Filamentous fungi‐copper (Cu) interactions are very important in the formation of natural ecosystems and the bioremediation of heavy metal pollution. However, important issues at the proteome level remain unclear. We compared six proteomes from Cu‐resistant wild‐type (WT) Penicillium janthinellum strain GXCR and a Cu‐sensitive mutant (EC‐6) under 0, 0.5, and 3 mmol/L Cu treatments using iTRAQ. A total of 495 known proteins were identified, and the following conclusions were drawn from the results: Cu tolerance depends on ATP generation and supply, which is relevant to glycolysis pathway activity; oxidative phosphorylation, the TCA cycle, gluconeogenesis, fatty acid synthesis, and metabolism are also affected by Cu; high Cu sensitivity is primarily due to an ATP energy deficit; among ATP generation pathways, Cu‐sensitive and Cu‐insensitive metabolic steps exist; gluconeogenesis pathway is crucial to the survival of fungi in Cu‐containing and sugar‐scarce environments; fungi change their proteomes via two routes (from ATP, ATP‐dependent RNA helicases (ADRHs), and ribosome biogenesis to proteasomes and from ATP, ADRHs to spliceosomes and/or stress‐adapted RNA degradosomes) to cope with changes in Cu concentrations; and unique routes exist through which fungi respond to high environmental Cu. Further, a general diagram of Cu‐responsive paths and a model theory of high Cu are proposed at the proteome level. Our work not only provides the potential protein biomarkers that indicate Cu pollution and targets metabolic steps for engineering Cu‐tolerant fungi during bioremediation but also presents clues for further insight into the heavy metal tolerance mechanisms of other eukaryotes.

The low-distortion processing of well-testing geological parameters is a key way to provide decision-making support for oil and gas field development. However, the classical processing methods face many problems, such as the stochastic nature of the data, the randomness of initial parameters, poor denoising ability, and the lack of data compression and prediction mechanisms. These problems result in poor real-time predictability of oil operation status and difficulty in offline interpreting the played back data. Given these, we propose a wavelet-based Kalman smoothing method for processing uncertain oil well-testing data. First, we use correlation and reconstruction errors as analysis indicators and determine the optimal combination of decomposition scale and vanishing moments suitable for wavelet analysis of oil data. Second, we build a ground pressure measuring platform and use the pressure gauge equipped with the optimal combination parameters to complete the downhole online wavelet decomposition, filtering, Kalman prediction, and data storage. After the storage data are played back, the optimal Kalman parameters obtained by particle swarm optimization are used to complete the data smoothing for each sample. The experiments compare the signal-to-noise ratio and the root mean square error before and after using different classical processing models. In addition, robustness analysis is added. The proposed method, on the one hand, has the features of decorrelation and compressing data, which provide technical support for real-time uploading of downhole data; on the other hand, it can perform minimal variance unbiased estimates of the data, filter out the interference and noise, reduce the reconstruction error, and make the data have a high resolution and strong robustness.

Abstract. A two-beam phase-sensitive optical time domain reflectometer (OTDR) is proposed and experimentally demonstrated. Optical pulses are launched into two distributed fibers. With the interference effect of two channel Rayleigh scattering signals and the positioning principle of the traditional OTDR, detection and location of vibration events along sensing fibers can be achieved using a new signal processing scheme. What is more, to investigate the sensitivity fading problem that often occurred when polarization controller (PC) is not utilized, the optical polarization model based on the equivalent Jones matrices of sensing fibers is established. Simulation results show that due to the polarization characteristic discrepancy of the sensing fibers, the signal amplitude varies a lot for different polarization states of the incident light. Experimental result shows that with the PC, the signal signal to noise ratio can be stabilized as 9.5 dB, which verifies the effectiveness of the sensor. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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