composition varied remarkably under 50%, 75%, and 100% WFPS treatments. However, the composition of nirS-containing denitrifying bacterial community gradually varied with an increase in soil moisture from 25% to 100% WFPS. Certain dominant OTUs of nirK- nirS- and nosZ-containing denitrifiers were highly abundant, especially under treatments of 50%, 75% and 100% WFPS, which were closely associated with the N2O flux. Thus, nirK, nirS and nosZ-containing denitrifiers respond to soil moisture differently, and enriched species might mainly be involved in controlling N2O flux in fallow paddy soils via denitrification, while the abundance of nirS-containing denitrifiers might affect N2O emission levels more significantly than that of nirK-containing denitrifiers.

This study develops a prognostic model to predict metastasis and prognosis in colorectal cancer liver metastases by identifying distinct macrophage subsets. Using scRNA-seq data from primary colorectal cancer and liver metastases, we dissected the cellular landscape to find unique macrophage subpopulations, particularly EEF1G + macrophages, which were prevalent in liver metastases. The study leveraged data from GSE231559, TCGA, and GEO databases to construct an 8-gene risk model named EMGS, based on the EEF1G + macrophage gene signature. Patients were divided into high-risk and low-risk groups using the median EMGS score, with the high-risk group showing significantly worse survival. This group also demonstrated upregulated pathways associated with tumor progression, such as epithelial-mesenchymal transition and angiogenesis, and downregulated metabolic pathways. Moreover, the high-risk group presented an immunosuppressive microenvironment, with a higher TIDE score indicating lower effectiveness of immunotherapy. The study identifies potential drugs targeting the high-risk group, suggesting therapeutic avenues to improve survival. Conclusively, the EMGS score identifies colorectal cancer patients at high risk of liver metastases, highlighting the role of specific macrophage subsets in tumor progression and providing a basis for personalized treatment strategies.

Previous studies have shown that phosphorus addition to P-limited soils increases gaseous N loss. A possible explanation for this phenomenon is element stoichiometry (specifically of C:N:P) modifying linked nutrient cycling, leading to enhanced nitrification and denitrification. In this study, we investigated how P stoichiometry influenced the dynamics of soil N-cycle functional genes. Rice seedlings were planted in P-poor soils and incubated with or without P application. Quantitative PCR was then applied to analyze the abundance of ammonia-oxidizing ( amoA ) and denitrifying ( narG nirK , nirS , nosZ ) genes in soil. P addition reduced bacterial amoA abundance but increased denitrifying gene abundance. We suggest this outcome is due to P-induced shifts in soil C:P and N:P ratios that limited ammonia oxidization while enhancing P availability for denitrification. Under P application, the rhizosphere effect raised ammonia-oxidizing bacterial abundance ( amoA gene) and reduced nirK , nirS , and nosZ in rhizosphere soils. The change likely occurred through greater C input and O 2 release from roots, thus altering C availability and redox conditions for microbes. Our results show that P application enhances gaseous N loss potential in paddy fields mainly through stimulating denitrifier growth. We conclude that nutrient availability and elemental stoichiometry are important in regulating microbial gene responses, thereby influencing key ecosystem processes such as denitrification. Graphical abstract ᅟ

Soil sickness a severe problem in tobacco production, leading to soil-borne diseases and reduce in tobacco yield. This occurs as a result of the interaction between root exudates and rhizosphere microorganisms, which is however, little studied until now. By combining the field investigation and pot experiment, we found the output yield consistently decreased during the first 10 years of continuous cropping in a tobacco field, but increased at the 15th year (15Y). The root exudate and rhizosphere bacterial community was further analyzed to reveal the underlying mechanism of the suppressive soil formation. Root exudate of 15Y tobacco enriched in amino acids and derivatives, while depleted in the typical autotoxins including phenolic acids and alkaloids. This was correlated to the low microbial diversity in 15Y, but also the changes in community composition and topological properties of the co-occurrence network. Especially, the reduced autotoxins were associated with low Actinobacteria abundance, low network complexity and high network modularity, which significantly correlated with the recovered output yield in 15Y. This study revealed the coevolution of rhizosphere microbiota and root exudate as the soil domesticated by continuous cropping of tobacco, and indicated a potential role of the autotoxins and theirs effect on the microbial community in the formation of suppressive soil.

