The multi-domain non-structural protein 3 (Nsp3) is the largest protein encoded by the coronavirus (CoV) genome, with an average molecular mass of about 200 kD. Nsp3 is an essential component of the ...replication/transcription complex. It comprises various domains, the organization of which differs between CoV genera, due to duplication or absence of some domains. However, eight domains of Nsp3 exist in all known CoVs: the ubiquitin-like domain 1 (Ubl1), the Glu-rich acidic domain (also called “hypervariable region”), a macrodomain (also named “X domain”), the ubiquitin-like domain 2 (Ubl2), the papain-like protease 2 (PL2pro), the Nsp3 ectodomain (3Ecto, also called “zinc-finger domain”), as well as the domains Y1 and CoV-Y of unknown functions. In addition, the two transmembrane regions, TM1 and TM2, exist in all CoVs. The three-dimensional structures of domains in the N-terminal two thirds of Nsp3 have been investigated by X-ray crystallography and/or nuclear magnetic resonance (NMR) spectroscopy since the outbreaks of Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) in 2003 as well as Middle-East Respiratory Syndrome coronavirus (MERS-CoV) in 2012. In this review, the structures and functions of these domains of Nsp3 are discussed in depth.
•Nonstructural protein 3 (∼200 kD) is a multifunctional protein comprising up to 16 different domains and regions.•Nsp3 binds to viral RNA, nucleocapsid protein, as well as other viral proteins, and participates in polyprotein processing.•The papain-like protease of Nsp3 is an established target for new antivirals.•Through its de-ADP-ribosylating, de-ubiquitinating, and de-ISGylating activities, Nsp3 counteracts host innate immunity.•Structural data are available for the N-terminal two thirds of Nsp3, but domains in the remainder are poorly characterized.
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Inflammatory signals from immunological cells may cause damage to intestinal epithelial cells (IECs), resulting in intestinal inflammation and tissue impairment. Interferon‐γ‐inducible protein 16 ...(IFI16) was reported to be involved in the pathogenesis of Behçet's syndrome (BS). This study aimed to investigate how inflammatory cytokines released by immunological cells and IFI16 participate in the pathogenesis of intestinal BS. RNA sequencing and real‐time quantitative PCR (qPCR) showed that the positive regulation of tumor necrosis factor‐α (TNF‐α) production in peripheral blood mononuclear cells (PBMCs) of intestinal BS patients may be related to the upregulation of polo like kinase 1 (PLK1) in PBMCs (P = 0.012). The plasma TNF‐α protein level in intestinal BS was significantly higher than in healthy controls (HCs; P = 0.009). PBMCs of intestinal BS patients and HCs were co‐cultured with human normal IECs (NCM460) to explore the interaction between immunological cells and IECs. Using IFI16 knockdown, PBMC‐NCM460 co‐culture, TNF‐α neutralizing monoclonal antibody (mAb), stimulator of interferon genes (STING) agonist 2′3′‐cGAMP, and the PLK1 inhibitor SBE 13 HCL, we found that PLK1 promotes the secretion of TNF‐α from PBMCs of intestinal BS patients, which causes overexpression of IFI16 and induces apoptosis of IECs via the STING–TBK1 pathway. The expressions of IFI16, TNF‐α, cleaved caspase 3, phosphorylated STING (pSTING) and phosphorylated tank binding kinase 1 (pTBK1) in the intestinal ulcer tissue of BS patients were significantly higher than that of HCs (all P < 0.05). PLK1 in PBMCs of intestinal BS patients increased TNF‐α secretion, inducing IEC apoptosis via activation of the IFI16–STING–TBK1 pathway. PLK1 and the IFI16–STING–TBK1 pathway may be new therapeutic targets for intestinal BS.
Model summarizing interactions between peripheral blood mononuclear cells (PBMCs) of intestinal Behçet's syndrome (BS) and intestinal epithelial cells (IECs). High expression of polo like kinase 1 (PLK1) promoted tumor necrosis factor‐α (TNF‐α) production and secretion from intestinal BS PBMCs that furthers the increase of interferon‐γ‐inducible protein 16 (IFI16) in IECs. IFI16 then induces IEC apoptosis via activation of the STING–TBK1 pathway.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to ...complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks.
