Migration Experiences and Reported Sexual Behavior Among Young, Unmarried Female Migrants in Changzhou, China.

BackgroundChina has a large migrant population, including many young unmarried women. Little is known about their sexual behavior, contraceptive use, and risk of unintended pregnancy.Methods475 unmarried female migrants aged 15-24, working in 1 of 6 factories in 2 districts of Changzhou city, completed an anonymous self-administered questionnaire in May 2012 on demographic characteristics, work and living situation, and health. We examined demographic and migration experience predictors of sexual and contraceptive behavior using bivariate and multivariate regressions.Results30.1% of the respondents were sexually experienced, with the average age at first sex of 19 years (standard deviation=3). 37.8% reported using contraception at first sex, 58.0% reported using consistent contraception during the past year, and 28.0% reported having at least 1 unintended pregnancy with all unintended pregnancies resulting in abortion. Those who had had at least 1 abortion reported having on average 1.6 abortions [SD=1] in total. Migrating with a boyfriend and changing jobs fewer times were associated with being sexually experienced. Younger age, less education, and changing jobs more times were associated with inconsistent contraceptive use.ConclusionThese findings demonstrate there is an unmet need for reproductive health education and services where these women work as well as in their hometown communities. This education must begin early to reach young women before they migrate

Self-training with dual uncertainty for semi-supervised medical image segmentation

In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the problem of insufficient labeled data by generating pseudo labels for iterative training. However, noise generated due to the model's uncertainty during training directly affects the segmentation results. Therefore, we added sample-level and pixel-level uncertainty to stabilize the training process based on the self-training framework. Specifically, we saved several moments of the model during pre-training, and used the difference between their predictions on unlabeled samples as the sample-level uncertainty estimate for that sample. Then, we gradually add unlabeled samples from easy to hard during training. At the same time, we added a decoder with different upsampling methods to the segmentation network and used the difference between the outputs of the two decoders as pixel-level uncertainty. In short, we selectively retrained unlabeled samples and assigned pixel-level uncertainty to pseudo labels to optimize the self-training process. We compared the segmentation results of our model with five semi-supervised approaches on the public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks. Keywords: Medical image segmentation, semi-supervised learning, self-training, uncertainty estimatio

Selection of Reference Genes for RT-qPCR Analysis in a Predatory Biological Control Agent, \u3cem\u3eColeomegilla maculata\u3c/em\u3e (Coleoptera: Coccinellidae)

Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for quantifying gene expression across various biological processes, of which requires a set of suited reference genes to normalize the expression data. Coleomegilla maculata (Coleoptera: Coccinellidae), is one of the most extensively used biological control agents in the field to manage arthropod pest species. In this study, expression profiles of 16 housekeeping genes selected from C. maculata were cloned and investigated. The performance of these candidates as endogenous controls under specific experimental conditions was evaluated by dedicated algorithms, including geNorm, Normfinder, BestKeeper, and ΔCt method. In addition, RefFinder, a comprehensive platform integrating all the above-mentioned algorithms, ranked the overall stability of these candidate genes. As a result, various sets of suitable reference genes were recommended specifically for experiments involving different tissues, developmental stages, sex, and C. maculate larvae treated with dietary double stranded RNA. This study represents the critical first step to establish a standardized RT-qPCR protocol for the functional genomics research in a ladybeetle C. maculate. Furthermore, it lays the foundation for conducting ecological risk assessment of RNAi-based gene silencing biotechnologies on non-target organisms; in this case, a key predatory biological control agent

Dual uncertainty-guided multi-model pseudo-label learning for semi-supervised medical image segmentation

Semi-supervised medical image segmentation is currently a highly researched area. Pseudo-label learning is a traditional semi-supervised learning method aimed at acquiring additional knowledge by generating pseudo-labels for unlabeled data. However, this method relies on the quality of pseudo-labels and can lead to an unstable training process due to differences between samples. Additionally, directly generating pseudo-labels from the model itself accelerates noise accumulation, resulting in low-confidence pseudo-labels. To address these issues, we proposed a dual uncertainty-guided multi-model pseudo-label learning framework (DUMM) for semi-supervised medical image segmentation. The framework consisted of two main parts: The first part is a sample selection module based on sample-level uncertainty (SUS), intended to achieve a more stable and smooth training process. The second part is a multi-model pseudo-label generation module based on pixel-level uncertainty (PUM), intended to obtain high-quality pseudo-labels. We conducted a series of experiments on two public medical datasets, ACDC2017 and ISIC2018. Compared to the baseline, we improved the Dice scores by 6.5% and 4.0% over the two datasets, respectively. Furthermore, our results showed a clear advantage over the comparative methods. This validates the feasibility and applicability of our approach

Stable Reference Gene Selection for RT-qPCR Analysis in Nonviruliferous and Viruliferous \u3cem\u3eFrankliniella occidentalis\u3c/em\u3e

Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for measuring and evaluating gene expression during variable biological processes. To facilitate gene expression studies, normalization of genes of interest relative to stable reference genes is crucial. The western flower thrips Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae), the main vector of tomato spotted wilt virus (TSWV), is a destructive invasive species. In this study, the expression profiles of 11 candidate reference genes from nonviruliferous and viruliferous F. occidentalis were investigated. Five distinct algorithms, geNorm, NormFinder, BestKeeper, the ΔCt method, and RefFinder, were used to determine the performance of these genes. geNorm, NormFinder, BestKeeper, and RefFinder identified heat shock protein 70 (HSP70), heat shock protein 60 (HSP60), elongation factor 1 α, and ribosomal protein l32 (RPL32) as the most stable reference genes, and the ΔCt method identified HSP60, HSP70, RPL32, and heat shock protein 90 as the most stable reference genes. Additionally, two reference genes were sufficient for reliable normalization in nonviruliferous and viruliferous F. occidentalis. This work provides a foundation for investigating the molecular mechanisms of TSWV and F. occidentalis interactions

Neural PDE Solvers for Irregular Domains

Neural network-based approaches for solving partial differential equations (PDEs) have recently received special attention. However, the large majority of neural PDE solvers only apply to rectilinear domains, and do not systematically address the imposition of Dirichlet/Neumann boundary conditions over irregular domain boundaries. In this paper, we present a framework to neurally solve partial differential equations over domains with irregularly shaped (non-rectilinear) geometric boundaries. Our network takes in the shape of the domain as an input (represented using an unstructured point cloud, or any other parametric representation such as Non-Uniform Rational B-Splines) and is able to generalize to novel (unseen) irregular domains; the key technical ingredient to realizing this model is a novel approach for identifying the interior and exterior of the computational grid in a differentiable manner. We also perform a careful error analysis which reveals theoretical insights into several sources of error incurred in the model-building process. Finally, we showcase a wide variety of applications, along with favorable comparisons with ground truth solutions

The Effect of Zn-Al-Hydrotalcites Composited with Calcium Stearate and β-Diketone on the Thermal Stability of PVC

A clean-route synthesis of Zn-Al-hydrotalcites (Zn-Al-LDHs) using zinc oxide and sodium aluminate solution has been developed. The as-obtained materials were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM). The effects of metal ions at different molar ratios on the performance of hydrotalcites were discussed. The results showed that the Zn-Al-hydrotalcites can be successfully synthesized at three different Zn/Al ratios of 3:1, 2:1 and 1:1. Thermal aging tests of polyvinyl chloride (PVC) mixed with Zn-Al-LDHs, calcium stearate (CaSt2) and β-diketone were carried out in a thermal aging test box by observing the color change. The results showed that Zn-Al-LDHs can not only enhance the stability of PVC significantly due to the improved capacity of HCl-adsorption but also increase the initial stability and ensure good-initial coloring due to the presence of the Zn element. The effects of various amounts of Zn-Al-LDHs, CaSt2 and β-diketone on the thermal stability of PVC were discussed. The optimum composition was determined to be 0.1 g Zn-Al-LDHs, 0.15 g CaSt2 and 0.25 g β-diketone in 5 g PVC

Multiparametric MRI in differentiating pulmonary artery sarcoma and pulmonary thromboembolism: a preliminary experience

PURPOSE:We aimed to define multiparametric magnetic resonance imaging (MRI) findings to differentiate between pulmonary artery sarcoma (PAS) and pulmonary thromboembolism (PTE).METHODS:Eleven patients with suspected PTE were prospectively included to undergo pulmonary MRI before surgery or biopsy. MRI protocol included an unenhanced sequence, diffusion-weighted imaging (DWI, b=800 s/mm2) and a dynamic contrast-enhanced sequence. Morphologic characteristics including distribution, filling defect, and intensity were observed on T1-, T2-, and fat-suppressed T2-weighted imaging, DWI, and contrast-enhanced MRI. Apparent diffusion coefficient (ADC) values were calculated.RESULTS:Six patients were pathologically diagnosed as PAS and the other five as chronic PTE. There were no significant differences in age, gender, presenting symptoms, D-dimer, and N-terminal pro-brain natriuretic peptide between the two groups (P > 0.05). Among MRI findings that were tested for their ability to diagnose PAS, area under the curve (AUC) was significantly higher than 0.5 for main pulmonary artery involvement (AUC, 0.83±0.13; P = 0.011), hyperintensity on fat-suppressed T2-weighted imaging (AUC, 0.82±0.14; P = 0.025), hyperintensity on DWI (AUC, 0.88±0.12; P = 0.002), contrast enhancement (AUC, 0.92±0.10; P < 0.001) and pleural effusion (AUC, 0.82±0.14; P = 0.025). Moreover, grape-like appearance in distal pulmonary artery and cardiac invasion had 100% specificity for diagnosis of PAS. However, ADC value of PAS was not significantly different than that of chronic PTE (U, 12.00; P = 0.584).CONCLUSION:Hyperintense filling defect in main pulmonary artery on fat-suppressed T2-weighted imaging and DWI and contrast enhancement may help to discriminate PAS from PTE