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.     

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.     

A randomized study on the effect of modified behavioral activation treatment for depressive symptoms in rural left-behind elderly

Objective: To evaluate the effectiveness of a modified behavioral activation treatment (MBAT) intervention on reducing depressive symptoms in rural left-behind elderly.

Method: This is a randomized study registered in Chinese Clinical Trial Registry (ChiCTR-IOR-17011289). Eighty rural left-behind elderly people who had a Geriatric Depression Scale (GDS) score between 11 and 25 were randomly assigned to the intervention (n?=?40) and control group (n?=?40). The intervention group received both MBAT and regular treatment for 8 weeks while the control group received regular treatment. Both groups were assessed with the GDS, Beck Anxiety Inventory (BAI), and Oxford Happiness Questionnaire (OHQ) at baseline, immediately post-intervention, and at 3 months post-intervention.

Results: There were a total of 73 participants that completed the intervention. The scores of GDS and BAI decreased significantly, but the scores of OHQ increased significantly in the intervention group after 8 sessions of MBAT (P?<?.01). The reduction in depression symptoms after the intervention was maintained at the 3-month follow-up. Significant differences in GDS, BAI, and OHQ scores were observed between the intervention group and the control group (P?<?.01).

Conclusion: MBAT produced a significantly greater reduction in depressive symptoms than regular care in rural left-behind elderly.

Clinical or methodological significance of this article: A modified behavioral activation (BA) psychotherapy can significantly reduce the recurrence and seriousness of depression symptoms in the left-behind elderly with mild to moderate depression. This study also suggests that further study of the MBAT as an intervention will provide a direction for the management of mental health in rural left-behind elders.     

血液系统恶性肿瘤患者化疗后合并中性粒细胞减少性肠炎的诊治研究

目的分析成人血液系统恶性肿瘤患者接受强烈化疗后中性粒细胞减少性肠炎（NE）的发生率、危险因素及预后情况。方法收集2004至2013年接受化疗的1804例血液系统恶性肿瘤患者，记录患者血常规、凝血检测和血液生化检测结果，并记录患者年龄、性别、原发病、既往化疗次数、既往化疗方案中是否使用阿糖胞苷、临床症状、肠壁厚度、中性粒细胞最低计数、中性粒细胞缺乏持续时间、NE的治疗方法和预后等，探讨NE起病诱因、临床特征、腹部B超特点、症状的预后意义及化疗药物对发病的影响等。结果1804例患者中226例（12.5%）化疗后合并NE，化疗后10~19d起病，中位起病时间为化疗后第14天。发生NE后26例患者死亡，病死率11.5%。化疗药物包括阿糖胞苷、临床症状≥4项、中性粒细胞缺乏持续超过7d以及B超下肠壁厚度≥10mm的患者病死率相对较高。结论NE是接受强烈化疗的血液系统肿瘤患者的严重的并发症，发生NE后患者病死率较高。     

低密度脂蛋白在2型糖尿病骨质疏松中的作用研究进展

2型糖尿病及骨质疏松已成为我国最主要的慢性代谢性疾病。2型糖尿病常伴有血脂紊乱，常表现为低密度脂蛋白升高，而越来越多的研究表明血脂通过不同的方式影响骨代谢，其机制可能是通过抑制骨髓间充质干细胞成骨分化，通过RANK/RNAKL/OPG信号通路及炎症反应调节破骨细胞等方式调节骨代谢。     

基于自动勾画和快速蒙特卡罗计算的放射治疗全身器官剂量数据库平台的可行性研究

目的:探讨建立一种放射治疗全身器官剂量数据库平台的可行性。方法:使用基于深度学习的自动勾画软件DeepViewer?1例食管癌患者的全身CT上勾画全身器官，然后利用基于GPU加速的蒙特卡罗软件ARCHER计算相应的器官剂量分布，最后利用Lyman-Kutcher-Burman（LKB）模型评估放疗患者正常组织并发症概率（NTCP）。结果:针对该病例，成功建立基于DeepViewer?ARCHER和LKB模型的全身器官剂量数据库，发现距离靶区越近的器官剂量越大，其中心脏与靶区间距离最小，剂量为14.11 Gy，但因其模型参数特殊，通过LKB模型计算的NTCP为0.00%；左、右肺的剂量分别为3.19和1.16 Gy，但是NTCP值却很大，分别为2.13%和1.60%。对于距离靶区较远的头颈部器官（视交叉、视神经和眼）和腹部器官（直肠、膀胱和股骨头）剂量分别约为9和2 mGy，并且NTCP均近似为0.00%。结论:研究结果证明通过自动勾画软件DeepViewer?蒙特卡罗软件ARCHER和LKB模型建立全身器官剂量数据库的可行性。     

Toxicity of ZnO nanoparticles (NPs) to THP-1 macrophages: interactions with saturated or unsaturated free fatty acids

In a biological microenvironment, free fatty acids (FFA) as ubiquitous biological molecules might interact with nanoparticles (NPs) and consequently change the toxicological responses. However, whether the chemical structures of FFA could influence their interactions with NPs remain unknown. This study investigated the interactions between ZnO NPs and saturated or unsaturated FFA (complexed to BSA), namely stearic acid (SA, C18:0), oleic acid (OA, C18:1), and α-linolenic acid (ALA, C18:3). It was shown that BSA, SA, OA, and ALA increased the atomic force microscope (AFM) heights as well the polydispersity index (PDI) of ZnO NPs. BSA modestly protected THP-1 macrophages from ZnO NP exposure, whereas OA and ALA led to relatively less cyto-protective effects of BSA. Moreover, only co-exposure to ZnO NPs and SA significantly promoted the release of interleukin-8. BSA, SA, OA, and ALA equally changed intracellular ROS and Zn ions associated with ZnO exposure, but co-exposure to ZnO NPs and OA/ALA particularly activated the expression of endoplasmic reticulum stress-apoptosis genes. In combination, these results showed that FFA could influence the colloidal aspects and toxicological signaling pathway of ZnO NPs, which is dependent on the number of unsaturated bonds of FFA.     

一种面向中医药数据的高效脱敏算法＊

目的 针对中医药大数据平台传统的加密方案效率不高的问题，提出一种高效的基于属性的内积加密的数据脱敏算法。方法 Hash（哈希）算法是应用广泛的高效的数据加密方法，但传统的哈希算法基于单一的控制策略，效率不高。本文提出一种基于属性的内积加密的数据脱敏算法，把批量的敏感数据分割为不同长度数据颗粒度，与特定密文的哈希进行内积处理。结果 在面对中医药大数据平台的海量数据加密的场景，与传统的哈希加密算法相比，本文提供的加密算法具有很好的性能。结论 为了保障个人隐私数据不被泄露，中医药大数据平台中的个人医疗数据需要加密脱敏后，才能进行分析处理或对外发布。本文提出的算法具备灵活的数据颗粒度、策略和高效的性能表现，适用于海量的中医药数据脱敏。