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The stimulator of interferon genes (STING) pathway serves as a crucial nexus in inflammatory responses and cell death. Despite its role in Mitochondria-Endoplasmic Reticulum Contact (MERC), the mechanistic contributions to inflammatory outcomes remain poorly understood. In clinical acute respiratory distress syndrome (ARDS) models of COVID-19 infection and animal models of LPS-induced acute lung injury (ALI), the STING pathway is closely associated with the pyroptosis pathway. The macrophage STING-N-GSDMD-mtDNA positive feedback loop, upon LPS challenge, induces inflammatory responses and pyroptosis. The GSDMD inhibitor disulfiram (DSF) specifically abrogates the N-terminal portion of GSDMD anchored to the mitochondrial membrane. Furthermore, macrophage STING mediates the direct interaction between Drp1 and N-GSDMD on mitochondrial membrane by regulating mitochondrial calcium, linking mitochondrial fission to the induction of inflammatory responses. Targeting STING-mediated mitochondrial homeostasis, both genetically and pharmacologically, may play a protective role in preventing and treating sepsis-induced acute lung injury. Overall, our study posits that STING deficiency mitigates the cooperative interaction between N-GSDMD and Drp1 in mediating mitochondrial permeabilization and rupture following LPS challenge, paving the way for further investigations into inflammation and pyroptosis. Graphical abstract

COVID-19 has spread globally. Epidemiological susceptibility to COVID-19 has been reported in patients with cancer. We aimed to systematically characterise clinical features and determine risk factors of COVID-19 disease severity for patients with cancer and COVID-19. In this multicentre, retrospective, cohort study, we included all adult patients (aged ≥18 years) with any type of malignant solid tumours and haematological malignancy who were admitted to nine hospitals in Wuhan, China, with laboratory-confirmed COVID-19 between Jan 13 and March 18, 2020. Enrolled patients were statistically matched (2:1) with patients admitted with COVID-19 who did not have cancer with propensity score on the basis of age, sex, and comorbidities. Demographic characteristics, laboratory examinations, illness severity, and clinical interventions were compared between patients with COVID-19 with or without cancer as well as between patients with cancer with non-severe or severe COVID-19. COVID-19 disease severity was defined on admission on the basis of the WHO guidelines. Univariable and multivariable logistic regression, adjusted for age, sex, comorbidities, cancer type, tumour stage, and antitumour treatments, were used to explore risk factors associated with COVID-19 disease severity. This study was registered in the Chinese Clinical Trial Register, ChiCTR2000030807. Between Jan 13 and March 18, 2020, 13 077 patients with COVID-19 were admitted to the nine hospitals in Wuhan and 232 patients with cancer and 519 statistically matched patients without cancer were enrolled. Median follow-up was 29 days (IQR 22–38) in patients with cancer and 27 days (20–35) in patients without cancer. Patients with cancer were more likely to have severe COVID-19 than patients without cancer (148 [64%] of 232 vs 166 [32%] of 519; odds ratio [OR] 3·61 [95% CI 2·59–5·04]; p<0·0001). Risk factors previously reported in patients without cancer, such as older age; elevated interleukin 6, procalcitonin, and D-dimer; and reduced lymphocytes were validated in patients with cancer. We also identified advanced tumour stage (OR 2·60, 95% CI 1·05–6·43; p=0·039), elevated tumour necrosis factor α (1·22, 1·01–1·47; p=0·037), elevated N-terminal pro-B-type natriuretic peptide (1·65, 1·03–2·78; p=0·032), reduced CD4+ T cells (0·84, 0·71–0·98; p=0·031), and reduced albumin–globulin ratio (0·12, 0·02–0·77; p=0·024) as risk factors of COVID-19 severity in patients with cancer. Patients with cancer and COVID-19 were more likely to deteriorate into severe illness than those without cancer. The risk factors identified here could be helpful for early clinical surveillance of disease progression in patients with cancer who present with COVID-19. China National Natural Science Foundation.

Annuals are an important component part of plant communities in arid and semiarid grassland ecosystems. Although it is well known that precipitation has a significant impact on productivity and species richness of community or perennials, nevertheless, due to lack of measurements, especially long-term experiment data, there is little information on how quantity and patterns of precipitation affect similar attributes of annuals. This study addresses this knowledge gap by analyzing how quantity and temporal patterns of precipitation affect aboveground biomass, interannual variation aboveground biomass, relative aboveground biomass, and species richness of annuals using a 29-year dataset from a dry steppe site at the Inner Mongolia Grassland Ecosystem Research Station. Results showed that aboveground biomass and relative aboveground biomass of annuals increased with increasing precipitation. The coefficient of variation in aboveground biomass of annuals decreased significantly with increasing annual and growing-season precipitation. Species richness of annuals increased significantly with increasing annual precipitation and growing-season precipitation. Overall, this study highlights the importance of precipitation for aboveground biomass and species richness of annuals.

