24 research outputs found
Unexpected Reaction Pathway for Butyrylcholinesterase-Catalyzed Inactivation of Hunger Hormone Ghrelin
Extensive computational modeling and simulations have been carried out, in the present study, to uncover the fundamental reaction pathway for butyrylcholinesterase (BChE)-catalyzed hydrolysis of ghrelin, demonstrating that the acylation process of BChE-catalyzed hydrolysis of ghrelin follows an unprecedented single-step reaction pathway and the single-step acylation process is rate-determining. The free energy barrier (18.8 kcal/mol) calculated for the rate-determining step is reasonably close to the experimentally-derived free energy barrier (~19.4 kcal/mol), suggesting that the obtained mechanistic insights are reasonable. The single-step reaction pathway for the acylation is remarkably different from the well-known two-step acylation reaction pathway for numerous ester hydrolysis reactions catalyzed by a serine esterase. This is the first time demonstrating that a single-step reaction pathway is possible for an ester hydrolysis reaction catalyzed by a serine esterase and, therefore, one no longer can simply assume that the acylation process must follow the well-known two-step reaction pathway
Binarized Neural Architecture Search
Neural architecture search (NAS) can have a significant impact in computer
vision by automatically designing optimal neural network architectures for
various tasks. A variant, binarized neural architecture search (BNAS), with a
search space of binarized convolutions, can produce extremely compressed
models. Unfortunately, this area remains largely unexplored. BNAS is more
challenging than NAS due to the learning inefficiency caused by optimization
requirements and the huge architecture space. To address these issues, we
introduce channel sampling and operation space reduction into a differentiable
NAS to significantly reduce the cost of searching. This is accomplished through
a performance-based strategy used to abandon less potential operations. Two
optimization methods for binarized neural networks are used to validate the
effectiveness of our BNAS. Extensive experiments demonstrate that the proposed
BNAS achieves a performance comparable to NAS on both CIFAR and ImageNet
databases. An accuracy of vs. is achieved on the CIFAR-10
dataset, but with a significantly compressed model, and a faster search
than the state-of-the-art PC-DARTS
Functional Supramolecular Gels Based on the Hierarchical Assembly of Porphyrins and Phthalocyanines
Supramolecular gels containing porphyrins and phthalocyanines motifs are attracting increased interests in a wide range of research areas. Based on the supramolecular gels systems, porphyrin or phthalocyanines can form assemblies with plentiful nanostructures, dynamic, and stimuli-responsive properties. And these π-conjugated molecular building blocks also afford supramolecular gels with many new features, depending on their photochemical and electrochemical characteristics. As one of the most characteristic models, the supramolecular chirality of these soft matters was investigated. Notably, the application of supramolecular gels containing porphyrins and phthalocyanines has been developed in the field of catalysis, molecular sensing, biological imaging, drug delivery and photodynamic therapy. And some photoelectric devices were also fabricated depending on the gelation of porphyrins or phthalocyanines. This paper presents an overview of the progress achieved in this issue along with some perspectives for further advances
Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets
Understanding the spatiotemporal patterns and key determinants of rural homestay industry agglomeration is crucial for the well-planning and well-management of rural tourism during the process of rural revitalization in China. By employing multi geospatial datasets, this study investigated the long-term spatiotemporal patterns and their key determinants of homestay inns during the period 2004–2019 in Moganshan, a well-known rural tourism destination in Zhejiang Province, China. The kernel density estimation and spatial autocorrelation were integrated to identify the hotspots of rural homestay inns at a fine scale. The key determinants were further uncovered using multiple stepwise regression and logistic regression models. The result shows that the overall growth of homestay inns was slow at the early stage and has progressed rapidly since 2014, with 94.2% of homestay inns newly opened during the period 2014–2019. The first hotspot was located in Moganshan National Park and then spread to the surrounding villages. Three hotspot zones have emerged, including the northern hotspot zone (Sihe-Xiantan), central hotspot zone (Houwu-Park-Liaoyuan), and southern hotspot zone (Ziling-Laoling-Lanshukeng) by 2019. The modeling indicates that government policy was an essential determinant for the increase in homestay inns, followed by entrepreneurship and investment. The new homestay inns were more likely to occur in settlements close to scenic spots, river networks, and cultivated land. Abundant scenic spots and heterogeneous landscapes were also preferred when selecting sites and executing landscape design for homestay inns. Our empirical study has provided practical insights for policy makers, entrepreneurs, and planners for future sustainable homestay industry development
Fine-Scale Monitoring of Industrial Land and Its Intra-Structure Using Remote Sensing Images and POIs in the Hangzhou Bay Urban Agglomeration, China
