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Among the current five Variants of Concern, infections caused by the SARS-CoV-2 B.1.617.2 (Delta) variant are often associated with the greatest severity. Despite recent advances on the molecular basis of elevated pathogenicity using recombinant proteins, architecture of intact Delta virions remains veiled. Moreover, molecular evidences for the detailed mechanism of S-mediated membrane fusion are missing. Here we reported the in situ structure and distribution of S on the authentic Delta variant, and discovered invagination in the distinctive Delta architecture. We also captured fusion snapshots from the virus-virus fusion events, provided structural evidences for Delta’s attenuated dependency on cellular factors for fusion activation, and proposed a model of S-mediated membrane fusion. Site-specific glycan analysis revealed increased oligomannose-type glycosylation of native Delta S over that of the Wuhan-Hu-1 S. Together, these results disclose distinctive factors of Delta being the most virulent SARS-CoV-2 variant.In BriefCryo-ET of intact SARS-CoV-2 Delta variant revealed its distinctive architecture and captured snapshots of its membrane fusion in action.

Monolayer α-phase GeS is promising for many novel applications due to its high carrier mobility and suitable band gap. Recently, the metal and nonmetal zigzag edges of monolayer α-phase GeS have been predicted to undergo universal ZZ(Ge-Tube)/ZZ(S-R) edge reconstruction. Therefore, studies on GeSNR should be reconsidered. In this paper, we study the quantum confinement effects on the electronic properties of edge reconstructed monolayer GeS nanoribbon (GeSNR) by using first-principles calculations. As width of the nanoribbon increases from 10 to 41 Å, the band gap keeps indirect and linearly decreases from 1.57 to 0.87 eV. Robust spatial separation of valence band maximum and conduction band minimum exist in reconstructed GeSNR with width larger than 19 Å. Moreover, high carrier mobility is expected in the reconstructed GeSNR. Our results suggest that reconstructed GeS nanoribbon is an important candidate for optoelectronics and photocatalytic.

The aim of this study was to investigate the association between retinol-binding protein 4 (RBP4) and diabetic retinopathy (DR) among patients with type 2 diabetes mellitus (T2DM). Databases PubMed, Embase, Web of Science, Chinese National Knowledge Infrastructure, VIP, and Wangfang were searched to July 30, 2019. The Newcastle–Ottawa Scale was applied to assess the quality of all identified studies, and those qualified were included in the meta-analysis. The Chi squared Q test and I2 statistics were conducted to evaluate heterogeneity. Standardized mean differences (SMD) and 95% confidence intervals (CI) among RBP4 within the DR and T2DM without retinopathy (DWR) groups were pooled using the random effects model depending on the heterogeneity. Subgroup analyses were conducted among the groups having different diabetes duration, detection methods, body mass index, and total cholesterol and triglyceride levels. The funnel plot was used to assess publication bias. Nineteen observational studies were included in our meta-analysis. RBP4 was significantly higher in both nonproliferative DR (SMD: 0.72, 95% CI 0.48–0.95, P

With the continuous integration of cold, heat, electricity and other energy systems and the market-oriented reform of energy transactions, the traditional power demand response can no longer meet the business needs of multi-energy coupling. Distribution network and heat pump energy storage coupled cooling and heating system is a combination of renewable energy utilization and energy storage technology, which is beneficial to improve the utilization rate of renewable energy. At the same time, by shifting peaks and filling valleys, the operating cost of the system can be reduced and the balanced dispatching of the power grid can be realized. In this paper, an electro-thermal complementary model of coupled heating and cooling system with energy storage of distribution network and heat pump is proposed, and through simulation analysis of an example, it is verified that the proposed method can effectively improve the flexibility of energy supply and operation economy of the system.

