Characterization of porcine ENO3: genomic and cDNA structure, polymorphism and expression

In this study, a full-length cDNA of the porcine ENO3 gene encoding a 434 amino acid protein was isolated. It contains 12 exons over approximately 5.4 kb. Differential splicing in the 5'-untranslated sequence generates two forms of mRNA that differ from each other in the presence or absence of a 142-nucleotide fragment. Expression analysis showed that transcript 1 of ENO3 is highly expressed in liver and lung, while transcript 2 is highly expressed in skeletal muscle and heart. We provide the first evidence that in skeletal muscle expression of ENO3 is different between Yorkshire and Meishan pig breeds. Furthermore, real-time polymerase chain reaction revealed that, in Yorkshire pigs, skeletal muscle expression of transcript 1 is identical at postnatal day-1 and at other stages while that of transcript 2 is higher. Moreover, expression of transcript 1 is lower in skeletal muscle and all other tissue samples than that of transcript 2, with the exception of liver and kidney. Statistical analysis showed the existence of a polymorphism in the ENO3 gene between Chinese indigenous and introduced commercial western pig breeds and that it is associated with fat percentage, average backfat thickness, meat marbling and intramuscular fat in two different populations

Defining genetic intra-tumor heterogeneity: a chronological annotation of mutational pathways

Tumor heterogeneity is believed to be important in tumor progression and its response to therapies. However, despite numerous mutations being reported in human tumors, genetic intra-tumor heterogeneity remains poorly defined. We have developed a novel strategy to provide a chronological annotation of mutational events in a tumor. We used an endometrial tumor from a patient and transplanted it into athymic mice to create many tumor xenografts. While the patient tumor xenografts were initially responsive to raloxifene treatment, xenografts created with cancer cell clones isolated from the same patient tumor showed dramatic differences in response to raloxifene, indicating existence of intra-tumor heterogeneity with some subpopulations inherently resistant to the drug. A 250K single nucleotide polymorphism (SNP) array from Affymetrix was used to profile genotype changes on 3 xenografts and 10 single cells from another 10 xenografts. We found 797 SNP sites containing loss of heterozygosity (LOH) common to all these specimens, indicating that genetic mutations in these regions may contain the earliest genetic events in the original patient tumor. Based upon the genotype information from the 10 single cancer cells, we developed a phylogenetic tree using neighbor-joining method. We showed that there are at least 3 distinct subpopulations in the patient tumor. Additionally, the phylogenetic tree was used to determine the order of genetic events, thus providing a chronological annotation to genetic mutations. Our approach represents an important analytic strategy for defining genetic intra-tumor heterogeneity and providing chronological annotations to the genetic landscape revealed by future whole genome sequencing in tumors

Pokemon GO in Melbourne CBD: A case study of the cyber-physical symbiotic social networks

[EN] The recent popular game, Pokemon GO, created two symbiotic social networks by location-based mobile augmented reality (LMAR) technique. One is in the physical world among players, and another one is in the cyber world among players' avatars. To date, there is no study that has explored the formation of each social network and their symbiosis. In this paper, we carried out a data-driven research on the Pokemon GO game to solve this problem. We accordingly organised the collection of two real datasets. For the first dataset, we designed a questionnaire to collect players' individual behaviours in Pokemon GO, and used maps of Melbourne (Australia) to track and record their usual playing areas. Based on the data that we collected, we modelled the formation of the symbiotic social networks in both physical world (i.e. for players) and cyber world (i.e. for avatars) as well as interactions between players and Pokemon GO elements (i.e. 'bridges' of the two worlds). By investigating the mechanism of network formation, we revealed the relatively weak correlation between the formation processes of the two networks. We further incorporated the real-world pedestrian dataset collected by sensors across Melbourne CBD into the study of their symbiosis. Based on the second dataset, we examined the changes of people's social behaviours in terms of most visited places. The results suggested that the existence of the cyber social network has reciprocally changed the structure of the symbiotic physical social network. (C) 2017 Elsevier B.V. All rights reserved.This research is partially supported by the Australian Research Council projects DP150103732, DP140103649, and LP140100816. The authors extend their appreciation to the International Scientific Partnership Program (ISPP) at King Saud University, Riyadh, Saudi Arabia for funding this work through the project No. ISPP#0069.Wang, D.; Wu, T.; Wen, S.; Liu, D.; Xiang, Y.; Zhou, W.; Hassan Mohamed, H.... (2018). Pokemon GO in Melbourne CBD: A case study of the cyber-physical symbiotic social networks. Journal of Computational Science. 26:456-467. https://doi.org/10.1016/j.jocs.2017.06.009S4564672

Expression pattern and polymorphism of three microsatellite markers in the porcine CA3 gene

Carbonic anhydrase III (CA3) is an abundant muscle protein characteristic of adult type-1, slow-twitch, muscle fibres. In order to further understand the functions of the porcine CA3 protein in muscle, the temporal and spatial distributions of its gene product were analysed and the association between the presence of specific polymorphisms and carcass traits in the pig was also examined. Real-time PCR revealed that the CA3 mRNA expression showed no differences with age in skeletal muscles from Yorkshire pigs at postnatal day-1, month-2, and month-4. We provide the first evidence that CA3 is differentially expressed in the skeletal muscle of Yorkshire and Meishan pig breeds. In addition, the whole pig genomic DNA sequence of CA3 was investigated and shown to contain seven exons and six introns. Comparative sequencing of the gene from three pig breeds revealed the existence of microsatellite SJ160 in intron 5 and microsatellite SJ158 and a novel microsatellite marker that includes a tandem repeat of (TC)n in intron 4. We also determined the allele number and frequencies of the three loci in seven pig breeds and found that they are low polymorphic microsatellite markers. Statistical analysis showed that the CA3 microsatellite polymorphism was associated with dressing percentage, internal fat rate, carcass length, rib number and backfat thickness in the pig

Geo6D: Geometric Constraints Learning for 6D Pose Estimation

Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters. Since the visible features of objects are implicitly influenced by their poses, the network allows inferring the pose by analyzing the differences in features in the visible region. However, due to the unpredictable and unrestricted range of pose variations, the implicitly learned visible feature-pose constraints are insufficiently covered by the training samples, making the network vulnerable to unseen object poses. To tackle these challenges, we proposed a novel geometric constraints learning approach called Geo6D for direct regression 6D pose estimation methods. It introduces a pose transformation formula expressed in relative offset representation, which is leveraged as geometric constraints to reconstruct the input and output targets of the network. These reconstructed data enable the network to estimate the pose based on explicit geometric constraints and relative offset representation mitigates the issue of the pose distribution gap. Extensive experimental results show that when equipped with Geo6D, the direct 6D methods achieve state-of-the-art performance on multiple datasets and demonstrate significant effectiveness, even with only 10% amount of data