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In order to enhance the intelligence and unmanned operation level of the rapid quantitative loading system for coal, achieve real-time detection of train loading quality, and prevent occurrences of train overloading or misalignment, this study addresses the shortcomings of existing non-contact loading quality detection systems by proposing a coal rapid loading misalignment detection method based on laser radar point clouds. Integrating the loading process at coal train stations, the system utilizes laser radar three-dimensional scanning technology and car number recognition technology to establish a train loading quality detection system. A Mahalanobis distance-based outlier filtering algorithm is proposed, which conducts statistical analysis on the neighborhood of each point, calculating its Mahalanobis distance to nearby points. This process eliminates a significant amount of random noise present in the train car point clouds, such as dust during loading, sprayed water mist, and environmental disturbances (rain, snow, coal dust), etc. A label-connected domain clustering algorithm is introduced to segment between adjacent train cars through point cloud connected domain region labeling and clustering. Additionally, a train car stitching algorithm based on PCA analysis is proposed for the three-dimensional stitching of train car point clouds. A point cloud extraction method for loading materials based on point cloud slicing is presented, improving computational speed by constructing local point cloud neighborhoods. Slicing is performed in the length and width directions of the train car to filter out point clouds in front and behind, as well as on the left and right of the train car. Finally, calculation methods for key indicators of train loading quality, including loading height, coal loading quantity, and misalignment quantity, are proposed. The intuitive display of detection results is achieved through surface three-dimensional reconstruction, facilitating the quality inspection of train loading. Experimental results demonstrate that the proposed method enables real-time scanning modeling and loading quality detection on the surface of the train during loading, with applicability to different-sized vehicle models, indicating its generality.

Abstract Electromagnetic (EM) waves are one of the important carriers for information exchange in seawater. The EM parameters of seawater are important factors that affect the performance of EM wave propagation. In the real marine environment, non-constant EM parameters make it impossible to consider seawater as a homogeneous media in the model. However, there are few targeted analyses in the existing studies. In this paper, an N-layered media model was established to investigate the influence of non-constant EM parameters on EM wave propagation in seawater. A direct global matrix method is proposed to solve the EM fields, whose result accuracy does not decrease with the increasing number of layers. The necessity and correctness of the model and methods are verified through numerical simulations and sea experiments. Combined with the existing marine environment database, the propagation characteristics of EM waves in different EM parameter profiles of seawater were analyzed through numerical experiments. The results indicated that the non-homogeneous seawater has a great impact on the intensity, phase, change rate, and spatial distribution of EM waves. Furthermore, the influence is not only reflected in the underwater EM field but also in the air and increases with frequency.

Abstract Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by hyperglycemia resulting from defects in insulin secretion and/or insulin action. Increasing evidence suggests that inflammation played an important role in the pathogenesis of T2DM. Prospective studies on the link between immunoglobulins concentrations and the risk of T2DM in adults are limited. We developed a cohort study including 7,093 adults without T2DM history. The incidence of T2DM was 16.45 per 1,000 person-years. Compared with the lowest quartiles, the hazard ratios (95% confidence intervals) of T2DM for the highest quartiles of IgG, IgE, IgM and IgA were 0.64 (0.48–0.85), 0.94 (0.72–1.23), 0.68 (0.50–0.92) and 1.62 (1.24–2.11) (P for trend was

Abstract DNA lesions are linked to cancer, aging, and various diseases. The recognition and sequencing of special DNA lesions are of great interest but highly challenging. In this paper, an unnatural‐base‐pair‐promoting method for sequencing highly mutagenic ethenodeoxycytidine (εC) DNA lesions that occurred frequently is developed. First, a promising unnatural base pair of dεC–dNaM to recognize εC lesions is identified, and then a conversion PCR is developed to site‐precise transfer dεC–dNaM to dTPT3–dNaM for convenient Sanger sequencing. The low sequence dependence of this method and its capacity for the enrichment of dεC in the abundance of as low as 1.6 × 10–6 nucleotides is also validated. Importantly, the current method can be smoothly applied for recognition, amplification, enrichment, and sequencing of the real biological samples in which εC lesions are generated in vitro or in vivo, thus offering the first sequencing methodology of εC lesions at single‐base resolution. Owing to its simple operations and no destruction of inherent structures of DNA, the unnatural‐base‐pair strategy may provide a new platform to produce general tools for the sequencing of DNA lesions that are hardly sequenced by traditional strategies.

Abstract Background Dihydroxyacetone (DHA) stands as a crucial chemical material extensively utilized in the cosmetics industry. DHA production through the dephosphorylation of dihydroxyacetone phosphate, an intermediate product of the glycolysis pathway in Escherichia coli, presents a prospective alternative for industrial production. However, insights into the pivotal enzyme, dihydroxyacetone phosphate dephosphorylase (HdpA), remain limited for informed engineering. Consequently, the development of an efficient tool for high-throughput screening of HdpA hypermutants becomes imperative. Results This study introduces a methylglyoxal biosensor, based on the formaldehyde-responding regulator FrmR, for the selection of HdpA. Initial modifications involved the insertion of the FrmR binding site upstream of the −35 region and into the spacer region between the −10 and −35 regions of the constitutive promoter J23110. Although the hybrid promoter retained constitutive expression, expression of FrmR led to complete repression. The addition of 350 μM methylglyoxal promptly alleviated FrmR inhibition, enhancing promoter activity by more than 40-fold. The methylglyoxal biosensor system exhibited a gradual increase in fluorescence intensity with methylglyoxal concentrations ranging from 10 to 500 μM. Notably, the biosensor system responded to methylglyoxal spontaneously converted from added DHA, facilitating the separation of DHA producing and non-producing strains through flow cytometry sorting. Subsequently, the methylglyoxal biosensor was successfully applied to screen a library of HdpA mutants, identifying two strains harboring specific mutants 267G > T and D110G/G151C that showed improved DHA production by 68% and 114%, respectively. Expressing of these two HdpA mutants directly in a DHA-producing strain also increased DHA production from 1.45 to 1.92 and 2.29 g/L, respectively, demonstrating the enhanced enzyme properties of the HdpA mutants. Conclusions The methylglyoxal biosensor offers a novel strategy for constructing genetically encoded biosensors and serves as a robust platform for indirectly determining DHA levels by responding to methylglyoxal. This property enables efficiently screening of HdpA hypermutants to enhance DHA production.

Carboxypeptidase A(CPA) has a great potential application in the food and pharmaceutical industry due to its capability to hydrolyze ochratoxin A(OTA) and remove the bitterness of peptide. However, CPA is a mesophilic enzyme that cannot adequately exert its catalytic activity at elevated temperatures, which seriously restricts its industrial application. In this study, the rational design of disulfide bonds was introduced to improve the thermostability of CPA. The highly flexible regions of CPA were predicted through the HotSpot Wizard program and molecular dynamics (MD) simulations. Then, DbD and MODIP online servers were conducted to predict potential residue pairs for introducing disulfide bonds in CPA. After the conservativeness analysis of the PSSM matrix and the structural analysis of the MD simulation, two mutants with potentially enhanced thermostability were screened. Results showed that these mutants D93C/F96C and K153C/S251C compared to the wild-type(WT) exhibited increase by 10 and 10 °C in Topt, 3.4 and 2.7 min in t1/2 at 65 °C, in addition to rise of 8.5 and 11.4 °C in T5015, respectively. Furthermore, the molecular mechanism responsible for thermostability was investigated from the perspective of advanced structure and molecular interactions. The enhanced thermostability of both mutants was not only associated with the more stable secondary structure and the introduction of disulfide bonds but also related to the changes in hydrogen bonds and the redistribution of surface charges in mutant regions. This study showed for the first time that the rational design of disulfide bonds is an effective strategy to enhance the thermostability of CPA, providing in this way a broader industrial application.

