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"Xu, Cheng"
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Insertion of ammonia into alkenes to build aromatic N-heterocycles
Ammonia is one of the most abundant and simple nitrogen sources with decent stability and reactivity. Direct insertion of ammonia into a carbon skeleton is an ideal approach to building valuable
N
-heterocycles for extensive applications with unprecedented atom and step economy. Here, we show an electrochemical dehydrogenative method in which ammonia is inserted directly into alkenes to build aromatic
N
-heterocycles in a single step without the use of any external oxidant. This new approach achieves 98–99.2% atom economy with hydrogen as the only byproduct. Quinoline and pyridine with diverse substitutions are readily available. In this work, electrochemistry was used to drive a 4-electron oxidation reaction that is hard to access by other protocols, providing a parallel pathway to nitrene chemistry. In a tandem transformation that included three distinct electrochemical processes, the insertion of ammonia further showcased the tremendous potential to manipulate heterocycles derived from Hantzsch ester to diazine via pyridine and pyrrole.
Aromatic heterocycles containing nitrogen are ubiquitous in biologically relevant small molecules. Here the authors show an unorthodox methodology for their synthesis, by inserting the nitrogen atom into a carbon ring, with ammonia in electrochemical conditions.
Journal Article
Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques
Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection.
Journal Article
Shinjuku incident
\"A simple Chinese immigrant wages a perilous war against one of the most powerful criminal organizations on the planet in this sprawling action drama directed by Derek Yee (One Night in Mongkok) and starring Jackie Chan, Daniel Wu, and Masaya Kato. In Japan, foreign migrants are shunned by mainstream society. Taunted by the yakuza, they live in constant fear of being discovered and repatriated. Into this world ventures Steelhead (Chan), a humble tractor repairman who has traveled to Tokyo in search of his missing girlfriend, Xiu Xiu (Xu Jinglei), who vanished into thin air shortly after arriving in the city. After locating his brother Joe (Daniel Wu), Steelhead discovers that Xiu Xiu has adapted a Japanese identity and married up-and-coming yakuza chief Eguchi (Masaya Kato), with whom he forms an uneasy alliance. Over time, Steelhead earns the respect of his fellow Chinese immigrants by establishing a place for them to gather. But the dark side still beckons to Steelhead, because after helping Eguchi dispense with a powerful rival, he is granted full control of Shinjuku's most popular nightspots. Resistant to the allure of the criminal lifestyle, Steelhead discovers a new love and opens a tractor-repair business just outside the city. When Eguchi begins using Steelhead's former compatriots as pawns to front the yakuza's drug trade, the vengeful immigrant returns to the city determined to exact justice, even if it means destroying the future of the woman he once loved\"--Allmovie.com, viewed October 18, 2018.
DVD
Cardiovascular disease in type 2 diabetes mellitus: progress toward personalized management
Cardiovascular diseases (CVDs) are the main cause of death among patients with type 2 diabetes mellitus (T2DM), particularly in low- and middle-income countries. To effectively prevent the development of CVDs in T2DM, considerable effort has been made to explore novel preventive approaches, individualized glycemic control and cardiovascular risk management (strict blood pressure and lipid control), together with recently developed glucose-lowering agents and lipid-lowering drugs. This review mainly addresses the important issues affecting the choice of antidiabetic agents and lipid, blood pressure and antiplatelet treatments considering the cardiovascular status of the patient. Finally, we also discuss the changes in therapy principles underlying CVDs in T2DM.
Journal Article
Aptamer-drug conjugate: targeted delivery of doxorubicin in a HER3 aptamer-functionalized liposomal delivery system reduces cardiotoxicity
The toxic side effects of doxorubicin (DOX) have limited its use in chemotherapy. Neither liposomal DOX nor pegylated liposomal DOX are able to completely resolve this issue. This is a proof-of-concept study testing aptamer-drug conjugate (ApDC) targeted delivery systems for chemotherapeutic drugs.
Aptamer library targeting human epidermal growth factor receptor 3 (HER3) was screened and affinity was determined by enzyme-linked immunosorbent assay. Specificity was tested in MCF-7
, BT474
, and 293T
cells using flow cytometry and confocal microscopy. We further developed a HER3 aptamer-functionalized liposome encapsulating DOX and the efficiency of this ApDC was detected by cellular uptake analysis and cell viability assay. In MCF-7 tumor-bearing mice, tumor targeting evaluation, efficacy, toxicity and preliminary pharmocokinetic study was performed.
The candidate #13 aptamer had highest affinity (Kd =98±9.7 nM) and specificity. ApDC effectively reduces the half maximal inhibitory concentration of DOX compared with lipsome-DOX and free DOX. In vivo imaging and preliminary distribution studies showed that actively targeted nanoparticles, such as Apt-Lip-DOX molecules, could facilitate the delivery of DOX into tumors in MCF-7-bearing mice. This targeted chemotherapy caused greater tumor suppression than other groups and alleviated side effects such as weight loss, low survival rate, and organ (heart and liver) injury demonstrated by H&E staining.
The results indicate that targeted chemotherapy using the aptamer-drug conjugate format could provide better tolerability and efficacy compared with non-targeted delivery in relatively low-dose toxic drugs.
Journal Article
A Driver’s Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model
Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing process using drivers’ physiology measurement data and vehicle dynamic data. All the data used in the proposed model were obtained by portable sensors with the capability of recording data in the actual driving process. A hidden Markov model (HMM) was proposed to link driving risk with drivers’ physiology information and vehicle dynamic data. The two-factor indicators were established to evaluate the performances of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk. The standard deviation of normal to normal R–R intervals of the heart rate (SDNN), fixation duration, saccade range, and average speed were then selected as the input of the HMM. The HMM was trained and tested using field-observed data collected in Xi’an City. The proposed model using the data from the physiology measurement sensor can identify dangerous driving state from normal driving state and predict the transition probability between these two states. The results match the perceptions of the tested drivers with an accuracy rate of 90.67%. The proposed model can be used to develop proactive crash prevention strategies.
Journal Article
Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction
Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS
2
/MoS
2
hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS
2
/MoS
2
phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems.
Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing. Here the authors, develop a neuro-inspired optical sensor based on NbS
2
/MoS
2
films that can operate with monolithically integrated functions of static image enhancement and dynamic trajectory registration.
Journal Article
Comparative analysis of cell lineage differentiation during hepatogenesis in humans and mice at the single-cell transcriptome level
During embryogenesis, the liver is the site of hepatogenesis and hematopoiesis and contains many cell lineages derived from the endoderm and mesoderm. However, the characteristics and developmental programs of many of these cell lineages remain unclear, especially in humans. Here, we performed single-cell RNA sequencing of whole human and mouse fetal livers throughout development. We identified four cell lineage families of endoderm-derived, erythroid, non-erythroid hematopoietic, and mesoderm-derived non-hematopoietic cells, and defined the developmental pathways of the major cell lineage families. In both humans and mice, we identified novel markers of hepatic lineages and an ID3
+
subpopulation of hepatoblasts as well as verified that hepatoblast differentiation follows the “default-directed” model. Additionally, we found that human but not mouse fetal hepatocytes display heterogeneity associated with expression of metabolism-related genes. We described the developmental process of erythroid progenitor cells during human and mouse hematopoiesis. Moreover, despite the general conservation of cell differentiation programs between species, we observed different cell lineage compositions during hematopoiesis in the human and mouse fetal livers. Taken together, these results reveal the dynamic cell landscape of fetal liver development and illustrate the similarities and differences in liver development between species, providing an extensive resource for inducing various liver cell lineages in vitro.
Journal Article