Blind Inpainting with Object-aware Discrimination for Artificial Marker Removal

Medical images often contain artificial markers added by doctors, which can negatively affect the accuracy of AI-based diagnosis. To address this issue and recover the missing visual contents, inpainting techniques are highly needed. However, existing inpainting methods require manual mask input, limiting their application scenarios. In this paper, we introduce a novel blind inpainting method that automatically completes visual contents without specifying masks for target areas in an image. Our proposed model includes a mask-free reconstruction network and an object-aware discriminator. The reconstruction network consists of two branches that predict the corrupted regions with artificial markers and simultaneously recover the missing visual contents. The object-aware discriminator relies on the powerful recognition capabilities of the dense object detector to ensure that the markers of reconstructed images cannot be detected in any local regions. As a result, the reconstructed image can be close to the clean one as much as possible. Our proposed method is evaluated on different medical image datasets, covering multiple imaging modalities such as ultrasound (US), magnetic resonance imaging (MRI), and electron microscopy (EM), demonstrating that our method is effective and robust against various unknown missing region patterns

Polymorphisms in thymidylate synthase gene and susceptibility to breast cancer in a Chinese population: a case-control analysis

BACKGROUND: Accumulative evidence suggests that low folate intake is associated with increased risk of breast cancer. Polymorphisms in genes involved in folate metabolism may influence DNA methylation, nucleotide synthesis, and thus individual susceptibility to cancer. Thymidylate synthase (TYMS) is a key enzyme that participates in folate metabolism and catalyzes the conversion of dUMP to dTMP in the process of DNA synthesis. Two potentially functional polymorphisms [a 28-bp tandem repeat in the TYMS 5'-untranslated enhanced region (TSER) and a 6-bp deletion/insertion in the TYMS 3'-untranslated region (TS 3'-UTR)] were suggested to be correlated with alteration of thymidylate synthase expression and associated with cancer risk. METHODS: To test the hypothesis that polymorphisms of the TYMS gene are associated with risk of breast cancer, we genotyped these two polymorphisms in a case-control study of 432 incident cases with invasive breast cancer and 473 cancer-free controls in a Chinese population. RESULTS: We found that the distribution of TS3'-UTR (1494del6) genotype frequencies were significantly different between the cases and controls (P = 0.026). Compared with the TS3'-UTR del6/del6 wild-type genotype, a significantly reduced risk was associated with the ins6/ins6 homozygous variant genotype (adjusted OR = 0.58, 95% CI = 0.35–0.97) but not the del6/ins6 genotype (OR = 1.09, 95% CI = 0.82–1.46). Furthermore, breast cancer risks associated with the TS3'-UTR del6/del6 genotype were more evident in older women, postmenopausal subjects, individuals with a younger age at first-live birth and individuals with an older age at menarche. However, there was no evidence for an association between the TSER polymorphism and breast cancer risks. CONCLUSION: These findings suggest that the TS3'-UTR del6 polymorphism may play a role in the etiology of breast cancer. Further larger population-based studies as well as functional evaluation of the variants are warranted to confirm our findings

Rib locating on chest direct radiography image using watershed algorithm and correlation matching

A rib locating method on chest direct radiography (DR) image using watershed algorithm and correlation matching is presented in this paper. Firstly, the body and spine are located by employing watershed algorithm; second, the body model is selected to remove other bones outside body; thirdly, the models of left and right ribs are resized and rotated to fit ribs of each side respectively; finally, the rib regions are extracted, each one of which contains only one rib. 70 DR images are used to test the method. The experiment result shows that the average error rate, accuracy, and sensitivity are respectively 0.067, 0.828 and 0.862

Recognizing mixed urban functions from human activities using representation learning methods

When various urban functions are integrated into one location, they form a mixture of functions. The emerging big data promote an alternative way to identify mixed functions. However, current methods are largely unable to extract deep features in these data, resulting in low accuracy. In this study, we focused on recognizing mixed urban functions from the perspective of human activities, which are essential indicators of functional areas in a city. We proposed a framework to comprehensively extract deep features of human activities in big data, including activity dynamics, mobility interactions, and activity semantics, through representation learning methods. Then, integrating these features, we employed fuzzy clustering to identify the mixture of urban functions. We conducted a case study using taxi flow and social media data in Beijing, China, in which five urban functions and their correlations with land use were recognized. The mixture degree of urban functions in each location was revealed, which had a negative correlation with taxi trip distance. The results confirmed the advantages of our method in understanding mixed urban functions by employing various representation learning methods to comprehensively depict human activities. This study has important implications for urban planners in understanding urban systems and developing better strategies

Electronic structure modulating for supported Rh catalysts toward CO2 methanation

