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Developing low-frequency acoustic senor with high sensitivity is crucial for diverse applications, ranging from seismic monitoring, military operations, to pipeline surveillance. Here, we have proposed a high-sensitivity graphene oxide (GO)-based Fabry-Perot low-frequency sensor, in which a 170 nm thick, large-area and uniformly GO film was prepared by a vacuum filtration method. To enhance the accuracy and stability of the sensor, a low-coherence interference system based on birefringent crystal blocks was designed utilizing a three-step phase-shifting arctangent algorithm. Our sensor exhibited a sensitivity of −93.48 dB re 1 rad/μPa at 6–60 Hz with a fluctuation of 0.6 dB. The minimum detectable pressure of the sensor was measured at 0.37 μPa/Hz1/2 @20 Hz with a signal to noise ratio of 135.41 dB. Overall, this sensor offers simplicity in preparation, high sensitivity, low detectable sound pressure, making it a significant asset for low-frequency acoustic applications.

High-precision intrusion positioning and recognition are crucial to promoting the practical application of long-distance distributed optical fiber vibration sensing systems in the engineering field. In this article, we propose a method for positioning and recognition in a long-distance asymmetric fiber-optic vibration sensing system based on bilinear time-frequency transform (BTFT) and convolutional neural network (CNN). First, a mean filter is conducted to obtain the endpoints of the vibration signal (VS). Second, the time-frequency feature of the VS is obtained by an affine smoothed pseudo Wigner-Ville distribution (ASPWVD), avoiding the incorrect positioning results caused by the phase mismatch of two signals. Third, the vibration position can be acquired by the time delay value obtained from the cross correlation. Meanwhile, a model capable of recognizing multiple types of perturbation events (PEs) is constructed by training the dataset containing ASPWVD of multiple VSs in CNN. Experimental results demonstrate that 94.8% of the positioning error reaches ±20 m, and the average positioning time is 302 ms, implementing the real-time positioning. The average recognition success rate of the five PEs and the average test time reaches 96.5% and 143 ms, respectively. Our work carries out the pattern recognition of PEs in the process of positioning. It is demonstrated that our work shows great prospects in the field of perimeter security.

As a basic physical parameter, temperature plays an important role in science and industry areas. The cavity optomechanics especially the spring effect provide an ideal platform for precision measurement. Here, we bridge between optical sensitization and optomechanical transduction by fabricating a liquid-core microbubble resonator to realize dual sensitization enhancement. The high thermo-optic coefficient liquid is injected into the microbubble to increase the temperature sensitivity of optical resonant peak shift. The optomechanical spring effect is used to transduce the amplified optical shift to mechanical frequency change and further enhance the temperature response. Through the enhancement combination of optical and mechanical methods, we have achieved a sensitivity of 8.1 MHz/ °C, which is at least two orders of magnitude higher than traditional optomechanical approaches. The temperature resolution is estimated as high as 5.3 × 10−5°C with mechanical frequency linewidth 8.6 kHz. A capillary ethanol evaporation experiment is constructed to demonstrate capability of the tiny temperature fluctuations measurement. The novel dual approach greatly enhanced the ultra-high resolution sensing capability and have a flexible sensitivity adjust potential with simply injecting different liquids.

Spectral interrogation has been an effective method for fiber-optic Fabry–Perot (F–P) sensors, but it still suffers from low sampling rate, poor dynamic response, and potential jump errors. In this article, we present a novel spectral interrogation system for the F–P sensor, with the advantages of error suppression and high speed. A peak-jump compensation method (P-JCM) is proposed to eliminate the potential jump errors by judging the difference between adjacent results, and a special spectrometer is employed for high-speed spectrum acquisition of the F–P sensor. We validate the system by performing high-speed pressure measurements of 0.01–3 MPa in the range of 25 °C–700 °C, with measurement rates up to 5 kHz. The measurement errors are less than 0.028 MPa and the actual resolution is 0.0024 MPa. It is the first spectral interrogation system that achieves high-speed measurement of dynamic pressure over a wide temperature range. Our new technique broadens the potential of spectral interrogation methods and offers a powerful F–P sensing system for various measurement fields.

