Alternative splicing of CsbHLH133 regulates geraniol biosynthesis in tea plants.

Geraniol is one of the most abundant aromatic compounds in fresh tea leaves and contributes to the pleasant odor of tea products. Additionally, it functions as an airborne signal that interacts with other members of the ecosystem. To date, the regulation of the geraniol biosynthesis in tea plants remains to be investigated. In this study, a correlation test of the content of geraniol and its glycosides with gene expression data revealed that nudix hydrolase, CsNudix26, and its transcription factor, CsbHLH133 are involved in geraniol biosynthesis. In vitro enzyme assays and metabolic analyses of genetically modified tea plants confirmed that CsNudix26 is responsible for the formation of geraniol. Yeast one-hybrid, dual-luciferase reporter, and EMSA assays were used to verify the binding of CsbHLH133 to the CsNudix26 promoter. Overexpression of CsbHLH133 in tea leaves enhanced CsNudix26 expression and geraniol accumulation, whereas CsbHLH133 silencing reduced CsNudix26 transcript levels and geraniol content. Interestingly, CsbHLH133-AS, produced by alternative splicing, was discovered and proved to be the primary transcript expressed in response to various environmental stresses. Furthermore, geraniol release was found to be affected by various factors that alter the expression patterns of CsbHLH133 and CsbHLH133-AS. Our findings indicate that distinct transcript splicing patterns of CsbHLH133 regulate geraniol biosynthesis in tea plants in response to different regulatory factors.

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.

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.

The potential, challenges, and prospects of the genus Spirulina polysaccharides as future multipurpose biomacromolecules.

Spirulina has been widely used worldwide as a food and medicinal ingredient for centuries. Polysaccharides are major bioactive constituents of Spirulina and are of interest because of their functional properties and unlimited application potential. However, the clinical translation and market industrialization of the polysaccharides from genus Spirulina (PGS) are retarded due to the lack of a further understanding of their isolation, bioactivities, structure-activity relationships (SARs), toxicity, and, most importantly, versatile applications. Herein, we provide an overview of the extraction, purification, and structural features of PGS; meanwhile, the advances in bioactivities, SARs, mechanisms of effects, and toxicity are discussed and summarized. Furthermore, the applications, potential developments, and future research directions are scrutinized and highlighted. This review may help fill the knowledge gap between theoretical insights and practical applications and guide future research and industrial application of PGS.

Clinical outcomes of proximal femoral reconstruction technique combined with THA in the treatment of high dislocation secondary to septic arthritis: a retrospective single-center study.

PURPOSE: The aim of this retrospective study was to examine the clinical outcomes and complications of proximal femur reconstruction (PFR) combined with total hip arthroplasty (THA) in patients with high hip dislocation secondary to septic arthritis (SA). METHODS: Between September 2016 to September 2021, we performed a series of 15 consecutive PFR combined with THA on patients with high dislocation of the hip secondary to SA, of these,12 hips were reviewed retrospectively, with a mean follow-up of 2.5 years (range, 1.5-6 years). The mean age of the patients at the time of surgery was 52 years (range, 40-70 years). RESULTS: All patients were followed up. At 1-year postoperative follow-up, the median HHS increased from 32.50 preoperatively to 79.50 postoperatively. The median VAS decreased from 7 before surgery to 2 at 1 year after surgery. The median LLD reduced from 45 mm preoperatively to 8 mm at 1 year after surgery. The mean operative time 125 ± 15 min (range 103-195 min). Mean estimated blood loss was500 ± 105ml (range 450-870 ml). Mean hospital days 9.5 days (range 6-15 days). Two patients developed nerve injuries that improved after nutritional nerve treatment. One patient had recurrent postoperative dislocation and underwent reoperation, with no recurrence dislocation during the follow-up. There were no cases of prosthesis loosening during the follow-up period. One patient developed acute postoperative periprosthetic joint infection (PJI) that was treated with Debridement, Antibiotics and Implant Retention (DAIR) plus anti-infective therapy, with no recurrence during 2 years of follow-up. CONCLUSION: This study indicates PFR combined with THA shows promise as a technique to manage high hip dislocation secondary to SA, improving early outcomes related to pain, function, and limb length discrepancy.

Two galactinol synthases contribute to the drought response of Camellia sinensis.