Methanation of carbon dioxide is a promising approach to ameliorate greenhouse effect. Rhodium based catalysts have been intensively investigated due to its ability in cleavage of CO bond, but the role of support in the catalysts was underestimated. In this regard, we explored the methanation of CO2 over Rh catalysts with three metal oxide supports (TiO2, Al2O3, and ZnO). It was found that Rh/TiO2 exhibited the highest catalytic activity with product yield of 455 mmol gcat-1 h−1 (CH4 and CO) at 370 °C and 2 MPa, which is 2 and 14 times higher than Rh/A12O3 and Rh/ZnO, respectively. Moreover, the CH4 selectivity over Rh/TiO2 was higher than 95 %. The superior catalytic performance of Rh/TiO2 can be mainly attributed to its unique electronic structure associated with stronger Rh-TiO2 interaction and the existence of Ti3+ ions on TiO2

Nitric oxide sensing revisited

Nitric oxide (NO) sensing is an ancient trait enabled by hemoproteins harboring a highly conserved Heme-Nitric oxide/OXygen (H-NOX) domain that operates throughout bacteria, fungi, and animal kingdoms including in humans, but that has long thought to be absent in plants. Recently, H-NOX-containing plant hemoproteins mediating crucial NO-dependent responses such as stomatal closure and pollen tube guidance have been reported. There are indications that the detection method that led to these discoveries will uncover many more heme-based NO sensors that operate as regulatory sites in complex proteins. Their characterizations will in turn offer a much more complete picture of plant NO responses at both the molecular and systems level

Enhanced Hybrid Ant Colony Optimization for Machining Line Balancing Problem with Compound and Complex Constraints

Targeted at the machining production line balancing problem, based on the precedence constraint relation of the present machining task, this article suggests adding practical constraints such as advanced station preparations, post-auxiliary tasks, and tool changing. The study introduced ‘tight’ and ’or’ constraints to bring the problem definition closer to the actual situation. For this problem, a mixed-integer programming model was constructed in this study. The model redefines the machining and auxiliary processing tasks and adds new time constraints to the station. The model considers two optimisation objectives: the number of stations and the machining line balancing rate. In view of the complexity of the problem, heuristic task set filtering mechanisms were designed and added to the ant colony optimisation, to satisfy the above compound and complex constraints. The processing task chain was constructed using the rules of ant colony pheromone accumulation and a random search mechanism. The study designed a Gantt chart generation module to improve the usability and visibility of the program. Ultimately, through an actual case study of a complex box part including 73 processing elements and realising the design and planning of machining production lines that meet complex constraints by substituting algorithms, the balance rates of several groups of optimisation schemes were higher than 90%, which showed that the algorithm is effective and has a good economy and practicability

The Optimization of Passengers’ Travel Time under Express-Slow Mode Based on Suburban Line

The suburban line connects the suburbs and the city centre; it is of huge advantage to attempt the express-slow mode. The passengers’ average travel time is the key factor to reflect the level of rail transport services, especially under the express-slow mode. So it is important to study the passengers’ average travel time under express-slow, which can get benefit on the optimization of operation scheme. First analyze the main factor that affects passengers’ travel time and then mine the dynamic interactive relationship among the factors. Second, a new passengers’ travel time evolution algorithm is proposed after studying the stop schedule and the proportion of express/slow train, and then membrane computing theory algorithm is introduced to solve the model. Finally, Shanghai Metro Line 22 is set as an example to apply the optimization model to calculate the total passengers’ travel time; the result shows that the total average travel time under the express-slow mode can save 1 minute and 38 seconds; the social influence and value of it are very huge. The proposed calculation model is of great help for the decision of stop schedule and provides theoretical and methodological support to determine the proportion of express/slow trains, improves the service level, and enriches and complements the rail transit operation scheme optimization theory system

The Optimization of Passengers’ Travel Time under Express-Slow Mode Based on Suburban Line

The suburban line connects the suburbs and the city centre; it is of huge advantage to attempt the express-slow mode. The passengers’ average travel time is the key factor to reflect the level of rail transport services, especially under the express-slow mode. So it is important to study the passengers’ average travel time under express-slow, which can get benefit on the optimization of operation scheme. First analyze the main factor that affects passengers’ travel time and then mine the dynamic interactive relationship among the factors. Second, a new passengers’ travel time evolution algorithm is proposed after studying the stop schedule and the proportion of express/slow train, and then membrane computing theory algorithm is introduced to solve the model. Finally, Shanghai Metro Line 22 is set as an example to apply the optimization model to calculate the total passengers’ travel time; the result shows that the total average travel time under the express-slow mode can save 1 minute and 38 seconds; the social influence and value of it are very huge. The proposed calculation model is of great help for the decision of stop schedule and provides theoretical and methodological support to determine the proportion of express/slow trains, improves the service level, and enriches and complements the rail transit operation scheme optimization theory system