In this article, we introduce an intensity demodulation method for the optical Vernier effect based on an intelligent algorithm. To reduce the requirement for a large number of pre-collected raw spectra for the training of the three-layer neural network, a spectrum interpolation scheme was incorporated. With the proposed method, the measurand can be demodulated by simply measuring the relative intensity at three wavelengths. A Vernier-effect temperature sensor consisting of two parallel fiber Fabry–Perot interferometers (FPIs) was fabricated and used to validate the proposed method. The experimental results demonstrate that the intensity demodulation method yields results in very good agreement with those of conventional spectral demodulation method. The sensor exhibits a sensitivity of 5.82 nm/°C and a relative error within ±1.23% in the range of 28 °C–44 °C. The proposed method enables the demodulation system to eliminate the dependence on the expensive optical spectrum analyzer (OSA). This has significant implications for reducing the cost of the optical Vernier-effect demodulation system and facilitating the practical application of such sensors.

Optical frequency-domain reflectometry (OFDR) offers a unique combination of ultrahigh spatial resolution and sensitivity, which makes OFDR one of the most rapidly developing fields of distributed optical fiber sensing (DOFS), especially in 3-D shape sensing, acoustic sensing, dynamic strain sensing, and refractive index (RI) sensing besides temperature and static strain. In this review, we covered key technological developments and insights of OFDR in recent years, which includes nonlinear tuning correction, fast spectrum analysis, Rayleigh backscattering spectra demodulation, Rayleigh scattering enhancement fiber, phase-based demodulation, high-temperature sensing, dynamic strain sensing, acoustic sensing, 3-D shape sensing, RI sensing, biochemical sensing, polarimetric OFDR sensing, and photonic integrated circuit-based OFDR. Finally, we presented the challenges and prospects of DOFS based on OFDR.

Polarization low-coherence interferometry (PLCI) is a promising technology for the demodulation of sensing signals. However, in such a system, the dispersion-induced fringe phase deviation will bring serious distortion to the demodulated results. In response to this challenge, a method is proposed to correct the fringe order jumps by exploiting the dispersion compensation equation. The distribution model of the actual optical path difference (OPD), corresponding to the ideal thickness of the birefringent optical wedge (BOW) in the system, is established. Combining the actual OPD distribution due to the birefringence and the interference fringes with double refraction dispersion variation, we can obtain the relationship between the deviation phase and the envelope peak (EP) of the interference fringes and can fully eliminate the fringe order jump error. A pressure experiment is carried out to verify the effectiveness of the proposed method. Compared with the conventional monochromatic frequency-domain absolute phase (MFDAP) algorithm, the proposed method can fundamentally suppress the fringe order jump problem induced by the dispersion without the loss of the demodulation accuracy and thus provides a new way for the precise demodulation of low-coherence interference fringes in large-range measurements.

In recent years, traditional perimeter security system is gradually replaced by optical fiber distributed vibration sensing system, as it has superior advantages such as high sensitivity, fast response, and simple structure. However, it is still challenging to accurately realize multievent pattern recognition in practical applications. Accurate pattern recognition can reduce the false alarm rate and significantly increase the stability of the optical fiber system. In this article, we proposed a pattern recognition approach based on short-time Fourier transform (STFT) and Resnet 152-based neural network. First, the vibration signal containing high-frequency information was extracted through a median filter. Second, STFT was used to convert a 1-D time-domain signal to a 2-D time–frequency signal. The feature dimension of optical signals was expanded. Third, the redundant information would be removed by dividing the high-, medium-, and low-energy segments. Finally, the preprocessed optical signals were sent to Resnet 152 convolutional neural network (CNN) model for pattern recognition. To verify the effectiveness of the proposed scheme, field tests with nine sensing events (climbing, crashing, cutting, kicking, knocking hard, knocking lightly, no intrusion, pulling, and waggling) have been experimentally carried out. It is demonstrated that the average recognition accuracy of the nine common sensing events is 96.67%, and the detection time is 0.2391 s. The feasibility of deep CNN in solving pattern recognition has been proved.

