DEPTOR is an inhibitor of the mTOR kinase which controls cell growth. DEPTOR consists of two DEP domains and a PDZ domain connected by an unstructured linker, and its stability is tightly regulated through post-translational modifications of its linker region that contains the 286SSGYFS291 degron. Based on the mTORC1 complex, our modelling suggests a possible spatial arrangement of DEPTOR which is characterised to form a dimer. Our model shows that the two PDZ domains of a DEPTOR dimer bind separately to the dimeric mTOR's FAT domains ~130 Å apart, while each of the two extended linkers is sufficiently long to span from the FAT domain to the kinase domain of mTOR and beyond to join a shared dimer of the DEP domains. This places the linker's S299 closest to the kinase's catalytic site, indicating that phosphorylation would start with it and successively upstream towards DEPTOR's degron. The CK1α kinase is reportedly responsible for the phosphorylation of the degron, and our docking analysis further reveals that CK1α contains sites to bind DEPTOR's pS286, pS287 and pT295, which may act as priming phosphates for the phosphorylation of the degron's S291. DEPTOR's linker can also be ubiquitylated by the UbcH5A-SCFβ-TrCP complex without its PDZ dissociating from mTOR according to the modelling. As the catalytic cleft of mTOR's kinase is restricted, interactions between the kinase's unstructured segment surrounding the cleft and DEPTOR's linker, which may involve S293 and S299, may be critical to controlling DEPTOR's access to the catalytic cleft and hence its phosphorylation by mTOR in a manner dependent on mTOR's activation.
Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.
Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.