An edge-directed interpolation method for fetal spine MR images

Abstract Background Fetal spinal magnetic resonance imaging (MRI) is a prenatal routine for proper assessment of fetus development, especially when suspected spinal malformations occur while ultrasound fails to provide details. Limited by hardware, fetal spine MR images suffer from its low resolution. High-resolution MR images can directly enhance readability and improve diagnosis accuracy. Image interpolation for higher resolution is required in clinical situations, while many methods fail to preserve edge structures. Edge carries heavy structural messages of objects in visual scenes for doctors to detect suspicions, classify malformations and make correct diagnosis. Effective interpolation with well-preserved edge structures is still challenging. Method In this paper, we propose an edge-directed interpolation (EDI) method and apply it on a group of fetal spine MR images to evaluate its feasibility and performance. This method takes edge messages from Canny edge detector to guide further pixel modification. First, low-resolution (LR) images of fetal spine are interpolated into high-resolution (HR) images with targeted factor by bi-linear method. Then edge information from LR and HR images is put into a twofold strategy to sharpen or soften edge structures. Finally a HR image with well-preserved edge structures is generated. The HR images obtained from proposed method are validated and compared with that from other four EDI methods. Performances are evaluated from six metrics, and subjective analysis of visual quality is based on regions of interest (ROI). Results All these five EDI methods are able to generate HR images with enriched details. From quantitative analysis of six metrics, the proposed method outperforms the other four from signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM) and mutual information (MI) with seconds-level time consumptions (TC). Visual analysis of ROI shows that the proposed method maintains better consistency in edge structures with the original images. Conclusions The proposed method classifies edge orientations into four categories and well preserves structures. It generates convincing HR images with fine details and is suitable in real-time situations. Iterative curvature-based interpolation (ICBI) method may result in crisper edges, while the other three methods are sensitive to noise and artifacts

Performance comparison of ultrasonography and magnetic resonance imaging in their diagnostic accuracy of placenta accreta spectrum disorders: a systematic review and meta-analysis

Objectives: Accurate prenatal diagnosis of placenta accrete spectrum disorder (PAS) remains a challenge, and the reported diagnostic value of ultrasonography (US) and magnetic resonance imaging (MRI) varies widely. This study aims to systematically evaluate the diagnostic accuracy of US as compared with MRI in the detection of PAS within the identical patient population. Methods: Medline, EMBASE, Google scholar and Cochrane library were searched. Pooled sensitivity, specificity, diagnostic odds ratio (DOR) and the area under the summary receiver operating characteristic (SROC) curve were calculated. Subgroup analysis was also performed to elucidate the heterogeneity of results. Results: A total of 18 articles comprising 861 pregnancies were included in the study. The overall diagnostic accuracy of US for identification of PAS was as follows: sensitivity [0.90 (0.86–0.93)], specificity [0.83 (0.79–0.86)], DOR [39.5 (19.6–79.7)]. The overall diagnostic accuracy of MRI for identification of PAS was as follows: sensitivity [0.89 (0.85-0.92)], specificity [0.87 (0.83–0.89)], DOR [37.4 (17.0–82.3)]. The pooled sensitivity (p = 0.808) and specificity (p = 0.413) between US and MRI are not significantly different. SROC analysis revealed that there was no statistical difference (p = 0.552) in US and MRI for the overall predictive accuracy of PAS. Furthermore, in the subgroup analysis of between retrospective and prospective studies, between earlier and most recent studies, there was no statistical difference (p > 0.05) in diagnostic accuracy of US and MRI for the detection of PAS. Conclusions: Both ultrasonography (US) and magnetic resonance imaging (MRI) showed comparable accuracy in the prenatal diagnosis of placenta accrete spectrum disorder (PAS). Routine employment of MRI with relatively high expense in the prenatal identification of PAS should not be recommended

A fault prediction method for catenary of high-speed rails based on meteorological conditions

Fault frequency of catenary is related to meteorological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation between catenary faults and meteorological conditions, and further the effect of meteorological conditions on catenary operation. Moreover, machine learning is used for catenary fault prediction. As with the single decision tree, only a small number of training samples can be classified correctly by each weak classifier, the AdaBoost algorithm is adopted to adjust the weights of misclassified samples and weak classifiers, and train multiple weak classifiers. Finally, the weak classifiers are combined to construct a strong classifier, with which the final prediction result is obtained. In order to validate the prediction method, an example is provided based on the historical data from a railway bureau of China. The result shows that the mapping relation between meteorological conditions and catenary faults can be established accurately by AdaBoost algorithm. The AdaBoost algorithm can accurately predict a catenary fault if the meteorological conditions are provided. Document type: Articl

Potential for pancreatic maturation of differentiating human embryonic stem cells is sensitive to the specific pathway of definitive endoderm commitment