We demonstrate a short-time long distance distributed high-temperature sensing by non-local Haar transform (NLH) in optical frequency domain reflectometry (OFDR). By searching similar pixels across a non-local region, NLH makes good use of incomplete space similarity of information contained in 3D cross-correlation distribution of local Rayleigh scattering spectra measured by OFDR, which can be used to enhance image denoising performance and retain rare details of original spectra data. With the proposed method, we have achieved a short-time distributed high-temperature sensing ranging from 950 °C to 1050 °C over 102 m by reduced-cladding single mode fiber (RC-SMF) with a sensing spatial resolution of 2 cm. Compared with traditional image denoising methods including Gaussian filter (GF), block-matching three dimensional filter (BM3D) and wavelet denoising (WD), the proposed NLH method has a best performance to restore the consistency of spectral shift distribution caused by the same temperature change without deterioration of sensing spatial resolution.

We present a fiber laser temperature sensor based on temperature dependence whisper gallery mode-based microcavity. Three different thermo-optic coefficient liquids are injected into a thin-wall thickness microtubule for the different temperature sensitivity of whisper gallery mode shift. The sensor temperature sensitivity increases with the absolute value of the liquid core thermo-optic coefficient. The theoretical model of whisper gallery mode temperature response is analyzed. A liquid core microtubule is inserted into the erbium-doped fiber ring laser (FRL) cavity as the optical filter and sensing unit simultaneously, so the lasing wavelength can be used to detect the temperature change of the medium. Due to the narrow spectral width of the sensing laser, a high sensing resolution system is experimentally confirmed. With the combination of the liquid core to increase the sensitivity and FRL cavity to increase sensing resolution, the high sensitivity of 112.8 pm/°C of the sensing system is demonstrated and the thermal resolution is $8.16\times 10^{-{3}}\,\,^{\circ }\text{C}$ .

In this work, we propose and demonstrate a high-stability polarization-based interrogation method using cross-correlation for optical fiber interferometric acoustic sensing. Specifically, an extrinsic Fabry-Perot interferometric (EFPI) sensor based on polyphenylene sulfide (PPS) diaphragm is firstly used for signal detection, and then a cross-correlation interrogation system is formed with a birefringent crystal based on polarization low-coherence interference technology. Finally, the direct current (DC) component of the interference signal can be acquired in real-time with the help of the birefringent crystals block of known length. The system does not require an orthogonal relationship between the interference signals. Moreover, it can still work when the initial cavity length of the sensor drifts due to the influence of the environment. Theoretical simulations and a series of experiments have been carried out to verify the feasibility of the proposed scheme. And experimental results show that the sensor's initial cavity length shifts from 90.082 μm to 88.451 μm or 91.692 μm under 1 kHz acoustic signal, the maximum relative demodulation error is 0.4539 nm, and the signal-to-noise ratio(SNR)can reach 80 dB. Compared with the reported schemes, the proposed interrogation scheme simplifies the system configuration, and has properties of low cost, strong robustness, and high-frequency, so it has great potential in practical engineering applications.