Rock mass structure rupture is an important factor that seriously restricts the construction and safe operation of underground space engineering projects such as mines, subways and tunnels. Realizing the identification of rock mass fracture state is one of the hotspots and emphases of current research. In this study, some experiments of sandstone loading failure under different conditions were carried out, the acoustic emission Mel-frequency cepstral coefficient (MFCC) and its fluctuation difference during the whole loading process were extracted, the variation law of the coefficient and its fluctuation difference during the whole loading failure process was studied, and the correlation characteristics of coefficient No.1 (According to the calculation of acoustic emission Mel-frequency cepstral coefficients, it can be seen that a set of acoustic emission Mel-frequency cepstral coefficient includes 12, and coefficient No.1 refers to the first Mel-frequency cepstral coefficient) and its fluctuation difference with the fracture state of sandstone were analyzed. Based on this, a method to identify sandstone fracture state using the Mel-frequency cepstral of acoustic emission was proposed, and the identification criteria was constructed. The identification effect was finally verified. The results show that with loading increase, the coefficient No.1 increases as a whole, and the coefficient value and its discreteness increase significantly in the failure stage and show significant regular fluctuations. The fluctuation difference of the coefficient has the characteristics of periodic variation. The size of the fluctuation difference and its fluctuation can characterize the fracture of sandstone. The overall increase and sudden increase of the fluctuation difference can reflect the macroscopic fracture of sandstone in the unstable deformation and post-peak failure stage, and the sudden increase level of the fluctuation difference can reflect the fracture degree of sandstone. The acoustic emission Mel-frequency cepstral coefficient and its fluctuation difference show good response characteristics to sandstone fracture, which is less affected by different loading conditions, thus they have applicability in reflecting sandstone fracture. The coefficient No.1 and its fluctuation difference have a good correlation with the fracture state of sandstone. The correlation can be divided into three stages as: in the micro-fracture stage of sandstone, the coefficient No.1 and its fluctuation difference are intensively distributed; in the unstable deformation stage just prior to the peak load, the distribution range increases sharply, the overall value increases and the high abnormal value appears; in the post-peak failure stage, the distribution range further increases, the overall value is higher, and more high abnormal values appear. The identification method and criteria of sandstone fracture state were constructed by using the 75% site value and outliers of coefficient No.1 and the 75% site value and outliers of the fluctuation difference of coefficient No.1. The effect of the identification criteria was tested by the confusion matrix of the three-classification model. The accuracy and precision of identification are 90.43% and 94.45%, respectively, which indicate the identification effect is good. The results can provide a reference for the identification of the fracture state of other types of coal and rocks, and for the monitoring and early warning of coal rock instability.

Rockbursts have become a significant hazard in underground mining, underscoring the need for a robust early warning model to ensure safety management. This study presents a novel approach for rockburst prediction, integrating the Mann-Kendall trend test (MKT) and multi-indices fusion to enable real-time and quantitative assessment of rockburst hazards. The methodology employed in this study involves the development of a comprehensive precursory index library for rockbursts. The MKT is then applied to analyze the real-time trend of each index, with adherence to rockburst characterization laws serving as the warning criterion. By employing a confusion matrix, the warning effectiveness of each index is assessed, enabling index preference determination. Ultimately, the integrated rockburst hazard index Q is derived through data fusion. The results demonstrate that the proposed model achieves a warning effectiveness of 0.563 for Q, surpassing the performance of any individual index. Moreover, the model's adaptability and scalability are enhanced through periodic updates driven by actual field monitoring data, making it suitable for complex underground working environments. By providing an efficient and accurate basis for decision-making, the proposed model holds great potential for the prevention and control of rockbursts. It offers a valuable tool for enhancing safety measures in underground mining operations.

The coal industry urgently needs real-time access to comprehensive information on coal quality and quantity for its digital transformation. Based on analyzing the industrial application requirements for comprehensive analysis of coal quality and quantity, this paper focuses on the technical principles, research status, and industrial application of spectroscopic techniques represented by laser-induced breakdown spectroscopy (LIBS) and multispectral fusion with other spectroscopic techniques. It also discusses the artificial intelligence-based coal quality and quantity detection methods represented by image analysis. Then, based on the industrial application scenarios of different technologies, we need to analyze the technical limitations of real-time coal quality and quantity online detection technology in industrial applications. These limitations include detection accuracy issue based on technical principles, equipment stability issue caused by complex environmental factors, algorithm analysis issue based on large-scale data processing, technical applicability, and flexibility issue in the entire coal industry chain, respectively. Finally, four development suggestions for future comprehensive analysis and online detection technology of coal quality and quantity were proposed, they are research on coal quality online detection technology considering geological conditions, research on industrial-scale multispectral fusion technology, research on comprehensive analysis of coal quality and quantity using spectroscopy and image analysis techniques and in-depth research on the application of intelligent technologies in real-time coal quality and quantity detection, respectively. Coal quality online detection is a complex field that involves multiple disciplines and specialized knowledge. It relies on interdisciplinary scientific and technological fields such as coal petrography, spectroscopy, instrument engineering, data processing, pattern recognition, artificial intelligence, and machine learning. Establishing industrial application scenario-coal quality and coal quantity parameters-actual application guidance databases is an important direction for achieving intelligent coal quality and coal quantity online detection and obtaining comprehensive coal quality and coal quantity information.

In this study, a series arc fault detection method combining load recognition and multilayer perceptron-support vector machine (MLP-SVM) is proposed. The method addresses the issue of interfering loads on series arc fault detection and the lack of significant arc fault features in some loads. Initially, the eigenvalues of the line currents for single and mixed loads are extracted in the time domain, both during arc fault and normal conditions. Subsequently, load recognition is performed using a complex matrix calculation method. Then, a feature matrix and history matrix are created for each load. The history matrix is then used to compare the data in the feature matrix to detect any abnormalities in the eigenvalues of each in the presence of any irregularity, the line current flowing through this load will be consistently gathered throughout several cycles, and processed to obtain the eigenvalues, then fed into the MLP-SVM model for training. The classification outcomes will be achieved by means of model train. The results demonstrate that the method effectively prevents misclassification of interfering loads, resulting in improved accuracy in series arc fault detection.

Abstract Background Epigenetic alterations contribute greatly to the development and progression of colorectal cancer, and effect of aberrant miR-622 expression is still controversial. This study aimed to discover miR-622 regulation in CRC proliferation. Methods miR-622 expression and prognosis were analyzed in clinical CRC samples from Nanfang Hospital. miR-622 regulation on cell cycle and tumor proliferation was discovered, and FOLR2 was screened as functional target of miR-622 using bioinformatics analysis, which was validated via dual luciferase assay and gain-of-function and loss-of-function experiments both in vitro and in vivo. Results miR-622 overexpression in CRC indicated unfavorable prognosis and it regulated cell cycle to promote tumor growth both in vitro and in vivo. FOLR2 is a specific, functional target of miR-622, which negatively correlates with signature genes in cell cycle process to promote CRC proliferation. Conclusions miR-622 upregulates cell cycle process by targeting FOLR2 to promote CRC proliferation, proposing a novel mechanism and treatment target in CRC epigenetic regulation of miR-622.

