Mixed event separation and identification based on a convolutional neural network trained with the domain transfer method for a φ-OTDR sensing system.

In phase-sensitive optical time domain reflectometer (φ-OTDR) based distributed acoustic sensing (DAS), correct identification of event types is challenging in complex environments where multiple events happen simultaneously. In this study, we have proposed a convolutional neural network (CNN) with a separation module and an identification module to simultaneously separate a mixed event into individual single-event components and identify each type of component contained in the mixed event. The domain transfer method is used in the training, fine-tuning, and testing of the proposed CNN, which saves 94% of the workload for massive DAS data collection and signal demodulation.

Simultaneous sensing of temperature and strain with enhanced performance using forward Brillouin scattering in highly nonlinear fiber.

Simultaneous temperature and strain sensing has been demonstrated for the first time to our knowledge by using forward Brillouin scattering (FBS) in a highly nonlinear fiber (HNLF). It is based on different responses of radial acoustic modes R and torsional-radial acoustic modes TR to the temperature and strain. High-order acoustic modes with large FBS gain in an HNLF are chosen to improve the sensitivity.

High-sensitivity acoustic impedance sensing based on forward Brillouin scattering in a highly nonlinear fiber.

By using radial acoustic modes induced forward Brillouin scattering (FBS) in a highly nonlinear fiber (HNLF), to the best of our knowledge we have demonstrated acoustic impedance sensing with the sensitivity reaching beyond 3MHz for the first time. Benefiting from the high acousto-optical coupling efficiency, both radial acoustic modes (R) and torsional-radial acoustic modes (TR) induced FBS in HNLF have larger gain coefficient and scattering efficiency than those in standard single-mode fiber (SSMF). This provides better signal-to-noise ratio (SNR) and hence larger measurement sensitivity.

Integrated denoising and extraction of both temperature and strain based on a single CNN framework for a BOTDA sensing system.

We have proposed and demonstrated a denoising and extraction convolutional neural network (DECNN) composed of 1D denoising convolutional autoencoder (DCAE) and 1D residual attention network (RANet) modules to extract temperature and strain simultaneously in a Brillouin optical time-domain analysis (BOTDA) system. With DCAE for high-fidelity denoising and RANet for accurate and robust information extraction, integrated denoising and extraction of both temperature and strain have been realized for the first time under a single CNN framework. Both simulation and experiment have been conducted to statistically analyze the performance of the proposed scheme and compare it with the conventional equation solving method (CESM), which show that DECNN has large noise tolerance and robustness over a wide range of temperature/strain and signal-to-noise ratio (SNR) conditions.

The mutation-free embryo for in vitro fertilization selected by MALBAC-PGD resulted in a healthy live birth from a family carrying PKD 1 mutation.

Background: Autosomal dominant polycystic kidney disease (ADPKD, autosomal dominant PKD or adult-onset PKD) is the most prevalent and potentially lethal kidney disease that is hereditary and lacks effective treatment. Preimplantation genetic diagnosis (PGD) of embryos in assistant reproductive technology (ART) helps to select mutation-free embryos for blocking ADPKD inheritance from the parents to their offspring. However, there are multiple pseudogenes in the PKD1 coding region, which make blocking ADPKD inheritance by PGD complicated and difficult.

[Detection of pathogenic mutations for methylmalonic acidemia using new-generation semiconductor targeted sequencing].

Objective: To detect the pathogenic mutation in a patient with methylmalonic acidemia using IonTorrent Personal Genome Machine (PGM) and assess the feasibility of such technology for analyzing complex monogenic diseases.

Methods: Peripheral blood sample was collected from the patient. Genomic DNA was isolated using a standard method and subjected to targeted sequencing using an Ion Ampliseq Inherited Disease Panel.