Noninvasive prenatal diagnosis of aneuploidy using cell-free nucleic acids in maternal blood: promises and unanswered questions

The discovery of cell-free fetal (cff) DNA and RNA in the maternal circulation has driven developments in noninvasive prenatal diagnosis (NIPD) for the past decade. Detection of paternally derived alleles in cff DNA is becoming well established. Now much interest is focussing on NIPD of fetal chromosomal abnormalities, such as trisomy 21, which is a considerable challenge because this demands accurate quantitative measurements of the amounts of specific cff DNA or cff RNA sequences in maternal blood samples. Emerging strategies for distinguishing and quantifying the fetal nucleic acids in the maternal circulation promise continued development of the field, and pose a number of unanswered questions. Copyright © 2007 John Wiley & Sons, Ltd.     

基于超声透射特性的密闭容器液位测量与装置设计*

针对金属密闭容器内液体液位测量问题,设计1种基于超声透射特性的非接触式液位测量装置。测量装置根据被测容器内液体介质与气体介质透射系数不同的原理,通过超声接收探头回波情况判断容器内液体位置,利用软件与硬件结合,调整超声发射探头与接收探头的放大倍数,实现对不同材料、厚度容器内液位的测量。测量装置可以将所测液位、容器倾斜程度等数据通过上位机、阿里云平台等多方位显示。研究结果表明:该测量装置可测量不同材料、厚度密闭容器内承装液体的液位,所测数据较为稳定,测量误差范围不大于3 mm。     

基于数字锁相技术的飞机货舱火灾烟雾光学参数方法研究*

为解决飞机货舱火灾光学探测易受环境杂光和空气中灰尘、水汽干扰,误报率高的问题,提出1种基于数字锁相的双波长火灾烟雾探测方法。利用数字锁相技术调制双波长光源,选择性地提取微弱光散射信号,并设计前、后双向散射光探测光路。采用常见火灾材料及干扰源进行真假火源实验,依据米氏散射理论及不同粒径气溶胶颗粒对不同波长光的散射效应差异,以不对称比（AR）和双波长光强比（DWIR）作为火灾检测参数对不同粒径颗粒进行区分,根据2种火灾检测参数建立数据样本,利用BP神经网络进行分析,并加入复合式传感器实验作为对比。研究结果表明:基于数字锁相技术的火灾烟雾光学探测方法能够在一定程度上区分火灾烟雾和干扰源,误报率小于3.5%。     

PS-InSAR监测矿区建筑物及道路动态沉降安全分析*

针对矿山井下开采引发的地表沉降,提出1种结合时序相干系数阈值、振幅离差指数阈值和强度信息阈值的三重阈值法PS点识别方法的PS-InSAR技术。基于哨兵1号A星（Sentinel-1A）的雷达图像,依据三重阈值法识别到淮北市郊一煤矿311工作面采区内重要建筑物和道路的PS点,分别分析开采沉陷阶段的地表沉降活跃期（2018年7月14日—2019年9月7日）和该工作面停采2个月后地表沉陷衰退期（2019年9月7日—2021年1月23日）的动态沉降速率、沉降大小。研究结果表明:三重阈值法能筛选出更有效的PS点,可提高PS点识别与PS-InSAR技术监测精度,说明利用三重阈值法识别PS点的PS-InSAR技术监测矿区建筑物及道路安全具有可行性。     

Leveraging social media and deep learning to detect rare megafauna in video surveys

Deep learning has become a key tool for the automated monitoring of animal populations with video surveys. However, obtaining large numbers of images to train such models is a major challenge for rare and elusive species because field video surveys provide few sightings. We designed a method that takes advantage of videos accumulated on social media for training deep-learning models to detect rare megafauna species in the field. We trained convolutional neural networks (CNNs) with social media images and tested them on images collected from field surveys. We applied our method to aerial video surveys of dugongs (Dugong dugon) in New Caledonia (southwestern Pacific). CNNs trained with 1303 social media images yielded 25% false positives and 38% false negatives when tested on independent field video surveys. Incorporating a small number of images from New Caledonia (equivalent to 12% of social media images) in the training data set resulted in a nearly 50% decrease in false negatives. Our results highlight how and the extent to which images collected on social media can offer a solid basis for training deep-learning models for rare megafauna detection and that the incorporation of a few images from the study site further boosts detection accuracy. Our method provides a new generation of deep-learning models that can be used to rapidly and accurately process field video surveys for the monitoring of rare megafauna.