Abstract Background Epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all RNA types. Precise recognition of RNA modifications is critical for understanding their functions and regulatory mechanisms. However, wet experimental methods are often costly and time-consuming, limiting their wide range of applications. Therefore, recent research has focused on developing computational methods, particularly deep learning (DL). Bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and the transformer have demonstrated achievements in modification site prediction. However, BiLSTM cannot achieve parallel computation, leading to a long training time, CNN cannot learn the dependencies of the long distance of the sequence, and the Transformer lacks information interaction with sequences at different scales. This insight underscores the necessity for continued research and development in natural language processing (NLP) and DL to devise an enhanced prediction framework that can effectively address the challenges presented. Results This study presents a multi-scale self- and cross-attention network (MSCAN) to identify the RNA methylation site using an NLP and DL way. Experiment results on twelve RNA modification sites (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um) reveal that the area under the receiver operating characteristic of MSCAN obtains respectively 98.34%, 85.41%, 97.29%, 96.74%, 99.04%, 79.94%, 76.22%, 65.69%, 92.92%, 92.03%, 95.77%, 89.66%, which is better than the state-of-the-art prediction model. This indicates that the model has strong generalization capabilities. Furthermore, MSCAN reveals a strong association among different types of RNA modifications from an experimental perspective. A user-friendly web server for predicting twelve widely occurring human RNA modification sites (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um) is available at http://47.242.23.141/MSCAN/index.php . Conclusions A predictor framework has been developed through binary classification to predict RNA methylation sites.

N6-methyladenosine (m6A) is a crucial RNA modification involved in various biological activities. Computational methods have been developed for the detection of m6A sites in Saccharomyces cerevisiae at base-resolution due to their cost-effectiveness and efficiency. However, the generalization of these methods has been hindered by limited base-resolution datasets. Additionally, RMBase contains a vast number of low-resolution m6A sites for Saccharomyces cerevisiae, and base-resolution sites are often inferred from these low-resolution results through post-calibration. We propose MTTLm6A, a multi-task transfer learning approach for base-resolution mRNA m6A site prediction based on an improved transformer. First, the RNA sequences are encoded by using one-hot encoding. Then, we construct a multi-task model that combines a convolutional neural network with a multi-head-attention deep framework. This model not only detects low-resolution m6A sites, it also assigns reasonable probabilities to the predicted sites. Finally, we employ transfer learning to predict base-resolution m6A sites based on the low-resolution m6A sites. Experimental results on Saccharomyces cerevisiae m6A and Homo sapiens m1A data demonstrate that MTTLm6A respectively achieved area under the receiver operating characteristic (AUROC) values of 77.13% and 92.9%, outperforming the state-of-the-art models. At the same time, it shows that the model has strong generalization ability. To enhance user convenience, we have made a user-friendly web server for MTTLm6A publicly available at http://47.242.23.141/MTTLm6A/index.php

Introduction: Podocyte infolding glomerulopathy (PIG) is a newly recognized rare glomerular injury. The clinical significance and mechanism of this injury pattern remains unclear. Methods: We conducted a retrospective study of renal biopsies from January 2018 to December 2020 in Kingmed Diagnostics. The renal biopsy features and clinical data were reviewed. Laser scanning microdissection and mass spectrometry (LMD/MS) was conducted to analyze the potential mechanism. Results: A total of 116 (0.092%) out of 126,086 biopsies were diagnosed as PIG during the period. Of these, 89 (76.7%) cases were found to have PIG coexisting with immune-complex associated glomerulonephritis (IC-PIG) whereas 27 (23.3%) were identified as isolated PIG without immunoglobulin or complement deposition. Systemic lupus erythematosus (SLE), especially with membranous lupus nephritis (LN), was diagnosed in most (70.8%) IC-PIG cases. Of the isolated PIG cases, 51.9% had no known underlying conditions; however, a relatively high positive rate (42.1%) of antinuclear antibody (ANA) was detected. Nearly half (47.5%) of the patients presented with nephrotic syndrome (NS). PIG grade was associated with proteinuria in isolated PIG (P = 0.035). LMD/MS revealed dysregulated cytoskeletal protein α-actinin4 (ACTN4) and tubulin beta-4 chain in PIG compared with normal donor kidney and minimal change disease (MCD). The displacement of ACTN4 into the glomerular basement membrane (GBM) was confirmed by the confocal microscope. Conclusion: PIG is a rare podocyte injury that can exist alone without underlying disease or be concurrent with various diseases, especially SLE. Podocyte cytoskeletal protein ACTN4 and tubulin beta-4 chain were dysregulated, which may be involved in the mechanism of PIG.

The extraction process of crude polysaccharides from Agrocybe aegerita was optimized, and the antioxidant and anti-aging effects of the crude polysaccharides were evaluated. The optimal extraction parameters for the polysaccharide were identified using the response surface methodology. The DPPH, hydroxy radical, and superoxide anion radical scavenging capacities were evaluated to determine the antioxidant properties of the AAPs. The effects of the AAPs on the lifespan, head-swing frequency, swallowing frequency, body-bending frequency, and stress resistance of Caenorhabditis elegans were determined. The optimal extraction conditions included a solid-to-liquid ratio that resulted in an extraction solution concentration of 0.034 g/mL, an extraction temperature of 92.64 °C, and an extraction time of 2.82 h. Under these conditions, the yield of the AAPs was 11.325% ± 0.996%. The IC50 of the AAPs for superoxide anion radical scavenging was 4.693 mg/mL. The AAPs reached their maximum activity at concentrations of about 2 mg/mL for DPPH and 5 mg/mL for the hydroxyl radical. The AAPs could prolong the lifespan and improve locomotion and the stress resistance of C. elegans. Our findings illustrate the potential of AAPs as an anti-aging and antioxidant agent, highlighting the use of this natural compound in the fields of food and pharmacology.

Landslide displacement monitoring can directly reflect the deformation process of a landslide. Predicting landslide displacements using monitored time series data through deep learning is a useful method for landslide early warning. Currently, existing prediction models mainly focus on single-point time series displacement prediction and do not consider the spatial relationship between monitoring points. To fully take into account the temporal and spatial correlation of displacement monitoring data, this paper proposes two models based on the graph convolutional network (GCN) to perform spatiotemporal prediction of the displacement of the Tangjiao 1# landslide. Firstly, the landslide monitoring system is transformed into a fully connected graph (FCG) to depict the spatial relationship among monitoring points on the landslide. Secondly, a temporal graph convolutional network (T-GCN) model and an attention temporal graph convolutional network (A3T-GCN) model of landslide displacement based on the GCN and GRU models are established respectively. Thirdly, the two models are used to predict the displacement of the Tangjiao 1# landslide. The results show that the established spatiotemporal prediction models are effective in predicting the displacement of the Tangjiao 1# landslide, and the proposed A3T-GCN model achieves the highest prediction accuracy. Our conclusion validates the effectiveness of the attention mechanism in predicting landslide displacement.