In this paper, the optimal extraction conditions of crude polysaccharide from Hippophae rhamnoidesL. were gained by response surface methodology (RSM) according to the principle of Box-Behnken design (BBD), and three purified fractions (PBP-1, PBP-2 and PBP-3) were isolated by multiple column chromatography. PBP-1 mainly contained arabinose, xylose, mannose, glucose and galactose in a molar ratio of 1.18: 1: 2.20: 32.17: 1.45. PBP-2 mainly contained xylose, mannose, glucose and galactose in a molar ratio of 1: 0.28: 1.02: 0.2. PBP-3 mainly contained xylose, glucose and galactose in a molar ratio of 1: 2.15: 0.28, and the average molecular weights were 26.5, 5.6 and 4.9 kDa, respectively. Furthermore, the CPBP and PBP-3 exhibited the higher effective scavenging activities against DPPH, ABTS and reducing power, and the CPBP indicated the highest protective effect against H2O2-induced oxidative injury in LO2 cells. These findings could provide useful information for the utilization and development of the resources of Hippophae rhamnoidesL.

The rock quality designation (RQD) is a commonly used index for the classification and evaluation of rock mass quality and is widely adopted in mining and geological engineering. The traditional method of obtaining RQD still requires manual measurement of core length and calculation of RQD, which is inefficient. To address this problem, we propose a framework for drilling core image segmentation and RQD estimation from digital images of cores in core boxes based on the deep learning algorithm. The proposed framework is generated by combining the Mask Region-Based Convolutional Neural Networks (Mask R-CNN) instance segmentation algorithm, the U-Shaped Convolutional Neural Networks (U-Net) semantic segmentation algorithm, and the image processing functions of the Open Source Computer Vision Library (OpenCV). To demonstrate the accuracy of the proposed method, seven boreholes in the Xiushuihe vanadium-titanium magnetite mine, located in the Sichuan Province, China, were used as the case study. According to the comparison of the manual measurements and calculations of the cores taken from seven boreholes used to conduct this study, the framework can record the length of the drill cores and calculate RQD within an average error rate of 3.42%, while it saves about 85% of the working time. The results illustrate that the proposed framework enhances the efficiency of RQD calculation while satisfying the engineering requirements.

We present a long distance distributed strain sensing in optical frequency domain reflectometry (OFDR) by shape-adaptive principal component analysis Block-Matching three-dimensional filter (BM3D-SAPCA) image denoising, which uses correlated patterns and high degree redundancy of sensing data for enhancing the performance of distributed sensing by image processing for removing noise and increasing the signal-to-noise ratio (SNR) of noisy measurements. Compared with 2D image denoising methods, BM3D-SAPCA method searches similar 2D image blocks and stacks them together in 3D arrays, which takes full advantage of the high level of similitude and redundancy contained on the multidimensional information to denoise. We find that the BM3D-SAPCA method can effectively suppresses noise aggravation along with an increasing of the sensing distance. Without modifying the hardware system of OFDR, we achieve a distributed strain sensing with a 5 cm spatial resolution, a 2 μϵ strain resolution on a 200 m all grating fiber. We compare the performance of distributed strain sensing by BM3D-SAPCA with Gaussian filter, wavelet denoising (WD) and non-local mean filter (NLM) using the same data. The mean maximal strain measurement error at loaded strain areas is reduced from 2.3791 μϵ to 0.6545 μϵ by BM3D-SAPCA. These mean errors by Gaussian filter, NLM and WD are 1.1177 μϵ, 1.6668 μϵ and 1.9721 μϵ, respectively. The mean standard deviations of strain measurement in eight repeat experiments is reduced from 1.5221 μϵ to 0.3134 μϵ after noise reduction by BM3D-SAPCA, which is 79.37% lower than the raw data. The mean standard deviations after noise reduction by Gaussian filter, NLM and WD are decreased by 68.85%, 64.24% and 14.41% respectively.

