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Reactions that lead to destruction of aromatic ring systems often require harsh conditions and, thus, take place with poor selectivities. Selective partial dearomatization of fused arenes is even more challenging but can be a strategic approach to creating versatile, complex polycyclic frameworks. Herein we describe a general organophotoredox approach for the chemo- and regioselective dearomatization of structurally diverse polycyclic aromatics, including quinolines, isoquinolines, quinoxalines, naphthalenes, anthracenes and phenanthrenes. The success of the method for chemoselective oxidative rupture of aromatic moieties relies on precise manipulation of the electronic nature of the fused polycyclic arenes. Mechanistic studies show that the addition of a hydrogen atom transfer (HAT) agent helps favor the dearomatization pathway over the more thermodynamically downhill aromatization pathway. We show that this strategy can be applied to rapid synthesis of biologically valued targets and late-stage skeletal remodeling en route to complex structures.

 

Recently, our groups have introduced the notion of optical parametric amplification based on non-Hermitian phase matching wherein the incorporation of loss can lead to gain in this nonlinear optical process. Previous simulation results using second-order nonlinear optical coupled-mode theory have demonstrated the potential of this technique as an alternative to the stringent phase-matching condition, which is often difficult to achieve in semiconductor platforms. Here we fortify this notion for the case of third-order nonlinearity by considering parametric amplification in silicon nanowires and illustrate the feasibility of these devices by employing rigorous finite-difference time-domain analysis using realistic materials and geometric parameters. Particularly, we demonstrate that by systematic control of the optical loss of the idler in a four-wave mixing process, we can achieve efficient unidirectional energy conversion from the pump to the signal component even when the typical phase-matching condition is violated. Importantly, our simulations show that a signal gain of$\sim <\#comment/>9\mathrm{d}\mathrm{B}$for a waveguide length of a few millimeters is possible over a large bandwidth of several hundreds of nanometers ($\sim <\#comment/>600\mathrm{n}\mathrm{m}$). This bandwidth is nearly 2 orders of magnitude larger than what can be achieved in the conventional silicon-photonics-based four-wave mixing process.

 

After eukaryotic fertilization, gamete nuclei migrate to fuse parental genomes in order to initiate development of the next generation. In most animals, microtubules control female and male pronuclear migration in the zygote. Flowering plants, on the other hand, have evolved actin filament (F-actin)-based sperm nuclear migration systems for karyogamy. Flowering plants have also evolved a unique double-fertilization process: two female gametophytic cells, the egg and central cells, are each fertilized by a sperm cell. The molecular and cellular mechanisms of how flowering plants utilize and control F-actin for double-fertilization events are largely unknown. Using confocal microscopy live-cell imaging with a combination of pharmacological and genetic approaches, we identified factors involved in F-actin dynamics and sperm nuclear migration inArabidopsis thaliana(Arabidopsis) andNicotiana tabacum(tobacco). We demonstrate that the F-actin regulator, SCAR2, but not the ARP2/3 protein complex, controls the coordinated active F-actin movement. These results imply that an ARP2/3-independent WAVE/SCAR-signaling pathway regulates F-actin dynamics in female gametophytic cells for fertilization. We also identify that the class XI myosin XI-G controls active F-actin movement in theArabidopsiscentral cell. XI-G is not a simple transporter, moving cargos along F-actin, but can generate forces that control the dynamic movement of F-actin for fertilization. Our results provide insights into the mechanisms that control gamete nuclear migration and reveal regulatory pathways for dynamic F-actin movement in flowering plants.

 

Road network is a basic component of intelligent transportation systems (ITS) in smart city. Informative representation of road networks is important as it is essential to a wide variety of ITS applications. In this paper, we propose a neural network representation learning model, namely Intersection of Road Network to Vector (IRN2Vec), to learn embeddings of road intersections that encode rich information in a road network by exploring geo-locality and intrinsic properties of intersections and moving behaviors of road users. In addition to model design, several issues unique to IRN2Vec, including data preparation for model training and various relationships among intersections, are examined. We evaluate the learned embeddings via extensive experiments on three real-world datasets using three downstream test cases, including prediction of traffic signals and crossings on intersections and travel time estimation. Experimental results show that the proposed IRN2Vec outperforms three existing methods, DeepWalk, LINE and Node2vec, in terms of F1-score in predicting traffic signals (22.21% to 23.84%) and crossings (8.65% to 11.65%), and mean absolute error (MAE) in travel time estimation (9.87% to 19.28%). 

Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which explores various intrinsic properties of road networks to learn embeddings of intersections and road segments in road networks. To implement the RLRN framework, we propose a new neural network model, namely Road Network to Vector (RN2Vec), to learn embeddings of intersections and road segments jointly by exploring geo-locality and homogeneity of them, topological structure of the road networks, and moving behaviors of road users. In addition to model design, issues involving data preparation for model training are examined. We evaluate the learned embeddings via extensive experiments on several real-world datasets using different downstream test cases, including node/edge classification and travel time estimation. Experimental results show that the proposed RN2Vec robustly outperforms existing methods, including (i) Feature-based methods : raw features and principal components analysis (PCA); (ii) Network embedding methods : DeepWalk, LINE, and Node2vec; and (iii) Features + Network structure-based methods : network embeddings and PCA, graph convolutional networks, and graph attention networks. RN2Vec significantly outperforms all of them in terms of F1-score in classifying traffic signals (11.96% to 16.86%) and crossings (11.36% to 16.67%) on intersections and in classifying avenue (10.56% to 15.43%) and street (11.54% to 16.07%) on road segments, as well as in terms of Mean Absolute Error in travel time estimation (17.01% to 23.58%).