Abstract Currently available guided bone regeneration (GBR) films lack active immunomodulation and sufficient osteogenic ability‐ in the treatment of periodontitis, leading to unsatisfactory treatment outcomes. Challenges remain in developing simple, rapid, and programmable manufacturing methods for constructing bioactive GBR films with tailored biofunctional compositions and microstructures. Herein, the controlled electroassembly of collagen under the salt effect is reported, which enables the construction of porous films with precisely tunable porous structures (i.e., porosity and pore size). In particular, bioactive salt species such as the anti‐inflammatory drug diclofenac sodium (DS) can induce and customize porous structures while enabling the loading of bioactive salts and their gradual release. Sequential electro‐assembly under pre‐programmed salt conditions enables the manufacture of a Janus composite film with a dense and DS‐containing porous layer capable of multiple functions in periodontitis treatment, which provides mechanical support, guides fibrous tissue growth, and acts as a barrier preventing its penetration into bone defects. The DS‐containing porous layer delivers dual bio‐signals through its morphology and the released DS, inhibiting inflammation and promoting osteogenesis. Overall, this study demonstrates the potential of electrofabrication as a customized manufacturing platform for the programmable assembly of collagen for tailored functions to adapt to specific needs in regenerative medicine.

Corneal transplantation is a prevailing treatment to repair injured cornea and restore vision but faces the limitation of donor tissue shortage clinically. In addition, suturing-needed transplantation potentially causes postoperative complications. Herein, we design a PEG-Lysozyme injective hydrogel as a suture-free, shape self-adaptive, bioactive implant for corneal stroma defect repair. This implant experiences a sol-gel phase transition via an in situ amidation reaction between 4-arm-PEG-NHS and lysozyme. The physicochemical properties of PEG-Lysozyme can be tuned by the components ratio, which confers the implant mimetic corneal modulus and provides tissue adhesion to endure increased intraocular pressure. In vitro tests prove that the implant is beneficial to Human corneal epithelial cells growth and migration due to the bioactivity of lysozyme. Rabbit lamellar keratoplasty experiment demonstrates that the hydrogel can be filled into defect to form a shape-adaptive implant adhered to native stroma. The implant promotes epithelialization and stroma integrity, recovering the topology of injured cornea to normal. A newly established animal forging behavior test prove a rapid visual restoration of rabbits when use implant in a suture free manner. In general, this work provides a promising preclinical practice by applicating a self-curing, shape self-adaptive and bioactive PEG-Lysozyme implant for suture-free stroma repair.

Abstract Rheological mechanical properties of the soft rock are affected significantly by its main physical characteristics-clay mineral. In this study, taking the mudstone on the roof and floor in four typical mining regions as the research object, firstly, the clay mineral characteristic was analyzed by the X-ray diffraction test. Subsequently, rheological mechanical properties of mudstone samples under different confining pressures are studied through triaxial compression and creep tests. The results show that the clay mineral content of mudstone in different regions is different, which leads to significant differences in its rheological properties, and these differences have a good correlation with the content of montmorillonite and illite-montmorillonite mixed layer. Taking the montmorillonite content as an example, compared with the sample with 3.56% under the lower stress level, the initial creep deformation of the sample with 11.19% increased by 3.25 times, the viscosity coefficient and long-term strength decreased by 80.59% and 53.94%, respectively. Furthermore, based on the test results, the damage variation is constructed considering the montmorillonite content and stress level, and the M–S creep damage constitutive model of soft rock is established. Finally, the test results can be fitted with determination coefficients ranging from 0.9020 to 0.9741, which proves that the constitutive relation can reflect the influence of the clay mineral content in the samples preferably. This study has an important reference for revealing the long-term stability control mechanism of soft rock roadway rich in clay minerals.

Models for maturity discrimination and polyphenol content determination of Aronia melanocarpa (AM) were established to nondestructively determinethe maturityand polyphenol content of AM based on the hyperspectral imaging (HSI) technology. The HSI technology was adopted to collect the image information of Fukangyuan No. 1 AM in different maturity levels, and the Folin-Ciocalteu colorimetric method was employed to determine the polyphenol content. The Monte Carlo method was used to eliminate the outliers. The original image information was preprocessed with the following procedures: Moving average, median filter, normalize, baseline calibration, multiple scattering correction, detrending, and standard normal variate. Meanwhile, the sample set partitioning based on joint x-y distance method was applied to divide the samples. The competitive adaptive reweighting sampling and uninformative variable elimination method were selected to extract the feature wavelengths, based on which the partial least squares model (PLS) and support vector machine (SVM) model were established and compared. The results showed that the UVE-SVM model after multiple scattering correction showed the best performance among all established discriminant models, with the comprehensive recognition rate of 94.62%, Rc2 of 0.9712, and accuracy of 100%. The CARS-SVM model after median filter exhibited the best efficiency in detection of polyphenol content, with the Rc2 of 0.8331. In addition, this work proved that the polyphenol content in AM was visual. This work provided a theoretical basis for the application of HSI technology in berry industry.

Abstract Background Neoadjuvant chemotherapy followed by radical cystectomy (RC) is the standard of care for patients with muscle‐invasive bladder cancer (MIBC). However, treatment outcomes are suboptimal. Camrelizumab, a PD‐1 blockade, has shown benefits in several tumors. This study aimed to investigate the efficacy and safety of neoadjuvant camrelizumab in combination with gemcitabine plus cisplatin (GC) followed by RC for MIBC patients. Methods This was a multi‐center, single‐arm study that enrolled MIBC patients with a clinical stage of T2‐4aN0‐1M0, and scheduled for RC. Patients received three 21‐day cycles of camrelizumab 200 mg on day 1, gemcitabine 1000 mg/m2 on day 1 and 8, and cisplatin 70 mg/m2 on day 2, followed by RC. The primary endpoint was pathologic complete response (pCR, pT0N0). Results From May 2020 to July 2021, 43 patients were enrolled and received study medications at nine centers in China. Three of them were deemed ineligible and excluded from efficacy analysis but included in safety analysis. In total 10 patients were unevaluable as they declined RC (two due to adverse events [AEs] and eight due to patient's willingness). Among 30 evaluable patients, 13 patients (43.3%) achieved pCR, and 16 patients (53.3%) achieved pathologic downstaging. No AEs leading to death were observed. The most common AEs were anemia (69.8%), decreased white blood cell count (65.1%), and nausea (65.1%). Immune‐related AEs were all grade 1 or 2. Pathologic response was not correlated with PD‐L1 expression status or tumor mutation burden. Individual genes as a biomarker for pathologic response were not identified. Conclusions Neoadjuvant treatment with camrelizumab and GC regimen demonstrated preliminary anti‐tumor activity for MIBC patients with manageable safety profiles. The study met its primary endpoint, and the following randomized trial is ongoing.

