In the last two decades, network science has blossomed and influenced various fields, such as statistical physics, computer science, biology and sociology, from the perspective of the heterogeneous ...interaction patterns of components composing the complex systems. As a paradigm for random and semi-random connectivity, percolation model plays a key role in the development of network science and its applications. On the one hand, the concepts and analytical methods, such as the emergence of the giant cluster, the finite-size scaling, and the mean-field method, which are intimately related to the percolation theory, are employed to quantify and solve some core problems of networks. On the other hand, the insights into the percolation theory also facilitate the understanding of networked systems, such as robustness, epidemic spreading, vital node identification, and community detection. Meanwhile, network science also brings some new issues to the percolation theory itself, such as percolation of strong heterogeneous systems, topological transition of networks beyond pairwise interactions, and emergence of a giant cluster with mutual connections. So far, the percolation theory has already percolated into the researches of structure analysis and dynamic modeling in network science. Understanding the percolation theory should help the study of many fields in network science, including the still opening questions in the frontiers of networks, such as networks beyond pairwise interactions, temporal networks, and network of networks. The intention of this paper is to offer an overview of these applications, as well as the basic theory of percolation transition on network systems.
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•Integrating aptamer with CRISPR-Cas12a enables SARS-CoV-2 antigen detection.•RCA-DNA architecture is a promising Cas12a-based electrochemical reporter.•RCA-DNA reporter enhances the ...electrochemical signal over normal ssDNA reporter.•Nucleocapsid antigen was sensitively and selectively detected in a label-free format.
Serological antigen testing has emerged as an important diagnostic paradigm in COVID-19, but often suffers from potential cross-reactivity. To address this limitation, we herein report a label-free electrochemical aptamer-based sensor for the detection of SARS-CoV-2 antigen by integrating aptamer-based specific recognition with CRISPR-Cas12a-mediated signal amplification. The sensing principle is based on the competitive binding of antigen and the preassembled Cas12a-crRNA complex to the antigen-specific aptamer, resulting in a change in the collateral cleavage activity of Cas12a. To further generate an electrochemical signal, a DNA architecture was fabricated by in situ rolling circle amplification on a gold electrode, which serves as a novel substrate for Cas12a. Upon Cas12a-based collateral DNA cleavage, the DNA architecture was degraded, leading to a significant decrease in impedance that can be measured spectroscopically. Using SARS-CoV-2 nucleocapsid antigen as the model, the proposed CRISPR-Cas12a-based electrochemical sensor (CRISPR-E) showed excellent analytical performance for the quantitative detection of nucleocapsid antigen. Since in vitro selection can obtain aptamers selective for many SARS-CoV-2 antigens, the proposed strategy can expand this powerful CRISPR-E system significantly for quantitative monitoring of a wide range of COVID-19 biomarkers.
We describe an efficient, bubble-free nanoscale motor consisting of a copper–platinum (Cu–Pt) segmented rod that operates as a nanobattery in dilute aqueous Br2 or I2 solutions. The motion of the rod ...is powered by self-electrophoresis caused by redox reactions occurring on the two different metal segments. Asymmetric ratchet-shaped pure copper nanorods were also found to rotate and tumble in aqueous Br2 solution because of the ion gradient arising from asymmetric dissolution of copper.
A novel strategy for the N‐arylation of NH‐sulfoximines has been developed by merging nickel catalysis and electrochemistry (in an undivided cell), thereby providing a practical method for the ...construction of sulfoximine derivatives. Paired electrolysis is employed in this protocol, so a sacrificial anode is not required. Owing to the mild reaction conditions, excellent functional group tolerance and yield are achieved. A preliminary mechanistic study indicates that the anodic oxidation of a NiII species is crucial to promote the reductive elimination of a C−N bond from the resulting NiIII species at room temperature.
A novel strategy for the N‐arylation of NH‐sulfoximines has been developed by merging nickel catalysis and electrochemistry (in an undivided cell), thereby providing a practical method for the synthesis of sulfoximine derivatives. Paired electrolysis is employed in this protocol, so a sacrificial anode is not required.