MAIN CONCLUSION: CsGolS2-1 and CsGolS2-2 are involved in the transcriptional mechanism and play an important role in the drought response of tea plants. GolS is critical for the biosynthesis of galactinol and has been suggested to contribute to drought tolerance in various plants. However, whether GolS plays a role in drought response and the underlying transcriptional mechanism of GolS genes in response to drought stress in tea plants is still unclear. In this study, we found that drought stress promotes the accumulation of galactinol in tea leaves and that the expression of CsGolS2-1 and CsGolS2-2, which encode proteins capable of catalyzing galactinol biosynthesis, is continuously and dramatically induced by drought stress. Moreover, transgenic Arabidopsis plants expressing CsGolS2-1 and CsGolS2-2 were more drought-tolerant than WT plants, as evidenced by increased cell membrane stability. In addition, the drought-responsive transcription factor CsWRKY2 has been shown to positively regulate the expression of CsGolS2-1 and CsGolS2-2 by directly binding to their promoters. Furthermore, CsVQ9 was found to interact with CsWRKY2 and promote its transcriptional function to activate CsGolS2-1 and CsGolS2-2 expression. Taken together, our findings provide insights not only into the positive role played by CsGolS2-1 and CsGolS2-2 in the drought response of tea plants but also into the transcriptional mechanisms involved.

SERS with Flexible ß-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network.

The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was developed using surface-enhanced Raman spectroscopy (SERS) based on a flexible substrate and lightweight deep learning network. The flexible SERS substrate was fabricated by assembling ß-cyclodextrin-modified gold nanoparticles (ß-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (ß-CD@AuNP/PTFE). The concentrations of benzo(a)pyrene (BaP), naphthalene (Nap), and pyrene (Pyr) residues on fruit and vegetable surfaces could be detected at 0.25, 0.5, and 0.25 µg/cm2, respectively, and all the relative standard deviations (RSD) were less than 10%, indicating that the ß-CD@AuNP/PTFE exhibited high sensitivity and stability. The lightweight network was then used to construct a classification model for identifying various PAH residues. ShuffleNet obtained the best results with accuracies of 100%, 96.61%, and 97.63% for the training, validation, and prediction datasets, respectively. The proposed method realised the in situ detection and identification of various PAH residues on fruit and vegetables with simplicity, celerity, and sensitivity, demonstrating great potential for the rapid, nondestructive analysis of surface contaminant residues in the food-safety field.

Determination of Tea Aroma Precursor Glycosides: An Efficient Approach via Liquid Chromatography-Tandem Mass Spectrometry.

Tea aroma components are often stored as glycosidically bound forms in the tea plant (Camellia sinensis). However, the determination of these glycosides in tea samples is far from optimal. In the present study, we developed a high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method for simultaneous quantification of eight primary aroma glycosides within 10 min. After systematic optimization of multiple reaction monitoring (MRM) parameters, the proposed method was highly sensitive and accurate. Optimization of the method permitted the efficient extraction of aroma glycosides. The developed method was applied to analyze the contents of aroma glycosides in different organs of tea plants, including the bud, leaves, and stem. Contents of aroma glycosides in the harvested 'Shaancha 1' ranged from 36.1 to 40454.4 µg kg-1. Geranyl glucoside and primeveroside mainly accumulated in young leaves, while other glycosides mainly accumulated in mature leaves. The findings document a rapid, reliable, and efficient analysis method. This method will be helpful in elucidating the biosynthesis and biotransformation mechanism of tea aroma glycosides and in promoting the development of the tea industry using advanced technological control approaches during the cultivation of tea plants and tea manufacture.

Detection of 1-OHPyr in human urine using SERS with injection under wet liquid-liquid self-assembled films of ß-CD-coated gold nanoparticles and deep learning.

1-Hydroxypyrene (1-OHPyr), a typical hydroxylated polycyclic aromatic hydrocarbon (OH-PAH), has been commonly regarded as a urinary biomarker for assessing human exposure and health risks of PAHs. Herein, a fast and sensitive method was developed for the determination of 1-OHPyr in urine using surface-enhanced Raman spectroscopy (SERS) combined with deep learning (DL). After emulsification, urinary 1-OHPyr was separated using simple liquid-liquid extraction. Gold nanoparticles with ß-cyclodextrin (ß-CD@AuNPs) were synthesized, and homogeneous and ordered ß-CD@AuNP films were prepared through a liquid-liquid interface self-assembly process. The separated 1-OHPyr was injected under wet assembled films for SERS detection. Concentration as low as 0.05 µg mL-1 of 1-OHPyr in urine could still be detected, and the relative standard deviation was 5.5 %, and this was ascribed to the adsorption of ß-CD and the high-probability contact between 1-OHPyr molecules and the nanogap of assembled films under the action of capillary force. Meanwhile, a convolutional neural network (CNN), a classical DL network architecture, was adopted to build the prediction model, and the model was further simplified by genetic algorithm (GA). CNN combined with a GA obtained optimized results with determination coefficient and a root mean square error of prediction sets of 0.9639 and 0.6327, respectively, outperforming other models. Overall, the proposed method achieves fast and accurate detection of 1-OHPyr in urine, improves the assessment human exposure to PAHs and is expected to have applications in the analysis of other OH-PAHs in complex environments.

