脊髓小脑共济失调2型家系ATXN2基因中间重复病例表型与分子遗传学特征

目的探讨脊髓小脑共济失调2型(SCA2)致病基因ATXN2异常等位基因中间重复个体的表型和分子遗传学特点。方法针对2005—2018年中日友好医院神经科运动障碍与神经遗传病研究中心收集的1383个常染色体显性遗传共济失调家系的先证者和部分家系成员,采用荧光标记毛细管电泳片段分析方法进行动态突变检测,对携带ATXN2基因中间重复的个体进行临床表型和遗传特征分析。结果共检出163个家系(包含先证者和家系成员共203人)携带异常扩展的ATXN2基因CAG重复序列,其中93个家系中有107例的异常扩展等位基因重复次数在29~34次之间。在其中的20个亲子对中,父系遗传16个,异常等位基因的代间扩展增加0~28次,母系遗传4个,异常等位基因的代间扩展增加0~4次。结论对于临床拟诊SCA2家系患者,需对其亲代或成年子代个体进行ATXN2基因检测,以免漏诊。动态突变基因检测有助于识别中间重复的个体,对明确家系致病基因和遗传咨询至关重要。     

Uncertainty evaluation at three spatial scales for the NDVI-based VWC estimation method used in the SMAP algorithm

Vegetation water content (VWC) is the key input parameter for a soil moisture retrieval algorithm based on microwave remote sensing, and VWC uncertainty can limit the estimated accuracy of soil moisture. There has been little research on VWC algorithm development and validation in China, and the uncertainty of the VWC estimation method has not been well evaluated. Therefore, the aim of this study is to evaluate the uncertainty of the VWC estimation method used in the SMAP (Soil Moisture Active Passive) algorithm on three spatial scales (the point-scale, 30 m scale, and 1 km scale) for maize in northeast China. Results from three ground experimental datasets showed that the SMAP VWC estimation method was strongly biased with an average overestimation of 1.16 kg m^?2,1.04 kg m^?2, and 1.13 kg m^?2 for the point-scale, 30 m scale, and 1 km scale respectively, and maximum bias occurred in the mid-stage of maize. Also, a new power relationship between NDVI (Normalized Difference Vegetation Index) and VWC was proposed for the 30 m scale based on Sentinel 2 NDVI and field VWC values from 2017 experiment, with respective R² (coefficient of determination) and Root Mean Squared Error (RMSE) values of 0.80 and 0.67 kg m^?2. The results confirmed that this power relationship was still suitable for VWC estimation at the 1 km scale, and it has smaller bias than the original SMAP VWC method. Future work will be carried out to evaluate the applicability of this VWC estimation method over a lager region. It is expected that it can improve the accuracy of soil moisture by providing high precision VWC input parameters.     

Troubled-Cell Indicator Based on Mean Value Property for Hybrid WENO Schemes

In this paper, we introduce a new type of troubled-cell indicator to improve hybrid weighted essentially non-oscillatory (WENO) schemes for solving the hyperbolic conservation laws. The hybrid WENO schemes selectively adopt the high-order linear upwind scheme or the WENO scheme to avoid the local characteristic decompositions and calculations of the nonlinear weights in smooth regions. Therefore, they can reduce computational cost while maintaining non-oscillatory properties in non-smooth regions. Reliable troubled-cell indicators are essential for efficient hybrid WENO methods. Most of troubled-cell indicators require proper parameters to detect discontinuities precisely, but it is very difficult to determine the parameters automatically. We develop a new troubled-cell indicator derived from the mean value theorem that does not require any variable parameters. Additionally, we investigate the characteristics of indicator variable; one of the conserved properties or the entropy is considered as indicator variable. Detailed numerical tests for 1D and 2D Euler equations are conducted to demonstrate the performance of the proposed indicator. The results with the proposed troubled-cell indicator are in good agreement with pure WENO schemes. Also the new indicator has advantages in the computational cost compared with the other indicators.     

Exact confidence intervals for the average causal effect on a binary outcome

Based on the physical randomization of completely randomized experiments, in a recent article in Statistics in Medicine, Rigdon and Hudgens propose two approaches to obtaining exact confidence intervals for the average causal effect on a binary outcome. They construct the first confidence interval by combining, with the Bonferroni adjustment, the prediction sets for treatment effects among treatment and control groups, and the second one by inverting a series of randomization tests. With sample size n, their second approach requires performing O(n⁴)randomization tests. We demonstrate that the physical randomization also justifies other ways to constructing exact confidence intervals that are more computationally efficient. By exploiting recent advances in hypergeometric confidence intervals and the stochastic order information of randomization tests, we propose approaches that either do not need to invoke Monte Carlo or require performing at most O(n²)randomization tests. We provide technical details and R code in the Supporting Information . Copyright © 2016 John Wiley & Sons, Ltd.