Abstract Meteorological conditions and source emissions in grassland areas are quite different from those in urban areas, which significantly impacts the spatiotemporal characteristics of black carbon (BC). To obtain the characteristics of BC in the typical grassland environment in China, continuous observations of BC were carried out in Etuoke Banner, a typical grassland environment in Ordos, from September 8 to December 1, 2022. BC in Etuoke Banner in autumn is 22.4–4667.5 ng m−3, and the average concentration is 456.6 ng m−3, accounting for 2.20% of the mass fraction of PM2.5. BCliquid (BC generated from the combustion of liquid fuels) is the main component of BC (accounting for 79.2%); the average concentration is 361.7 ng m−3. The diurnal variations of BC, BCliquid, and BCsolid (BC generated from the combustion of solid fuels) are bimodal, with peaks at 08:00 and 18:00. The first peak is mainly related to traffic sources, cooking sources, and incomplete combustion of carbon‐containing substances; the second peak may be caused by emissions from residential cooking sources under the influence of meteorological conditions unfavorable to diffusion. The diurnal variation of absorption Ångström exponent (AAE) is unimodal, with the peak at 14:00. With the increase in BC mass concentration, AAE and visibility gradually decreased, wind speed first decreased and then increased, P and RH gradually increased, and the contribution of biomass combustion sources to BC decreased. In contrast, the contribution of traffic sources to BC increased. The evolution characteristics of atmospheric pollutants differed with the increase in BC concentration. The potential sources and affecting areas of BC and PM2.5 are mainly concentrated around Etuoke Banner and can affect the North China Plain after 48 h of transmission.

Abstract To date, genome‐wide association studies (GWASs) have discovered 35 susceptible loci of leprosy; however, the cumulative effects of these loci can only partially explain the overall risk of leprosy, and the causal variants and genes within these loci remain unknown. Here, we conducted out new GWASs in two independent cohorts of 5007 cases and 4579 controls and then a meta‐analysis in these newly generated and multiple previously published (2277 cases and 3159 controls) datasets were performed. Three novel and 15 previously reported risk loci were identified from these datasets, increasing the known leprosy risk loci of explained genetic heritability from 23.0 to 38.5%. A comprehensive fine‐mapping analysis was conducted, and 19 causal variants and 14 causal genes were identified. Specifically, manual checking of epigenomic information from the Epimap database revealed that the causal variants were mainly located within the immune‐relevant or immune‐specific regulatory elements. Furthermore, by using gene‐set, tissue, and cell‐type enrichment analyses, we highlighted the key roles of immune‐related tissues and cells and implicated the PD‐1 signaling pathways in the pathogenetic mechanism of leprosy. Collectively, our study identified candidate causal variants and elucidated the potential regulatory and coding mechanisms for genes associated with leprosy.

Background and purposeFavorable wall apposition of a flow diverter (FD) is essential for the treatment of intracranial aneurysms. The irretrievability and final drop point uncertainty of the proximal tail of the FD increase the difficulty of achieving good tail apposition. Therefore, understanding the factors associated with FD tail malapposition would be helpful for clinical practice.MethodsA total of 153 patients with 161 FD deployments in the carotid artery between 2020 and 2023 were retrospectively collected from our center’s database for this study. Patient demographics, aneurysm characteristics, FDs, carotid artery anatomy, periprocedural complications, discharge modified Rankin scale (MRS) scores, and follow-up outcomes were investigated by comparing patients with and without FD tail malapposition. Comparisons were made with t tests or Kruskal–Wallis tests for continuous variables and the Pearson χ2 or Fisher exact test for categorical variables. Logistic regression was conducted to determine the predictors of malapposition.ResultsTail malapposition occurred for 41 out of the 161 FDs (25.5%). Univariate analysis revealed that the FD brand, FD length, FD distal to proximal vessel diameter ratio, FD tail position (straight or curved), and curvature of the vessel curve were significantly associated with FD tail malapposition (p

Abstract A low concentration of Te4+ doping is found to be capable of endowing the lead‐free Cs2SnCl6 perovskites with excellent photoluminescence quantum yield (PLQY), while further increasing Te4+ concentration leads to PLQY deterioration. The mechanism behind the improved PLQY is intensively studied and reported elsewhere. However, little work is conducted to understand the decreased PLQY at high doping levels and to explore its implications for non‐PL‐related applications. Here, it is demonstrated that the Te4+‐incorporated Cs2SnCl6 can be promising candidate for efficient CO2 photocatalysis. An optimum photocatalytic performance is achieved when Te4+ concentration reaches as high as 50%, at which point significant PL quenching has occurred. Through a detailed spectral characterization, such concentration‐dependent functionality is attributed to systematic changes in both electronic and local crystal structure, which allow a robust regulation of excitation energy relaxation channels. These findings expand the scope of available photocatalysts for CO2 reduction and also inform synthetic planning for the preparation of multifunctional Pb‐free metal halide perovskites.

In order to improve the extraction rate of crude polysaccharide from Schizophyllum mycelium and explore the biological activity of purified intracellular polysaccharide, this study adopted the method of ultrasonic-assisted hot water extraction to extract intracellular crude polysaccharide from Schizophyllum mycelium and the extraction process was optimized by response surface method. On this basis, DEAE-52 ion-exchange column and Sephadex G-100 dextran gel column chromatography were used to separate and purify the crude polysaccharide, and then the structural characterization, antioxidant capacity and antibacterial activity of the purified polysaccharide were carried out. Results showed that the optimal extraction conditions of intracellular crude polysaccharides were as follows: Under the extraction temperature of 90 ℃, the water-material ratio 30:1, the ultrasonic time 30 min, the ultrasonic power 230 W. Under these conditions, the extraction rate of intracellular crude polysaccharide was 18.14%±0.33%. The purified polysaccharide fraction NSPG-1 was a β-pyranose sugar with the molecular weight of 1.05×106 Da. NSPG-1 had good antioxidant capacity and the IC50 values for DPPH radical, hydroxyl radical and superoxide anion radical were 6.97 1.07 and 11.41 mg/mL, respectively. NSPG-1 had good antibacterial activity and the IC50 values against Escherichia coli, Bacillus subtilis and Staphylococcus aureus were 7.56, 12.54 and 10.42 mg/mL, respectively. The results would provide a basis for the industrial production and utilization of Schizophyllan.

Abstract Escherichia coli (E. coli) is an opportunistic pathogen that can cause clinical mastitis in dairy cows worldwide. Mastitis produces severe symptoms in dairy cows, such as udder inflammation, the production of harmful substances, reduced milk production, and altered milk quality. Intramammary injections of rifaximin have a beneficial effect on dairy cow mastitis, especially for mastitis caused by E. coli. However, we do not know whether the currently accepted clinical administration scheme is reasonable. Therefore, the purpose of this experiment was to evaluate the clinical dosing regimen for curing mastitis induced by E. coli. In this study, the pharmacokinetics of four single dose groups (50, 100, 200, and 400 µg/gland) were studied in CD-1 lactating mice, and the main pharmacokinetic parameters were obtained by non-compartment and two-compartment model of Phoenix 8.1 software. A total of 5,000 colony-forming units (CFU) of E. coli ATCC25922 were injected into the mammary glands of mice under anatomic microscope guidance. After 12 h of growth in vivo, the mouse mastitis model was successfully developed. In pharmacodynamics experiment, 12 different dosing regimens (doses ranged from 25 to 800 µg/gland and two dosing intervals of 12 and 24 h) were used to study the therapeutic potential of rifaximin for mastitis. The PK/PD model was established by integrating pharmacokinetics and pharmacodynamics using the inhibitory sigmoid Emax model. The optimal antibacterial effect was 2log10CFU/gland reduction of bacterial colony counts in vivo, when the magnitude of AUC24/MIC exceeded 57.80 h. A total of 57.80 h of AUC24/MIC was defined as a target value in the Monte Carlo simulation. The clinically recommended dosage regimen of 100 mg/gland every 12 h in a day achieved a 91.08% cure rate for the treatment of bovine mastitis caused by E. coli infection.