Learning to Learn Variational Quantum Algorithm.

Variational quantum algorithms (VQAs) use classical computers as the quantum outer loop optimizer and update the circuit parameters to obtain an approximate ground state. In this article, we present a meta-learning variational quantum algorithm (meta-VQA) by recurrent unit, which uses a technique called "meta-learner." Motivated by the hybrid quantum-classical algorithms, we train classical recurrent units to assist quantum computing, learning to find approximate optima in the parameter landscape. Here, aiming to reduce the sampling number more efficiently, we use the quantum stochastic gradient descent method and introduce the adaptive learning rate. Finally, we deploy on the TensorFlow Quantum processor within approximate quantum optimization for the Ising model and variational quantum eigensolver for molecular hydrogen (H2), lithium hydride (LiH), and helium hydride cation (HeH+). Our algorithm can be expanded to larger system sizes and problem instances, which have higher performance on near-term processors.

Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo.

In this paper, we propose some efficient multi-view stereo methods for accurate and complete depth map estimation. We first present our basic methods with Adaptive Checkerboard sampling and Multi-Hypothesis joint view selection (ACMH & ACMH+). Based on our basic models, we develop two frameworks to deal with the depth estimation of ambiguous regions (especially low-textured areas) from two different perspectives: multi-scale information fusion and planar geometric clue assistance. For the former one, we propose a multi-scale geometric consistency guidance framework (ACMM) to obtain the reliable depth estimates for low-textured areas at coarser scales and guarantee that they can be propagated to finer scales. For the latter one, we propose a planar prior assisted framework (ACMP). We utilize a probabilistic graphical model to contribute a novel multi-view aggregated matching cost. At last, by taking advantage of the above frameworks, we further design a multi-scale geometric consistency guided and planar prior assisted multi-view stereo (ACMMP). This greatly enhances the discrimination of ambiguous regions and helps their depth sensing. Experiments on extensive datasets show our methods achieve state-of-the-art performance, recovering the depth estimation not only in low-textured areas but also in details. Related codes are available at https://github.com/GhiXu.

Robust Self-Testing of Four-Qubit Symmetric States.

Quantum verification has been highlighted as a significant challenge on the road to scalable technology, especially with the rapid development of quantum computing. To verify quantum states, self-testing is proposed as a device-independent concept, which is based only on the observed statistics. Previous studies focused on bipartite states and some multipartite states, including all symmetric states, but only in the case of three qubits. In this paper, we first give a criterion for the self-testing of a four-qubit symmetric state with a special structure and the robustness analysis based on vector norm inequalities. Then we generalize the idea to a family of parameterized four-qubit symmetric states through projections onto two subsystems.

Unearthing the Alleviatory Mechanisms of Brassinolide in Cold Stress in Rice.

Cold stress inhibits rice germination and seedling growth. Brassinolide (BR) plays key roles in plant growth, development, and stress responses. In this study, we explored the underlying mechanisms whereby BR helps alleviate cold stress in rice seedlings. BR application to the growth medium significantly increased seed germination and seedling growth of the early rice cultivar "Zhongzao 39" after three days of cold treatment. Specifically, BR significantly increased soluble protein and soluble sugar contents after three days of cold treatment. Moreover, BR stimulated the activity of superoxide dismutase, catalase, peroxidase, and ascorbate peroxidase; thereby alleviating cold-induced damage and increasing glutathione content and the GSH/GSSG ratio while concomitantly reducing H2O2 content. BR upregulated the expression levels of cold-response-related genes, including OsICE1, OsFer1, OsCOLD1, OsLti6a, OsSODB, OsMyb, and OsTERF2, and downregulated that of OsWRKY45, overall alleviating cold stress symptoms. Thus, BR not only upregulated cellular osmotic content and the antioxidant enzyme system to maintain the physiological balance of reactive oxygen species under cold but, additionally, it regulated the expression of cold-response-related genes to alleviate cold stress symptoms. These results provide a theoretical basis for rice breeding for cold resistance using young seedlings.

