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, which involves the combined use of 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 subjected to long-term different fertilization regimes, namely CK (no fertilizer control), synthetic fertilizer only (NPK), organic manure only (M) and organic manure plus synthetic fertilizer (MNPK). AOA dominated the NH3 oxidation in M treatment, whereas AOB dominated all the NH3 oxidation and N2O production processes in all but M treatment, and comammox played a minor role in both NH3 oxidation and N2O production in 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 paper 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.

A Novel Point Cloud Registration Method Based on ROPNet.

Point cloud registration is a crucial preprocessing step for point cloud data analysis and applications. Nowadays, many deep-learning-based methods have been proposed to improve the registration quality. These methods always use the sum of two cross-entropy as a loss function to train the model, which may lead to mismatching in overlapping regions. In this paper, we designed a new loss function based on the cross-entropy and applied it to the ROPNet point cloud registration model. Meanwhile, we improved the ROPNet by adding the channel attention mechanism to make the network focus on both global and local important information, thus improving the registration performance and reducing the point cloud registration error. We tested our method on ModelNet40 dataset, and the experimental results demonstrate the effectiveness of our proposed method.

A comparative study on adsorption of cadmium and lead by hydrochars and biochars derived from rice husk and Zizania latifolia straw.

In this study, hydrochars and biochars were prepared from rice husk (RH) and Zizania latifolia straw (ZL) at various pyrolysis temperatures as absorbents, for removing toxic ions from single and competitive solutions of cadmium (Cd) and/or lead (Pb). The adsorption efficiencies of Cd and Pb in both hydrochars and biochars were lower in the competitive solution than in the single solution, and the absorbents had a stronger affinity for Pb than for Cd. Compared to hydrochars, biochars showed more favorable Cd and Pb adsorption capacities in the single or competitive solutions, and the ZL biochars had the maximum adsorption capacity among them. The SEM and FTIR analyses suggest that the predominant adsorption mechanisms of biochars and hydrochars are surfaces monolayer adsorption, precipitation, complexation, and coordination with π electrons. However, hydrochars derived from ZL exhibited an optimal additional Pb adsorption capacity in the high-level (5 ~ 10 mg L-1 Cd and Pb) competitive solution. This extra Pb adsorption of hydrochars was likely attributed to the Si-O-Si groups and more bumpy structure. Zizania latifolia straw biochar had a huge potential removal of Cd or/and Pb, and applying hydrochars as absorbents was beneficial to the removal of Cd and Pb in polluted solutions.

Silicon fertilization influences microbial assemblages in rice roots and decreases arsenic concentration in grain: A five-season in-situ remediation field study.

Microbial mechanism of in-situ remediation of arsenic (As) in As-contaminated paddy fields by silicon (Si) fertilization has been rarely reported, especially under continuous rice cultivation and Si applications. In this study, two Si fertilizers were applied for three phases in five consecutive rice seasons to investigate the long-lasting impacts on in-situ remediation of As, and the underpinning microbial mechanism of root-associated compartments (bulk soil, rhizosphere and endosphere) was explored using the last double-cropping rice. Repeated application of Si fertilizers as base manure had a long-lasting effect on reducing As concentrations in rice grains. Application of Si fertilizer at an adequate amount resulted in an extended in-situ remediation effect from endosphere to rhizosphere. The microbial diversity and richness in rhizosphere soil and endosphere were significantly impacted by Si fertilization, the effects depending on application doses and prolonged seasons. Si fertilization can immobilize As in the root or rhizosphere, and Fe concentrations and the As- and Fe-transforming microorganisms (i.e. Geobacteraceae) are the determinants of As uptake in rice. We recommend more extensive supplementation of Si fertilizer at a higher rate to decrease grain As concentration for in-situ remediation. This study sheds light on the microbial-mediated mechanism underlying Si fertilization effect on decreased As uptake in paddy fields.

The inhibitory efficacy of procyanidin on soil denitrification varies with N fertilizer type applied.

Denitrification is a major process of the nitrogen (N) cycle by converting nitrate (NO3-) back to gaseous nitrogen (N2), which leads to massive losses of N, including fertilizer N, from agricultural systems. One mitigation strategy for these N losses involves denitrification inhibition by plant-derived biological denitrification inhibitors (BDIs). Procyanidin was recently identified as a new class of BDI in root extracts from Fallopia spp. However, the efficacy of this compound on soil denitrification under different N fertilizer sources is not well understood. Here, a 14-day microcosm experiment was conducted using three nitrate-based fertilizers (NH4NO3, KNO3, and Ca(NO3)2) to investigate the impact of procyanidin on soil denitrification and associated microbial pathways. Results showed that procyanidin inhibited denitrification activity regardless of the source of N fertilizer applied, but the inhibitory efficacy of procyanidin varied with N fertilizer types. Addition of procyanidin had greater denitrification inhibition in the soils applied with NH4NO3 than with other types of N fertilizer. Moreover, nitrate reductase activity was significantly suppressed by procyanidin addition across all three N fertilizers tested. Quantification of denitrifying functional genes (nirS, nirK, and nosZ) demonstrated that procyanidin inhibited the activity and growth of nirS- and nirK-type denitrifiers, but stimulated the growth of nosZI-containing denitrifiers. These findings indicate that the inhibition of soil denitrification by procyanidin was mainly a result of the suppression of nitrate reductase activity and nirS- and nirK-type denitrifiers abundance. The use of procyanidin together with N fertilizers, especially NH4NO3, can be an effective way to reduce the N losses by denitrification.

Silicon alleviates salt stress-induced potassium deficiency by promoting potassium uptake and translocation in rice (Oryza sativa L.).

Under salt stress, plants suffer from potassium (K) deficiency caused by excess salts in growth substrate. Silicon (Si) can promote K status in many plant species under salt stress, however, the underlying mechanisms remain unclear. In this study, we assessed the effects of Si on K homeostasis in rice under salt stress and investigated the mechanisms behind using two low-Si rice mutants (lsi1 and lsi2) and their wild types (WTs). After five days' treatment with Si, plant growth was improved and salt stress-induced K deficiency was alleviated in WTs but not in mutants. Simultaneously, Si significantly enhanced K accumulation content, K uptake index and shoot K distribution rate in WTs but not in mutants. Besides, Si enhanced K concentration in xylem sap in WTs but not in mutants. Scanning ion-selected electrode technique (SIET) analysis showed net K influx rate was raised by Si addition under salt stress in WTs but not in mutants. Moreover, Si up-regulated the expression of genes responsible for K uptake (OsAKT1 and OsHAK1) and xylem loading (OsSKOR) in WTs but not in mutants. Overall, our results strongly indicate that Si can improve K uptake and translocation by up-regulating the expression of relevant genes, thereby promoting K status and alleviating salt stress in rice.