Multi-class change detection can make various ground monitoring projects more efficient and convenient. With the development of deep learning, the multi-class change detection methods have introduced Deep Neural Network (DNN) to improve the accuracy and efficiency of traditional methods. The class imbalance in the image will affect the feature extraction effect of DNN. Existing deep learning methods rarely consider the impact of data on DNN. To solve this problem, this paper proposes a class rebalancing algorithm based on data distribution. The algorithm iteratively trains the SSL model, obtains the distribution of classes in the data, then expands the original dataset according to the distribution of classes, and finally trains the baseline SSL model using the expanded dataset. The trained semantic segmentation model is used to detect multi-class changes in two-phase images. This paper is the first time to introduce the image class balancing method in the multi-class change detection task, so a control experiment is designed to verify the effectiveness and superiority of this method for the unbalanced data. The mIoU of the class rebalancing algorithm in this paper reaches 0.4615, which indicates that the proposed method can effectively detect ground changes and accurately distinguish the types of ground changes.

As the key equipment of the coal transportation system, the intelligence of the belt conveyor is crucial to the safe and efficient transportation of coal mines. And it is of great significance to improve the safety level of coal mines and ensure a stable supply of coal. Driven by the new technological revolution, the field of belt conveyor intelligence is in the stage of accelerated development. Key intelligent technologies such as information system platforms, intelligent control, measurement and sensing have made continuous progress, showing a trend of multi-point breakthroughs and all-around development. However, the relevant technologies are mostly applied experimentally in coal mining enterprises and have not been widely promoted. This paper reviews the research and development trend of key technologies for intelligent belt conveyors in recent years and summarizes the problems encountered in engineering applications. First, the authors give an overview of the development of belt conveyor intellectualization, focusing on the technical characteristics of belt conveyors with different carrying types and the technical requirements for intelligent development. Then, the key technical problems that need to be solved for idler belt conveyor intelligence are pointed out. The current research status of four key directions, such as energy saving technology of belt conveyors, coal flow sensing technology, health status monitoring technology, and safety assurance technology, is introduced. The article analyzes the application of computer vision, big data analysis, intelligent control, and decision-making technologies in engineering, and discusses the existing problems and future development trends. Finally, the development suggestions for belt conveyors are proposed from the perspective of intelligent monitoring and control research, combined with the analysis of related technology research results at home and abroad. The future research directions of intelligent key technologies such as non-contact coal flow intelligent sensing of belt conveyors, transparent conveying based on multi-source information fusion, frequency conversion and energy-saving control, and virtual reality are discussed.

The energy conservation of existing buildings plays a significant role in reducing global carbon emissions and building energy consumption. The research on building energy conservation focuses on discussing existing and new buildings. However, the research on energy conservation of existing buildings is relatively single, and there is a lack of systematic and in-depth review. Based on the bibliometric and qualitative analysis methods, this study selects the relevant literature from 2008 to 2022 for network mapping analysis and summarizes the research results. The seven indicators of impact factor, total publications, average publication year, total/average citations, and total/average normative citations were selected to analyze the selected articles by country, institution, journal, keywords, and highly cited literature. Three major research themes of existing building energy efficiency are obtained by analyzing the quantitative results of keywords and highly cited literature: (1) influencing factors and energy-saving obstacles of energy consumption, (2) energy-saving measures, and (3) energy-saving optimization and its evaluation method. Finally, based on the bibliometric results, this research qualitatively analyzes the highly cited literature and the latest research results to obtain the future development trends and possible directions of energy conservation in the existing buildings throughout their life cycle. This study provides innovative ideas for the energy-saving renovation of existing buildings: (1) Technology empowerment (using renewable energy, negative carbon technology, “artificial intelligence + Internet of Things + 5G” and cogeneration, etc.) and (2) Formulating regulations and policy incentives for different decision-makers (implementing demand response strategies such as time-of-use pricing to guide and restrict user behavior, considering carbon tax and carbon trading, etc.). on the premise of utilizing renewable energy and increasing the capacity resilience of the distribution grids, the existing buildings will be energy-efficient, economical, intelligent and comfortable in the future.

As a new alternative fuel vehicle for energy reserve strategy, the development of methanol vehicles in China has been at the forefront. Completed pilot projects have demonstrated that methanol vehicles can effectively reduce fuel costs and pollutant emissions during driving. In addition to methanol vehicles, alternative fuel vehicles such as electric vehicles and compressed natural gas vehicles also maintain a sound development momentum in China. It is essential to understand which one has more advantages from sustainable development. This paper uses the Energy, Environment, and Economy (3E) evaluation method to analyze the energy consumption, environmental emission, and economy of methanol, electric, gas, and gasoline vehicles from the whole life cycle perspective. A comprehensive index evaluation model was constructed, and the development potential of the three alternative fuel vehicles was ranked under four different scenarios (energy-oriented, environment-oriented, economic-oriented, and equilibrium scenarios). This research shows that methanol vehicles have higher total energy consumption than gasoline vehicles in the whole life cycle. In terms of greenhouse gas emissions, compared with gasoline vehicles, methanol, and compressed natural gas vehicles decreased by 8.79 tons and 12.45 tons, respectively. At the same time, methanol vehicles have a better economic benefit than gasoline vehicles (37%), which is the same as the completed pilot projects. In addition, results also show that due to the advantages of low renovation cost and fuel price, the user’s cost of methanol vehicles is more down than blade electric vehicles, which makes them have better development potential compared with electric and compressed natural gas vehicles in the economic-oriented and equilibrium scenario. Therefore, methanol vehicles can be used as alternative fuel vehicles for long-term development in China.

With the increasing global attention to environmental protection, microgrids with efficient usage of renewable energy have been widely developed. Currently, the intermittent nature of renewable energy and the uncertainty of its demand affect the stable operation of a microgrid. Additionally, electric vehicles (EVs), as an impact load, could severely affect the safe dispatch of the microgrid. To solve these problems, a multi-objective optimization model was established based on the economy and the environmental protection of a microgrid including EVs. The linear weighting method based on two-person zero-sum game was used to coordinate the full consumption of renewable energy with the full bearing of load, and balance the two objectives better. Moreover, the adaptive simulated annealing particle swarm optimization algorithm (ASAPSO) was used to solve the multi-objective optimization model, and obtain the optimal solution in the unit. The simulation results showed that the multi-objective weight method could diminish the influence of uncertainty factors, promoting the full absorption of renewable energy and full load-bearing. Additionally, the orderly charging and discharging mode of EVs could reduce the operation cost and environmental protection cost of the microgrid. Therefore, the improved optimization algorithm was capable of improving the economy and environmental protection of the microgrid.