The urbanization in China is “incomplete” and the migration of non-hukou migrants is circular, wherein rural migrants often keep their rural land in the home village as a social safety net. The ...informal housing market is one of the main housing providers for migrant workers. Existing studies see informal housing as the migrants’ passive choice under the discriminatory hukou system, while underplaying the migrants’ familial multi-site tenure strategies between village homes and city places. As suggested by New Economics of Labor Migration (NELM), attachment to a place of origin (such as keeping hometown lands), while choosing informal housing at the destination, is a familial utility maximization strategy that can control risks when migrating between locations. Informal housing areas, therefore, become a trans-local rural-urban gradient and semi-urban landscape. We use the 2017 Migrant Dynamics Monitoring Survey data and the binary logistic regression to examine (a) whether hometown landowning is a significant predictor of the migrants’ choosing of a temporary stay in informal settlements in urban destinations, and (b) which kind of hometown land arrangement (farmland or homestead holding or both of them) is the strongest indicator of the higher probability of staying in informal settlements in urban destinations? The data analysis reveals that homestead in hometown is a more prominent pulling factor than farmland to “glue” rural migrants together within an integrated rural land “insurance regime” between the migrant-sending and -receiving places. The land-use and informal housing governance (including urban village demolition) ignore the trans-local nature of the migratory networks and semi-urbanizing dynamics. The traditional analysis of the rural-urban gradient with many landscapes should consider the functional and tenurial linkage between the locations at different points along with the complex migration activities.
In the electronics industry, the efficient recovery and capture of sulfur hexafluoride (SF6) from SF6/N2 mixtures is of great importance. Herein, three metal–organic frameworks with fine‐tuning pore ...structures, Cu(peba)2, Ni(pba)2, and Ni(ina)2, were designed for SF6 capture. Among them, Ni(ina)2 has perfect pore sizes (6 Å) that are comparable to the kinetic diameter of sulfur hexafluoride (5.2 Å), affording the benchmark binding affinity for SF6 gas. Ni(ina)2 exhibits the highest SF6/N2 selectivity (375.1 at 298 K and 1 bar) and ultra‐high SF6 uptake capacity (53.5 cm3 g−1 at 298 K and 0.1 bar) at ambient conditions. The remarkable separation performance of Ni(ina)2 was verified by dynamic breakthrough experiments. Theoretical calculations and the SF6‐loaded single‐crystal structure provided critical insight into the adsorption/separation mechanism. This porous coordination network has the potential to be used in industrial applications.
The metal–organic framework (MOF) Ni(ina)2 has pore sizes (6 Å) that are perfectly compatible with the kinetic diameter of sulfur hexafluoride (5.2 Å), affording the benchmark binding affinity for this potent greenhouse gas that is used in the electronics industry.
High-frequency (HF) signal injection methods have been widely employed in the sensorless control of interior permanent magnet synchronous motor (IPMSM) drives from zero to low speed. However, the ...control performance will be deteriorated severely when the motor subjects to the disturbance. To cope with this issue, an enhanced linear active disturbance rejection control (LADRC)-based HF pulse voltage signal injection method is proposed in this paper. The cascaded extended state observer is established to guarantee relatively timely and accurate estimation of the total disturbance. The linear control law is generated to compensate for the total disturbance in a feedforward way, which reduces the plant to approximate a canonical first-order integral. The tracking performance and the stability of the enhanced LADRC are analyzed theoretically. Maximum torque per ampere control is adopted to reduce the estimation burden by making full use of the reluctance torque, which helps to further improve the tracking performance of the enhanced LADRC. Finally, the validity of the proposed sensorless control scheme is verified on a 2.2-kW IPMSM drive platform.
