An enzymatic platform for the asymmetric amination of primary, secondary and tertiary C(sp³)–H bonds

The ability to selectively functionalize ubiquitous C–H bonds streamlines the construction of complex molecular architectures from easily available precursors. Here we report enzyme catalysts derived from a cytochrome P450 that use a nitrene transfer mechanism for the enantioselective amination of primary, secondary and tertiary C(sp³)–H bonds. These fully genetically encoded enzymes are produced and function in bacteria, where they can be optimized by directed evolution for a broad spectrum of enantioselective C(sp³)–H amination reactions. These catalysts can aminate a variety of benzylic, allylic and aliphatic C–H bonds in excellent enantioselectivity with access to either antipode of product. Enantioselective amination of primary C(sp³)–H bonds in substrates that bear geminal dimethyl substituents furnished chiral amines that feature a quaternary stereocentre. Moreover, these enzymes enabled the enantioconvergent transformation of racemic substrates that possess a tertiary C(sp³)–H bond to afford products that bear a tetrasubstituted stereocentre, a process that has eluded small-molecule catalysts. Further engineering allowed for the enantioselective construction of methyl–ethyl stereocentres, which is notoriously challenging in asymmetric catalysis

Comparative Proteomic Analysis Provides Insight into the Key Proteins Involved in Cucumber (Cucumis sativus L.) Adventitious Root Emergence under Waterlogging Stress

Waterlogging is a common abiotic stress in both natural and agricultural systems, and it primarily affects plant growth by the slow oxygen diffusion in water. To sustain root function in the hypoxic environment, a key adaptation for waterlogging tolerant plants is the formation of adventitious roots (ARs). We found that cucumber waterlogging tolerant line Zaoer-N seedlings adapt to waterlogging stress by developing a larger number of ARs in hypocotyls, while almost no AR is generated in sensitive line Pepino. To understand the molecular mechanisms underlying AR emergence, the iTRAQ-based quantitative proteomics approach was employed to map the proteomes of hypocotyls cells of the Zaoer-N and Pepino under control and waterlogging conditions. A total of 5,508 proteins were identified and 146 were differentially regulated proteins (DRPs), of which 47 and 56 DRPs were specific to tolerant and sensitive line, respectively. In the waterlogged Zaoer-N hypocotyls, DRPs related to alcohol dehydrogenases (ADH), 1-aminocyclopropane-1-carboxylicacid oxidases, peroxidases, 60S ribosomal proteins, GSDL esterases/lipases, histone deacetylases and histone H5 and were strongly overrepresented to manage the energy crisis, promote ethylene release, minimize oxidative damage, mobilize storage lipids, and stimulate cell division, differentiation and growth. The evaluations of ethylene production, ADH activity, pyruvate decarboxylase (PDC) activity and ethanol production were in good agreement with the proteomic results. qRT-PCR analysis of the corresponding 146 genes further confirmed the accuracy of the observed protein abundance. These findings shed light on the mechanisms underlying waterlogging triggered cucumber ARs emergence, and provided valuable information for the breeding of cucumber with enhanced tolerance to waterlogging

Electronic properties and quantum transports in functionalized graphene Sierpinski carpet fractals

Recent progress in controllable functionalization of graphene surfaces enables the experimental realization of complex functionalized graphene nanostructures, such as Sierpinski carpet (SC) fractals. Herein, we model the SC fractals formed by hydrogen and fluorine functionalized patterns on graphene surfaces, namely, H-SC and F-SC, respectively. We then reveal their electronic properties and quantum transport features. From calculated results of the total and local density of state, we find that states in H-SC and F-SC have two characteristics: (i) low-energy states inside about |E/t|<1 (with t as the near-neighbor hopping) are localized inside free graphene regions due to the insulating properties of functionalized graphene regions, and (ii) high-energy states in F-SC have two special energy ranges including -2.3<E/t<-1.9 with localized holes only inside free graphene areas and 3<E/t<3.7 with localized electrons only inside fluorinated graphene areas. The two characteristics are further verified by the real-space distributions of normalized probability density. We analyze the fractal dimension of their quantum conductance spectra and find that conductance fluctuations in these structures follow the Hausdorff dimension. We calculate their optical conductivity and find that several additional conductivity peaks appear in high energy ranges due to the adsorbed H or F atoms

