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Abstract Natural herbs, which contain pharmacologically active compounds, have been used historically as medicines. Conventionally, the analysis of chemical components in herbal medicines requires time-consuming sample separation and state-of-the-art analytical instruments. Nanopore, a versatile single molecule sensor, might be suitable to identify bioactive compounds in natural herbs. Here, a phenylboronic acid appended Mycobacterium smegmatis porin A (MspA) nanopore is used as a sensor for herbal medicines. A variety of bioactive compounds based on salvianolic acids, including caffeic acid, protocatechuic acid, protocatechualdehyde, salvianic acid A, rosmarinic acid, lithospermic acid, salvianolic acid A and salvianolic acid B are identified. Using a custom machine learning algorithm, analyte identification is performed with an accuracy of 99.0%. This sensing principle is further used with natural herbs such as Salvia miltiorrhiza, Rosemary and Prunella vulgaris. No complex sample separation or purification is required and the sensing device is highly portable.

‘Akizuki’ pear (Pyrus pyrifolia Nakai) corky disease is a physiological disease that strongly affects the fruit quality of ‘Akizuki’ pear and its economic value. In this study, Raman spectroscopy was employed to develop an early diagnosis model by integrating support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) modeling techniques. The effects of various pretreatment methods and combinations of methods on modeling results were studied. The relative optimal index formula was utilized to identify the SG and SG+WT as the most effective preprocessing methods. Following the optimal preprocessing method, the performance of the majority of the models was markedly enhanced through the process of model reconditioning, among which XGBoost achieved 80% accuracy under SG+WT pretreatment, and F1 and kappa both performed best. The results show that RF, GBDT, and XGBoost are more sensitive to the pretreatment method, whereas SVM and CNN are more dependent on internal parameter tuning. The results of this study indicate that the early detection of Raman spectroscopy represents a novel approach for the nondestructive identification of asymptomatic ‘Akizuki’ pear corky disease, which is of paramount importance for the realization of large-scale detection across orchards.

The early symptoms of cork spot disorder in 'Akizuki' pear (Pyrus pyrifolia Nakai) are challenging to distinguish from those in healthy fruits, hindering early identification in production. In this study, samples of cork-browned 'Akizuki' pears, asymptomatic fruits and healthy fruits were examined to determine the content of relevant mineral elements. A micro near-infrared spectrometer collected spectral information, and various pretreatment methods were applied to the near-infrared spectral data. Support vector machine (SVM) modelling using the original data achieved the highest overall recognition accuracy of 84.65% and an F1 value of 84.06%. For identifying fruits without cork spot disease, Autokeras modelled data processed with the SG method, achieving the best accuracy of 90%. These findings establish a reliable basis for the early identification and diagnosis of cork spot disorder in 'Akizuki' pear, enhancing pear production management.

This study clarified the characteristics and influencing factors of sap flow in Populus tomentosa Carr. and Salix babylonica L., and verified the applicability of Granier’s original formula for measuring the sap flow of the two species, aimed to provide a basis for the accurate assessment of tree transpiration. P. tomentosa and S. babylonica were used as research objects, their sap flow was measured by the thermal dissipation probe method (TDP), together with changes in meteorological factors and soil water content. Meanwhile, the transpiration of both species was measured by the liquid level equilibrium method (LLE) to verify the applicability of Granier’s original formula. We found that: (1) the sap flow velocity of P. tomentosa and S. babylonica under typical sunny and cloudy conditions showed unimodal or bimodal changes, which were highly significantly correlated with meteorological factors (P < 0.01), but they were all small and poorly correlated with meteorological factors on rainy days. (2) The sap flow velocity of both species was significantly and negatively correlated (P < 0.05) with the daily change in stem and soil water content at 10–20 cm. (3) Compared to that calculated with the LLE method, the sap flows of the two species calculated by the TDP technique using Granier’s original formula were seriously underestimated, with error rates of -60.96% and -63.37%, respectively. The Granier’s correction formulas for P. tomentosa and S. babylonica established by the LLE method were Fd = 0.0287K1.236 (R2 = 0.941) and Fd = 0.0145K0.852 (R2 = 0.904), respectively, and the combined correction formula was Fd = 0.0235K1.080 (R2 = 0.957). It was verified that the errors of sap flow calculated by the specific correction formulas for P. tomentosa and S. babylonica were -6.18% and -5.86%, and those calculated by the combined correction formula were -12.76% and -2.32%, respectively. Therefore, the characteristics of the sap flow velocity of P. tomentosa and S. babylonica on sunny, cloudy and rainy days were different and significantly influenced by meteorological factors. The original Granier’s formula for calculating their sap flow resulted in a large error, but can be measured more accurately by constructing specific correction and combination formulas through the LLE method.

