Pass-back chain extension expands multimodular assembly line biosynthesis.

Modular nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) enzymatic assembly lines are large and dynamic protein machines that generally effect a linear sequence of catalytic cycles. Here, we report the heterologous reconstitution and comprehensive characterization of two hybrid NRPS-PKS assembly lines that defy many standard rules of assembly line biosynthesis to generate a large combinatorial library of cyclic lipodepsipeptide protease inhibitors called thalassospiramides. We generate a series of precise domain-inactivating mutations in thalassospiramide assembly lines, and present evidence for an unprecedented biosynthetic model that invokes intermodule substrate activation and tailoring, module skipping and pass-back chain extension, whereby the ability to pass the growing chain back to a preceding module is flexible and substrate driven. Expanding bidirectional intermodule domain interactions could represent a viable mechanism for generating chemical diversity without increasing the size of biosynthetic assembly lines and challenges our understanding of the potential elasticity of multimodular megaenzymes

Impact Dynamics of Droplet Containing Particle Suspensions on Deep Liquid Pool

Droplet impact on surfaces is ubiquitous in many natural and industrial processes. While the impact dynamics of droplets composed of simple fluids have been studied extensively, droplets containing particles are less explored, but are more application relevant. The non-Newtonian behavior of particle suspension introduces new physics affecting the impact dynamics. Here, we investigated the impact dynamics of droplets containing cornstarch particles on a deep water pool and systematically characterized the impact outcomes with various Weber number and particle volume fractions. Distinctive phenomena compared to Newtonian droplet impact have been observed. A regime map of the impact outcomes is unveiled and the transition boundaries are quantified with scaling analysis. Rheology of the suspension is found to play a pivotal role in giving rise to distinct impact outcomes. The results lay the foundation for further characterization of the dynamics of suspension droplet impacting on liquid surfaces and can be translated to other suspension fluids

Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD

Electrowetting Using a Microfluidic Kelvin Water Dropper

The Kelvin water dropper is an electrostatic generator that can generate high voltage electricity through water dripping. A conventional Kelvin water dropper converts the gravitational potential energy of water into electricity. Due to its low current output, Kelvin water droppers can only be used in limited cases that demand high voltage. In the present study, microfluidic Kelvin water droppers (MKWDs) were built in house to demonstrate a low-cost but accurately controlled miniature device for high voltage generation. The performance of the MKWDs was characterized using different channel diameters and flow rates. The best performed MKWD was then used to conduct experiments of the electrowetting of liquid on dielectric surfaces. Electrowetting is a process that has been widely used in manipulating the wetting properties of a surface using an external electric field. Usually electrowetting requires an expensive DC power supply that outputs high voltage. However, in this research, it was demonstrated that electrowetting can be conducted by simply using an MKWD. Additionally, an analytic model was developed to simulate the electrowetting process. Finally, the model’s ability to well predict the liquid deformation during electrowetting using MKWDs was validated

The thickness of the ventral medial prefrontal cortex predicts the prior-entry effect for allocentric representation in near space

Neuropsychological studies have demonstrated that the preferential processing of near-space and egocentric representation is associated with the self-prioritization effect (SPE). However, relatively little is known concerning whether the SPE is superior to the representation of egocentric frames or near-space processing in the interaction between spatial reference frames and spatial domains. The present study adopted the variant of the shape-label matching task (i.e., color-label) to establish an SPE, combined with a spatial reference frame judgment task, to examine how the SPE leads to preferential processing of near-space or egocentric representations. Surface-based morphometry analysis was also adopted to extract the cortical thickness of the ventral medial prefrontal cortex (vmPFC) to examine whether it could predict differences in the SPE at the behavioral level. The results showed a significant SPE, manifested as the response of self-associated color being faster than that of stranger-associated color. Additionally, the SPE showed a preference for near-space processing, followed by egocentric representation. More importantly, the thickness of the vmPFC could predict the difference in the SPE on reference frames, particularly in the left frontal pole cortex and bilateral rostral anterior cingulate cortex. These findings indicated that the SPE showed a prior entry effect for information at the spatial level relative to the reference frame level, providing evidence to support the structural significance of the self-processing region

Triplet Contrastive Learning for Unsupervised Vehicle Re-identification

Part feature learning is a critical technology for finegrained semantic understanding in vehicle re-identification. However, recent unsupervised re-identification works exhibit serious gradient collapse issues when directly modeling the part features and global features. To address this problem, in this paper, we propose a novel Triplet Contrastive Learning framework (TCL) which leverages cluster features to bridge the part features and global features. Specifically, TCL devises three memory banks to store the features according to their attributes and proposes a proxy contrastive loss (PCL) to make contrastive learning between adjacent memory banks, thus presenting the associations between the part and global features as a transition of the partcluster and cluster-global associations. Since the cluster memory bank deals with all the instance features, it can summarize them into a discriminative feature representation. To deeply exploit the instance information, TCL proposes two additional loss functions. For the inter-class instance, a hybrid contrastive loss (HCL) re-defines the sample correlations by approaching the positive cluster features and leaving the all negative instance features. For the intra-class instances, a weighted regularization cluster contrastive loss (WRCCL) refines the pseudo labels by penalizing the mislabeled images according to the instance similarity. Extensive experiments show that TCL outperforms many state-of-the-art unsupervised vehicle re-identification approaches. The code will be available at https://github.com/muzishen/TCL.Comment: Code: https://github.com/muzishen/TC

Theoretical framework for informal groups of construction workers : a grounded theory study

The current construction industry, which has a high accident rate and declining labor productivity, urgently requires efficient and practical management policies. Research has shown that social norms within informal groups have considerable influence on construction workers, while studies on informal groups of construction workers (IGCWs) have been scarce. Current theories of informal groups have not been analyzed in combination with construction industry characteristics. The purpose of this paper is to develop a theoretical framework of IGCWs, including definitions, types, characteristics, causes, and functions. First, on the basis of existing theoretical research of informal groups, two semistructured interviews were designed to collect data from managers and workers. Then, a qualitative approach using grounded theory with NVivo software was employed to code the interview information, and 25 subcategories were obtained: 5 types, 10 characteristics, 4 causes, and 6 functions of IGCWs. Eventually, a conceptual model was established to explain the definition of IGCWs according to the interview data and subcategories identified. This study not only contributes to improving behavioral science theory, especially group behavior theory and human relations theory, but also contributes to constructing an informal group theory of the construction industry. In practical terms, the targeted identification of IGCWs is useful for managers in taking measures to more effectively manage construction workers