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•Literatures on simulation-based building envelope optimization were reviewed.•Optimization algorithms, objectives and popular tools were compared and discussed.•Single-objective optimization was the ...major measure while energy was the top concern.•Limitation in comfort evaluation and occupant behavior requires further attention.•Comprehensive building envelope design involves energy efficiency and comfort issue.
Green building design is presently among the hottest research topics in the world. Maintaining a comfortable indoor environment with minimum energy consumption is a challenging task that attracts the attention of experts around the world. With the recent advances in building performance simulation tools, it is now possible to predict and assess building performance at the design stage. Simulation-based optimization of building design is a potential application that connects building performance simulation with optimization algorithms. In this paper, numerous studies on the optimization of building envelope design were assembled and reviewed. Popular optimization algorithms were compared and discussed. Targeted objectives were collected and summarized. Based on the statistical results, the limitations in this research area were identified, and some potential breakthroughs were suggested.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
China is planning to introduce emission trading scheme (ETS) to decrease CO2 emission. As low carbon energy (LCE) will play a pivotal role in reducing CO2 emissions, our paper is to assess the extent ...and the conditions under which a carbon ETS can deliver LCE investment in China. We chose wind technology as a case study and a real-option based model was built to explore the impact of a number of variables and design features on investment decisions, e.g. carbon and electricity price, carbon market risk, carbon price floor and ceiling and on-grid ratio. We compute critical values of these variables and features and explore trade-offs among them. According to our work, a carbon ETS has a significant effect on wind power plant investment although it cannot support investment in wind power on its own. Carbon price stabilization mechanisms such as carbon price floor can significantly improve the effect of carbon ETS but the critical floor to support investment is still much higher than the carbon price in China pilot ETSs. Our results show that other policy measures will be needed to promote low-carbon energy development in China.
•The impact of Chinese emission trading scheme on low carbon energy investment is assessed.•A real-option based investment decision model under uncertainty is built and employed.•Key variables and features of ETS influencing wind power investment are explored.•Chinese carbon ETS cannot support low carbon energy investment on its own.•Other policy measures complementing ETS are still needed and should be coordinated.
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Solar radiation through glazing area is one major source of the space cooling load in subtropical cooling-dominant climates. Application of energy-efficient glazing system can significantly reduce ...the energy consumption of air-conditioning systems in summer, thus has become a hot research topic. In this paper, a super-insulating glazing system was studied, which was formed by two layers of conventional single clear glass panes and a layer of silica aerogel filled in between. Several glazing samples were prepared. The thermal and optical parameters were measured. An annual HVAC (heating, ventilation and air conditioning) system energy analysis was also conducted based on the space cooling load simulation. The result indicated that in humid subtropical climates like Hong Kong, the application of silica aerogel glazing system can reduce the annual space cooling load by around 4% in a typical commercial building. With respect to the envelope heat gain, the reduction could be around 60%. It was also found that the silica aerogel glazing system performed better if the internal heat source in a building took a small proportion in the total space cooling load.
•We evaluated the application of silica aerogel glazing in cooling-dominant climates.•A silica aerogel-filled window system sample is constructed and measured.•HVAC energy consumption was reduced by 4% with silica aerogel glazing application.•More than 60% cooling load caused by building envelope heat gain can be reduced.•It is the first time silica aerogel window is considered in cooling dominant climates.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
GeTe and (Bi,Sb)2Te3 are two representative thermoelectric (TE) materials showing maximum performance at middle and low temperature, respectively. In order to achieve higher performance over the ...whole temperature range, their segmented one‐leg TE modules are designed and fabricated by one‐step spark plasma sintering (SPS). To search for contact and connect layers, the diffusion behavior of Fe, Ni, Cu, and Ti metal layers in GeTe is studied systematically. The results show that Ti with a similar linear expansivity (10.80 × 10−6 K−1) to GeTe, has low contact resistance (3 µΩ cm2) and thin diffusion layer (0.4 µm), and thus is an effective metallization layer for GeTe. The geometric structure of the GeTe/(Bi,Sb)2Te3 segmented one‐leg TE module and the ratio of GeTe to (Bi,Sb)2Te3 are determined by finite element simulation method. When the GeTe height ratio is 0.66, its theoretical maximum conversion efficiency (ηmax) can reach 15.9% without considering the thermal radiation and thermal/electrical contact resistance. The fabricated GeTe/(Bi,Sb)2Te3 segmented one‐leg TE module showed a ηmax up to 9.5% with a power density ≈ 7.45 mW mm−2, which are relatively high but lower than theoretical predictions, indicating that developing segmented TE modules is an effective approach to enhance TE conversion efficiency.
