无偿献血宣传招募工作的实践和思考

该文总结该市2017年1月-2018年12月之间,在无偿献血宣传招募方面工作内容。结果显示2018年较2017年献血总量有所提升,其中团体献血所占比例逐渐上升,街头献血所占比例逐渐下降。通过大力宣传无偿献血工作,扩大无偿献血团体招募工作,完善并深化无偿献血各项制度,使得普通群众的无偿献血认知度不断提高。     

Rabs and axonal regeneration

Membrane trafficking processes are presumably vital for axonal regeneration after injury, but mechanistic understanding in this regard has been sparse. A recent loss-of-function screen had been carried out for factors important for axonal regeneration by cultured cortical neurons and the results suggested that the activity of a number of Rab GTPases might act to restrict axonal regeneration. A loss of Rab27b, in particular, is shown to enhance axonal regeneration in vitro, as well as in C. elegans and mouse central nervous system injury models in vivo. Possible mechanisms underlying this new finding, which has important academic and translational implication, are discussed.     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

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.     

Genome-wide haplotypic testing in a Finnish cohort identifies a novel association with low-density lipoprotein cholesterol

We performed genome-wide tests for association between haplotype clusters and each of 9 metabolic traits in a cohort of 5402 Northern Finnish individuals genotyped for 330 000 single-nucleotide polymorphisms. The metabolic traits were body mass index, C-reactive protein, diastolic blood pressure, glucose, high-density lipoprotein (HDL), insulin, low-density lipoprotein (LDL), systolic blood pressure, and triglycerides. Haplotype clusters were determined using Beagle. There were LDL-associated clusters in the chromosome 4q13.3-q21.1 region containing the albumin (ALB) and platelet factor 4 (PF4) genes. This region has not been associated with LDL in previous genome-wide association studies. The most significant haplotype cluster in this region was associated with 0.488 mmol/l higher LDL (95% CI: 0.361–0.615 mmol/l, P-value: 6.4 × 10⁻¹⁴). We also observed three previously reported associations: Chromosome 16q13 with HDL, chromosome 1p32.3-p32.2 with LDL and chromosome 19q13.31-q13.32 with LDL. The chromosome 1 and chromosome 4 LDL associations do not reach genome-wide significance in single-marker analyses of these data, illustrating the power of haplotypic association testing.     

化浊解毒方对溃疡性结肠炎的治疗作用及机制

目的探讨化浊解毒方治疗溃疡性结肠炎(UC)的治疗作用及机制。方法120例UC患者按照随机数字表法分为观察组、对照组各60例。观察组予中药化浊解毒方口服,每日1剂,早晚2次温服;对照组予美沙拉嗪肠溶片口服,1.0 g/次,3次/d。2组疗程均4周。对比2组治疗前后Geboes指数、结肠镜下黏膜表现、生活质量评分、疾病活动指数及血清炎性因子IL-8、IL-35水平,凝血指标血清FIB水平,统计治疗后1年内复发情况。结果治疗后,观察组Geboes指数、疾病活动指数及血清炎性因子IL-8水平、凝血指标血清FIB水平均较本组治疗前降低,生活质量评分、血清炎性因子IL-35水平升高(P<0.05);且观察组治疗后疾病活动指数及血清炎性因子IL-8水平、凝血指标血清FIB水平均低于对照组,生活质量评分、血清炎性因子IL-35水平均高于对照组(P<0.05)。与对照组相比,治疗组糜烂、溃疡改善不明显(P>0.05),充血水肿、颗粒样变等肠镜表现改善情况均优于对照组(P<0.05)。治疗结束1年观察组复发率为10.64%,对照组为23.53%,2组差异有统计学意义(P<0.05)。结论化浊解毒方能改善UC患者临床症状,修复肠黏膜病理损伤,降低复发率;其机制可能与调节血清炎性因子IL-8、IL-35和凝血因子FIB水平有关。