This study provides a detailed experimental and mathematical analysis of the impact of the initial pathway of definitive endoderm (DE) induction on later stages of pancreatic maturation. Human embryonic stem cells (hESCs) were induced to insulin-producing cells following a directed-differentiation approach. DE was induced following four alternative pathway modulations. DE derivatives obtained from these alternate pathways were subjected to pancreatic progenitor (PP) induction and maturation and analyzed at each stage. Results indicate that late stage maturation is influenced by the initial pathway of DE commitment. Detailed quantitative analysis revealed WNT3A and FGF2 induced DE cells showed highest expression of insulin, are closely aligned in gene expression patterning and have a closer resemblance to pancreatic organogenesis. Conversely, BMP4 at DE induction gave most divergent differentiation dynamics with lowest insulin upregulation, but highest glucagon upregulation. Additionally, we have concluded that early analysis of PP markers is indicative of its potential for pancreatic maturation. © 2014 Jaramillo et al

Loop closure detection of visual SLAM based on variational autoencoder

Loop closure detection is an important module for simultaneous localization and mapping (SLAM). Correct detection of loops can reduce the cumulative drift in positioning. Because traditional detection methods rely on handicraft features, false positive detections can occur when the environment changes, resulting in incorrect estimates and an inability to obtain accurate maps. In this research paper, a loop closure detection method based on a variational autoencoder (VAE) is proposed. It is intended to be used as a feature extractor to extract image features through neural networks to replace the handicraft features used in traditional methods. This method extracts a low-dimensional vector as the representation of the image. At the same time, the attention mechanism is added to the network and constraints are added to improve the loss function for better image representation. In the back-end feature matching process, geometric checking is used to filter out the wrong matching for the false positive problem. Finally, through numerical experiments, the proposed method is demonstrated to have a better precision-recall curve than the traditional method of the bag-of-words model and other deep learning methods and is highly robust to environmental changes. In addition, experiments on datasets from three different scenarios also demonstrate that the method can be applied in real-world scenarios and that it has a good performance

Resveratrol reduces the inflammatory response in adipose tissue and improves adipose insulin signaling in high-fat diet-fed mice

Background Obesity-induced glucose metabolism disorder is associated with chronic, low-grade, systemic inflammation and is considered a risk factor for diabetes and metabolic syndrome. Resveratrol (RES), a natural anti-inflammatory compound, is observed to improve glucose tolerance and insulin sensitivity in obese rodents and humans. This study aimed to test the effects of RES administration on insulin signaling and the inflammatory response in visceral white adipose tissue (WAT) caused by a high-fat diet (HFD) in mice. Methods A total of 40 wild-type C57BL/6 male mice were divided into four groups (10 in each group): the standard chow diet (STD) group was fed a STD; the HFD group was fed a HFD; and the HFD-RES/L and HFD-RES/H groups were fed a HFD plus RES (200 and 400 mg/kg/day, respectively). The L and H in RES/L and RES/H stand for low and high, respectively. Glucose tolerance, insulin sensitivity, circulating inflammatory biomarkers and lipid profile were determined. Quantitative PCR and Western blot were used to determine the expression of CC-chemokine receptor 2 (CCR2), other inflammation markers, glucose transporter 4 (GLUT4), insulin receptor substrate 1 (IRS-1) and pAkt/Akt and to assess targets of interest involving glucose metabolism and inflammation in visceral WAT. Results HFD increased the levels of total cholesterol, triglycerides, low-density lipoprotein cholesterol and proinflammatory cytokines in serum, decreased the high-density lipoprotein cholesterol level in serum, and induced insulin resistance and WAT inflammation in mice. However, RES treatment alleviated insulin resistance, increased the expressions of pAkt, GLUT4 and IRS-1 in WAT, and decreased serum proinflammatory cytokine levels, macrophage infiltration and CCR2 expression in WAT. Conclusion Our results indicated that WAT CCR2 may play a vital role in macrophage infiltration and the inflammatory response during the development of insulin resistance in HFD-induced obesity. These data suggested that administration of RES offers protection against abnormal glucose metabolism and inflammatory adaptations in visceral WAT in mice with HFD-induced obesity

Layered tungsten-based composites and their pseudocapacitive and electrocatalytic performance

With the rapid development of heterostructured electrocatalysts, the potential application of transition metal dichalcogenide (TMD)-based composites for electrocatalysis have attracted intense attraction owing to their unique optical, electronic, and mechanical properties. Herein, a facile solvothermal method to obtain heterostructured composites consisting of TMD (WS2) and graphitic carbon nitride (g-C3N4) is reported. DFT calculation results demonstrates that the interface interaction between g-C3N4 and WS2 optimizes the electronic structure of composite materials and activates the active sites. The WS2–g-C3N4 composites with surface sulfur and nitrogen vacancies exhibit high specific capacitance of 1156 F g−1 and excellent cycling stability with no capacitance loss over 2000 charge–discharge cycles, demonstrating huge potential in applications for pseudocapacitive energy storage. In addition, WS2–g-C3N4 composites can attain excellent hydrogen production activity to reach a current density of 10 mA cm−2 at an overpotential of −0.170 V (vs. RHE) and Tafel slope of 59 mV dec−1. This work provides an effective way for the synthesis of heterostructured electrocatalysts with efficient activity for energy conversion and storage