Leveraging nationally representative survey data on 443,680 respondents from January to March 2021, this study examines the temporal, spatial, and sociodemographic variations in COVID-19 vaccine ...hesitancy in the U.S. Findings reveal multidimensional determinants of vaccination intentions involving confidence, complacency, and circumspection factors. Using descriptive analyses and multilevel mixed-effects regression models, we find persistent partisan divide across states and significant racial disparities, with Blacks more likely to develop vaccine hesitancy due to confidence and circumspection than Whites. Vaccine hesitancy among Blacks declines dramatically across time but varies little across states, indicating new directions to effectively address inequalities in vaccination. Results also show nuanced gender differences, with women more likely to develop hesitancy due to circumspection and men more likely to have hesitancy due to complacency. Moreover, we find important intersection between race, gender, and education that calls for efforts to adequately address the concerns of the most vulnerable and disadvantaged groups.
•COVID-19 vaccine hesitancy (VH) involves factors like confidence, circumspection, and complacency.•Blacks are more likely to develop VH due to confidence and circumspection than Whites.•VH among Blacks declines dramatically across time but varies little across states.•The effect of education on VH is very small among Hispanics.•Women are more likely to develop VH due to circumspection and men more likely to have VH due to complacency.
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Organic electrosynthesis has been widely used as an environmentally conscious alternative to conventional methods for redox reactions because it utilizes electric current as a ...traceless redox agent instead of chemical redox agents. Indirect electrolysis employing a redox catalyst has received tremendous attention, since it provides various advantages compared to direct electrolysis. With indirect electrolysis, overpotential of electron transfer can be avoided, which is inherently milder, thus wide functional group tolerance can be achieved. Additionally, chemoselectivity, regioselectivity, and stereoselectivity can be tuned by the redox catalysts used in indirect electrolysis. Furthermore, electrode passivation can be avoided by preventing the formation of polymer films on the electrode surface. Common redox catalysts include N-oxyl radicals, hypervalent iodine species, halides, amines, benzoquinones (such as DDQ and tetrachlorobenzoquinone), and transition metals. In recent years, great progress has been made in the field of indirect organic electrosynthesis using transition metals as redox catalysts for reaction classes including C–H functionalization, radical cyclization, and cross-coupling of aryl halides-each owing to the diverse reactivity and accessible oxidation states of transition metals. Although various reviews of organic electrosynthesis are available, there is a lack of articles that focus on recent research progress in the area of indirect electrolysis using transition metals, which is the impetus for this review.
Breast cancer is associated with the highest morbidity rates for cancer diagnoses in the world and has become a major public health issue. Early diagnosis can increase the chance of successful ...treatment and survival. However, it is a very challenging and time-consuming task that relies on the experience of pathologists. The automatic diagnosis of breast cancer by analyzing histopathological images plays a significant role for patients and their prognosis. However, traditional feature extraction methods can only extract some low-level features of images, and prior knowledge is necessary to select useful features, which can be greatly affected by humans. Deep learning techniques can extract high-level abstract features from images automatically. Therefore, we introduce it to analyze histopathological images of breast cancer via supervised and unsupervised deep convolutional neural networks. First, we adapted Inception_V3 and Inception_ResNet_V2 architectures to the binary and multi-class issues of breast cancer histopathological image classification by utilizing transfer learning techniques. Then, to overcome the influence from the imbalanced histopathological images in subclasses, we balanced the subclasses with Ductal Carcinoma as the baseline by turning images up and down, right and left, and rotating them counterclockwise by 90 and 180 degrees. Our experimental results of the supervised histopathological image classification of breast cancer and the comparison to the results from other studies demonstrate that Inception_V3 and Inception_ResNet_V2 based histopathological image classification of breast cancer is superior to the existing methods. Furthermore, these findings show that Inception_ResNet_V2 network is the best deep learning architecture so far for diagnosing breast cancers by analyzing histopathological images. Therefore, we used Inception_ResNet_V2 to extract features from breast cancer histopathological images to perform unsupervised analysis of the images. We also constructed a new autoencoder network to transform the features extracted by Inception_ResNet_V2 to a low dimensional space to do clustering analysis of the images. The experimental results demonstrate that using our proposed autoencoder network results in better clustering results than those based on features extracted only by Inception_ResNet_V2 network. All of our experimental results demonstrate that Inception_ResNet_V2 network based deep transfer learning provides a new means of performing analysis of histopathological images of breast cancer.
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