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Abstract This study aims to develop a trigger operator based on the Overlap Volume Histogram (OVH) and examined its effectiveness in enhancing plan quality to minimize radiation-induced lung injury in postoperative radiotherapy for breast cancer. This trigger operator was applied for plan re-optimization to the previous Volumetric Modulated Arc Therapy (VMAT) plans of 16 left breast conserving surgery cases. These cases were categorized into a Contiguous Group (CG) and a Separated Group (SG) based on the relative position between the target and the Left-Lung (L-Lung). We investigated the changes in Vx, mean dose, and Normal Tissue Complication Probability (NTCP) values of organs-at-risk (OARs) before and after using the trigger operator. The Pairwise Sample T test was employed to evaluate the differences in indices between the two groups before and after optimizations. The trigger operator effectively initiated plan re-optimization. The values of V5, V10, V20, V30, and V40 of the L-Lung, as well as the mean dose of the heart, all decreased after re-optimization. The Pairwise Sample T test results showed statistically significant differences in the V20, V30, and V40 of the L-Lung in the CG (P

The hemodynamic characteristics of venous reflux are associated with infertility in patients with varicocele; however, an effective method for quantifying the structural distribution of the reflux is lacking. This study aimed to predict surgical outcomes using a new software for venous reflux quantification. This was a retrospective cohort study of a consecutive series of 105 patients (age range: 22–44 years) between July 2017 and September 2019. Venous reflux of the varicocele was obtained using the Valsalva maneuver during scrotal Doppler ultrasonography before microsurgical varicocelectomy. Using this software, the colored reflux signals were segmented, and the gray scale of the color pixels representing the reflux velocity was comprehensively quantified into the mean reflux velocity of the green layer (MRVG) and the reflux velocity standard deviation of the green layer (RVSDG). Spontaneous pregnancy and changes from baseline in the semen parameters were assessed during a 12-month follow-up period. Data were analyzed using logistic regression analysis. An association of the high MRVG group with impaired progressive motility (odds ratio [OR] = 2.868, 95% confidence interval [CI]: 1.133–7.265) and impaired sperm concentration (OR = 2.943, 95% CI: 1.196–7.239) was found during multivariate analysis. High MRVG (OR = 2.680, 95% CI: 1.086–6.614) and high RVSDG (OR = 2.508, 95% CI: 1.030–6.111) were found to be independent predictors of failure to achieve pregnancy following microsurgical repair. In summary, intense venous reflux is an independent predictor of impaired progressive motility, sperm concentration, and pregnancy outcomes after microsurgical varicocelectomy.

Atmospheric inversion of CO2 Emissions is based on the correction of prior carbon dioxide flux inventories using concentration monitoring data and atmospheric transport models to obtain posterior carbon dioxide flux. In atmospheric inversion studies, fixed covariance functions are commonly used to generate covariance matrices, and the hyperparameters in the covariance functions are empirically estimated. In this study, we design and implement an ideal experiment based on meteorological data from the central urban area of Zhengzhou, using WRF-STILT to generate sensitivity matrices and construct real carbon emission inventories and prior inventories. Based on the real carbon emission inventories and sensitivity matrices of monitoring stations, simulated observation concentration values are generated. Firstly, based on the observed concentration values, sensitivity matrices of monitoring stations, prior inventories, and constructed covariance matrices, the values of hyperparameters are determined based on maximum marginal likelihood estimation. Then, the influence of different prior covariance functions on the inversion results is tested, and it is found that the prior covariance matrix generated by the balgovind covariance function is most suitable for the experimental data.

The transportation of wind fields plays a crucial role in the dispersion and distribution of air pollutants, and accurate wind speed prediction is essential for assessing pollutant concentrations. In this study, we constructed a Stacking ensemble learning model using three models, namely Random Forest, LightGBM, and XGBoost, as base learners, and the LASSO regression model as the meta-learner to optimize wind speed data forecasting for Zhengzhou City’s urban area using WRF. Firstly, based on Pearson correlation coefficients, we selected meteorological variables that have a significant impact on near-surface wind speed and derived historical lagged features. Bayesian TPE was utilized for hyperparameter tuning and model building. Finally, the performance of the trained model was evaluated by comparing it with ground observation data. The results showed that compared to the WRF model, Random Forest, LightGBM, and XGBoost effectively reduced forecast errors and significantly improved wind speed predictions. Both LightGBM and XGBoost demonstrated similar performance in the correction models across the 11 stations and outperformed Random Forest. The Stacking method integrated the advantages of the base learners and exhibited improvement capabilities over individual models, highlighting the potential of machine learning in enhancing localized and accurate weather forecasting.

