The role of microorganisms in petroleum degradation: Current development and prospects.

Petroleum hydrocarbon compounds are persistent organic pollutants, which can cause permanent damage to ecosystems due to their biomagnification. Bioremediation of oil is currently the main solution for the remediation of petroleum hydrocarbon pollutants in ecosystems. Despite several lab studies on oil microbial biodegradation efficiency, still there are various challenges for microorganisms to perform efficiently in outside environments. Herewith, investigating efficient biodegradation technologies through discovering new microorganisms, biodegradation pathways modification, and new bioremediations technologies are in great demand. The degradation of petroleum pollutants by microorganisms and the remediation of contaminated soils are achieved through their key enzymes and metabolic pathways. Although, several challenges hinder the effective biodegradation processes such as the toxic environment, long chains and versatility of petroleum hydrocarbons and the existence of the full metabolism pathways in a single microorganism. There are several developed oil biodegradation strategies by microorganisms such as synthetic biology, biofilm, recombinant technology and microbial consortia. Herewith, the application of multi-omics technology to discover oil-contaminated environments microbial communities, synthetic biology, microbial consortia, and other technologies would help improve the efficiency of microbial remediation.

Human Parsing with Contextualized Convolutional Neural Network.

In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, semantic edge context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network. Given an input human image, Co-CNN produces the pixelwise categorization in an end-to-end way. First, the cross-layer context is captured by our basic local-to-global-to-local structure, which hierarchically combines the global semantic information and the local fine details across different convolutional layers. Second, the global image-level label prediction is used as an auxiliary objective in the intermediate layer of the Co-CNN, and its outputs are further used for guiding the feature learning in subsequent convolutional layers to leverage the global image-level context. Third, semantic edge context is further incorporated into Co-CNN, where the high-level semantic boundaries are leveraged to guide pixel-wise labeling. Finally, to further utilize the local super-pixel contexts, the within-super-pixel smoothing and cross-super-pixel neighbourhood voting are formulated as natural sub-components of the Co-CNN to achieve the local label consistency in both training and testing process. Comprehensive evaluations on two public datasets well demonstrate the significant superiority of our Co-CNN over other state-of-the-arts for human parsing. In particular, the F-1 score on the large dataset [1] reaches 81.72 percent by Co-CNN, significantly higher than 62.81 percent and 64.38 percent by the state-of-the-art algorithms, M-CNN [2] and ATR [1], respectively. By utilizing our newly collected large dataset for training, our Co-CNN can achieve 85.36 percent in F-1 score.