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BACKGROUND: To determine the congenital heart defect (CHD) prevalence and identify the associated risk factors in children within the multi-ethnic Yunnan Region of China. METHODS: This is a prospective matched case-control screening study. Screening for CHD in children residing within 28 county districts of Yunnan Province during the period of January 2001 to December 2016 was conducted. A total of 2,421 and CHD cohort and 24,210 control cohort were derived from a total population of 400,855 children (under 18 years of age). RESULTS: A total of 2,421 children were diagnosed with CHD, yielding a CHD prevalence of 6.04 cases per 1,000 children. The prevalence of CHD by sex was 6.54 per 1,000 females versus 5.59 per 1,000 males. The ethnic groups displaying the highest CHD prevalence were the Lisu (15.51 per 1,000), Achang (13.18 per 1,000), Jingpo (12.32 per 1,000), Naxi (9.68 per 1,000), and Tibetan (8.57 per 1,000), respectively. The most common CHD was atrial septal defect, amounting to 1.94 instances per 1,000 children. We identified a number of child-associated parameters that significantly correlated with greater CHD risk, such as lower mass at birth, shorter duration of gestation, and younger age at the time of screening. We also identified a number of maternal and familial risk factors. CONCLUSIONS: This ultrasonic color Doppler imaging study revealed a relatively commonplace prevalence of CHD. Moreover, the prevalence of CHD in Yunnan Region significantly varied with sex and ethnic status. Certain child-associated, maternal, and familial risk factors may contribute to CHD risk.

Identifying a set of critical nodes with high propagation in complex networks to achieve maximum influence is an important task in the field of complex network research, especially in the background of the current rapid global spread of COVID-19. In view of this, some scholars believe that nodes with high importance in the network have stronger propagation, and many classical methods are proposed to evaluate node importance. However, this approach makes it difficult to ensure that the selected spreaders are dispersed in the network, which greatly affects the propagation ability. The VoteRank algorithm uses a voting-based method to identify nodes with strong propagation in the network, but there are some deficiencies. Here, we solve this problem by proposing the DILVoteRank algorithm. The VoteRank algorithm cannot properly reflect the importance of nodes in the network topology. Based on this, we redefine the initial voting ability of nodes in the VoteRank algorithm and introduce the degree and importance of the line (DIL) ranking method to calculate the voting score so that the algorithm can better reflect the importance of nodes in the network structure. In addition, the weakening mechanism of the VoteRank algorithm only weakens the information of neighboring nodes of the selected nodes, which does not guarantee that the identified initial spreaders are sufficiently dispersed in the network. On this basis, we consider all the neighbors nodes of the node’s nearest and next nearest neighbors, so that the crucial spreaders identified by our algorithm are more widely distributed in the network with the same initial node ratio. In order to test the algorithm performance, we simulate the DILVoteRank algorithm with six other benchmark algorithms in 12 real-world network datasets based on two propagation dynamics model. The experimental results show that our algorithm identifies spreaders that achieve stronger propagation ability and propagation scale and with more stability compared to other benchmark algorithms.

Fast computation of the landing footprint of a space-to- ground vehicle is a basic requirement for the deployment of parking orbits, as well as for enabling decision makers to develop real-time programs of transfer trajectories. In order to address the usually slow computational time for the determination of the landing footprint of a space-to-ground vehicle under finite thrust, this work proposes a method that uses polynomial equations to describe the boundaries of the landing footprint and uses back propagation (BP) neural networks to quickly determine the landing footprint of the space-to-ground vehicle. First, given orbital parameters and a manoeuvre moment, the solution model of the landing footprint of a space-to-ground vehicle under finite thrust is established. Second, given arbitrary orbital parameters and an arbitrary manoeuvre moment, a fast computational model for the landing footprint of a space-to-ground vehicle based on BP neural networks is provided. Finally, the simulation results demonstrate that under the premise of ensuring accuracy, the proposed method can quickly determine the landing footprint of a space-to-ground vehicle with arbitrary orbital parameters and arbitrary manoeuvre moments. The proposed fast computational method for determining a landing footprint lays a foundation for the parking-orbit configuration and supports the design of real-time transfer trajectories.

Given the limited fuel capacity of an on-orbit service vehicle (OSV), proper OSV allocation to satellites during each service mission is critical for economic fuel consumption. This allocation problem can be formulated as an optimization problem with many continuous and discrete design variables of wide domains. This problem can be effectively handled through the proposed approach that combines the tabu search with the discrete particle swarm optimization algorithm (DPSO-TS). First of all, Pontryagin’s minimum principle and genetic algorithm (GA) are exploited to find the most fuel-efficient transfer trajectory. This fuel efficiency maximization can then serve as the performance index of the OSV allocation optimization model problem. In particular, the maximization of the minimum residual fuel over individual OSVs is proposed as a performance index for OSV allocation optimization. The optimization problem is numerically solved through the proposed DPSO-TS algorithm. Finally, the simulation results demonstrate that the DPSO-TS algorithm has a higher accuracy compared to the DPSO, the DPSO-PDM and the DPSO-CSA algorithms in the premise that these four algorithms have the basically same computational time. The DPSO-TS algorithm can effectively solve the OSV allocation optimization problem.

Yaxiong Li,* Yongping Zhang,* Jiansheng LiuDepartment of General Surgery, First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, People’s Republic of China*These authors contributed equally to this work Purpose: The biological functions of neuropilin and tolloid-like 2 (NETO2) in the progression of pancreatic cancer remained unexplored. We aimed to investigate the biological roles and underlying molecular mechanisms of NETO2 in pancreatic cancer.Materialsand methods: Thirty paired pancreatic tumor tissue samples and corresponding nontumor tissues were obtained from 30 pancreatic cancer patients who did not receive preoperative chemotherapy or radiotherapy. The changes in multiple cellular functions associated with tumor progression were assessed after NETO2 knockdown/overexpression in pancreatic cancer cell lines. Additionally, a mouse-xenograft model was developed to verify the in vitro results.Results: NETO2 was upregulated in pancreatic tumor tissues. Elevated expression of NETO2 was not only associated with an advanced tumor stage, but was also a prediction of poor prognosis for pancreatic cancer patients. Knockdown of NETO2 in pancreatic cancer cell lines arrested the cell cycle and inhibited cell proliferation, colony formation, invasion, and migration; in contrast, overexpression of NETO2 had an opposite effect on all of these parameters. A STAT3 specific inhibitor, cryptotanshinone, reversed the tumor-promoting effects induced by NETO2 overexpression in pancreatic cancer. Western blot analysis showed that invasion and migration were closely related to epithelial–mesenchymal transition, and that the STAT3 signaling pathway was involved in NETO2-mediated oncogenic transformation in pancreatic cancer cells. Furthermore, NETO2 knockdown significantly inhibited the growth of pancreatic tumor xenografts in nude mice.Conclusion: NETO2 has an important role in the progression and metastasis of pancreatic cancer and could serve as a novel candidate for targeted therapy of pancreatic cancer.Keywords: pancreatic cancer, NETO2, proliferation, metastasis, STAT3