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Summary Acyltransferase (AT)‐less type I polyketide synthases (PKSs) produce complex natural products due to the presence of many unique tailoring enzymes. The 3‐hydroxy‐3‐methylglutaryl coenzyme A synthases (HCSs) are responsible for β‐alkylation of the growing polyketide intermediates in AT‐less type I PKSs. In this study, we discovered a large group of HCSs, closely associated with the characterized and orphan AT‐less type I PKSs through in silico genome mining, sequence and genome neighbourhood network analyses. Using HCS‐based probes, the survey of 1207 in‐house strains and 18 soil samples from different geographic locations revealed the vast diversity of HCS‐containing AT‐less type I PKSs. The presence of HCSs in many AT‐less type I PKSs suggests their co‐evolutionary relationship. This study provides a new probe to study the abundance and diversity of AT‐less type I PKSs in the environment and microbial strain collections. Our study should inspire future efforts to discover new polyketide natural products from AT‐less type I PKSs.

Noise tends to limit the quality of wide field electromagnetic method (WFEM) data and exploration results. The existing WFEM denoising methods lack the signal identification process and are only able to filter or eliminate abnormalities in the time or frequency domain, which easily leads to the loss of more abundant real data and to low data quality. Thus, we built the WFEM data sample library to extract the multi-domain features. Then, neighborhood search and location sharing were used to improve the grey wolf optimizer (IGWO) algorithm. The support vector machine (SVM) parameters were optimized by IGWO to train multi-domain features, and an IGWO-SVM data model was generated. We used the data model to quantitatively test the WFEM signal and noise in the simulation and measured data. This method can effectively identify the WFEM signal and noise, eliminate the identified noise, and use the identified signal to reconstruct the effective data. Finally, the digital coherence technique was used to extract the spectrum amplitude of the effective frequency points. The experiments demonstrated the advantage of the convergence of IGWO algorithms and the comparison of the SVM parameters optimization techniques. The proposed method can quickly and effectively search the optimal SVM parameters, significantly improve the identification effect of WFEM signal noise, and completely remove the abnormal noise waveform in the reconstructed data. The more stable electric field curves in the results verify the effectiveness of the algorithm design and optimized identification method.

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Powerline interference in the controlled source electromagnetic method has traditionally been one of the biggest conundrums plaguing geophysicists, and its conventional denoising methods primarily include filtering and noise estimation. The filter method leaches noise at specific frequency points, which might also filter useful signals; the noise estimation method significantly eliminates interference, whereas the premise is that the noise is stable after a short time and a recorder is necessary in the field. In the present study, using the periodicity and symmetry of powerline noise, we propose a subtraction and an addition method for cancellation of the powerline noise. First, the transmitted signal is optimized so that the equivalent transmitted signal is an m sequence; then the response signal is processed by using the cancellation method; subsequently, the correlation identification is applied and finally, we solve the earth impulse response by means of the Wiener filter deconvolution method. Simulation experiments and field data tests demonstrate that the powerline noise can be well suppressed by the cancellation method proposed in the present study, so that the system identification accuracy is greatly improved. The method is simple in principle and effective in removing powerline noise, which presents a novel perspective on noise elimination for system identification.