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Metabolic engineering strategies for terpenoid production have mainly focused on bottlenecks in the supply of precursor molecules and cytotoxicity to terpenoids. In recent years, the strategies involving compartmentalization in eukaryotic cells has rapidly developed and have provided several advantages in the supply of precursors, cofactors and a suitable physiochemical environment for product storage. In this review, we provide a comprehensive analysis of organelle compartmentalization for terpenoid production, which can guide the rewiring of subcellular metabolism to make full use of precursors, reduce metabolite toxicity, as well as provide suitable storage capacity and environment. Additionally, the strategies that can enhance the efficiency of a relocated pathway by increasing the number and size of organelles, expanding the cell membrane and targeting metabolic pathways in several organelles are also discussed. Finally, the challenges and future perspectives of this approach for the terpenoid biosynthesis are also discussed.

Strobilation is a well-organized metamorphosis process in scyphozoan jellyfish, through which the benthic polyp develops into the pelagic medusa. So far, knowledge on the molecular mechanisms behind the drastic morphological and physiological changes remains limited. In the present study, the metabolic profiles in polyp and strobila stages of jellyfish Rhopilema esculentum were examined using ultra-performance liquid chromatography coupled to a mass spectrometer. A total of 3 071 metabolites with putative annotation were detected, of which 167 were identified as differential metabolites between the polyp and strobila. Among the metabolites, 31 significantly decreased and 136 significantly increased in abundance in the strobila. Thyroxin, one of the previously proposed strobilation inducer of Aurelia, was not detected in this study. The indole-containing compounds are known for triggering strobilation in Discomedusae. Two indole derivatives and an indole-isomer containing metabolite were detected among the most significantly increased metabolites, which may be potential endogenous molecules for strobilation initiation. The membrane-associated phospholipids and the inflammation and oxidative stress-related eicosanoids were found to have significant changes. Although the functions of these metabolites in strobilation are not yet completely known, they provide some clues to the induction of the process of strobilation and metabolic responses that take place during strobilation.

The main challenges of ReID is the intra-class variations caused by color deviation under different camera conditions. Simultaneously, we find that most of the existing adversarial metric attacks are realized by interfering with the color characteristics of the sample. Based on this observation, we first propose a local transformation attack (LTA) based on color variation. It uses more obvious color variation to randomly disturb the color of the retrieved image, rather than adding random noise. Experiments show that the performance of the proposed LTA method is better than the advanced attack methods. Furthermore, considering that the contour feature is the main factor of the robustness of adversarial training, and the color feature will directly affect the success rate of attack. Therefore, we further propose joint adversarial defense (JAD) method, which include proactive defense and passive defense. Proactive defense fuse multi-modality images to enhance the contour feature and color feature, and considers local homomorphic transformation to solve the over-fitting problem. Passive defense exploits the invariance of contour feature during image scaling to mitigate the adversarial disturbance on contour feature. Finally, a series of experimental results show that the proposed joint adversarial defense method is more competitive than a state-of-the-art methods., Comment: Accepted by CVPR2022 Workshops (https://openaccess.thecvf.com/content/CVPR2022W/HCIS/html/Gong_Person_Re-Identification_Method_Based_on_Color_Attack_and_Joint_Defence_CVPRW_2022_paper.html)

The archives of Jinhua 金華 City, Zhejiang 浙江 Province, contain a Japanese Jinhua Gyerim Association articles and register (金華雞林會會則及名簿 Jinhua jilinhui huize ji mingbu). This Korean townsmen associati...

One of the challenges of computer vision is that it needs to adapt to color deviations in changeable environments. Therefore, minimizing the adverse effects of color deviation on the prediction is one of the main goals of vision task. Current solutions focus on using generative models to augment training data to enhance the invariance of input variation. However, such methods often introduce new noise, which limits the gain from generated data. To this end, this paper proposes a strategy eliminate deviation with deviation, which is named Random Color Dropout (RCD). Our hypothesis is that if there are color deviation between the query image and the gallery image, the retrieval results of some examples will be better after ignoring the color information. Specifically, this strategy balances the weights between color features and color-independent features in the neural network by dropouting partial color information in the training data, so as to overcome the effect of color devitaion. The proposed RCD can be combined with various existing ReID models without changing the learning strategy, and can be applied to other computer vision fields, such as object detection. Experiments on several ReID baselines and three common large-scale datasets such as Market1501, DukeMTMC, and MSMT17 have verified the effectiveness of this method. Experiments on Cross-domain tests have shown that this strategy is significant eliminating the domain gap. Furthermore, in order to understand the working mechanism of RCD, we analyzed the effectiveness of this strategy from the perspective of classification, which reveals that it may be better to utilize many instead of all of color information in visual tasks with strong domain variations.

Most existing concept-drifting data streams classification approaches assume that the true label of the instance in the data streams can be accessed right after it is classified and utilize it to detect concept drift as well as adjust the current model. It is impractical in real-world applications because manual labelling of data is both costly and time consuming. We apply a novel technique to overcome the problem mentioned above. The proposed method takes advantage of the model clusters generated by the fast KNNModel algorithm to classify the instances in the data streams. With the unlabeled testing instances, the arrival of a novel class and the drift in the underlying concept of a class are detected when the number of instances which are not covered by any model clusters increases rapidly at a certain significance level than that of before. The domain experts are asked to label a few instances to adjust the current model if and only if concept drift happens. Experimental results on both synthetic and real data streams show that compared with the traditional classification algorithms, our method acquires the comparable or better efficacy and efficiency using only a small amount of labelled data.

Nanofluids have enhanced heat transfer capacity compared to conventional fluids. In the present paper, the effects of the thermophysical properties and thermal dispersion on the convective heat transfer coefficients of nanofluids have been investigated. The analytical results show that the effective thermal conductivities of a nanofluid is enhanced with the addition of nanopaticles into the base fluid. The collisions between nanoparticles, fluid molecules, and the wall of flow-tube are intensified, leading to enhanced mixing agitation and turbulence. Improved thermophysical properties and thermal dispersion result in the convective heat transfer coefficient enhancement of nanofluids.