The metabolism of substances such as amino acids and carbohydrates plays a crucial role in the growth and development of silkworms. Analyzing the differential expression of key genes associated with these metabolic processes can help elucidate the molecular mechanisms underlying abnormal development in silkworm mutants. This study conducted and compared transcriptome analyses of individuals from the silkworm mutant perforated small cocoon( psc ) and the wild-type XueSong KD(XSKD) at the third-instar larval stage. A total of 716 differentially expressed genes were identified, including 354 upregulated genes and 362 downregulated genes. Functional annotation based on the KEGG database indicates that these differentially expressed genes were mainly enriched in metabolic pathways related to amino acids, carbohydrates, and lipids, as well as pathways involving neuroactive ligand-receptor interactions. Some key enzyme genes involved in substance metabolism and important neuroreceptor genes played a crucial role in the formation of the psc . In addition, by selecting some differentially expressed genes for qRT-PCR verification, the results indicated that the identification of differentially expressed genes were reliable. This study utilized RNA sequencing technology to screen for differentially expressed genes between the psc mutant and the XSKD. These findings provide important insights into the molecular mechanisms underlying the formation of the abnormal phenotypes in the psc mutant, and they also have certain guiding significance for the breeding of high-quality large cocoon silkworm varieties.

Biological nitrogen (N₂) fixation as a source of new N input into the soil by free-living diazotrophs is important for achieving sustainable rice agriculture. However, the dominant environmental drivers or factors influencing N₂ fixation and the functional significance of the diazotroph community structure in paddy soil across a climatic gradient are not yet well understood. Thus, we characterized the diazotroph community and identified the ecological predictors of N₂ fixation potential in four different climate zones (mid-temperate, warm-temperate, subtropical, and tropical paddy soils) in eastern China. Comprehensive nifH gene sequencing, functional activity detection, and correlation analysis with environmental factors were estimated. The potential nitrogenase activity (PNA) was highest in warm-temperate regions, where it was 6.2-, 2.9-, and 2.2-fold greater than in the tropical, subtropical, and mid-temperate regions, respectively; nifH gene abundance was significantly higher in warm-temperate and subtropical zones than in the tropical ormid-temperate zones. Diazotroph diversitywas significantly higher in the tropical climate zone and significantly lower in the mid-temperate zone. Non-metric multidimensional scaling and canonical correlation analysis indicated that paddy soil diazotroph populations differed significantly among the four climate zones, mainly owing to differences in climate and soil pH. Structural equation models and automatic linear models revealed that climate and nutrients indirectly affected PNA by affecting soil pH and diazotroph community, respectively, while diazotroph community, C/P, and nifH gene abundance directly affected PNA. And C/P ratio, pH, and the diazotroph community structure were the main predictors of PNA in paddy soils. Collectively, the differences in diazotroph community structure have ecological significance, with important implications for the prediction of soil N₂-fixing functions under climate change scenarios.

A paddy soil, with microbial biomass considerably reduced by chloroform fumigation, was treated with low-crystalline ferrihydrite and high-crystalline goethite and with 13C-labeled acetate. In the first 10 days of the incubation, CH4 was produced mainly from the added acetate (56‒91%). After day 30, however, 3‒11% of the total CH4 emissions originated from the added acetate. Chloroform fumigation reduced the microbial biomass by 43‒87%, leading to the decrease in the CH4 emission from the fumigated soil for 352‒1127 times compared to that from the unfumigated soil. Acetate only contributed to 0‒6% of the total CH4 emission from the fumigated soil during the entire incubation period. Thus, chloroform fumigation largely reduced the abundance of methanogens, and the reduction in the abundance of acetotrophic methanogens was high. Iron oxide additions reduced CH4 emissions from the added acetate and from other sources. The reduction was stronger in the fumigated soil compared to that in the unfumigated soil because the lower abundance of methanogens in the fumigated soil decreased the competition for substrates with iron reducers. The effect of ferrihydrite on CH4 emission from non-acetate sources was stronger than that of goethite before day 6; however, this effect became weaker thereafter, because of the reduced number of reactive sites after acetate sorption by ferrihydrite. We conclude that the marked reduction in the microbial biomass, and especially methanogens, decreased the methane production, changed the CH4 sources, and increased the relative effects of iron oxides on CH4 production.

Background: Colorectal cancer (CRC) remains a significant health burden globally, necessitating a deeper understanding of its molecular landscape and prognostic markers. This study characterized ferroptosis-related genes (FRGs) to construct models for predicting overall survival (OS) across various CRC datasets. Methods: In TCGA-COAD dataset, differentially expressed genes (DEGs) were identified between tumor and normal tissues using DESeq2 package. Prognostic genes were identified associated with OS, disease-specific survival, and progression-free interval using survival package. Additionally, FRGs were downloaded from FerrDb website, categorized into unclassified, marker, and driver genes. Finally, multiple models (Coxboost, Elastic Net, Gradient Boosting Machine, LASSO Regression, Partial Least Squares Regression for Cox Regression, Ridge Regression, Random Survival Forest [RSF], stepwise Cox Regression, Supervised Principal Components analysis, and Support Vector Machines) were employed to predict OS across multiple datasets (TCGA-COAD, GSE103479, GSE106584, GSE17536, GSE17537, GSE29621, GSE39084, GSE39582, and GSE72970) using intersection genes across DEGs, OS, disease-specific survival, and progression-free interval, and FRG categories. Results: Six intersection genes (ASNS, TIMP1, H19, CDKN2A, HOTAIR, and ASMTL-AS1) were identified, upregulated in tumor tissues, and associated with poor survival outcomes. In the TCGA-COAD dataset, the RSF model demonstrated the highest concordance index. Kaplan-Meier analysis revealed significantly lower OS probabilities in high-risk groups identified by the RSF model. The RSF model exhibited high accuracy with AUC values of 0.978, 0.985, and 0.965 for 1-, 3-, and 5-year survival predictions, respectively. Calibration curves demonstrated excellent agreement between predicted and observed survival probabilities. Decision curve analysis confirmed the clinical utility of the RSF model. Additionally, the model’s performances were validated in GSE29621 dataset. Conclusions: The study underscores the prognostic relevance of 6 intersection genes in CRC, providing insights into potential therapeutic targets and biomarkers for patient stratification. The RSF model demonstrates robust predictive performance, suggesting its utility in clinical risk assessment and personalized treatment strategies.