Facing the aggravation of air pollution and serious wind curtailment in North China, a new way to utilize excess wind power is presented, which is to use the excess nighttime wind from the surrounding areas to provide electric heating in Beijing at night. First, the mechanism of the nighttime electric heating price calculation and its environmental benefits are explained from both the supply side and the demand side. Second, considering the economic and environmental characteristics of electric heating, the electricity price-ceiling calculation model on the demand side, the electricity price-floor calculation model on the supply side, and the environmental benefit-analysis model are formed. Finally, the models are tested by practical data from Beijing, showing that the proposed models are scientific and practicable. The study shows several conclusions. (1) When no subsidy policy is carried out, the regenerative electric heater and the air-sourced heat pump are not economically justified to replace the rural-household coal-fired boiler and coal-burning boiler and the public coal-fired boiler. (2) With the existing subsidy policy, the regenerative electric heater is economically justified to replace the rural-household coal-fired boiler, self-owned coal-burning boiler, and public coal-fired boiler, and the air-sourced heat pump can be the substituted for the rural-household coal-fired boiler. Under different wind–thermal-power ratios, the feasible intervals for the electricity price are respectively [47.12, 48.62], [44.31, 48.62], and [41.51, 48.62]. (3) Compared with the traditional coal-fired heating, wind-sourced electric heating contributes fewer pollutant emissions and more environmental benefits.

Driven by the “third industrial revolution”, energy internet has become the core of modern economic and social development. It combines with internet, big data, artificial intelligence and other technologies to jointly promote intelligent, clean, efficient and sustainable development. This paper first briefly analyzes the background and significance of energy internet, and points out that compared with traditional energy network, energy internet has the advantages of electrification and integration. Then, this paper studies the framework and functional forms of the energy internet, and roughly divides the energy internet into energy production and consumption layer, energy transmission layer, integrated energy management big data platform, and application layer. At the same time, an effective method is proposed to combine the energy system with emerging technologies such as the internet, smart grid, cloud computing, and big data to solve the problem of low energy efficiency of the energy system. On this basis, the development trend of the energy internet is predicted, and the development focus of the energy internet field is proposed from different angles, which has certain guiding significance for the development of the energy internet.

With the development of energy and digital revolution, the concept of integrated energy service (IES) attracts many attentions due to its green, high efficient and low carbon characteristics. Applying digital platform to promote the usage of multi-source data and the construction of mechanism model will bring positive effects on integrated energy system planning, control and operation. However, the development of integrated energy service platform is still in its infancy, the strategy and the corresponding architecture design is needed. To deal with these issues, this paper firstly presents a comprehensive analysis about the present status of IES digital platforms, such as their types, features and key functionalities are deeply studied. Then the current problems include development route, main stream technology and business models are summarized and the follow future trends of IES platform is concluded. Finally, the key points in the development process and the architecture of IES platform is given.

Based on the multi-model ensemble average results of the CMIP5 program, we predict the changes of global terrestrial wind and solar energy resources from 2020 to 2030 under different future climate change scenarios. The results show that the multi-mode ensemble average results have high confidence in the simulation of global wind and solar energy resources. Under different climate scenarios (RCPs), the changes in global terrestrial wind and solar energy resources in the next 2020-2030 (relative to 1986-2005) will have significant regional differences. Among them, wind resources in the Americas, Africa and Australia increased, while European wind-rich areas decreased; those in Asia (e.g., Northwest China and Central Asia) increased in RCP2.6, but decreased in RCP4.5 and RCP8.5. Global terrestrial solar energy resources are increasing in different RCPs scenarios in the future, especially in European solar energy-rich areas. Wind energy and solar energy resources on the global land have obvious seasonal variation characteristics, and the seasonal variation rate varies greatly in different regions. The change trend and change range of wind energy and solar energy resources in different rich areas are different. There are some differences in the RCPs scenario. It shows the complexity of future changes in wind and solar energy resources in response to global climate change.

Obesity increases the risk of diabetes; however, not everyone who is obese develops diabetes. In this study, the dataset GSE54350 was downloaded from the Gene Expression Omnibus database (GEO), including obese diabetic and non-diabetic samples as control. Differentially expressed genes (DEGs) between obese diabetic and non-diabetic samples were selected using GEO2R plugin. Gene ontology (GO) enrichment and protein-protein interaction (PPI) analysis of these DEGs were performed using Metascape. The results showed 1073 DEGs, including 496 up-regulated genes and 577 down-regulated genes. These DEGs were enriched in biological pathways involved in hemostasis, regulation of organelle assembly, apoptotic signaling, myeloid leukocyte activation, apoptosis, etc. We revealed that Caspase 3 (CASP3) and tissue inhibitor of metalloproteinase 3 (TIMP3) might serve as marker genes and potential therapeutic targets for obesity-related diabetes, which deserves further investigations for validation.