The fuel efficiency of the transportation sector has become a key factor to reduce greenhouse gas emissions and fuel consumption in response to the negative impacts of global warming. As an approach to energy saving and environmental sustainability, eco-driving has attracted considerable research interest in the past decades. This review aims to provide a comprehensive review of the research on eco-driving using methodologies of literature bibliometrics and content analysis through VOSviewer software. The following keywords “ecological-driving”, “ecological-routing”, “ecological-bus”, “ecological-car”, “ecological-vehicle”, “eco-driving”, “eco-routing”, “eco-driver”, “eco-bus”, “eco-car” and “eco-vehicle” are used for paper retrieval. The query was conducted on January 20, 2021. The results take account of all journal articles, proceedings papers, and reviews without time limitation. Finally, a total of 767 documents were retrieved as total publications, which were viewed over the period 2001–2020 based on the Web of Science (WoS) Core Collection database. The publication year, leading countries, leading sources, leading institutions, leading authors, document citation, and document co-citation were analyzed to explore the primary trends. The In-depth analysis reveals five clusters of keywords, and the review of relevant studies on eco-driving from five different perspectives is carried out to identify potential trends and future research hot spots of eco-driving.

Ghost peaks refer to these peaks on backscattering trace measured by a reflectometer similar to Fresnel reflection peaks appearing at the position of non-reflection events in optical fiber or optical waveguide networks testing, which will cause ambiguity in the interpretation of reflection events on backscattering trace. We present a simple and effective method to eliminate side lobe ghost peaks in optical frequency domain reflectometry (OFDR) using a Wiener deconvolution filter. An OFDR system usually contains a main interferometer that is used to measure backscattering trace of fiber umber test (FUT) and an auxiliary interferometer that is used to measure the phase of tunable laser source (TLS) to compensate tuning nonlinearity. Multi-peaks in the spectrum of TLS and reflections in the auxiliary interferometer will generate side lobes on point spread function (PSF) of OFDR system, which will cause ghost peaks on FUT trace. By performing a Wiener deconvolution filtering on the auxiliary interferometer, we can obtain the PSF of an auxiliary interferometer, which is used as a convolution kernel to perform a Wiener deconvolution filtering on FUT trace from the main interferometer. Comparing the main interferometer spatial signal before and after deconvolution filtering, the peak amplitude whose reduction higher than threshold is identified to a ghost peak and is eliminated. By this ghost peak elimination method, we can acquire a pure FUT trace by an OFDR using a low-cost distributed feedback (DFB) TLS with a 26 m test length and an mm level spatial resolution.

The early diagnosis of myocardial infarction can significantly improve the survival rate in emergency treatment, which is mainly implemented by the immunoassay for myocardial infarction biomarkers such as cardiac troponins in blood. In this work, a disposable optofluidic microtubule whispering gallery mode (WGM) immunosensor for label-free cardiac troponin I-C (cTnI-C) complex detection has been proposed and demonstrated with active interrogation enhancement. The disposable microtubule is simply fabricated by a silica capillary with pressurized tapering technology for thin-wall, and the cTnI antibodies are immobilized on the inner wall surface of the microtubule through the self-adherent polydopamine substrate. By configuring the two coupling microfibers, the double-fiber-coupled microtubule cavity can serve as a tunable filter for the mutual-coupled polarimetric fiber ring laser (FRL), whose output laser wavelength is determined by the cTnI-C concentration in the optofluidic microtubule with inherent microfluidic channel. Due to the cyclic-cumulative gain of the FRL, the characteristic resonant peak of optical sensing signal is enhanced in the spectral width compression and the optical signal-to-noise ratio improvement, and therefore the optical immunosensor for cTnI-C can be achieved by tracking the output laser wavelength of the FRL conveniently. The dynamic binding and unbinding process of cTnI-C antigen–antibody is illustrated by monitoring the lasing peak wavelength continuously. Our all-fiber immunosensor demonstrated here has the advantages of fast label-free detection, real-time monitor, high sensitivity and disposable sensing element, which can be an innovative detecting tool in early diagnosis of myocardial infarction.

