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Background Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood. Methods In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response). Results Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased. Conclusion Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron (B.1.1.529) variant is highly transmissible with potential immune escape. We conducted a test-negative case–control study to evaluate mRNA-1273 vaccine effectiveness (VE) against infection and hospitalization with Omicron or Delta. The large, diverse study population included 26,683 SARS-CoV-2 test-positive cases with variants determined by S gene target failure status (16% Delta and 84% Omicron). The two-dose VE against Omicron infection at 14–90 days was 44.0% (95% confidence interval, 35.1–51.6%) but declined quickly. The three-dose VE was 93.7% (92.2–94.9%) and 86.0% (78.1–91.1%) against Delta infection and 71.6% (69.7–73.4%) and 47.4% (40.5–53.5%) against Omicron infection at 14–60 days and >60 days, respectively. The three-dose VE was 29.4% (0.3–50.0%) against Omicron infection in immunocompromised individuals. The three-dose VE against hospitalization with Delta or Omicron was >99% across the entire study population. Our findings demonstrate high, durable three-dose VE against Delta infection but lower effectiveness against Omicron infection, particularly among immunocompromised people. However, three-dose VE of mRNA-1273 was high against hospitalization with Delta and Omicron variants. A test-negative case–control analysis using data from a diverse population in California, USA, demonstrates that vaccine efficacy of a three-dose regimen of the mRNA-1273 COVID-19 vaccine is reduced against infection with the Omicron SARS-CoV-2 variant in comparison to Delta, but that efficacy against hospitalization remained high for both variants.

An ideal anti-counterfeiting technique has to be inexpensive, mass-producible, nondestructive, unclonable and convenient for authentication. Although many anti-counterfeiting technologies have been developed, very few of them fulfill all the above requirements. Here we report a non-destructive, inkjet-printable, artificial intelligence (AI)-decodable and unclonable security label. The stochastic pinning points at the three-phase contact line of the ink droplets is crucial for the successful inkjet printing of the unclonable security labels. Upon the solvent evaporation, the three-phase contact lines are pinned around the pinning points, where the quantum dots in the ink droplets deposited on, forming physically unclonable flower-like patterns. By utilizing the RGB emission quantum dots, full-color fluorescence security labels can be produced. A convenient and reliable AI-based authentication strategy is developed, allowing for the fast authentication of the covert, unclonable flower-like dot patterns with different sharpness, brightness, rotations, amplifications and the mixture of these parameters. Anti-counterfeiting technologies should ideally be unclonable, yet simple to fabricate and decode. Here, the authors develop an inkjet-printable and unclonable security label based on random patterning of quantum dot inks, and accompany it with an artificial intelligence decoding mechanism capable of authenticating the patterns.

The operating lifetime of blue quantum-dot light-emitting diodes (QLED) is currently a short slab for this emerging display technology. To pinpoint the origin of device degradation, here we apply multiple techniques to monitor the electric-field distribution and space-charge accumulation across the multilayered structure before and after lifetime tests. Evident by charge-modulated electro-absorption and capacitance-voltage characteristics, the excited electrons in blue quantum dots (QD) are prone to cross the type II junction between the QD emission layer and the electron-transporting layer (ETL) due to the offset of conduction band minimum, leading to space-charge accumulation and operating-voltage rise in the ETL. Therefore, unlike those very stable red devices, of which the lifetime is primarily limited by the slow degradation of hole-transporting layer, the poor lifetime of blue QLED originates from the fast degradation at the QD-ETL junction. Materials engineering for efficient electron injection is prerequisite for the boost of operating lifetime. Wide application of quantum dot light emitting diodes (QLED) in display technology is hindered by the poor lifetime of the blue QLEDs. Here, the degradation mechanism is shown to originate from space charge accumulation in the electron-transporting layer enabling improvements in blue QLED lifetimes.

For the state-of-the-art quantum dot light-emitting diodes, while the ZnO nanoparticle layers can provide effective electron injections into quantum dots layers, the hole transporting materials usually cannot guarantee sufficient hole injection owing to the deep valence band of quantum dots. Developing proper hole transporting materials to match energy levels with quantum dots remains a great challenge to further improve the device efficiency and operation lifetime. Here we demonstrate high-performance quantum dot light-emitting diodes with much extended operation lifetime using quantum dots with tailored energy band structures that are favorable for hole injections. These devices show a T 95 operation lifetime of more than 2300 h with an initial brightness of 1000 cd m −2 , and an equivalent T 50 lifetime at 100 cd m −2 of more than 2,200,000 h, which meets the industrial requirement for display applications. The commercialization of light-emitting diodes based on emissive quantum dots (e.g. QLEDs) is hindered by their inherent poor operational lifetime. Using an intelligent energy-level design strategy, Qian et al. demonstrate QLEDs with operational lifetime that meets industrial display standards.