Integrating Social Circles and Network Representation Learning for Item Recommendation

With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates social circles and network representation learning for item recommendation. Specifically, we first infer domain-specific social trust circles based on original users’ rating information and social network information. Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships into domain-specific matrix factorization model to learn latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms

ALL IN ONE NETWORK FOR DRIVER ATTENTION MONITORING

Nowadays, driver drowsiness and driver distraction is considered as a major risk for fatal road accidents around the world. As a result, driver monitoring identifying is emerging as an essential function of automotive safety systems. Its basic features include head pose, gaze direction, yawning and eye state analysis. However, existing work has investigated algorithms to detect these tasks separately and was usually conducted under laboratory environments. To address this problem, we propose a multi-task learning CNN framework which simultaneously solve these tasks. The network is implemented by sharing common features and parameters of highly related tasks. Moreover, we propose Dual-Loss Block to decompose the pose estimation task into pose classification and coarse-to-fine regression and Objectcentric Aware Block to reduce orientation estimation errors. Thus, with such novel designs, our model not only achieves SOA results but also reduces the complexity of integrating into automotive safety systems. It runs at 10 fps on vehicle embedded systems which marks a momentous step for this field. More importantly, to facilitate other researchers, we publish our dataset FDUDrivers which contains 20000 images of 100 different drivers and covers various real driving environments. FDUDrivers might be the first comprehensive dataset regarding driver attention monitorin

NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition

Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs). Recently, there has been a growing interest in introducing physics constraints into training neural PDE solvers to reduce the use of costly data and improve the generalization ability. However, these physics constraints, based on certain finite dimensional approximations over the function space, must resolve the smallest scaled physics to ensure the accuracy and stability of the simulation, resulting in high computational costs from large input, output, and neural networks. This paper proposes a general acceleration methodology called NeuralStagger by spatially and temporally decomposing the original learning tasks into several coarser-resolution subtasks. We define a coarse-resolution neural solver for each subtask, which requires fewer computational resources, and jointly train them with the vanilla physics-constrained loss by simply arranging their outputs to reconstruct the original solution. Due to the perfect parallelism between them, the solution is achieved as fast as a coarse-resolution neural solver. In addition, the trained solvers bring the flexibility of simulating with multiple levels of resolution. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations, which leads to an additional $10\sim100\times$ speed-up. Moreover, the experiment also shows that the learned model could be well used for optimal control.Comment: ICML 2023 accepte

An enzymatic platform for the asymmetric amination of primary, secondary and tertiary C(sp³)–H bonds

The ability to selectively functionalize ubiquitous C–H bonds streamlines the construction of complex molecular architectures from easily available precursors. Here we report enzyme catalysts derived from a cytochrome P450 that use a nitrene transfer mechanism for the enantioselective amination of primary, secondary and tertiary C(sp³)–H bonds. These fully genetically encoded enzymes are produced and function in bacteria, where they can be optimized by directed evolution for a broad spectrum of enantioselective C(sp³)–H amination reactions. These catalysts can aminate a variety of benzylic, allylic and aliphatic C–H bonds in excellent enantioselectivity with access to either antipode of product. Enantioselective amination of primary C(sp³)–H bonds in substrates that bear geminal dimethyl substituents furnished chiral amines that feature a quaternary stereocentre. Moreover, these enzymes enabled the enantioconvergent transformation of racemic substrates that possess a tertiary C(sp³)–H bond to afford products that bear a tetrasubstituted stereocentre, a process that has eluded small-molecule catalysts. Further engineering allowed for the enantioselective construction of methyl–ethyl stereocentres, which is notoriously challenging in asymmetric catalysis