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目的总结我国南方城市肺毛霉菌病（PM）的临床特征及预后，同时探讨宏基因组二代测序（mNGS）在PM诊断中的价值。方法回顾性分析中山大学附属第一医院2019年1月1日至2022年1月31日期间诊断为PM且曾行mNGS检测的病例，共纳入14例，确诊 4例，临床诊断10 例，收集其临床表现、诊治及预后资料。结果所有患者均有基础疾病，血液系统恶性肿瘤和糖尿病最常见。最常见的症状是发热（10例）、咳嗽（9例）和气促（9例）。胸部CT形态以实变影最多见，其次为肿块影，多伴空洞。实验室检查方面，CD4+T淋巴细胞下降、CD8+T淋巴细胞升高以及CD4+/CD8+比下降提示预后不良。CT影像学合并胸腔积液的患者预后更差。mNGS诊断的阳性率为 78.5 %，明显高于组织病理（50%）及肺泡灌洗液深部真菌快速荧光染色直接镜检（61.5%）、真菌培养（23.1%）。结论肺毛霉菌病好发于有基础疾病或免疫功能低下患者，临床表现缺乏特异性，较低的CD4/CD8 比率及CT影像学合并胸腔积液的患者预后更差，mNGS诊断PM快速便捷、敏感性高，在诊断PM中具有优势。

采用理论推导和有限元方法，对冷成型不锈钢矩形管截面受弯构件的挠度发展与计算方法进行研究。在考虑截面应力分布和弯角影响的基础上推导出截面边缘屈服弯矩的计算公式，通过与301个算例的数值积分结果进行比较，验证了该计算公式的准确性。采用有限元软件ABAQUS建立了考虑几何初始缺陷和冷弯残余应力的精细有限元分析模型，依据现有试验结果对模型的准确性进行了验证。基于有限元分析结果对近似曲率法的初始刚度进行了修正，提出了修正的冷成型不锈钢矩形管截面受弯构件的挠度计算方法。将修正的计算方法与现有的其他3种计算方法进行比较，结果表明修正的近似曲率法计算公式能够更为准确地反映受弯构件的挠度发展。

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Wheat is one of the most crucial global staple crops for food security. However, the continuous rainy weather during its growth, particularly at maturation, can easily cause ear germination and moldiness, thus severely impacting the yield and quality. This study aims to accurately monitor and evaluate the germination and moldiness of wheat ears under continuous rainy weather stress during the maturity period. A case study was also conducted on the continuous rainy weather in the western part of the Huang-Huai region of China in late May 2023. The wheat ear germination and moldiness were tackled using meteorological and satellite remote sensing data, with emphasis on the disaster risk elements. Then, meteorological hazard factors were determined from the weather stress mechanisms. The resilience was also characterized using remote sensing parameters, according to the state and environment of the wheat. Thirdly, the modeling factors were selected for subsequent analysis. Spearman correlation and ReliefF method were also used for the feature selection in binary and severity classification tasks, while Pearson correlation was employed to predict the ear germination and moldiness index (EGMI). The optimal factors were then combined to form the SCF, PCF, and RFF factor groups, according to the meteorological and remote sensing types. Subsequently, five classification models (including Logistic regression, LGR) and five regression methods (including multiple linear regression, MLR) were applied for the binary classification and severity grading of wheat ear germination and moldiness, in order to predict and simulate the EGMI. The effectiveness of these models was then compared to identify and grade the wheat ear germination and moldiness. The results showed that the optimal factors were achieved in the identification and severity grading of germination and moldiness using different classifiers, from the perspective of the disaster-causing process of continuous rain and the three elements of disaster risk. The accuracy score (AC) ranged from 0.649 to 0.811 in the binary classification of wheat ear germination and moldiness identification, with the Kappa coefficients between 0.245 and 0.600. In the three-category classification of severity grading, the AC value ranged from 0.432 to 0.622, with the Kappa values between 0.099 and 0.414. The R² value of EGMI prediction ranged from 0.10 to 0.25, with an average mean absolute error (MAE) of 12.93 and an average root mean square error (RMSE) of 16.74. The PCF-XGBR model performed the best, with the R², RMSE, and MAE values of 0.25, 15.69, and 12.05, respectively, as well as the standard deviation (SDEV) and centered root-meansquare deviation (CRMSD) values of 13.10 and 15.55, respectively. Comparative analysis of the three models showed that the remote sensing model was superior to the meteorological model, in terms of the identification of germination and moldiness. While the meteorological model outperformed the remote sensing model, in terms of grading the severity of germination and moldiness. The meteorological-remote sensing model was integrated to balance their shortcomings for better performance and robustness. The estimation of continuous rainy weather disasters was achieved in the western Huang-Huai region, thus filling the technological gap in monitoring wheat ear germination and moldiness. The finding can provide the technical support to reduce the wheat disaster in post-disaster assessment. [ABSTRACT FROM AUTHOR]

