Fundamental Research最新文献

A magnetorheological self-centering brace (MR–SCB) has been proposed to improve the energy dissipation capability of the brace. In this paper, a 15-story MR–SCB braced frame is numerically analyzed to examine its seismic performance and resilience. The MR–SCB provides higher lateral stiffness than the buckling restrained brace and greater energy dissipation capability than the existing self-centering brace. The brace also exhibits a reliable recentering capacity. Under rare earthquakes, the maximum average residual deformation ratio of the structure is less than the 0.5% limit. Under mega earthquakes, the maximum average interstory drift ratio of the structure does not exceed the 2.0% elastoplastic limit, and its maximum average floor acceleration ratio is 1.57. The effects of mainshock and aftershock on the structural behavior are also investigated. The interstory drift and residual deformation of the structure increase with the increase of the intensity of the aftershock. Under aftershocks with the same intensity as the mainshocks, the maximum increment of the residual deformation ratio of the structure is 81.8%, and the average interstory drift ratios of the 12^th, 7^th, and 3^rd stories of the structure are increased by 13.4%, 9.2% and 7.5%, respectively. The strong aftershock may significantly cause increased damage to the structure, and increase its collapse risk and residual deformation.

Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.

On April 29, 2020, China entered a normalization stage of prevention and control. By December 2021, more than 40 outbreaks had occurred in China, which reflected the shortcomings of the pandemic prevention and control measures in China at that time. As the capital city of China, Beijing faces more pressure in epidemic prevention and control. We used the COVID-19 cluster containment evaluation indicators to determine the effects of prevention and control measures on four COVID-19 outbreaks in Beijing. After considering the specificity and operability of evaluation indicators and the availability of evaluation data, the evaluation system in our study consisted of six dimensions: epidemic prevention and control effect, discovery and detection ability, precision prevention and control capability, public protection effect, medical treatment and nosocomial infection prevention and control ability, and information release and public opinion response ability. The composite scores of the prevention and control effects of the Xinfadi, Shunyi, Daxing, and Ejina Banner–associated COVID-19 outbreaks in Beijing were 62, 82, 87, and 76, respectively. In the six dimensions, the epidemic prevention and control effect, discovery and detection ability, precision prevention and control capability, and public protection effect scores for the Xinfadi outbreak were lower than those for the Shunyi, Daxing and Ejina Banner–associated outbreaks. The medical treatment and nosocomial infection prevention and control ability scores for the outbreak associated with Ejina Banner were lower than those for the Xinfadi, Shunyi, and Daxing outbreaks. In managing cluster outbreaks, Beijing was able to detect index cases early enough to reduce the scale and duration of the outbreak and consistently release official information to reduce public panic, standardize the management of centralized quarantine sites to prevent cross-infection, adhere to the “dynamic COVID‐zero” strategy to accurately prevent and control outbreaks, reduce the societal influence of the pandemic, and coordinate the epidemic prevention and control and socio-economic development.

The sudden onset of the coronavirus disease 2019 (COVID-19) in January 2020 has affected essential global health services. Cancer-screening services that can reduce cancer mortality are strongly affected. However, the specific role of COVID-19 in cancer screening is not fully understood. This study aimed to assess the efficiency of global cancer screening programs before and during the COVID-19 pandemic and to promote potential cancer-screening strategies for the next pandemic. Electronic searches in PubMed, Embase, and Web of Science, and manual searches were performed between January 1, 2020 and March 1, 2023. Cohort studies that reported the number of participants who underwent cancer screening before and during the COVID-19 pandemic were included. The methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale. Differences in cancer-screening rates were estimated using the incidence rate ratio (IRR). Fifty-five cohort studies were included in this meta-analysis. The screening rates of colorectal cancer using invasive screening methods (Pooled IRR = 0.52, 95% CI: 0.42 to 0.65, p < 0.01), cervical cancer (Pooled IRR = 0.56, 95% CI: 0.47 to 0.67, p < 0.01), breast cancer (Pooled IRR = 0.57, 95% CI: 0.49 to 0.66, p < 0.01) and prostate cancer (Pooled IRR = 0.71, 95% CI: 0.56 to 0.90, p < 0.01) during the COVID-19 pandemic were significantly lower than those before the COVID-19 pandemic. The screening rates of lung cancer (Pooled IRR = 0.77, 95% CI: 0.58 to 1.03, p = 0.08) and colorectal cancer using noninvasive screening methods (Pooled IRR = 0.74, 95% CI: 0.50 to 1.09, p = 0.13) were reduced with no statistical differences. The subgroup analyses revealed that the reduction in cancer-screening rates varied across economies. Our results suggest that the COVID-19 pandemic has had a noteworthy impact on colorectal, cervical, breast, and prostate cancer screening. Developing innovative cancer-screening technologies is important to promote the efficiency of cancer-screening services in the post-COVID-19 era and prepare for the next pandemic.