The constant increase in temperatures under global warming has led to a prolonged aestivation period for Apostichopus japonicus, resulting in considerable losses in production and economic benefits. However, the specific mechanism of aestivation has not been fully elucidated. In this study, we first tried to illustrate the biological mechanisms of aestivation from the perspective of the gut microbiota and metabolites. Significant differences were found in the gut microbiota of aestivating adult A. japonicus (AAJSD group) compared with nonaestivating adult A. japonicus (AAJRT group) and young A. japonicus (YAJRT and YAJSD groups) based on 16S rRNA gene high-throughput sequencing analysis. The abundances of Desulfobacterota, Myxococcota, Bdellovibrionota, and Firmicutes (4 phyla) in the AAJSD group significantly increased. Moreover, the levels of Pseudoalteromonas, Fusibacter, Labilibacter, Litorilituus, Flammeovirga, Polaribacter, Ferrimonas, PB19, and Blfdi19 genera were significantly higher in the AAJSD group than in the other three groups. Further analysis of the LDA effect size showed that species with significant variation in abundance in the AAJSD group, including the phylum Firmicutes and the genera Litorilituus, Fusibacter, and Abilibacter, might be important biomarkers for aestivating adult A. japonicus. In addition, the results of metabolomics analysis showed that there were three distinct metabolic pathways, namely biosynthesis of secondary metabolites, tryptophan metabolism, and sesquiterpenoid and triterpenoid biosynthesis in the AAJSD group compared with the other three groups. Notably, 5-hydroxytryptophan was significantly upregulated in the AAJSD group in the tryptophan metabolism pathway. Moreover, the genera Labilibacter, Litorilituus, Ferrimonas, Flammeovirga, Blfdi19, Fusibacter, Pseudoalteromonas, and PB19 with high abundance in the gut of aestivating adult A. japonicus were positively correlated with the metabolite 5-HTP. These findings suggest that there may be potential biological associations among the gut microbiota, metabolites, and aestivation in A. japonicus. This work may provide a new perspective for further understanding the aestivation mechanism of A. japonicus.

Objective To establish a neural network model based on enhanced CT for distinguishing ISUP grade of clear cell renal cell carcinoma (ccRCC). Methods We collected 131 cases of ccRCC, with 92 cases of low ISUP grade and 39 cases of high ISUP grade. Patients were divided into training set and validation set according to 5:5 stratified sampling. The enhanced CT images of each ccRCC patient were evaluated by the radiologist. Recursive feature elimination (RFE) was used to reduce the dimension of patients' general features and enhanced CT features, which was used for neural network modeling and validation. Results Patients' general features and enhanced CT features were verified by RFE method and then reduced to 14 features. The top 5 features were growth pattern, necrosis, enlargement of lymph nodes, tumor size and capsule. The AUC of the neural network model based on these 5 features in training set was 0.8844 (95%CI: 0.8062-0.9626), sensitivity was 89.47% and specificity was 82.61%; and those in validation set were 0.7924 (95%CI: 0.6567-0.9280), 75.00% and 86.96%, respectively. Conclusion The neural network model of ccRCC ISUP grade based on enhanced CT has relatively high diagnostic efficiency.

Abstract Bacterial enteritis is the most important disease in lamb for breeding greatly affects the growth of animals. Changes in the community of intestinal flora can cause the disorder of the colonic environment induced diarrhea. This study aimed to investigate the relationship between the incidence of bacterial enteritis and the number of intestinal microbiome, then the prevalence of drug-resistant genes was detected. Fecal samples were collected at five fattening sheep farms with different incidence of bacterial enteritis, pathogenic bacteria were isolated and identified, drug sensitivity tests were performed. Then, changes in number and structure of intestinal flora were compared by 16S rDNA V3-V4 region high-throughput sequencing, and the ARGs were detected using high-throughput real-time PCR. Our results revealed that the microbial communities were positively correlated with the incidence of bacterial enteritis in different farms. Bacterial communities were higher in YJ (with highest incidence of diarrhea) than any other farms. However, the ARGs seemed not to be more affected by the incidence of bacterial enteritis, but one of the significant findings to emerge from this study is that MCR-1 and NDM are detected in manure. This study has provided an insight of the changes occurring in intestinal flora and AGRs in fattening sheep farms with diverse incidence of bacterial enteritis.

Structural vibrations in Tip-Tilt modes usually affect the closed-loop performance of astronomically optical telescopes. In this paper, the state of art control methods-proportional integral (PI) control, linear quadratic Gaussian (LQG) control, disturbance feed forward (DFF) control, and disturbance observer control (DOBC) of Tip-Tilt mirror to reject vibrations are first reviewed, and then compared systematically and comprehensively. Some mathematical transformations allow PI, LQG, DFF, and DOBC to be described in a uniform framework of sensitivity function that expresses their advantages and disadvantages. In essence, feed forward control based-inverse model is the main idea of current techniques, which is dependent on accuracies of models in terms of Tip-Tilt mirror and vibrations. DOBC can relax dependences on accuracy model, and therefore this survey concentrates on concise tutorials of this method with clear descriptions of their features in the control area of disturbance rejections. Its applications in various conditions are reviewed with emphasis on the effectiveness. Finally, the open problems, challenges and research prospects of DOBC of Tip-Tilt mirror are discussed.