The low efficiency and fast degradation of devices from ink-jet printing process hinders the application of quantum dot light emitting diodes on next generation displays. Passivating the trap states caused by both anion and cation under-coordinated sites on the quantum dot surface with proper ligands for ink-jet printing processing reminds a problem. Here we show, by adapting the idea of dual ionic passivation of quantum dots, ink-jet printed quantum dot light emitting diodes with an external quantum efficiency over 16% and half lifetime of more than 1,721,000 hours were reported for the first time. The liquid phase exchange of ligands fulfills the requirements of ink-jet printing processing for possible mass production. And the performance from ink-jet printed quantum dot light emitting diodes truly opens the gate of quantum dot light emitting diode application for industry. Designing efficient and scalable quantum dot LEDs meeting industrial requirements remains a challenge. Here, the authors, by leveraging the liquid phase exchange of d-MX 2 ligands, present printed quantum dot LEDs with external quantum efficiency over 16% and half lifetime of more than 1,721,000 hours.

With the rapid development of information technology, internet enterprises have sprung up, bringing about huge power consumption due to constantly expanding enterprise scale, which in turn leads to significant carbon emissions. Additionally, the large influence of internet enterprises on the public and other businesses makes it particularly necessary to pay attention to their carbon emission reduction efforts. To explore the evolution path and patterns of carbon emission reduction among internet enterprises under the carbon neutrality goal, this paper constructs an evolutionary game model for internet enterprises to enter the carbon emissions trading market based on Externality and Sustainable Development Theories, while considering constraints from the carbon market, financial institutions and the public. The model utilizes Python 3.8.2 software for numerical simulations, aiming to push internet enterprises towards low-carbon development. The research findings indicate that: (1) Carbon emission reduction behavior of internet enterprises exhibits significant externality, and when constraints are weak or incentives are not evident, the motivation for enterprises to reduce carbon emissions is insufficient. (2) The carbon market can effectively promote carbon emission reduction among internet enterprises, and the strategy of entering the carbon market becomes the preferred option for these enterprises gradually. (3) Multiple constraints, including emission reduction costs, penalty for non-compliance, government subsidies, financing costs, opportunity losses, and reputation losses, can force internet enterprises towards low-carbon development.

Detecting small objects in complex remote sensing environments presents significant challenges, including insufficient extraction of local spatial information, rigid feature fusion, and limited global feature representation. In addition, improving model performance requires a delicate balance between improving accuracy and managing computational complexity. To address these challenges, we propose the SMA-YOLO algorithm. First, we introduce the Non-Semantic Sparse Attention (NSSA) mechanism in the backbone network, which efficiently extracts non-semantic features related to the task, thus improving the model’s sensitivity to small objects. In the model’s throat, we design a Bidirectional Multi-Branch Auxiliary Feature Pyramid Network (BIMA-FPN), which integrates high-level semantic information with low-level spatial details, improving small object detection while expanding multi-scale receptive fields. Finally, we incorporate a Channel-Space Feature Fusion Adaptive Head (CSFA-Head), which fully handles multi-scale features and adaptively handles consistency problems of different scales, further improving the robustness of the model in complex scenarios. Experimental results on the VisDrone2019 dataset show that SMA-YOLO achieves a 13% improvement in mAP compared to the baseline model, demonstrating exceptional adaptability in small object detection tasks for remote sensing imagery. These results provide valuable insights and new approaches to further advance research in this area.

Outer membrane vesicles (OMVs) are spherical, bilayered, and nanosized membrane vesicles that are secreted from gram-negative bacteria. OMVs play a pivotal role in delivering lipopolysaccharide, proteins and other virulence factors to target cells. Multiple studies have found that OMVs participate in various inflammatory diseases, including periodontal disease, gastrointestinal inflammation, pulmonary inflammation and sepsis, by triggering pattern recognition receptors, activating inflammasomes and inducing mitochondrial dysfunction. OMVs also affect inflammation in distant organs or tissues via long-distance cargo transport in various diseases, including atherosclerosis and Alzheimer’s disease. In this review, we primarily summarize the role of OMVs in inflammatory diseases, describe the mechanism through which OMVs participate in inflammatory signal cascades, and discuss the effects of OMVs on pathogenic processes in distant organs or tissues with the aim of providing novel insights into the role and mechanism of OMVs in inflammatory diseases and the prevention and treatment of OMV-mediated inflammatory diseases.

Abstract Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.