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels.

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception-attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception-attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.

Aircraft Landing Gear Retraction/Extension System Fault Diagnosis with 1-D Dilated Convolutional Neural Network.

The faults of the landing gear retraction/extension(R/E) system can result in the deterioration of an aircraft's maneuvering conditions; how to identify the faults of the landing gear R/E system has become a key issue for ensuring aircraft take-off and landing safety. In this paper, we aim to solve this problem by proposing the 1-D dilated convolutional neural network (1-DDCNN). Aiming at developing the limited feature information extraction and inaccurate diagnosis of the traditional 1-DCNN with a single feature, the 1-DDCNN selects multiple feature parameters to realize feature integration. The performance of the 1-DDCNN in feature extraction is explored. Importantly, using padding dilated convolution to multiply the receptive field of the convolution kernel, the 1-DDCNN can completely retain the feature information in the original signal. Experimental results demonstrated that the proposed method has high accuracy and robustness, which provides a novel idea for feature extraction and fault diagnosis of the landing gear R/E system.

Alternative splicing of CsJAZ1 negatively regulates flavan-3-ol biosynthesis in tea plants.

Flavan-3-ols are abundant in the tea plant (Camellia sinensis) and confer tea with flavor and health benefits. We recently found that alternative splicing of genes is likely involved in the regulation of flavan-3-ol biosynthesis; however, the underlying regulatory mechanisms remain unknown. Here, we integrated metabolomics and transcriptomics to construct metabolite-gene networks in tea leaves, collected over five different months and from five spatial positions, and found positive correlations between endogenous jasmonic acid (JA), flavan-3-ols, and numerous transcripts. Transcriptome mining further identified CsJAZ1, which is negatively associated with flavan-3-ols formation and has three CsJAZ1 transcripts, one full-length (CsJAZ1-1), and two splice variants (CsJAZ1-2 and -3) that lacked 3' coding sequences, with CsJAZ1-3 also lacking the coding region for the Jas domain. Confocal microscopy showed that CsJAZ1-1 was localized to the nucleus, while CsJAZ1-2 and CsJAZ1-3 were present in both the nucleus and the cytosol. In the absence of JA, CsJAZ1-1 was bound to CsMYC2, a positive regulator of flavan-3-ol biosynthesis; CsJAZ1-2 functioned as an alternative enhancer of CsJAZ1-1 and an antagonist of CsJAZ1-1 in binding to CsMYC2; and CsJAZ1-3 did not interact with CsMYC2. In the presence of JA, CsJAZ1-3 interacted with CsJAZ1-1 and CsJAZ1-2 to form heterodimers that stabilized the CsJAZ1-1-CsMYC2 and CsJAZ1-2-CsMYC2 complexes, thereby repressing the transcription of four genes that act late in the flavan-3-ol biosynthetic pathway. These data indicate that the alternative splicing variants of CsJAZ1 coordinately regulate flavan-3-ol biosynthesis in the tea plant and improve our understanding of JA-mediated flavan-3-ol biosynthesis.

Isolation and Functional Characterization of Multiple NADPH-Cytochrome P450 Reductase Genes from Camellia sinensis in View of Catechin Biosynthesis.

Catechins are critical constituents for the sensory quality and health-promoting benefits of tea. Cytochrome P450 monooxygenases are required for catechin biosynthesis and are dependent on NADPH-cytochrome P450 reductases (CPRs) to provide reducing equivalents for their activities. However, CPRs have not been identified in tea, and their relationship to catechin accumulation also remains unknown. Thus, three CsCPR genes were identified in this study, all of which had five CPR-related conserved domains and were targeted to the endoplasmic reticulum. These three recombinant CsCPR proteins could reduce cytochrome c using NADPH as an electron donor. Heterologous co-expression in yeast demonstrated that all the three CsCPRs could support the enzyme activities of CsC4H and CsF3'H. Correlation analysis indicated that the expression level of CsCPR1 (or CsCPR2 or CsCPR3) was positively correlated with 3',4',5'-catechin (or total catechins) content. Our results indicate that the CsCPRs are involved in the biosynthesis of catechins in tea leaves.