China has experienced rapid industrial land growth over last three decades, which has brought about diverse social and environmental issues. Hence, it is extremely significant to monitor industrial land and intra-structure dynamics for industrial land management and industry transformation, but it is still a challenging task to effectively distinguish the internal structure of industrial land at a fine scale. In this study, we proposed a new framework for sensing the industrial land and intra-structure across the urban agglomeration around Hangzhou Bay (UAHB) during 2010–2015 through data on points of interest (POIs) and Google Earth (GE) images. The industrial intra-structure was identified via an analysis of industrial POI text information by employing natural language processing and four different machine learning algorithms, and the industrial parcels were photo-interpreted based on Google Earth. Moreover, the spatial pattern of the industrial land and intra-structure was characterized using kernel density estimation. The classification results showed that among the four models, the support vector machine (SVM) achieved the best predictive ability with an overall accuracy of 84.5%. It was found that the UAHB contains a huge amount of industrial land: the total area of industrial land rose from 112,766.9 ha in 2010 to 132,124.2 ha in 2015. Scores of industrial clusters have occurred in the urban-rural fringes and the coastal zone. The intra-structure was mostly traditional labor-intensive industry, and each city had formed own industrial characteristics. New industries such as the electronic information industry are highly encouraged to build in the core city of Hangzhou and the subcore city of Ningbo. Furthermore, the industrial renewal projects were also found particularly in the core area of each city in the UAHB. The integration of POIs and GE images enabled us to map industrial land use at high spatial resolution on a large scale. Our findings can provide a detailed industrial spatial layout and enable us to better understand the process of urban industrial dynamics, thus highlighting the implications for sustainable industrial land management and policy making at the urban-agglomeration level
Dual Distribution Alignment Network for Generalizable Person Re-Identification
Domain generalization (DG) offers a preferable real-world setting for Person Re-Identification (Re-ID), which trains a model using multiple source domain datasets and expects it to perform well in an unseen target domain without any model updating. Unfortunately, most DG approaches are designed explicitly for classification tasks, which fundamentally differs from the retrieval task Re-ID. Moreover, existing applications of DG in Re-ID cannot correctly handle the massive variation among Re-ID datasets. In this paper, we identify two fundamental challenges in DG for Person Re-ID: domain-wise variations and identity-wise similarities. To this end, we propose an end-to-end Dual Distribution Alignment Network (DDAN) to learn domain-invariant features with dual-level constraints: the domain-wise adversarial feature learning and the identity-wise similarity enhancement. These constraints effectively reduce the domain-shift among multiple source domains further while agreeing to real-world scenarios. We evaluate our method in a large-scale DG Re-ID benchmark and compare it with various cutting-edge DG approaches. Quantitative results show that DDAN achieves state-of-the-art performance
Sensitivity enhancement of graphene Hall sensors modified by single-molecule magnets at room temperatureâ€
Single-molecule magnets (SMMs) possess many unique magnetic properties and thus attract a wide range of attention. However, the applications of SMMs always need a strict atmosphere, i.e. low temperature, high vacuum and strong magnetic field. In this work, we report the preparation and characterization of sensitivity enhanced graphene Hall elements (GHEs) decorated with Tb-core SMMs. By comparing the magnetic sensing and electronic tests of the GHEs before and after the SMMs modifications, the sensitivity of the GHEs increases by 44.9% in voltage mode and 59.0% in current mode compared with pristine GHEs. The increase of sensitivity may result from the magnetic center introduction of SMMs at roomtemperature. Moreover, the magnetic molecules may affect the graphene field environment leading to a Hall signal change. In addition, the SMMs modified GHEs present excellent linearity, offset voltage, repeatability and stability in magnetic sensing. This study paves the way to apply SMMs into practical use at room temperature and atmospheric pressure without strong magnetic field excitations.National Natural Science Foundation of China [61390502]; National Major State Basic Research Development Program [2013CB933403]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB 12030100]SCI(E)ARTICLE41776-1781
Heteroatomic Se<sub><i>n</i></sub>S<sub>8–<i>n</i></sub> Molecules Confined in Nitrogen-Doped Mesoporous Carbons as Reversible Cathode Materials for High-Performance Lithium Batteries
A reversible
cathode material in an ether-based electrolyte for high-energy lithium
batteries was successfully fabricated by homogeneously confining heteroatomic
Se<sub><i>n</i></sub>S<sub>8–<i>n</i></sub> molecules into nitrogen-doped mesoporous carbons (NMCs) <i>via</i> a facile melt–impregnation route. The resultant
Se<sub><i>n</i></sub>S<sub>8–<i>n</i></sub>/NMC composites exhibit highly reversible electrochemical behavior,
where selenium sulfides are recovered through the reversible conversion
of polysulfoselenide intermediates during discharge–charge
cycles. The recovery of selenium sulfide molecules endows the Se<sub><i>n</i></sub>S<sub>8–<i>n</i></sub>/NMC
cathodes with the rational integration of S and Se cathodes. Density
functional theory calculations further reveal that heteroatomic selenium
sulfide molecules with higher polarizability could bind more strongly
with NMCs than homoatomic sulfur molecules, which provides more efficient
suppression of the shuttling phenomenon. Therefore, with further assistance
of mesopore confinement of the nitrogen-doped carbons, the Se<sub>2</sub>S<sub>6</sub>/NMC composite with an optimal Se/S mole ratio
of 2/6 presents excellent cycle stability with a high initial Coulombic
efficiency of 96.5% and a high reversible capacity of 883 mAh g<sup>–1</sup> after 100 cycles and 780 mAh g<sup>–1</sup> after 200 cycles at 250 mA g<sup>–1</sup>. These encouraging
results suggest that the heteroatomization of chalcogen (such as S,
Se, or Te) molecules in mesostructured carbon hosts is a promising
strategy in enhancing the electrochemical performances of chalcogen/carbon-based
cathodes for Li batteries