Potato (Solanum tuberosum L.) is a globally important food crop, but its tetrasomic inheritance and diploid self-incompatibility have limited the discovery of potato genes and progress in breeding. Here, we developed an F2 segregating population consisting of 174 lines by crossing a self-compatible genome-homozygous diploid line (Y8, female parent) with a heterozygous diploid line (IVP101, male parent), followed by selfing. Using whole-genome resequencing, we constructed a high-density genetic map containing 4464 recombinant bin markers with an average physical distance of 165.51 Kb. Phenotypic evaluation of 8 traits related to yield, tuber shape, and tuber eye number across three environments revealed significant parental differences and wide phenotypic variation within the F2 population. QTL (Quantitative trait loci) mapping using this genetic map and multi-environment phenotypic data identified 89 QTLs, including 7 previously reported QTLs/genes. In addition, 10 QTLs were stably detected across multiple seasons (stable QTLs). Further genetic effect analysis showed that favorable alleles of these stable QTLs significantly enhanced phenotypic values. Notably, two pleiotropic QTLs were identified on chromosomes 5 and 12; the major-effect QTL on chromosome 12 (qTY-12-6, qTS-12-3, and qTE-12-4) exhibited high phenotypic variance explained (PVE). Its favorable allele from Y8 significantly increased mean tuber weight, tuber number per plant, and promoted rounder tuber shape while reducing eye number, simultaneously improving yield and quality. Collectively, this study provides a reference for genetic mapping using homozygous and heterozygous diploid parents, and the identified QTLs offer valuable genetic resources for potato breeding and molecular mechanism research, enhancing our understanding of the genetic regulation of yield, tuber shape, and eye number in potato.

Using an emergy and system dynamics model, this paper proposes a dynamic evaluation method for the sustainable development of eco-industrial parks and conducts an empirical analysis of the Shenyang Economic and Technological Development Zone (SETDZ). Four SETDZ’s development scenarios were designed, including inertia, economic, environmental protection, and science and technology scenarios, and the sustainable development status of each scenario was simulated and dynamically evaluated. In this paper, emergy analysis and SD method are used to simulate the changes of system functional elements and emergy evaluation indexes of SETDZ. The results show that under the coordinated development of the economy and environment, the science and technology scenario based on high-tech investment is the best development strategy for SETDZ. The sustainability of the SETDZ greatly improved on the implementation of the circular economic model, and the sustainable development indicator of the science and technology scenario increased from 3.59E-02 (2014) to 8.16E-02 (2024). Furthermore, SETDZ could achieve the coordinated development of the economy and environment owing to the reasonable layout of industrial enterprises, integration of public resources, effective utilization and disposal of waste, establishment of an enterprise symbiosis system, development of cleaner production, and other measures.

Eco-friendly SnTe based thermoelectric materials are intensively studied recently as candidates to replace PbTe; yet the thermoelectric performance of SnTe is suppressed by its intrinsically high carrier concentration and high thermal conductivity. In this work, we confirm that the Ag and La co-doping can be applied to simultaneously enhance the power factor and reduce the thermal conductivity, contributing to a final promotion of figure of merit. On one hand, the carrier concentration and band offset between valence bands are concurrently reduced, promoting the power factor to a highest value of ∼2436 µW·m −1 ·K −2 at 873 K. On the other hand, lots of dislocations (∼3.16×10 7 mm −2 ) associated with impurity precipitates are generated, resulting in the decline of thermal conductivity to a minimum value of 1.87 W·m −1 ·K −1 at 873 K. As a result, a substantial thermoelectric performance enhancement up to zT ≈ 1.0 at 873 K is obtained for the sample Sn 0.94 Ag 0.09 La 0.05 Te, which is twice that of the pristine SnTe ( zT ≈ 0.49 at 873 K). This strategy of synergistic manipulation of electronic band and microstructures via introducing rare earth elements could be applied to other systems to improve thermoelectric performance.

The fungal microbiota is an important component of the human gut microbiome and may be linked to gastrointestinal disease. In this study, the fungal microbiota of biopsy samples from adenomas and adjacent tissues was characterized by deep sequencing. Ascomycota, Glomeromycota and Basidiomycota were identified as the dominant phyla in both adenomas and adjacent tissues from all subjects. Among the 60 genera identified, the opportunist pathogens Phoma and Candida represented an average of 45% of the fungal microbiota. When analyzed at the operational taxonomic unit (OTU) level, however, a decreased diversity in adenomas was observed and three OTUs differed significantly from the adjacent tissues. Principal Component Analysis (PCA) revealed that the core OTUs formed separate clusters for advanced and non-advanced adenomas for which the abundance of four OTUs differed significantly. Moreover, the size of adenomas and the disease stage were closely related to changes in the fungal microbiota in subjects with adenomas. This study characterized the fungal microbiota profile of subjects with adenomas and identified potential diagnostic biomarkers closely related to different stages of adenomas.

