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Background Thyroid eye disease (TED) is the most common orbital pathology that occurs in up to 50% of patients with Graves’ disease. Herein, we aimed at discovering the possible hub genes and pathways involved in TED based on bioinformatical approaches. Results The GSE105149 and GSE58331 datasets were downloaded from the Gene Expression Omnibus (GEO) database and merged for identifying TED-associated modules by weighted gene coexpression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. EdgeR was run to screen differentially expressed genes (DEGs). Transcription factor (TF), microRNA (miR) and drug prediction analyses were performed using ToppGene suite. Function enrichment analysis was used to investigate the biological function of genes. Protein–protein interaction (PPI) analysis was performed based on the intersection between the list of genes obtained by WGCNA, lmQCM and DEGs, and hub genes were identified using the MCODE plugin. Based on the overlap of 497 genes retrieved from the different approaches, a robust TED coexpression network was constructed and 11 genes (ATP6V1A, PTGES3, PSMD12, PSMA4, METAP2, DNAJA1, PSMA1, UBQLN1, CCT2, VBP1 and NAA50) were identified as hub genes. Key TFs regulating genes in the TED-associated coexpression network, including NFRKB, ZNF711, ZNF407 and MORC2, and miRs including hsa-miR-144, hsa-miR-3662, hsa-miR-12136 and hsa-miR-3646, were identified. Genes in the coexpression network were enriched in the biological processes including proteasomal protein catabolic process and proteasome-mediated ubiquitin-dependent protein catabolic process and the pathways of endocytosis and ubiquitin-mediated proteolysis. Drugs perturbing genes in the coexpression network were also predicted and included enzyme inhibitors, chlorodiphenyl and finasteride. Conclusions For the first time, TED-associated coexpression network was constructed and key genes and their functions, as well as TFs, miRs and drugs, were predicted. The results of the present work may be relevant in the treatment and diagnosis of TED and may boost molecular studies regarding TED.

Low blood glucose levels and high urea nitrogen levels affect patient prognosis, but few studies have investigated whether the blood urea nitrogen to glucose (BGR) ratio predicts the risk of death.This retrospective research examined the connection between the BGR and 365-day mortality in patients with chronic kidney disease (CKD) stages 1–4 admitted to an intensive care unit (ICU). The study utilized data from 6,380 patients in the Medical Information Mart for Intensive Care IV version 2.2 (MIMIC-IV v2.2), taking into account confounding factors such as demographics, vital signs, laboratory indicators, and comorbidities. The study employed both univariate and multivariate Cox regression analyses stratified by BGR quartiles. Additionally, restricted cubic spline regression and inflection point analysis were used to explore the linear relationship between BGR and 365-day mortality, while Kaplan-Meier curve analysis was used to observe mortality changes under different BGR stratifications. Subgroup and mediating effect analyses were performed to evaluate the robustness of BGR’s effect on 365-day mortality. The study found a cumulative 365-day mortality rate of 34.2% among CKD stages 1–4 patients, with a 2.43-fold increase in the risk of death associated with BGR and at least a 44% increase in the risk of death for each unit increase in BGR ( P  = 0.022). A significant nonlinear relationship was identified, showing a stepwise change in the risk of death with a marked increase in the slope of the curve for BGR values below 0.52 and above 0.9 ( P  < 0.001). Subgroup analyses indicated interactions between BGR and factors such as age, sepsis, first-day antibiotic use, and cerebrovascular disease ( P  < 0.05). In conclusion, this study confirms that BGR is a significant and stable predictor of 1-year mortality risk in patients with CKD stages 1–4. Interventions aimed at timely adjustment, correction of metabolic imbalances, reduction of inflammation, and management of BGR levels are beneficial for reducing mortality in this patient population.