‘Huangguan’ pear has excellent quality, strong adaptability, and good socioeconomic value. Iron is one of the important trace elements in plants, and iron imbalance seriously affects the growth and development of pear trees and reduces their economic benefits. If the iron content in pear fruit can be easily and non-destructively detected using modern technology during the critical period of fruit development, it will undoubtedly help guide actual production. In this study, ‘Huangguan’ pear fruit was used as the research object, and the possibility of using the more convenient near-infrared spectroscopy (900~1700 nm) technology for nondestructive detection of the iron content in the peel and pulp of ‘Huangguan’ pear was explored. First, 12 algorithms were used to preprocess the original spectral data, and based on the original and the preprocessed spectral data, partial least squares regression and gradient boosting regression tree algorithms were used. A full-band prediction model of the iron content in the peel and pulp of ‘Huangguan’ pear was established, and the genetic algorithm was used to extract characteristic wavelengths, establish a characteristic wavelength prediction model, and evaluate the prediction effect of each model according to the coefficient of determination R² and the relative analysis error RPD. After comparison, we found that the prediction model with the best prediction of the iron content in the peel and pulp of ‘Huangguan’ pear reaches class A, and the prediction effect is good and meets expectations. This experiment shows that the use of near-infrared spectroscopy can achieve better prediction of the iron content in the peel and pulp of ‘Huangguan’ pear.

Objective Germline alterations in the breast cancer susceptibility genes type 1 and 2, BRCA1 and BRCA2, predispose individuals to hereditary cancers, including breast, ovarian, prostate, pancreatic, and stomach cancers. Accumulating evidence suggests inherited genetic susceptibility to lung cancer. The present study aimed to survey the prevalence of pathogenic germline BRCA mutations (gBRCAm) and explore the potential association between gBRCAm and disease onset in Chinese advanced non-small cell lung cancer (NSCLC) patients.Methods A total of 6,220 NSCLC patients were screened using capture-based ultra-deep targeted sequencing to identify patients harboring germline BRCA1/2 mutations.Results Out of the 6,220 patients screened, 1.03% (64/6,220) of the patients harbored the pathogenic gBRCAm, with BRCA2 mutations being the most predominant mutations (49/64, 76.5%). Patients who developed NSCLC before 50 years of age were more likely to carry gBRCAm (P = 0.036). Among the patients harboring classic lung cancer driver mutations, those with concurrent gBRCAm were significantly younger than those harboring the wild-type gBRCA (P = 0.029). By contrast, the age of patients with or without concurrent gBRCAm was comparable to those of patients without the driver mutations (P = 0.972). In addition, we identified EGFR-mutant patients with concurrent gBRCAm who showed comparable progression-free survival but significantly longer overall survival (P = 0.002) compared to EGFR-mutant patients with wild-type germline BRCA.Conclusions Overall, our study is the largest survey of the prevalence of pathogenic gBRCAm in advanced Chinese NSCLC patients. Results suggested a lack of association between germline BRCA status and treatment outcome of EGFR-TKI. In addition, results showed a positive correlation between pathogenic gBRCAm and an early onset of NSCLC.

Failuremode and effects analysis (FMEA) is a widely used safety assessmentmethod inmany fields. Z-number was previously applied in FMEA since it can take both possibility and reliability of information into consideration. However, the use of fuzzy weighted mean to integrate Z-valuations may have some drawbacks and is not suitable for some situations. In this paper, an improved method is proposed based on Z-numbers and the graded mean integration representation (GMIR) to deal with the uncertain information in FMEA. First, Z-numbers are constructed based on the evaluations of risk factors O, S, D for each failure mode by different experts. Second, weights of the three risk factors and experts are determined. Third, the integration representations of Z-numbers are obtained by a newmethod based on the GMIRmethod. Finally, risk priorities of the failure modes are derived considering the weights of experts and risk factors. Two examples and a case study are given to show the use of the proposed method and comparison with other methods. The results show that the proposed method is more reasonable, universal and simple in calculation. [ABSTRACT FROM AUTHOR]