Objective: To evaluate the performance of noninvasive prenatal testing for all fetal chromosomal aneuploidies in an extremely high-risk group undergoing first trimester combined Down syndrome screening. Method: A multicenter cohort prospective study in Taiwan was performed between June and December 2012. Maternal plasma was collected and shotgun massive parallel sequencing was performed on each fetal chromosome. 201 Taiwanese pregnant women at >12 weeks' gestation from 11 medical centers were enrolled in this trial. The extremely high-risk group was defined as a Down syndrome risk cutoff >1:30 or nuchal translucency >3.0 mm (n = 100), while the low-risk group was defined as a Down syndrome cutoff <1:1,500 (n = 101). Amniocentesis confirmation was performed and birth outcome was also recorded. Results: There were 11 cases of trisomy 21, 8 cases of trisomy 18, 3 cases of trisomy 13, 1 case of trisomy 16, 3 cases of 45,X, and 1 case of 47,XYY detected prenatally in 100 extremely high-risk gravidas [n = 27/100 (27%)]. The overall autosomal or sex chromosome aneuploidy detection rate was 96% (27/28) because of an insufficient amount maternal plasma for one fetus with Turner syndrome. In the low-risk group, no chromosomal abnormalities were detected (specificity = 100%). There were no false-positive cases in this study. Conclusions: This first trial in Taiwan shows that noninvasive prenatal testing for whole chromosome aneuploidies can be efficiently applied in extremely high- and low-risk populations.
Obtaining optimal solutions for groundwater resources planning problems, while simultaneously considering both fixed costs and time-varying pumping rates, is a challenging task. Application of conventional optimization algorithms such as linear and nonlinear programming is difficult due to the discontinuity of the fixed cost function in the objective function and the combinatorial nature of assigning discrete well locations. Use of conventional discrete algorithms such as integer programming or discrete dynamic programming is hampered by the large computational burden caused by varying pumping rates over time. A novel procedure that integrates a genetic algorithm ͑GA͒ with constrained differential dynamic programming ͑CDDP͒ calculates optimal solutions for a groundwater resources planning problem while simultaneously considering fixed costs and time-varying pumping rates. The GA determines the number and locations of pumping wells with operating costs then evaluated using CDDP. This study demonstrates that fixed costs associated with installing wells significantly impact the optimal number and locations of wells.
The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a complex AF detection system, which results in heavy training computation and high implementation cost. This paper proposes an AF detection method based on an end-to-end 1D convolutional neural network (CNN) architecture to raise the detection accuracy and reduce network complexity. By investigating the impact of major components of a convolutional block on detection accuracy and using grid search to obtain optimal hyperparameters of the CNN, we develop a simple, yet effective 1D CNN. Since the dataset provided by PhysioNet Challenge 2017 contains ECG recordings with different lengths, we also propose a length normalization algorithm to generate equal-length records to meet the requirement of CNN. Experimental results and analysis indicate that our method of 1D CNN achieves an average F1 score of 78.2%, which has better detection accuracy with lower network complexity, as compared with the existing deep learning-based methods.
In time-varying ground water remediation, the lack of an optimal control algorithm to simultaneously consider fixed costs and time-varying operating costs makes it nearly impossible to obtain an optimal solution. This study presents a novel algorithm that integrates a genetic algorithm (GA) and constrained differential dynamic programming (CDDP) to solve this time-varying ground water remediation problem. A GA can easily incorporate the fixed costs associated with the installation of wells. However, using a GA to solve for time-varying policies would dramatically increase the computational resources required. Therefore, the CDDP is used to handle the subproblems associated with time-varying operating costs. A hypothetical case study that incorporates fixed and time-varying operating costs is presented to demonstrate the effectiveness of the proposed algorithm. Simulation results indicate that the fixed costs can significantly influence the number and locations of wells, and a notable total cost savings can be realized by applying the novel algorithm herein.
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