The optical fiber curvature sensor is an important device that is widely used in the field of medical instruments. In this paper, we proposed a high sensitivity curvature sensor based on a long period fiber grating (LPFG), which is inscribed in the two-mode fiber (TMF) by CO_2 laser pulses. Unlike the traditional LPFG in the single-mode fiber, TMF-LPFG couples the high-order mode LP_11 in the core with the modes in the cladding. From the experiment results, the wavelength shifts to the short-wave direction as the sensor bends. This sensor offers a high curvature sensitivity of −23.67nm/m^−1 in the range of 1.137−4.684m^−1 and −18.95nm/m^−1 in the range of 4.766−6.132m^−1, respectively. Moreover, this sensor has the advantage of low transmission loss, which is less than −4dB.

By continuously injecting four groups of heterogeneous frequency dual pulses into the sensing fiber with weak fiber Bragg gratings (WFBGs), a quasi-distributed acoustic sensing method based on frequency-division multiplexing is proposed. Each group of pulses generates interference signals with different carrier frequencies after being reflected by the WFBGs. Through the discrete Fourier transform phase demodulation method, each carrier frequency interference signal is demodulated and then the phase is spliced. The feasibility of this method is theoretically analyzed, and a detection with a bandwidth of 2 kHz is realized on a 70 km sensing fiber with a spatial resolution of 10 m.

A model reference adaptive sliding mode control for the position control of the permanent magnet synchronous motor is developed in this article. First of all, a fast sliding surface is designed to achieve faster convergence than the ordinary sliding mode control. Then, the adaptive laws are developed to make the control parameters, especially the switching gain, updated online. Therefore, the chattering can be reduced effectively and the disturbance can be rejected well. Finally, a reference model which produces an exponential decay curve is applied for the position error to follow. Thus, not only fast error convergence can be guaranteed, but also the dynamic process of the system response can be controlled easily by modifying the decay rate of the reference model. The proposed model reference adaptive sliding mode control scheme combines the advantages of the sliding mode control and the model reference adaptive control. Simulation and experimental results reveal that faster and more accurate performance with smoother control signal and better robustness is obtained compared with other methods. Also, the model reference adaptive sliding mode control method can maintain good performance when the system inertia or the position reference varies in a wide range.

An all-optical fiber multi-layer surface plasmon resonance (SPR) biosensor based on a sandwich structure of polydopamine-MoSe_2@Au nanoparticles-polydopamine (PDA-MoSe_2@AuNPs-PDA) was designed for the detection of specific immunoreactions. By optimizing the multi-layer structure and the ratio of MoSe_2: AuNPs, a sensitivity of 5117.59 nm/RIU has been obtained, which is more than double that of the only Au-filmed optical fiber SPR sensor. A large surface area was produced by integrating the MoSe_2 primitive unit cell and the AuNPs into a hybrid plasmonic nanostructure of MoSe_2@AuNPs, leading to optical fiber SPR signal amplification. The nanostructure of MoSe_2@AuNPs was surrounded by the PDA layer to guarantee the efficient immobilization of the protein molecules on the optical fiber by strong covalent bond. This biosensor achieved a detection limit of 54.05 ng/mL for detecting the goat-anti-rabbit IgG, which demonstrated enhancements of 12.1%, 23.3% and 184.6% in comparison with three reported SPR biosensors decorated with PDA-AuNPs-PDA, PDA and Cysteamine-MoSe_2@AuNPs-Cysteamine nanostructure, respectively. This biosensor achieved favorable selectivity and outstanding sensitivity compared with the reported SPR immuno-sensors, which will provide a miniaturized, rapid-response and label-free optical fiber bio-sensing platform for clinical diagnosis in the future.