Simultaneous localization and mapping (SLAM) algorithm is a prerequisite for unmanned ground vehicle (UGV) localization, path planning, and navigation, which includes two essential components: frontend odometry and backend optimization. Frontend odometry tends to amplify the cumulative error continuously, leading to ghosting and drifting on the mapping results. However, loop closure detection (LCD) can be used to address this technical issue by significantly eliminating the cumulative error. The existing LCD methods decide whether a loop exists by constructing local or global descriptors and calculating the similarity between descriptors, which attaches great importance to the design of discriminative descriptors and effective similarity measurement mechanisms. In this paper, we first propose novel multi-channel descriptors (CMCD) to alleviate the lack of point cloud single information in the discriminative power of scene description. The distance, height, and intensity information of the point cloud is encoded into three independent channels of the shadow-casting region (bin) and then compressed it into a two-dimensional global descriptor. Next, an ORB-based dynamic threshold feature extraction algorithm (DTORB) is designed using objective 2D descriptors to describe the distributions of global and local point clouds. Then, a DTORB-based similarity measurement method is designed using the rotation-invariance and visualization characteristic of descriptor features to overcome the subjective tendency of the constant threshold ORB algorithm in descriptor feature extraction. Finally, verification is performed over KITTI odometry sequences and the campus datasets of Jilin University collected by us. The experimental results demonstrate the superior performance of our method to the state-of-the-art approaches.

Both organic matter and iron oxide (FeO) dynamics pose key roles in soil cadmium (Cd) bioavailability. However, the microbially driven transformation of soil organic matter and FeO and their linkages to Cd fractions remain unclear under reductive soil disinfestation (RSD) with different organic sources, which limits our mechanistic understanding of Cd immobilization by RSD. To address this gap, we conducted a 45 day microcosm experiment using a paddy soil contaminated with 22.8 mg/kg Cd. Six treatments were established: untreated control (CK), waterlogged (WF), and RSD-amended soils with 0.7% or 2.1% wheat straw (LWD, HWD) or soybean meal (LSD, HSD). We systematically assessed soil Cd fractionation, organic carbon and FeO concentrations, and bacterial community structure, aiming to clarify differences in Cd immobilization efficiency and the underlying mechanisms between wheat straw and soybean meal. For strongly extractable Cd, wheat straw RSD reduced the soil Cd concentrations from 6.02 mg/kg to 4.32 mg/kg (28.2%), whereas soybean meal RSD achieved a maximum reduction to 2.26 mg/kg (62.5%). Additionally, the soil mobility factor of Cd decreased from 44.6% (CK) to 39.2% (HWD) and 32.5% (HSD), while the distribution index increased from 58.5% (CK) to 62.2% (HWD) and 66.8% (HSD). Notably, the HWD treatment increased soil total organic carbon, humus, and humic acid concentrations by 34.8%, 24.6%, and 28.3%, respectively. Regarding amorphous FeO, their concentrations increased by 19.1% and 33.3% relative to CK. RSD treatments significantly altered soil C/N ratios (5.91–12.5). The higher C/N ratios associated with wheat straw stimulated r-strategist bacteria (e.g., Firmicutes, Bacteroidetes), which promoted carbohydrate degradation and fermentation, thereby enhancing the accumulation of humic substances. In contrast, the lower C/N ratios of soybean meal increased dissolved organic carbon and activated iron-reducing bacteria (FeRB; e.g., Anaeromyxobacter, Clostridium), driving iron reduction and amorphous iron oxide formation. PLS-PM analysis confirmed that wheat straw RSD immobilized Cd primarily through humification, whereas soybean meal RSD relied on FeRB-mediated FeO amorphization. These findings suggest that Cd immobilization in soil under RSD may be regulated by microbially mediated organic matter transformation and iron oxide dynamics, which was affected by organic materials of different C/N ratios.