GeTe/(Bi,Sb)2Te3 segmented one‐leg thermoelectric modules are successfully designed and fabricated. Its maximum efficiency reaches 9.5 % under the adiabatic boundary condition of cold side heat flow via Mini‐PEM, indicating a good application prospect.
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Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can ...provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.
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Much attention has been paid to imidoyl radical-involved reactions in recent years. As a divergent reactive intermediate, imidoyl radicals are used for the synthesis of functionalized heterocycles, ...nitriles, imines, amines,
etc
. This review is intended to highlight some recent progress in the past decade.
This review discusses the recent progress in imidoyl radical-involved reactions including the synthesis of functionalized heterocycles, nitriles, imines, amines,
etc
.
Summary Background Current staging methods do not accurately predict the risk of disease recurrence and benefit of adjuvant chemotherapy for patients who have had surgery for stage II colon cancer. ...We postulated that expression patterns of multiple microRNAs (miRNAs) could, if combined into a single model, improve postoperative risk stratification and prediction of chemotherapy benefit for these patients. Method Using miRNA microarrays, we analysed 40 paired stage II colon cancer tumours and adjacent normal mucosa tissues, and identified 35 miRNAs that were differentially expressed between tumours and normal tissue. Using paraffin-embedded specimens from a further 138 patients with stage II colon cancer, we confirmed differential expression of these miRNAs using qRT-PCR. We then built a six-miRNA-based classifier using the LASSO Cox regression model, based on the association between the expression of every miRNA and the duration of individual patients' disease-free survival. We validated the prognostic and predictive accuracy of this classifier in both the internal testing group of 138 patients, and an external independent group of 460 patients. Findings Using the LASSO model, we built a classifier based on the six miRNAs: miR-21-5p, miR-20a-5p, miR-103a-3p, miR-106b-5p, miR-143-5p, and miR-215. Using this tool, we were able to classify patients between those at high risk of disease progression (high-risk group), and those at low risk of disease progression (low-risk group). Disease-free survival was significantly different between these groups in every set of patients. In the initial training group of patients, 5-year disease-free survival was 89% (95% CI 77·3–94·4) for the low-risk group, and 60% (46·3–71·0) for the high-risk group (hazard ratio HR 4·24, 95% CI 2·13–8·47; p<0·0001). In the internal testing set of patients, 5-year disease-free survival was 85% (95% CI 74·3–91·8) for the low-risk group, and 57% (42·8–68·5) for the high-risk group (HR 3·63, 1·86–7·01; p<0·0001), and in the independent validation set of patients, was 85% (79·6–89·0) for the low-risk group and 54% (46·4–61·1) for the high-risk group (HR 3·70, 2·56–5·35; p<0·0001). The six-miRNA-based classifier was an independent prognostic factor for, and had better prognostic value than, clinicopathological risk factors and mismatch repair status. In an ad-hoc analysis, the patients in the high-risk group were found to have a favourable response to adjuvant chemotherapy (HR 1·69, 1·17–2·45; p=0·0054). We developed two nomograms for clinical use that integrated the six-miRNA-based classifier and four clinicopathological risk factors to predict which patients might benefit from adjuvant chemotherapy after surgery for stage II colon cancer. Conclusion Our six-miRNA-based classifier is a reliable prognostic and predictive tool for disease recurrence in patients with stage II colon cancer, and might be able to predict which patients benefit from adjuvant chemotherapy. It might facilitate patient counselling and individualise management of patients with this disease. Funding Natural Science Foundation of China.
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