We proposed a hybrid Fabry–Perot fiber-optic sensor based on the microelectromechanical system (MEMS) technique for measuring temperature and liquid refractive index simultaneously, and we verify the consistency of four sensors in the same batch. The sensor consists of a groove-array structured glass wafer and two silicon wafers, which are connected by double-sided anodic bonding. The three parts form two independent Fabry–Perot cavities for temperature and liquid refractive index sensing, respectively. We randomly selected three sensors in the same batch and conducted temperature and refractive index experiments to establish the sensing equation. The experimental results demonstrate their high consistency with temperature sensitivities of 81.6, 81.8, and 81.4 pm/°C in the range of 10°C to 80°C, and refractive index sensitivities of 1040.11, 1044.24, 1042.91 nm/RIU in the range of 1.333−1.374. The sensors have low cross-sensitivities that are less than 5.86×10^−6 RIU/°C and high precisions of 0.047°C, 2.14×10^−6RIU, respectively. To verify the validity of the sensing equation, we made another sensor in the same batch and got maximum errors of 0.36°C and 7.7×10^−5RIU, respectively.

It is difficult to obtain a large amount of labeled data, which has become a bottleneck for the application of deep learning to analyze one-dimensional optical time series signals. In order to solve this problem, a deep convolutional generative adversarial network model suitable for augmenting optical time series signals is proposed. Based on the acoustic emission (AE) data set obtained by an optical sensor with a small amount, the model can learn the corresponding data features and apply them to generate new data. The analysis results show that our model can generate stable and diverse AE fragments in epoch 500, and there is no model collapse. All the features between the generated data and the original data are not significantly different at the 0.05 level, which confirms that the method in this paper can generate the optical time series signals effectively.

We present a wall-thickness-controlled microbubble fabrication model for whispering-gallery-mode (WGM)-based application. The process of fabricating the model is divided into three sequenced steps: geometry size change of the microcapillary during drawing, expanding the process under internal injection air pressure, and microcapillary waist swell into a microbubble. Experiments were carried out to verify the effectiveness of the model. Experiment results show that wall thickness can reach 1.28 µm–1.46 µm at different injection pressure ranges of 50 kPa. The expected wall thickness of the microbubble can be achieved by changing injection pressure while keeping the diameter, which helps to prepare the required microbubble for practical application.

Prostate cancer is the second most common cancer in men worldwide1. Over the past decade, large-scale integrative genomics efforts have enhanced our understanding of this disease by characterizing its genetic and epigenetic landscape in thousands of patients2,3. However, most tumours profiled in these studies were obtained from patients from Western populations. Here we produced and analysed whole-genome, whole-transcriptome and DNA methylation data for 208 pairs of tumour tissue samples and matched healthy control tissue from Chinese patients with primary prostate cancer. Systematic comparison with published data from 2,554 prostate tumours revealed that the genomic alteration signatures in Chinese patients were markedly distinct from those of Western cohorts: specifically, 41% of tumours contained mutations in FOXA1and 18% each had deletions in ZNF292and CHD1. Alterations of the genome and epigenome were correlated and were predictive of disease phenotype and progression. Coding and noncoding mutations, as well as epimutations, converged on pathways that are important for prostate cancer, providing insights into this devastating disease. These discoveries underscore the importance of including population context in constructing comprehensive genomic maps for disease.

In this paper, we introduce a cryogen-adaptive sensor based on a micro-electromechanical system (MEMS) for level measurement of cryogenic fluids. The sensor is fabricated by an optical fiber inserted in a glass ferrule and an integrated Fabry–Perot (FP) chip using the MEMS technique. We carried a liquid nitrogen level measurement experiment to verify the performance of the sensor and a low coherent interference system is used to transform the liquid level to absolute phase. The measuring range is 24 cm and can be expanded more widely. The experimental results show that the sensor has a good monotonic linear response (coefficient determination ${{\rm R}^2} \gt {0.998}$R^2>0.998), and the measurement error is less than ${ \pm 5}\;{\rm mm}$±5mm in liquid nitrogen. The excellent cryogenic temperature performance from $ - {260}^\circ {\rm C}$−260^∘C to $ - {100}^\circ {\rm C}$−100^∘C also is demonstrated, which shows the potential application in level measurement of various cryogenic liquids.