Pseudomonas putida KT2442 has been a well-studied producer of medium-chain-length (mcl) polyhydroxyalkanoate (PHA) copolymers containing C6 ~ C14 monomer units. A mutant was constructed from P. putida KT2442 by deleting its phaG gene encoding R-3-hydroxyacyl-ACP-CoA transacylase and several other β-oxidation related genes including fadB, fadA, fadB2x, and fadAx. This mutant termed P. putida KTHH03 synthesized mcl homopolymers including poly(3-hydroxyhexanoate) (PHHx) and poly(3-hydroxyheptanoate) (PHHp), together with a near homopolymer poly(3-hydroxyoctanoate-co-2 mol% 3-hydroxyhexanoate) (PHO*) in presence of hexanoate, heptanoate, and octanoate, respectively. When deleted with its mcl PHA synthase genes phaC1 and phaC2, the recombinant mutant termed P. putida KTHH08 harboring pZWJ4-31 containing PHA synthesis operon phaPCJ from Aeromonas hydrophila 4AK4 accumulated homopolymer poly(3-hydroxyvalerate) (PHV) when valerate was used as carbon source. The phaC deleted recombinant mutant termed P. putida KTHH06 harboring pBHH01 holding PHA synthase PhbC from Ralstonia eutropha produced homopolymers poly(3-hydroxybutyrate) (PHB) and poly(4-hydroxybutyrate) using γ-butyrolactone was added as precursor. All the homopolymers were physically characterized. Their weight average molecular weights ranged from 1.8 x 10⁵ to 1.6 x 10⁶, their thermal stability changed with side chain lengths. The derivatives of P. putida KT2442 have been developed into a platform for production of various PHA homopolymers.

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With the development of deep learning algorithms, more and more deep learning algorithms are being applied to remote sensing image classification, detection, and semantic segmentation. The landslide semantic segmentation of a remote sensing image based on deep learning mainly uses supervised learning, the accuracy of which depends on a large number of training data and high-quality data annotation. At this stage, high-quality data annotation often requires the investment of significant human effort. Therefore, the high cost of remote sensing landslide image data annotation greatly restricts the development of a landslide semantic segmentation algorithm. Aiming to resolve the problem of the high labeling cost of landslide semantic segmentation with a supervised learning method, we proposed a remote sensing landslide semantic segmentation with weakly supervised learning method combing class activation maps (CAMs) and cycle generative adversarial network (cycleGAN). In this method, we used the image level annotation data to replace pixel level annotation data as the training data. Firstly, the CAM method was used to determine the approximate position of the landslide area. Then, the cycleGAN method was used to generate the fake image without a landslide, and to make the difference with the real image to obtain the accurate segmentation of the landslide area. Finally, the pixel-level segmentation of the landslide area on remote sensing image was realized. We used mean intersection-over-union (mIOU) to evaluate the proposed method, and compared it with the method based on CAM, whose mIOU was 0.157, and we obtain better result with mIOU 0.237 on the same test dataset. Furthermore, we made a comparative experiment using the supervised learning method of a u-net network, and the mIOU result was 0.408. The experimental results show that it is feasible to realize landslide semantic segmentation in a remote sensing image by using weakly supervised learning. This method can greatly reduce the workload of data annotation.

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