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Abstract Background Owing to the widespread use of chemical pesticides to control agricultural pests, pesticide tolerance has become a serious problem. In recent years, it has been found that symbiotic bacteria are related to pesticides tolerance. To investigate the potential role of microorganisms in the pesticide tolerance of Chilo suppressalis, this study was conducted. Results The insect was fed with tetracycline and cefixime as the treatment group (TET and CFM, respectively), and did not add antibiotics in the control groups (CK). The 16S rDNA sequencing results showed that antibiotics reduced the diversity of C. suppressalis symbiotic microorganisms but did not affect their growth and development. In bioassays of the three C. suppressalis groups (TET, CFM, and CK), a 72 h LC50 fitting curve was calculated to determine whether long-term antibiotic feeding leads to a decrease in pesticide resistance. The CK group of C. suppressalis was used to determine the direct effect of antibiotics on pesticide tolerance using a mixture of antibiotics and pesticides. Indirect evidence suggests that antibiotics themselves did not affect the pesticide tolerance of C. suppressalis. The results confirmed that feeding C. suppressalis cefixime led to a decrease in the expression of potential tolerance genes to chlorantraniliprole. Conclusions This study reveals the impact of antibiotic induced changes in symbiotic microorganisms on the pesticide tolerance of C. suppressalis, laying the foundation for studying the interaction between C. suppressalis and microorganisms, and also providing new ideas for the prevention and control of C. suppressalis and the creation of new pesticides.

Abstract Background Previous studies of maternal iron and birth outcomes have been limited to single indicators that do not reflect the comprehensive relationship with birth outcomes. We aimed to investigate the relationship between maternal iron metabolism and neonatal anthropometric indicators using comprehensive iron-related indicators. Methods A total of 914 Chinese mother-child dyads were enrolled in this prospective study. Subjects’ blood samples were collected at ≤ 14 weeks of gestation. Serum concentrations of iron-related indicators were measured by enzyme-linked immunosorbent assay (ELISA). Femur length was measured by B-ultrasound nearest delivery. Neonatal anthropometric indicators were collected from medical records. Results After adjustment for potential covariates, higher iron (per one standard deviation, SD increase) was detrimentally associated with − 0.22 mm lower femur length, whereas higher transferrin (per one SD increase) was associated with 0.20 mm higher femur length. Compared with normal subjects (10th-90th percentiles), subjects with extremely high (> 90th percentile) iron concentration were detrimentally associated with lower femur length, birth weight, and chest circumference, and a higher risk of low birth weight, LBW (HR: 3.92, 95%CI: 1.28, 12.0). Subjects with high concentration of soluble transferrin receptor, sTFR and transferrin (> 90th percentile) were associated with higher femur length. Subjects with low concentration of iron and ferritin concentrations (

Background: Despite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy. Methods: We retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3. Findings: A total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001). Interpretation: IASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

The effects of key parameters on locked mode induced disruption (LMiD) are investigated in EAST experiments. The experimental data for locked mode are collected from 2015 to 2022 when the externally applied resonant magnetic perturbation is successfully employed in EAST. In this dataset, ∼42% of the total shots are LMiD, while the remaining 58% are LM without disruption. To better analyze the LMiD, an intuitive physical process is proposed. The LMiD process can be divided into two stages, the evolution of magnetic islands and the loss of plasma stored energy. The LMiD can also be related to the evolution of the other 8 physical quantities. On the basis of this physical process analysis, the time scale and the influencing factors for LMiD are investigated using statistical analysis. It is found that the density (ne), the distance from the magnetic island outer boundary to the plasma last closed surface (dedge), the loop-voltage (Vloop), and the plasma core electron temperature (Te), which are consistent with the intuitive physical model, are key parameters to LMiD. In addition, other potentially important parameters, the relevant reasons, and statistical analysis on the parameter intervals where rapid disruption with greater harmfulness occurred have also been investigated.

The classification of jade grade has always been a very critical part of the jade industry, and improving the accuracy of jade grade classification is of great significance to the sustainable development of the jade industry. The study constructs a mineral identification classification model based on Raman spectroscopy + PCA through Raman spectroscopy and PCA principal component analysis and analyzes the data of jade grades and constituents. The actual performance of this paper’s model is explored by comparing its effectiveness with other algorithmic models in jade classification and the accuracy of classification parameters. The model in this paper is feasible in classifying the four grades of Hetian jade (seed material, gobi material, shanliushui material, and shanmu material). Green dense jade’s main minerals are -quartz and a few other minerals, including albite, hematite, graphite, and tourmaline. The main compositions of the sample jade are SiO2, Al2O3, and K2O. The overall accuracy of this paper’s model in classifying Xinjiang Hotan jade grades is 97.9%, which is significantly higher than that of the KNN classification algorithm and SVM classification algorithm. The total accuracy of this paper’s model on each parameter of jade grade is 85, which is higher than the 60 of the KNN algorithm and the 62 of the SVM algorithm, and the classification accuracy grade is high.