The Congestion Evolution of Jingzang Expressway and the Analysis on Participants’ Behavior

Road transportation networks are experiencing ever growing recurrent congestion and non-recurrent in developing China, which is a concurrent event. This paper takes Jingzang Expressway(G6) as an example, describes the saturation flow along the G6 compared with its designed capacity by the actual volume of each segment according to the density and structural characteristics of cars and trucks, and presents the congestion evolution in the past three years. Then provide inharmonious surveillance analysis among regions along this highway and game behavior between administers and carriers based on cost analysis. Finally, we point out that congestion is not only the road itself problems but also a social system problem, which should be transformed in the long term. Now we can apply some Intelligent Transport System to mitigate congestion

Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation (SSS) has recently gained increasing research interest as it can reduce the requirement for large-scale fully-annotated training data. The current methods often suffer from the confirmation bias from the pseudo-labelling process, which can be alleviated by the co-training framework. The current co-training-based SSS methods rely on hand-crafted perturbations to prevent the different sub-nets from collapsing into each other, but these artificial perturbations cannot lead to the optimal solution. In this work, we propose a new conflict-based cross-view consistency (CCVC) method based on a two-branch co-training framework which aims at enforcing the two sub-nets to learn informative features from irrelevant views. In particular, we first propose a new cross-view consistency (CVC) strategy that encourages the two sub-nets to learn distinct features from the same input by introducing a feature discrepancy loss, while these distinct features are expected to generate consistent prediction scores of the input. The CVC strategy helps to prevent the two sub-nets from stepping into the collapse. In addition, we further propose a conflict-based pseudo-labelling (CPL) method to guarantee the model will learn more useful information from conflicting predictions, which will lead to a stable training process. We validate our new CCVC approach on the SSS benchmark datasets where our method achieves new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/CCVC.Comment: accepted by CVPR202

Incremental hashing with sample selection using dominant sets

In the world of big data, large amounts of images are available in social media, corporate and even personal collections. A collection may grow quickly as new images are generated at high rates. The new images may cause changes in the distribution of existing classes or the emergence of new classes, resulting in the collection being dynamic and having concept drift. For efficient image retrieval from an image collection using a query, a hash table consisting of a set of hash functions is needed to transform images into binaryhash codeswhich are used as the basis to find similar images to the query. If the image collection is dynamic, the hash table built at one time step may not work well at the next due to changes in the collection as a result of new images being added. Therefore, the hash table needs to be rebuilt or updated at successive time steps. Incremental hashing (ICH) is the first effective method to deal with the concept drift problem in image retrieval from dynamic collections. In ICH, a new hash table is learned based on newly emerging images only which represent data distribution of the current data environment. The new hash table is used to generate hash codes for all images including old and new ones. Due to the dynamic nature, new images of one class may not be similar to old images of the same class. In order to learn new hash table that preserves within-class similarity in both old and new images,incremental hashing with sample selection using dominant sets(ICHDS) is proposed in this paper, which selects representative samples from each class for training the new hash table. Experimental results show that ICHDS yields better retrieval performance than existing dynamic and static hashing methods

Temporal variability in zooplankton community in the western Yellow Sea and its possible links to green tides

Large-scale macro-algal blooms of Ulva prolifera (also called green tides) have appeared each summer since 2008 in the western Yellow Sea. In this study, we investigated the temporal variability in zooplankton community in the western Yellow Sea and its possible links to green tides using data from a long-term plankton survey off the coast of Qingdao, China. Environmental conditions observed in the study area during the green tide period (GTP: June–August, 2008–2013) were compared to the non-green tide period (NGTP: June–August, 2005–2007), to support the contention that variations observed in zooplankton community may be attributed to the green tides, as opposed to natural climatic or environmental variations. Zooplankton assemblage structure observed during the GTP was then compared to the NGTP. Significant variations were detected both in zooplankton abundance and assemblage structure between the two defined periods. The abundance of zooplankton, mainly copepods, was significantly decreased during the GTP. Meanwhile, the relative abundance of copepods decreased by approximately 10% and that of gelatinous zooplankton, including appendicularians, chaetognaths, and medusae, almost doubled (ca. increased by 6.4%). The dominant species of meroplankton completely changed, specifically, polychaeta, and echinoderm larvae were more dominant than decapod and bivalve larvae. With regard to zooplankton size structure, the NGTP showed a higher size diversity with more small-sized organisms, while the GTP showed a lower size diversity in the community. According to general linear models, the interannual variation in summer zooplankton abundance was significantly correlated with green tides. These results indicate that the temporal changes in zooplankton community may have a close link to the green tides

Microtomography-based numerical simulations of heat transfer and fluid flow through β-SiC open-cell foams for catalysis

β-SiC open-cell foams are promising materials for catalytic supports with improved heat and mass transfer at moderate pressure drops. In this work, 3-dimensional (3D) models of a 30 ppi (pores per inch) β-SiC open-cell foam were generated using X-ray microtomography data. The resulting foam models were then used for finite element analysis (FEA) and computational fluid dynamics (CFD) simulations of heat transfer and fluid flow on the pore-scale. The FEA results demonstrate that (i) the overall effective thermal conductivity from direct simulations is comparable to the results estimated by experimental measurement, and are in the order of 10−1 W m−1 K−1 and (ii) thermal transport through fluid-saturated β-SiC foams depends on the solid-to-fluid conductivity ratio. By employing realistic foam models, pore-scale CFD simulations of fluid flows revealed the microscopic characteristics of laminar flow through open-cell foams. The anisotropic feature of realistic foam models promotes the axial and radial mixing of fluids in and after the foam element. The diffusion coefficient of laminar flow within foams was estimated at 10−4 m2 s−1, which is much larger than the molecular diffusion coefficient in a typical laminar flow in an open channel

Inflammation mobilizes copper metabolism to promote colon tumorigenesis via an IL-17-STEAP4-XIAP axis.

