Long-term addition of organic manure stimulates the growth and activity of comammox in a subtropical Inceptisol.

The discovery of complete ammonia oxidizers (comammox) has dramatically altered our perception of nitrogen (N) biogeochemistry. However, their functional importance vs. the canonical ammonia oxidizers (i.e., ammonia oxidizing-archaea (AOA) and bacteria (AOB)) in agroecosystems is still poorly understood. Accordingly, a new assay using acetylene, 3,4-dimethylpyrazole phosphate (DMPP), and 1-octyne was adopted to assess the ammonia (NH3) oxidation and nitrous oxide (N2O) production activity of these functional guilds in a subtropical Inceptisol under long-term different fertilization regimes. These regimes include CK (no fertilizer control), synthetic fertilizer only (NPK), organic manure only (M) and organic manure plus synthetic fertilizer (MNPK). AOA dominated NH3 oxidation in the M treatment, while AOB dominated both NH3 oxidation and N2O production in all treatments except M. Comammox always played a minor role in both NH3 oxidation and N2O production across all treatments. Both M and MNPK treatments significantly increased the activity and growth of comammox. Compared to NPK, comammox exhibited increases of 270 % and 326 % in the NH3 oxidation rates, and increases of 1472 % and 563 % in the N2O production rates in M and MNPK, respectively. Random forest model revealed that copper (Cu), comammox abundance, and dissolved organic nitrogen (DON) were the most important predictors for the NH3 oxidation rates of comammox. Redundancy analyses (RDA) showed that fertilizer treatments significantly altered the community composition of NH3 oxidizers, and pH was the overarching parameter underpinning the community shift of the NH3 oxidizers. Overall, this study provides evidence that comammox play a minor yet unneglectable role in the nitrification of agroecosystems, and the long-term addition of organic manure stimulates the growth and activity of comammox in a subtropical Inceptisol.

Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit.

This study aimed to develop a machine learning (ML)-based tool for early and accurate prediction of in-hospital mortality risk in patients with spontaneous intracerebral hemorrhage (sICH) in the intensive care unit (ICU). We did a retrospective study in our study and identified cases of sICH from the MIMIC IV (n = 1486) and Zhejiang Hospital databases (n = 110). The model was constructed using features selected through LASSO regression. Among five well-known models, the selection of the best model was based on the area under the curve (AUC) in the validation cohort. We further analyzed calibration and decision curves to assess prediction results and visualized the impact of each variable on the model through SHapley Additive exPlanations. To facilitate accessibility, we also created a visual online calculation page for the model. The XGBoost exhibited high accuracy in both internal validation (AUC = 0.907) and external validation (AUC = 0.787) sets. Calibration curve and decision curve analyses showed that the model had no significant bias as well as being useful for supporting clinical decisions. XGBoost is an effective algorithm for predicting in-hospital mortality in patients with sICH, indicating its potential significance in the development of early warning systems.

The response patterns of r- and K-strategist bacteria to long-term organic and inorganic fertilization regimes within the microbial food web are closely linked to rice production.

Soil microbial food web is crucial for maintaining crop production, while its community structure varies among fertilization regimes. Currently, the mechanistic understanding of the relationships between microbial food web and crop production under various nutrient fertilizations is poor. This knowledge gap limits our capacity to achieve precision agriculture for ensuring yield stability. In this study, we investigated the abiotic (i.e., soil chemical properties) and biotic factors (i.e., microbial food web, including bacteria, fungi, archaea and nematodes) that were closely associated with rice (Oryza sativa L.) production, using soils from seven fertilization regimes in distinct sampling locations (i.e., bulk vs rhizosphere soil) at a long-term experimental site. Organic manure alone fertilization (M) and integrated fertilization (NPKM) combining manure with inorganic fertilizers increased soil pH by 0.21-0.41 units and organic carbon content by 49.1 %-65.2 % relative to the non-fertilization (CK), which was distinct with inorganic fertilization. The principal coordinate analysis (PCoA) revealed that soil microbial and nematode communities were primarily shaped by fertilization rather than sampling locations. Organic fertilization (M, NPKM) increased the relative abundance of both r-strategist bacteria, specific taxa within the fungal (i.e., Pezizales) and nematode communities (i.e., omnivores-predators), whereas inorganic fertilization increased K-strategist bacteria abundances relative to the CK. Correspondingly, network analysis showed that the keystone taxa in the amplicon sequence variants (ASVs) enriched by organic manure and inorganic fertilization were mainly affiliated with r- and K-strategist bacteria, respectively. Structural equation modeling (SEM) analysis found that r- and K-strategist bacteria were positively correlated with rice production under organic and inorganic fertilization, respectively. Our results demonstrate that the response patterns of r/K-strategists to nutrient fertilization largely regulate rice yield, suggesting that the enhanced soil fertility and r-strategists contribute to the highest crop production in NPKM fertilization.