Urban agglomerations are becoming an increasingly important factor in advancing regional development and reshaping a new pattern of regional competition. However, few studies are focused on the impact of expanding urban agglomerations on reducing carbon emissions and its possible mechanism. Based on 285 city-level panel data from 2006 to 2017, this paper uses a staggered Difference-in-Differences (DID) model to explore the reduction effect and its possible mechanism of sustainable regional development policy, characterized by urban agglomeration expansion policy in the Yangtze River Delta, on carbon emissions with policy shocks in 2010 and 2013. The results are as follows: (1) The urban agglomeration expansion policy shows a significant marginal contribution to the reduction of carbon emissions, especially for the later joined (new) cities, and the reduction effect is particularly significant in the first and third years after the expansion, indicating that there are significant short-term and long-term reduction effects of the expansion policy. (2) The heterogeneities of reduction effect among three provinces are significant. Zhejiang Province enjoys the largest proportion carbon emission reduction effect, followed by Anhui and Jiangsu provinces. To be specific, urban agglomeration expansion in Zhejiang Province reduced carbon emissions and carbon emissions intensity in the overall, incumbent cities and new cities, while it only increased the total carbon emissions of the incumbent cities in Jiangsu province. (3) The heterogeneities of reduction effect brought by 2010 and 2013 are also significant. The urban agglomeration expansion policy in 2010 reduced carbon emissions on the whole cities and the incumbent cities with later joined cities excluded, while it had a significant reduction effect on the total, incumbent cities, and the new cities in 2013. (4) There are two possible mechanisms of this reduction effect. One is the strengthening of economic ties and enhanced environmental synergy between governments, called the market integration mechanism, which only has a significant effect on carbon emission reduction in the incumbent cities. Another is through the upgrade of the structure of regional industries, which has a significant effect in both the incumbent and new cities. These findings suggest that when formulating urban agglomerations polices, governments must take into account the carbon emissions effect, and advance the upgrading of industrial structure in the urban agglomeration.

Efficient and valuable strategies provided by large amount of available data are urgently needed for a sustainable electricity system that includes smart grid technologies and very complex power system situations. Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open-sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self-developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real-system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision-making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.

Road link speed is one of the important indicators for traffic states. In order to incorporate the spatiotemporal dynamics and correlation characteristics of road links into speed prediction, this paper proposes a method based on LDA and GCN. First, we construct a trajectory dataset from map-matched GPS location data of taxis. Then, we use the LDA algorithm to extract the semantic function vectors of urban zones and quantify the spatial dynamic characteristics of road links based on taxi trajectories. Finally, we add semantic function vectors to the dataset and train a graph convolutional network to learn the spatial and temporal dependencies of road links. The learned model is used to predict the future speed of road links. The proposed method is compared with six baseline models on the same dataset generated by GPS equipped on taxis in Shenzhen, China, and the results show that our method has better prediction performance when semantic zoning information is added. Both composite and single-valued semantic zoning information can improve the performance of graph convolutional networks by 6.46% and 8.35%, respectively, while the baseline machine learning models work only for single-valued semantic zoning information on the experimental dataset.

Exploring the spatiotemporal characteristics of park visitors and the “push and pull” factors that shape this mobility is critical to designing and managing urban parks to meet the demands of rapid urbanization. In this paper, 56 parks in Shenzhen were studied in 2019. First, cell phone signaling data were used to extract information on visitors’ departure locations and destination parks. Second, the bivariate Moran’s I and bivariate local Moran’s I (BiLISA) methods were used to identify the statistical correlation between the factors of the built environment and the park recreation trips. Finally, linear regression models were constructed to quantify the factors influencing the attractiveness of the park. Our study showed the following: (1) Recreation visitors at large parks varied significantly among population subgroups. Compared with younger adults, teenagers and older adults traveled lower distances and made fewer trips, and in particular, older adults of different genders differed significantly in park participation. (2) Recreational trips in large parks were related to the functional layout of the built environment around their residence. In areas with rich urban functions (e.g., southern Shenzhen), trips to large parks for leisure are more aggregated. (3) The findings reinforce the evidence that remote sensing data for urban vegetation can be an effective factor in characterizing park attractiveness, but the explanatory power of different vegetation data varies widely. Our study integrated the complementary human activity and remote sensing data to provide a more comprehensive understanding of urban park use and preferences. This will be important for future park planning.

