Background: Previous genome-wide association study (GWAS) has revealed the association between MYP10 at 8p23 and MYP15 at 10q21.1 and high myopia (HM) in a French population. This study is managed to discover the connection between some single nucleotide polymorphism (located at MYP10 and MYP15) and Han Chinese HM.
Methods and Results: This case-control association study contained 1673 samples, including 869 ophthalmic patients and 804 controls. Twelve tag SNPs have been selected from the MYP10 and MYP15 loci and genotyped by SNaPshot method. Among 12 SNPs, rs4840437 and rs6989782 in TNKS gene were found significant association with HM. Carriers of rs4840437G allele and rs4840437GG genotype created a low risk of high myopia (P = .036, OR = 0.81, 95%CI = 0.71–0.93; P = .016, OR = 0.73, 95%CI = 0.56–0.96; respectively). Carriers of rs6989782T allele and rs6989782TT+CT genotype also had a decreased risk of high myopia (P = .048, OR = 0.82, 95%CI = 0.71–0.94; P = .006, OR = 0.74, 95%CI = 0.59–0.92; respectively). Other 10 SNPs displaced nonsignificant association with HM. Additionally, the risk haplotype AC and the protective haplotype GT, generated by two SNPs in TNKS, were considerably more likely to be association with HM (for AC, P = .002 and OR = 1.26; for GT, P = .027 and OR = 0.84).
Conclusions: Our results demonstrated that some heritable variants in the TNKS gene are associated with HM in the Han population. The possible functions of TNKS in the development and pathogenesis of hereditary high myopia still require further researches to identify. 相似文献
目的:探讨40岁以上高龄女性体外受精-胚胎移植(IVF-ET)的妊娠结局,旨在为高龄女性提供生育咨询以及为改善高龄女性个体化辅助生殖治疗结局提供临床依据。方法:选择我院生殖中心2015年1月—2017年12月女方年龄≥40岁且使用自身卵子行体外受精的共2 467个治疗周期资料,对各项临床数据进行回顾性分析。结果:40岁及以上行辅助生殖治疗的患者,随着女性年龄增加获卵数明显减少(40~48岁女性平均获卵数分别为2.97、 2.69、2.17、2.01、1.77、1.61、1.68、1.29和1.00,44~48岁与40~43岁依次组间比较均P<0.05),尤其是44岁以上女性胚胎发育潜能明显降低(40~48岁囊胚形成率分别为48.90%、43.72%、33.67%、34.29%、24.39%、21.14%、26.32%、16.67%和0%,44~48岁与40~43岁组间依次比较均P<0.05)。共有518个周期行新鲜胚胎移植,结果显示,随女性年龄增加,临床妊娠率(40~48岁临床妊娠率分别为26.92%、21.15%、20.79%、10.96%、18.87%、11.11%、5.88%、0%和0%,43~48岁与40~42岁组间依次比较均P<0.05)、种植率(40~48岁种植率分别为23.65%、19.51%、17.70%、8.54%、7.49%、10.81%、5.56%、0%和0%,43~48岁与40~42岁组间依次比较均P<0.05)和活产率均显著降低(40~46岁活产率分别为18.46%、10.58%、9.90%、5.48%、5.66%、2.78%和5.88%,43~46岁与40~42岁组间依次比较均P<0.05),43岁以上者结局更差。44岁以上女性自然流产率明显增高(40~45岁流产率分别为31.43%、50.00%、52.38%、50.00%、70.00%和75.00%,44~45岁与40~43岁组间依次比较均P<0.05)。46岁女性仅1例妊娠并分娩,47岁和48岁女性均无成功妊娠。与抗苗勒管激素(AMH)>1.0 ng/mL组相比,AMH≤1.0 ng/mL组妊娠率、种植率及活产率均显著下降(27.04% vs. 14.74%,22.99% vs. 13.50%,15.88% vs. 7.37%;均P<0.05),流产率明显升高(41.27% vs. 50.00%,P<0.05)。结论:≥40岁高龄女性随年龄增长生育力逐渐降低。40~43岁年龄段女性助孕仍有一定的价值,尤其是卵巢仍有一定储备者(AMH>1.0 ng/mL),但44岁以上女性原则上不再建议ART助孕,对于46岁以上卵巢功能衰竭的女性强烈建议卵子捐赠或收养。 相似文献
Conservation laws are considered to be fundamental laws of nature. It has
broad applications in many fields, including physics, chemistry, biology, geology, and
engineering. Solving the differential equations associated with conservation laws is a
major branch in computational mathematics. The recent success of machine learning,
especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an
MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on
1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner.
We will show that the problem of solving conservation laws can be naturally viewed
as a sequential decision-making process, and the numerical schemes learned in such a
way can easily enforce long-term accuracy. Furthermore, the learned policy network
is carefully designed to determine a good local discrete approximation based on the
current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to
discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some
state-of-the-art numerical solvers such as WENO schemes, and supervised learning
based approach L3D and PINN, and how well it generalizes. 相似文献