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The purpose of this study is to explore the feasibility of using continuous wavelet transform to extract spectral difference information of different vigor desiccated cotton species.A method of filtering wavelet features (WFs) based on correlation and feature importance is proposed to extract the fine structure and complex information of spectral features of seeds with different vigor.Different vigor classes of desiccated cotton seeds were obtained through artificial aging experiments. And its high spectral image was collected.The raw spectra are preprocessed with Savitzky-Golay smoothing, multivariate scattering correction, first-order differentiation, and second-order differentiation.Then, the WFs extracted by wavelet basis functions such as gauss4, mexh and bior6.8 were compared.Spectral wavelet features (SFs) and WFs were downscaled using principal component analysis.Based on machine learning algorithms such as support vector machines (SVM), random forest (RF), extreme learning machines (ELM), and back propagation neural network (BPNN), a seed vigor detection model was developed for SFs principal components and WFs principal components. The accuracy of the seed vigor detection model was compared between SFs principal components and WFs principal components.The fine spectral information in WFs was further extracted based on correlation analysis and random forest feature importance evaluation. Including the 1% | R | -WFs feature set with the correlation with seed vigor at the top 1%, the 1% Importance-WFs feature set with the feature importance at the top 1% in seed vigor recognition, and the 1% | R | + 1% Importance-WFs feature set with the combination of the two, and bring these three WFs feature sets into the above machine learning model.The results showed that: 1) The bior6.8 function extracted better WFs for different vigor desiccated cotton species.Other wavelet basis functions show a clear ringing effect when extracting WFs.2) The modeling accuracy of the WFs principal components is higher than that of the SFs principal components in all machine learning models for each species.The model based on 1% |R|+1% Importance-WFs has the highest accuracy. 3) The optimal models for seed vigor detection of Jinke 21 and Jinke 20 were : 1% | R | + 1% Importance-WFs + ELM. The optimal model of Xinluzao 64 seed vigor detection is : 1% | R | + 1% Importance-WFs + any machine model and PCA-WFs + ELM / BPNN.The accuracies of the training set and test set of the optimal model of Jinke 21 are 99.63% and 98.28%, and the accuracies of the training set and test set of the optimal model of Jinke 20 and Xinluzao 64 are both 100%. The results indicate that the method proposed in this paper based on correlation and feature importance can effectively extract spectral difference information of different vitality dried cottonseeds, providing a new spectral characterization approach for seed vitality hyperspectral detection. [ABSTRACT FROM AUTHOR]

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Protein fibers are characterized by the self-assembled linear aggregates of proteins or peptides. Furthermore, the protein fibers can be expected to serve as an effective delivery carrier for water-insoluble bioactive compounds in food systems. Specifically, a better carrier can be generated for the delivery of hydrophobic bioactive substances, due mainly to its unique structure and high aspect ratio. Conventionally, protein fibrous aggregates are usually formed by proteins rich in β-sheet structure under the conditions of far below isoelectric point pH value and low ionic strength using long-term heating. However, the low preparation efficiency and high energy consumption have greatly limited to select raw materials for protein fibers. The traditional acid-thermal method has also been used to obtain the zein hydrophobic plant proteins at the low conversion rate, due to its special solubility and secondary structure. The application of such protein fiber is still lacking in the field of protein fibrosis. Among them, zein is one of the most important by-products in the process of industrial extraction of corn starch. The self-assembly properties of zein can have promising potential to be an ideal substrate for the fabrication of nano-biocomposite delivery systems in food and nutritional applications. Alternatively, the plasmas were the quasi-neutral particle systems of semigas and semi-fluid mixtures with high active moieties. The green, emerging, and non-thermal plasma technology has also great application potential in the food industry. This study aims to improve the fiber conversion rate of zein using plasma technology. An assessment treatment was innovatively used to modify the zein before acid-heat induction. A systematic investigation was then implemented to explore the influence of different process parameters during acid-heat induction on the degree of fibrosis of zein after plasma treatment. Single factor and response surface experiments were conducted to obtain the optimal condition of the fibrosis process in the plasma treatment power, plasma treatment time, heating time, heating temperature, and zein concentration. As such, the thioflavin T (ThT) fluorescence intensity showed that the plasma-assisted acid-heat induction effectively improved the degree of fibrosis of zein. The optimal parameters of fibrotic zein were as follows, plasma treatment power of 64 W, plasma treatment time of 61 s, heating time of 10 h, heating temperature of 70℃, and the concentration of zein of 30 mg/mL. After that, the ThT fluorescence intensity of fibrotic zein reached 2 272±23, which was significantly higher than that of the fibrotic zein without plasma treatment (1 239±19). The fitting analysis was then performed on the amide I region (1700-1600 cm-1 ) in the Fourier transform infrared (FT-IR) spectrum. It was found that the plasma treatment increased the βsheet structure, but decreased the α-helix structure of zein. Transmission electron microscopy (TEM) images showed that the particle size of zein decreased significantly after fibrosis, where the fiber structure was formed by linear aggregation of spherical protein particles along the horizontal direction. Therefore, the plasma treatment can be expected to induce the formation of more zein fiber structures. The finding can provide a strong reference and theoretical support for the development of high-efficiency fibrosis of zein, thus extending the application field of plasma technology in protein fibrosis. [ABSTRACT FROM AUTHOR]