Accidents caused by vehicles changing lanes occur frequently on highways. Moreover, frequent lane changes can severely impact traffic flow during peak commuting hours and on busy roads. A novel framework based on a multi-relational graph convolutional network (MR-GCN) is herein proposed to address these challenges. First, a dynamic multilevel relational graph was designed to describe interactions between vehicles and road objects at different spatio-temporal granularities, with real-time updates to edge weights to enhance understanding of complex traffic scenarios. Second, an improved spatio-temporal interaction graph generation method was introduced, focusing on spatio-temporal variations and capturing complex interaction patterns to enhance prediction accuracy and adaptability. Finally, by integrating a dynamic multi-relational graph convolutional network (DMR-GCN) with dynamic scene sensing and interaction learning mechanisms, the framework enables real-time updates of complex vehicle relationships, thereby improving behavior prediction’s accuracy and real-time performance. Experimental validation on multiple benchmark datasets, including KITTI, Apollo, and Indian, showed that our algorithmic framework achieves significant performance improvements in vehicle behavior prediction tasks, with Map, Recall, and F1 scores reaching 90%, 88%, and 89%, respectively, outperforming existing algorithms. Additionally, the model achieved a Map of 91%, a Recall of 89%, and an F1 score of 90% under congested road conditions in a self-collected high-speed traffic scenario dataset, further demonstrating its robustness and adaptability in high-speed traffic conditions. These results show that the proposed model is highly practical and stable in real-world applications such as traffic control systems and self-driving vehicles, providing strong support for efficient vehicle behavior prediction.

Crizotinib carries an FDA hepatotoxicity warning, yet analysis of the FAERS database suggests that the severity of its hepatotoxicity risks, including progression to hepatitis and liver failure, might be underreported. However, the underlying mechanism remains poorly understood, and effective intervention strategies are lacking. Here, mRNA-sequencing analysis, along with KEGG and GO analyses, revealed that DEGs linked to Crizotinib-induced hepatotoxicity predominantly associate with the ferroptosis pathway which was identified as the principal mechanism behind Crizotinib-induced hepatocyte death. Furthermore, we found that ferroptosis inhibitors, namely Ferrostatin-1 and Deferoxamine mesylate, significantly reduced Crizotinib-induced hepatotoxicity and ferroptosis in both in vivo and in vitro settings. We have also discovered that overexpression of AAV8-mediated Nrf2 could mitigate Crizotinib-induced hepatotoxicity and ferroptosis in vivo by restoring the imbalance in glutathione metabolism, iron homeostasis, and lipid peroxidation. Additionally, both Stat1 deficiency and the Stat1 inhibitor NSC118218 were found to reduce Crizotinib-induced ferroptosis. Mechanistically, Crizotinib induces the phosphorylation of Stat1 at Ser727 but not Tyr701, promoting the transcriptional inhibition of Nrf2 expression after its entry into the nucleus to promote ferroptosis. Meanwhile, we found that MgIG and GA protected against hepatotoxicity to counteract ferroptosis without affecting or compromising the anti-cancer activity of Crizotinib, with a mechanism potentially related to the Stat1/Nrf2 pathway. Overall, our findings identify that the phosphorylation activation of Stat1 Ser727, rather than Tyr701, promotes ferroptosis through transcriptional inhibition of Nrf2, and highlight MgIG and GA as potential therapeutic approaches to enhance the safety of Crizotinib-based cancer therapy. Highlights The primary mechanism of hepatocyte death induced by Crizotinib is through ferroptosis, and the utilization of ferroptosis inhibitors effectively reverses Crizotinib-induced hepatotoxicity. Crizotinib triggers the phosphorylation of Stat1 at Ser727 rather than Tyr701, which facilitates the transcriptional suppression of Nrf2 expression upon nuclear entry, promoting ferroptosis, and leading to liver toxicity. Both MgIG and GA demonstrate the ability to mitigate Crizotinib-induced hepatotoxicity without impacting its anti-tumor effectiveness.

In addition to traditional call centers, companies have increasingly started developing a new type of customer contact center over the Internet, rather than using phone calls, where customers communicate with agents online through a website. Apart from lining up for an agent service, customers have the option of an interactive self-help response and thus can obtain faster service. In such a new customer contact center, managers are inclined to induce customers to use an automated service to reduce manpower costs. Under such circumstances, delay announcements play an important role in guiding customer behavior derived from an avoid-the-crowd option into a self-help choice. Motivated by this, we characterize the underlying stochastic processes by establishing a fluid model in overloaded regimes, and propose that the key to the model is a customer response resulting from system interactions based on a deterministic approximation. Furthermore, we prove that the fluid limit of the model is a unique solution to a complicated queueing system using differential equations. Based on numerical examples, the approximation is shown to be effective in overloaded regimes through comparisons with simulations.A key managerial insight is that the lower the service level is required for staffing, the more effective automated service and the guidance of delay announcement are.