Isolation of the inorganic phosphorus-solubilizing bacteria Lysinibacillus sphaericus and assessing its role in promoting rice growth.

Soluble phosphorus scarcity severely limits plant growth and crop yield. In this study, a strain of inorganic phosphorus-solubilizing bacteria, Lysinibacillus sphaericus, was isolated from rice rhizosphere soil. The available phosphorus content in liquid inorganic phosphorus identification medium and in L. sphaericus-inoculated soil increased from 204.28 mg/L to 1124.68 mg/L and from 4.75 mg/kg to 7.04 mg/kg, respectively. The pH decreased significantly from 6.87 to 6.14. Incubation with L. sphaericus significantly increased malic and succinic acid content in the liquid inorganic phosphorus identification medium and increased acid phosphatase and alkaline phosphatase activity in the soil. Inoculation with L. sphaericus significantly increased rice growth, chlorophyll a/b content, and photosynthesis by increasing the soluble phosphorus content in the rice rhizosphere soil under phosphorus-deficient conditions. Further analysis revealed that L. sphaericus improved soil phosphorus release by decreasing soil pH and promoting acid phosphatase and alkaline phosphatase activity. This study supports the production of microbial fertilizers to improve rice yield in phosphorus-deficient conditions.

Predicting osteoporotic fractures post-vertebroplasty: a machine learning approach with a web-based calculator.

PURPOSE: The aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use. METHODS: A retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms. Model performance was assessed using the area under the receiver operating characteristic curve (ROC_AUC), and clinical utility was assessed by clinical decision curve analysis (DCA). The best models were analysed for interpretability using SHapley Additive exPlanations (SHAP) and the models were deployed visually using a web calculator. RESULTS: Training and test groups were split using time. The SVM model performed best in both the training group tenfold cross-validation (CV) and validation group AUC, with an AUC of 0.77. DCA showed that the model was beneficial to patients in both the training and test sets. A network calculator developed based on the SHAP-based SVM model can be used for clinical risk assessment ( https://nicolazhang.shinyapps.io/refracture_shap/ ). CONCLUSIONS: The SVM-based ML model was effective in predicting the risk of new-onset OVCF after PVP, and the network calculator provides a practical tool for clinical decision-making. This study contributes to personalised care in spinal surgery.

Study on the removal efficacy and mechanism of phosphorus from wastewater by eggshell-modified biochar.

The excessive discharge of phosphorus from rural domestic sewage is a problem that worthy of attention. If the phosphorus in the sewage were recovered, addressing this issue could significantly contribute to mitigating the global phosphorus crisis. In this study, corn straw, a common agricultural waste, was co-pyrolytically modified with eggshells, a type of food waste from university cafeterias. The resulting product, referred to as corn straw eggshell biochar (EGBC) was characterized using SEM, XRD, XPS, XRF, and other methods. Batch adsorption experiments were conducted to determine the optimal preparation conditions of EGBC and to explore its adsorption characteristics. EGBC showed strong adsorption effectiveness within a pH range of 5-12. The adsorption isotherm closely followed the Sips model (R2 > 0.9011), and the adsorption kinetics were more consistent with the pseudo-second-order model (R2 > 0.9899). The process was found to be both spontaneous and endothermic. Under optimal conditions, the phosphorus adsorption capacity of EGBC was measured to be 288.83 mg/g. This demonstrates the high efficiency of EGBC for phosphorus removal and illustrates an effective method of utilizing food waste for environmental remediation. PRACTITIONER POINTS: Biochar prepared from waste eggshell was used to removal and recovery phosphorus in wastewater treatment. EGBC has an impressive adsorption capacity that can reach up to 288.83 mg/g. EGBC has excellent adsorption and filtration capabilities, and there is a sudden increase in concentration at 900 min in the breakthrough curve of EGBC. EGBC has good regeneration performance, with an adsorption effect of 65% and an adsorption capacity of 121 mg/g after four desorption and regeneration cycles.

PSDF: Prior-Driven Neural Implicit Surface Learning for Multi-view Reconstruction.

Surface reconstruction has traditionally relied on the Multi-View Stereo (MVS)-based pipeline, which often suffers from noisy and incomplete geometry. This is due to that although MVS has been proven to be an effective way to recover the geometry of the scenes, especially for locally detailed areas with rich textures, it struggles to deal with areas with low texture and large variations of illumination where the photometric consistency is unreliable. Recently, Neural Implicit Surface Reconstruction (NISR) combines surface rendering and volume rendering techniques and bypasses the MVS as an intermediate step, which has emerged as a promising alternative to overcome the limitations of traditional pipelines. While NISR has shown impressive results on simple scenes, it remains challenging to recover delicate geometry from uncontrolled real-world scenes which is caused by its underconstrained optimization. To this end, the framework PSDF is proposed which resorts to external geometric priors from a pretrained MVS network and internal geometric priors inherent in the NISR model to facilitate high-quality neural implicit surface learning. Specifically, the visibility-aware feature consistency loss and depth prior-assisted sampling based on external geometric priors are introduced. These proposals provide powerfully geometric consistency constraints and aid in locating surface intersection points, thereby significantly improving the accuracy and delicate reconstruction of NISR. Meanwhile, the internal prior-guided importance rendering is presented to enhance the fidelity of the reconstructed surface mesh by mitigating the biased rendering issue in NISR. Extensive experiments on Tanks and Temples datasets show that PSDF achieves state-of-the-art performance on complex uncontrolled scenes.