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Water hammer can often occur in a pressurized pipeline flow system, leading to the pipeline explosions and even casualties. Therefore, it is of great significance to accurately predict the water hammer in the pressure pipelines for the safe operation. Among them, the weighting function-based (WFB) unsteady friction can be represented by the ZIELKE model in the simulation of water hammer, in order to effectively describe the frictional effects in the transient flow. However, the flow rates are required from all previous time steps at each time. In computational efficiency, the ZIELKE weighting function has been utilized to approximately sum a finite number of exponential terms, and then combine with the recursive formulas for the unsteady friction terms. But, the existing approximation models have also been limited to the low range of applicability or high computational costs. In this study, a two-stage least squares fitting (TSLSF) has been introduced to simulate the unsteady friction of transient flow in the pressurized pipeline. The initial parameters were obtained to fit the segmentation-based least squares segmentation of the ZIELKE weighting function in dimensionless time. After that, the new approximate weighting function was followed by a global least square fitting over the entire dimensionless time. Finally, 19 exponential terms were included in the TSLSF approximate weighting function. The TSLSF approximate weighting function was better fitted the ZIELKE weighting function than before, indicating the relative percentage error close to 0. Moreover, a broader range of applicability was also achieved to effectively simulate the unsteady flows. The experiment of water hammer was carried out in a reservoir-pipeline-valve system, in order to compare the computational efficiency and accuracy of the TSLSF friction model with the existing WFB. The computational accuracy of TSLSF model was closely aligned with the ZIELKE in the pressure wave curves at the midpoint and valve. Furthermore, the maximum relative percentage errors of the pressure head were 0.15% and 0.17%, respectively, similar to the URBANOWICZ model with the value of 0.15%. Therefore, the TSLSF and URBANOWICZ model exhibited the high computational accuracy. The computational efficiency was then compared in the calculation time of different friction models at different time steps. Specifically, the calculation time of the TSLSF model was smaller than that of the ZIELKE and the URBANOWICZ model. The computational efficiency of TSLSF model was improved by about 90% and 30%, respectively, compared with the ZIELKE and URBANOWICZ. In summary, the TSLSF friction model demonstrated the better performance in the applicability, computational accuracy, and computational efficiency. The broader range of applicability was achieved to improve the computational efficiency and accuracy. The accurate and efficient simulation of water hammer was realized in the pressurized pipelines. This finding can also provide the theoretical support and reference for the accurate and efficient simulation of transient flow in the pressurized pipelines [ABSTRACT FROM AUTHOR]

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目的　观察干扰素/维甲酸诱导凋亡相关基因 (GRIM-19)、小鼠双微体2 (MDM2)在肺腺癌组织中的表达 变化，并探讨其与患者临床特征的关系。方法　选择行手术治疗的肺腺癌患者60例，术中收集其肺腺癌组织和癌 旁正常肺组织各60例份，采用免疫组化法检测GRIM-19、MDM2蛋白阳性表达情况。比较不同临床特征的肺腺癌患 者癌组织GRIM-19、MDM2阳性表达率，分析肺腺癌组织GRIM-19、MDM2表达的相关性。结果　肺腺癌组织和癌旁 正常肺组织 GRIM-19 阳性表达率分别为 55% (35/60)、78% (47/60)，MDM2 阳性表达率分别为 43% (26/60)、23% (14/60)，两者比较 P均<0. 05。有淋巴结转移、TNM 分期Ⅲ期和低分化的肺腺癌患者癌组织 GRIM-19阳性表达率 分别低于无淋巴结转移、TNM分期Ⅰ期及Ⅱ期和高分化者，MDM2阳性表达率分别高于无淋巴结转移、TNM分期Ⅰ期及Ⅱ 期和高分化者 (P均<0. 05)。肺腺癌组织GRIM-19与MDM2的表达呈负相关关系 (r=-0. 530，P<0. 05)。结论　肺腺癌组 织中GRIM-19表达降低而MDM2表达升高，两者可能共同参与了肺腺癌的发生发展。 [ABSTRACT FROM AUTHOR]

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