In order to solve the problem of huge and messy data in the process of analyzing energy consumption structure in different regions, an energy consumption structure analysis method based on K-means clustering algorithm is proposed, and the elbow method and contour coefficient method are used to analyze the data in Qinghai Province. The consumption structure was analyzed and the algorithm was verified. The results show that the algorithm can efficiently and quickly perform data mining and clustering based on local economic and environmental characteristics, which greatly improve the convenience of energy consumption structure analysis.

Combined cooling, heating, and power (CCHP) microgrids are important means of solving the energy crisis and environmental problems. Multidimensional composite energy storage systems (CESSs) are vital to promoting the absorption of distributed renewable energy using CCHP microgrids and improving the level of energy cascade utilization. In this context, this paper proposes a multi-energy coupling structure that includes a multidimensional CESS with a compressed air energy storage (CAES) connected to a CCHP microgrid. Dividing design and operation causes some problems, such as low operating efficiency and difficult energy matching of CESSs. To solve the existing problems, an integrated design method is proposed that considers the capacity configuration of the equipment and the optimal operation of the system on a multi-timescale. The optimization result of the capacity configuration level is used as the constraint of the operational control level, and the equipment output plan of the operational control level is used as the optimized operation strategy and parameters of the system. The C-NSGA-II algorithm is adopted at the capacity configuration level and day-ahead scheduling level. Rolling optimization is solved using the PSO algorithm. The final result that satisfied the output design was obtained after several iterations. The average daily cost and CO2 emission reduction rate (CO2ERR) of capacity configuration levels are $2241 and 45.02%. The best CO2ERRs of day-ahead scheduling optimization levels are 39.9% and 45.9% in summer and winter, where the operating cost saving rate (OCSR) are 30.5% and 38.3% separately. Examples show that the integrated design method presented in this paper has significant advantages in enhancing energy-grade matching and improving the economy and environmental protection of the system.

Retina is a window reflecting the state of the general system and the condition of the eye. The traditional heavy-manual quantification of the morphological characteristics of retina is in need of improving. Therefore, we aim to automatically segment the retinal vessels and lesions and further analyze segmented results. Three public datasets including DRIVE, DIARETDB, IDRID were selected, and the images from them were preprocessed and augmented with a series of enhancements, random rotation and gamma transformation. Attention gate (AG) U-Net framework was used to segment retinal vessels, OD and exudates respectively. The performance of AG U-Net model was evaluated. Furthermore, the segmented results were analyzed with different shape descriptors using ImageJ. The geometric features such as width, length, area, circumference, and roundness were extracted and statistically analyzed in grade 2 and 3 DR images. The results approved the superiority of Attention-U-Net in retinal structure segmentation and the geometric features were successfully extracted and analyzed. In conclusion, the proposed AG U-Net empowered automatic segmentation and ImageJ based analytic framework are worthy of applications on retinal images, thus fostering the clinical science investigations.

With the exponential increase of the Internet information, it is more difficult to collect and distinguish the useful information online. Corpus plays an important role in information retrieval and recognition. This paper proposes a corpus construction method based on data mining and machine learning for the energy and power industry. Firstly, the information data of the power industry are obtained by web crawler technology. Then they are stored in a database in structured manner. In order to construct a useful power industry corpus, the Long Short-Term Memory - Conditional Random Fields (LSTM-CRF) model are adopted for word segmentation, the Inverse Document Frequency (IDF) feature and Accessor Variety feature are used for new word discovery. The final electricity corpus consists of words, paragraphs and chapters. Based on the information retrieval and knowledge map, the built electricity corpus is demonstrated that is benefit to energy and power research.