The semi-transparency property of smoke integrates it highly with the background contextual information in the image, which results in great visual differences in different areas. In addition, the limited annotation of smoke images from real forest scenarios brings more challenges for model training. In this paper, we design a semi-supervised learning strategy, named smoke-aware consistency (SAC), to maintain pixel and context perceptual consistency in different backgrounds. Furthermore, we propose a smoke detection strategy with triple classification assistance for smoke and smoke-like object discrimination. Finally, we simplified the LFNet fire-smoke detection network to LFNet-v2, due to the proposed SAC and triple classification assistance that can perform the functions of some specific module. The extensive experiments validate that the proposed method significantly outperforms state-of-the-art object detection algorithms on wildfire smoke datasets and achieves satisfactory performance under challenging weather conditions.

The caseinolytic protease (Clp) system has recently emerged as a promising anti-tuberculosis target. The anti-cancer drug bortezomib exhibits potent anti-mycobacterial activity and binds to Mycobacterium tuberculosis ( Mtb ) Clp protease complexes. We determine cryo-EM structures of Mtb ClpP1P2, ClpC1P1P2 and ClpXP1P2 complexes bound to bortezomib in different conformations. Structural and biochemical data indicate that sub-stoichiometric binding by bortezomib to the protease active sites orthosterically activates the Mtb ClpP1P2 complex. Bortezomib activation of Mtb ClpP1P2 induces structural changes promoting the recruitment of the chaperone-unfoldases, Mtb ClpC1 or Mtb ClpX, facilitating holoenzyme formation. The structures of the Mtb ClpC1P1P2 holoenzyme indicate that Mtb ClpC1 motion, induced by ATP rebinding at the Mtb ClpC1 spiral seam, translocates the substrate. In the Mtb ClpXP1P2 holoenzyme structure, we identify a specialized substrate channel gating mechanism involving the Mtb ClpX pore-2 loop and Mtb ClpP2 N-terminal domains. Our results provide insights into the intricate regulation of the Mtb Clp system and suggest that bortezomib can disrupt this regulation by sub-stoichiometric binding at the Mtb Clp protease sites. The study reveals how bortezomib activates Mycobacterium tuberculosis Clp protease complexes. Cryo-EM structures reveal how sub-stoichiometric bortezomib binding triggers structural changes, mediates holoenzyme formation and disrupts Clp system regulation.

EGFR mutations have been identified in 20,000 reported NSCLC (non-small cell lung cancer) samples, and exon 19 deletions and L858R mutations at position 21, known as \"classical\" mutations, account for 85-90% of the total EGFR (epidermal growth factor receptor) mutations. In this paper, two series of EGFR kinase inhibitors were designed and synthesised. Among them, compound B1 showed an IC 50 value of 13 nM for kinase inhibitory activity against EGFR L858R/T790M and more than 76-fold selectivity for EGFR WT . Furthermore, in an in vitro anti-tumour activity test, compound B1 showed an effective anti-proliferation activity against H1975 cells with an IC 50 value of 0.087 μΜ. We also verified the mechanism of action of compound B1 as a selective inhibitor of EGFR L858R/T790M by cell migration assay and apoptosis assay.

This study is to empirically investigate the impact of urbanization through improving transportation infrastructure, reflected by introducing high-speed rail (HSR), on the regional development with the interaction of the socioeconomic factors reflected by industrial structure. An advanced quantitative tool named multi-period difference-in-differences (DID) method is applied. We find the impact of urbanization through HSR on regional development is mixed while interacting with industrial structure helps to explain heterogeneities of the impact. The more the industrial structure tends to be agricultural, the greater the negative impact of HSR opening on regional economic development; meanwhile, the more the industrial structure evolves to be service-oriented, the greater the positive impact of HSR. This study highlights the importance of the interaction between urban growth and socioeconomic factors, which would provides a reference for government and urban planners to make decisions on introducing HSR or improving transportation infrastructure.