Copper levels are known to be elevated in inflamed and malignant tissues. But the mechanism underlying this selective enrichment has been elusive. In this study, we report a axis by which inflammatory cytokines, such as IL-17, drive cellular copper uptake via the induction of a metalloreductase, STEAP4. IL-17-induced elevated intracellular copper level leads to the activation of an E3-ligase, XIAP, which potentiates IL-17-induced NFκB activation and suppresses the caspase 3 activity. Importantly, this IL-17-induced STEAP4-dependent cellular copper uptake is critical for colon tumor formation in a murine model of colitis-associated tumorigenesis and STEAP4 expression correlates with IL-17 level and XIAP activation in human colon cancer. In summary, this study reveals a IL-17-STEAP4-XIAP axis through which the inflammatory response induces copper uptake, promoting colon tumorigenesis

GmDAD1, a Conserved Defender Against Cell Death 1 (DAD1) From Soybean, Positively Regulates Plant Resistance Against Phytophthora Pathogens

Initially identified as a mammalian apoptosis suppressor, defender against apoptotic death 1 (DAD1) protein has conserved plant orthologs acting as negative regulators of cell death. The potential roles and action mechanisms of plant DADs in resistance against Phytophthora pathogens are still unknown. Here, we cloned GmDAD1 from soybean and performed functional dissection. GmDAD1 expression can be induced by Phytophthora sojae infection in both compatible and incompatible soybean varieties. By manipulating GmDAD1 expression in soybean hairy roots, we showed that GmDAD1 transcript accumulations are positively correlated with plant resistance levels against P. sojae. Heterologous expression of GmDAD1 in Nicotiana benthamiana enhanced its resistance to Phytophthora parasitica. NbDAD1 from N. benthamiana was shown to have similar role in conferring Phytophthora resistance. As an endoplasmic reticulum (ER)-localized protein, GmDAD1 was demonstrated to be involved in ER stress signaling and to affect the expression of multiple defense-related genes. Taken together, our findings reveal that GmDAD1 plays a critical role in defense against Phytophthora pathogens and might participate in the ER stress signaling pathway. The defense-associated characteristic of GmDAD1 makes it a valuable working target for breeding Phytophthora resistant soybean varieties

Association between platelet distribution width and serum uric acid in Chinese population

© 2019 International Union of Biochemistry and Molecular Biology Platelet distribution width (PDW) is a simple and inexpensive parameter, which could predict activation of coagulation efficiently. And it has been confirmed to have a significant role in many diseases. We aimed to explore the association between PDW and hyperuricemia in a large Chinese cohort. This cross-sectional study recruited 61,091 ostensible healthy participants (29,259 males and 31,832 females) after implementing exclusion criteria. Clinical data of the enrolled population included anthropometric measurements and serum parameters. Database was sorted by gender, and the association between PDW and hyperuricemia was analyzed after dividing PDW into quartiles. Crude and adjusted odds ratios of PDW for hyperuricemia with 95% confidence intervals were analyzed using binary logistic regression models. We found no significant difference in PDW values between the genders. Males showed significantly higher incidence of hyperuricemia than females. From binary logistic regression models, significant hyperuricemia risks only were demonstrated in PDW quartiles 2 and 3 in males (P < 0.05). This study displayed close association between PDW and hyperuricemia as a risk factor. It is meaningful to use PDW as a clinical risk predictor for hyperuricemia in males. © 2019 BioFactors, 45(3):326–334, 2019

X-Ray Repair Cross-Complementing Group 1 (XRCC1) Genetic Polymorphisms and Risk of Childhood Acute Lymphoblastic Leukemia: A Meta-Analysis

Background: Recently, there have been a number of studies on the association between XRCC1 polymorphisms and childhood acute lymphoblastic leukemia (ALL) risk. However, the results of previous reports are inconsistent. Thus, we performed a meta-analysis to clarify the effects of XRCC1 variants on childhood ALL risk. Methods: A meta-analysis was performed to examine the association between XRCC1 polymorphisms (Arg399Gln, Arg194Trp, and Arg280His) and childhood ALL risk. We critically reviewed 7 studies with a total of 880 cases and 1311 controls for Arg399Gln polymorphism, 3 studies with a total of 345 cases and 554 controls for Arg280His polymorphism, and 6 studies with a total of 783 cases and 1180 controls for Arg194Trp polymorphism, respectively. Odds ratio (OR) and its 95% confidence interval (CI) were used. Results: Significant association between XRCC1 Arg399Gln polymorphism and childhood ALL risk was observed in total population analyses (OR additive model = 1.501, 95 % CI 1.112–2.026, P OR = 0.008; OR dominant model = 1.316, 95 % CI = 1.104–1.569, POR = 0.002) and Asian subgroup analyses (ORadditive model = 2.338, 95%CI = 1.254–4.359, POR = 0.008; ORdominant model = 2.108, 95%CI = 1.498–2.967, POR = 0.000). No association was detected in Caucasians, Metizo and mixed populations. Ethnicity was considered as a significant source of heterogeneity in the meta-regression model. For the other two XRCC1 polymorphisms, no association with childhood ALL risk was found