BackgroundM. leprae preferentially infects Schwann cells (SCs) in the peripheral nerves leading to nerve damage and irreversible disability. Knowledge of how M. leprae infects and interacts with host SCs is essential for understanding mechanisms of nerve damage and revealing potential new therapeutic strategies.Methodology/principal findingsWe performed a time-course single-cell sequencing analysis of SCs infected with M. leprae at different time points, further analyzed the heterogeneity of SCs, subpopulations associated with M. leprae infection, developmental trajectory of SCs and validated by Western blot or flow cytometry. Different subpopulations of SCs exhibiting distinct genetic features and functional enrichments were present. We observed two subpopulations associated with M. leprae infection, a stem cell-like cell subpopulation increased significantly at 24 h but declined by 72 h after M. leprae infection, and an adipocyte-like cell subpopulation, emerged at 72 h post-infection. The results were validated and confirmed that a stem cell-like cell subpopulation was in the early stage of differentiation and could differentiate into an adipocyte-like cell subpopulation.Conclusions/significanceOur results present a systematic time-course analysis of SC heterogeneity after infection by M. leprae at single-cell resolution, provide valuable information to understand the critical biological processes underlying reprogramming and lipid metabolism during M. leprae infection of SCs, and increase understanding of the disease-causing mechanisms at play in leprosy patients as well as revealing potential new therapeutic strategies.

Abstract Background Recent research recommends that epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all sorts of RNA. Exact identification of RNA modification is vital for understanding their purposes and regulatory mechanisms. However, traditional experimental methods of identifying RNA modification sites are relatively complicated, time-consuming, and laborious. Machine learning approaches have been applied in the procedures of RNA sequence features extraction and classification in a computational way, which may supplement experimental approaches more efficiently. Recently, convolutional neural network (CNN) and long short-term memory (LSTM) have been demonstrated achievements in modification site prediction on account of their powerful functions in representation learning. However, CNN can learn the local response from the spatial data but cannot learn sequential correlations. And LSTM is specialized for sequential modeling and can access both the contextual representation but lacks spatial data extraction compared with CNN. There is strong motivation to construct a prediction framework using natural language processing (NLP), deep learning (DL) for these reasons. Results This study presents an ensemble multiscale deep learning predictor (EMDLP) to identify RNA methylation sites in an NLP and DL way. It organically combines the dilated convolution and Bidirectional LSTM (BiLSTM), which helps to take better advantage of the local and global information for site prediction. The first step of EMDLP is to represent the RNA sequences in an NLP way. Thus, three encodings, e.g., RNA word embedding, One-hot encoding, and RGloVe, which is an improved learning method of word vector representation based on GloVe, are adopted to decipher sites from the viewpoints of the local and global information. Then, a dilated convolutional Bidirectional LSTM network (DCB) model is constructed with the dilated convolutional neural network (DCNN) followed by BiLSTM to extract potential contributing features for methylation site prediction. Finally, these three encoding methods are integrated by a soft vote to obtain better predictive performance. Experiment results on m1A and m6A reveal that the area under the receiver operating characteristic(AUROC) of EMDLP obtains respectively 95.56%, 85.24%, and outperforms the state-of-the-art models. To maximize user convenience, a user-friendly webserver for EMDLP was publicly available at http://www.labiip.net/EMDLP/index.php ( http://47.104.130.81/EMDLP/index.php ). Conclusions We developed a predictor for m1A and m6A methylation sites.

Abstract Wireless data transmission near the sea surface can be realized by electromagnetic (EM) waves. The transmitting antenna is one of the most important parts of the EM communication system. The optimal antenna form can greatly improve communication performance. In this work, theoretical and experimental studies were presented for analysing the communication performance of magnetic dipoles near the sea surface. The influence of the seabed on the EM waves propagation near the sea surface was analysed. The model based on vector potential was built to study the radiation of magnetic dipoles in three‐layered conducting media. The experiments on the sea were conducted to comprehensively verify the EM waves propagation from seawater to seawater, seawater to air, air to air, and air to seawater. The good agreement of the theoretical and experimental results indicates that the performance of a horizontal magnetic dipole is superior to that of a vertical magnetic dipole in both‐way communications between seawater and air. The studies in this work are helpful for antenna design and system development in wireless data transmission near the sea surface.

Abstract In the process of yielding biofuels from cellulose degradation, traditional enzymatic hydrolysis, such as β-glucosidase catalyzing cellobiose, can barely resolve the contradiction between cellulose degradation and bioenergy conservation. However, it has been shown that cellobiose phosphorylase provides energetic advantages for cellobiose degradation through a phosphorolytic pathway, which has attracted wide attention. Here, the cellobiose phosphorylase gene from Caldicellulosiruptor bescii (CbCBP) was cloned, expressed, and purified. Analysis of the enzymatic properties and kinetic mechanisms indicated that CbCBP catalyzed reversible phosphorolysis and had good thermal stability and broad substrate selectivity. In addition, the phosphorolytic reaction of cellobiose by CbCBP proceeded via an ordered Bi Bi mechanism, while the synthetic reaction proceeded via a ping pong Bi Bi mechanism. The present study lays the foundation for optimizing the degradation of cellulose and the synthesis of functional oligosaccharides.

Porous covalent organic frameworks have arisen as tunable photocatalysts for H2 production. Here, authors report frameworks engineered with co-catalyst binding sites to improve photocatalytic H2 evolution performances.

The COVID-19 pandemic, which was caused by SARS-CoV-2, has had a significant effect on global public health, economies, and societies worldwide. Serum antibody testing is a critical method for the diagnosis of COVID-19 and can complement RT-PCR in the diagnosis of COVID-19 patients; however, the performance of rapid antibody assays in the clinical setting has not been established. Rapid antibody assays were evaluated by investigating 296 COVID-19-positive individuals and 542 negative individuals confirmed by clinical diagnosis. The clinical diagnostic results were used as controls to evaluate the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), kappa, and 95% confidence interval (CI) of the rapid tests. IgM-positivity had a sensitivity of 86.1% and specificity of 99.1%. IgG-positivity had a sensitivity of 86.5% and specificity of 98.7%. The sensitivity of combined IgM- and IgG-positivity in clinically confirmed patients was 73.1% in the early stage (1-7 days after symptom onset) and reached 99% 15 days after symptom onset. The concordance between rapid antibody-positive tests and clinical diagnosis-positivity had a kappa value of 0.93. In addition, the false-positive rate of IgM and IgG combined nucleic acid detection was 30% in the early stage. The combined use of IgM and IgG could serve as a more suitable alternative detection method for patients with COVID-19. The rapid antibody test can be considered as an excellent supplementary approach for detecting SARS-CoV-2 in clinical application.

Cuproptosis, a newly discovered form of programmed cell death, plays a vital role in the occurrence and development of tumors. However, the role of cuproptosis in the bladder cancer tumor microenvironment remains unclear. In this study, we developed a method for predicting the prognostic outcomes and guiding the treatment selection for patients with bladder cancer. We obtained 1001 samples and survival data points from The Cancer Genome Atlas database and Gene Expression Omnibus database. Using cuproptosis-related genes (CRGs) identified in previous studies, we analyzed CRG transcriptional changes and identified two molecular subtypes, namely high- and low-risk patients. The prognostic features of eight genes (PDGFRB, COMP, GREM1, FRRS1, SDHD, RARRES2, CRTAC1, and HMGCS2) were determined. The CRG molecular typing and risk scores were correlated with clinicopathological features, prognosis, tumor microenvironment cell infiltration characteristics, immune checkpoint activation, mutation burden, and chemotherapy drug sensitivity. Additionally, we constructed an accurate nomogram to improve the clinical applicability of the CRG_score. qRT-PCR was used to detect the expression levels of eight genes in bladder cancer tissues, and the results were consistent with the predicted results. These findings may help us to understand the role of cuproptosis in cancer and provide new directions for the design of personalized treatment and prediction of survival outcomes in patients with bladder cancer.