Traumatic optic neuropathy leads to bidirectional degeneration of retinal ganglion cells and axons and results in optic nerve scaring, which inhibits the regeneration of damaged axons. Compared with its glial counterpart, the fibrotic response causing nerve scar tissue is poorly permissive to axonal regeneration. Using collagen1α1-GFP reporter mice, we characterize the development of fibrotic scar formation following optic nerve crush injury. We observe that perivascular collagen1α1 cells constitute a major cellular component of the fibrotic scar. We demonstrate that extracellular molecules and monocytes are key factors contributing to the pathogenesis of optic nerve fibrotic scar formation, with a previously unrecognized encapsulation of this scar. We also characterize the distribution of collagen1α1 cells in the retina after optic nerve crush injury based on in vivo and whole-mount retinal imaging. Our results identify collagen1α1 cells as a major component of fibrotic scarring following ONC and are a potential molecular target for promoting axonal regeneration after optic nerve injury.

To propose and implement a crowdsourcing framework for retinal image annotations to improve the annotation efficiency. In this study, open-source Bluelight was taken as backbone of the front end for online manual retinal image annotation for image semantic annotation and report documents, and based on that intelligent annotation and classification with deep learning (DL) was supplemented. For DL modules, we trained Mask-RCNN model to explicitly label the area of optic disc and macula. Furthermore, we trained Inception V3 model to classify diabetic retinopathy (DR) and normal retina. Then, we used Flask as the backend serving DL models. Finally, the implementation of interoperable annotation reports documentation and retrieval were conducted based on Lucene. The crowdsourcing framework was specially designed for professional doctors and computer researchers who have the ability to annotate. It efficiently and quickly completed the annotation of the retinal image and the macular area, and at the same time classified DR. Under this Browser/Server architecture, the tool achieved good cross-platform performance. In particular, the framework could provide annotation report documents to facilitate the optimization of subsequent DL models. Such crowdsourcing framework and reports documentation for retina semantic annotation could improve the effect of annotation and classification and worth further improvement and clinical validation.

Accelerating the transformation of the energy consumption pattern in western China and promoting the development of clean energy are the main problems facing the energy consumption system. This paper bases on the characteristics of China’s western region the industrial structure, the energy consumption structure, and analyses the energy consumption model transformation trend of typical of the west, on this basis, the combination of time series analysis and ARIMA model is used to set up different typical energy consumption in the field of forecast and analysis, and put forward according to the results of the analysis of energy consumption model transformation.

Chinese word segmentation is one of the foundation and core tasks for Chinese natural language processing. Although some achievements have been made for Chinese word segmentation system in general domains, it is far away to meet practical requirements in energy domain. We focus on Chinese word segmentation standard and segmentation technology in the energy domain which consists of 13283 energy basic terms. This paper firstly proposes a conditional random field segmentation model. Then, the character features, character type features and conditional entropy features which influence the word segmentation performance are chose and described. Finally, the proposed model is tested on the dataset of the State Grid energy literature and compared with current word segmentation tools, such as the Harbin Institute of Technology's Language Technology Platform and the Tsinghua's THU Lexical Analyzer for Chinese language processing tools. The F1 value of the best result of the proposed model is 0.8319.

In this paper, we propose a novel object tracking method via semi-supervised metric learning and edge detection. Firstly, we construct the object appearance model by extracting sparse codes features on different layers to exploit local information and holistic information. To utilize unlabeled samples information, the semi-supervised metric learning is introduced to measure candidates. In addition, based on ranking results from the learned metric classifier, we adopt an edge detection to alleviate the error accumulation. Both the proposed tracker and several selected trackers are tested on some challenging videos, where the target objects undergo pose change, illumination and occlusion. The experimental results demonstrate that the better performance of the proposed tracker compared with state-of-the-art methods.

With the advent of the mobile Internet era, the dissemination and diffusion of information has become faster and faster, and the dissemination and generation of information has also increased exponentially. More and more information is generated and diffused in the Internet, and the subsequent problem is that the collection and determination of information increases with its complexity. Therefore, it is necessary to propose and apply a new method to complete the analysis and processing of the information on the Internet. This paper uses data mining technology and machine learning intelligent algorithm to obtain and classify the information data of the power industry on the Internet, so as to construct the power industry corpus.