Background and objectiveBranch atheromatous disease (BAD) makes patients prone to early neurological deterioration (END), resulting in poor prognosis. The aim of this study was to investigate the association between SUA/SCr and END in BAD stroke patients.MethodsWe conducted a retrospective study that included 241 patients with BAD-stroke within 48 h of symptom onset. We divided the patients into the END group and the no END group. END was defined as an NIHSS score increase of more than 2 points within 1 week. SUA/SCr was calculated by the concentration of serum uric acid and creatine (serum uric acid/serum creatine) on admission. Univariate and multivariate analyses were used to identify independent predictors of END in BAD-stroke patients.ResultsEND was observed in 24.1% (58/241) of the patients in our study. Multiple logistic regression analyses showed that SUA/SCr (aOR, 0.716; 95% CI, 0.538–0.952; P = 0.022) and female sex (aOR, 0.469; 95% CI, 0.245–0.898; P = 0.022) were associated with END after adjusting for confounding factors. The predicted value of SUA/Scr for END was a sensitivity of 79.3%, a specificity of 44.8%, and an AUC of 0.609 (95% CI, 0.527–0.691, P < 0.05). The optimal cut-off value was 4.76.ConclusionSUA/SCr was negatively associated with the risk of END in BAD stroke patients.

Dihydrocapsaicin is the main bioactive component in Capsicum plants, which is widely used in China and India as a food drug and additive. In this study, the biotransformation of dihydrocapsaicin was performed using four cultivated human intestinal fungal strains in vitro. Eight metabolites, including seven previously undescribed metabolites (1 and 3−8) and one known analog (2), were obtained. Numerous spectroscopic data, such as NMR and HRESIMS, were collected to determine their structures. Based on the structures of the dihydrocapsaicin metabolites, the main biotransformation reactions were revealed to be hydroxylation, alcohol oxidation, and lactylation. In particular, the lactylation of hydroxyl groups is mainly mediated by Rhizopus oryzae R2701. In addition, metabolite 1 showed significant inhibitory effect on lysine-specific demethylase 1 (LSD1) (IC50 1.99 μM). Therefore, the biotransformation of dihydrocapsaicin by intestinal fungi afforded various derivatives, which were important resources for developing LSD1 inhibitors and potential application in cancer treatment.

BackgroundThe primitive hypoglossal artery (PHA) is an anastomotic vessel of the carotid-basilar artery system that is prevalent only transiently during the embryonic period. Persistent primitive hypoglossal artery (PPHA) is a rare vessel variation in which PHA exists persistently in adulthood and occurs in approximately 0.02–0.1% of the population. Tortuosity of the extracranial internal carotid artery (ICA) is relatively common, impacting 10–43% of the population, and is caused by either congenital or acquired factors. It is still unknown whether PPHA and tortuosity of extracranial ICA are associated. Here, we present a case report of the concurrence of three types of pathologies of the carotid artery: extreme coiling of the extracranial internal carotid artery, multiple aneurysms and persistent primitive hypoglossal artery.Case descriptionA 66-year-old woman suffered intermittent headaches, dizziness and numbness of the right eyelid for 5 years. Magnetic resonance angiography performed in a local hospital reported an aneurysm of the posterior communicating artery segment of the left ICA and a left PPHA. Digital subtraction angiography conducted after admission showed a PPHA originating from the left cervical ICA and an extremely coiling segment of the ICA distal to the beginning of PPHA. Except for the aneurysm of the posterior communicating artery segment of the left ICA, multiple aneurysms were found at the coiling segment of the ICA.ConclusionTo the best of our knowledge, this is the first report of PPHA accompanied by an adjacent, extremely coiling ICA. There are no reports of similar tortuous ICAs to this extent or at this position. Including aneurysms, three types of pathologies suggest their congenital origin, and a review of the literature infers the probable association of these lesions.

The core computer room of Hebei Earthquake Agency has businesses such as earthquake quick report and earthquake prediction. This paper analyzes the current situation of the old core computer room, and points out that the relocation of the computer room takes the minimization of business impact as the overall principle. Relocation implementation is completed through the steps of preparation and inspection of the new computer room, data backup and system detection, equipment sorting and inspection, operators’ optical cable fusion and link cutover, relocation and dust removal, testing and acceptance on equipment power. The summary and reflection on the problems arising from the relocation provide a material basis and strong support for the future operation of various businesses such as earthquake measurement, early warning and emergency of Hebei Earthquake Agency, and provide reference for the relocation of computer rooms of other Earthquake Agencies.

RationaleFructose-1,6-bisphosphatase (FBP1) is a tumor suppressor and a key enzyme negatively regulating Warburg effect in cancer. However, regulation of FBP1 protein expression and its exact role in prostate cancer (PCa) is largely unclear. Phosphatase and tensin homolog (PTEN) is one of the most frequently deleted tumor suppressor genes in human PCa. However, the role of PTEN loss in aberrant Warburg effect in cancer remains poorly understood.MethodsExpression of PTEN and FBP1 was analyzed in several PCa cell lines and prostate tumor tissues in mice. Western blot (WB) and RT-PCR approaches were used to examine how PTEN regulates FBP1 expression. Co-immunoprecipitation (co-IP) and in vivo ubiquitination assays were used to define the regulatory mechanisms. A PCa xenograft model was employed to determine the impact of PTEN regulation of FBP1 on PCa growth in vivo.ResultWe demonstrated that in a manner dependent of PI3K/AKT signal pathway PTEN regulated FBP1 expression in various PCa cell lines and tumors in mice. We confirmed that this regulation took place at the protein level and was mediated by SKP2 E3 ubiquitin ligase. Mechanistically, we showed that serine 271 phosphorylation of FBP1 by cyclin-dependent kinases (CDKs) was essential for SKP2-mediated degradation of FBP1 protein induced by PTEN loss. Most importantly, we further showed that loss of PTEN expression enhanced Warburg effect and PCa growth in mice in a manner dependent, at least partially on FBP1 protein degradation.ConclusionsOur results reveal a novel tumor-suppressive feature of PTEN in restraining FBP1 degradation and the Warburg effect. These results also suggest that prohibiting FBP1 protein degradation could be a viable therapeutic strategy for PTEN-deficient PCa.

Antibiotic resistance genes (ARGs) in the environment pose a threat to human and animal health. Dairy cows are important livestock in China; however, a comprehensive understanding of antibiotic resistance in their production environment has not been well clarified. In this study, we used metagenomic methods to analyze the resistomes, microbiomes, and potential ARG bacterial hosts in typical dairy farm environments (including feces, wastewater, and soil). The ARGs resistant to tetracyclines, MLS, β-lactams, aminoglycoside, and multidrug was dominant in the dairy farm ecosystem. The abundance and diversity of total ARGs in dairy feces and wastewater were significantly higher than in soil (P