Single-walled carbon nanotubes (SWCNTs) present excellent electronic and mechanical properties desired in wearable and flexible devices. The preparation of SWCNT films is the first step for fabricating various devices. This work developed a scalable and feasible method to assemble SWCNT thin films on water surfaces based on Marangoni flow induced by surface tension gradient. The films possess a large area of 40 cm × 30 cm (extensible), a tunable thickness of 15∼150 nm, a high transparency of up to 96%, and a decent conductivity. They are ready to be directly transferred to various substrates, including flexible ones. Flexible strain sensors were fabricated with the films on flexible substrates. These sensors worked with high sensitivity and repeatability. By realizing multi-functional human motion sensing, including responding to voices, monitoring artery pulses, and detecting knuckle and muscle actions, the assembled SWCNT films demonstrated the potential for application in smart devices.

The European Commission has proposed a Carbon Border Adjustment Mechanism (CBAM) to reduce carbon leakage and create a level playing field for its domestic products and imported goods. Nevertheless, the effectiveness of the proposal remains unclear, especially when it triggers threats of retaliation from trading partners of the European Union. We apply a Computable General Equilibrium model - Global Trade Analysis Project - to assess the economic and environmental impacts of different CBAM schemes. Here we show that the effectiveness of the CBAM to address carbon leakage risks is rather limited, and the CBAM raises concerns over global welfare costs, Correct to Gross Domestic Product (GDP) losses, and violation of equality principles. Trade retaliation leads to multiplied welfare losses, which would mostly be borne by poor countries. Our results question the carbon leakage reduction effect of a unilateral trade policy and suggest that climate change mitigation still needs to be performed within the framework of international cooperation.

In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the short-term prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R² of 0.125 (original ConvLSTM: RMSE = 0.702, R² = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R² = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.

The pursuit of compact and integrated devices has stimulated a growing demand for multifunctional sensors with rapid and accurate responses to various physical parameters, either separately or simultaneously. Fluorescent fiber sensors have the advantages of robust stability, light weight, and compact geometry, enabling real-time and noninvasive signal detection by monitoring the fluorescence parameters. Despite substantial progress in fluorescence sensors, achieving multifunctional sensing in a single optical fiber remains challenging. To solve this problem, in this study, we present a bottom-up strategy to design and fabricate thermally drawn multifunctional fiber sensors by incorporating functional nanocrystals with temperature and pressure fluorescence responses into a transparent glass matrix. To generate the desired nanocrystal-in-glass composite (NGC) fiber, the fluorescent activators, incorporated nanocrystals, glassy core materials, and cladding matrix are rationally designed. Utilizing the fluorescence intensity ratio technique, a self-calibrated fiber sensor is demonstrated, with a bi-functional response to temperature and pressure. For temperature sensing, the NGC fiber exhibits temperature-dependent near-infrared emission at temperatures up to 573 K with a maximum absolute sensitivity of 0.019 K⁻¹. A pressure-dependent upconversion emission is also realized in the visible spectral region, with a linear slope of -0.065. The successful demonstration of multifunctional NGC fiber sensors provides an efficient pathway for new paradigms of multifunctional sensors as well as a versatile strategy for future hybrid fibers with novel combinations of magnetic, optical, and mechanical properties.

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.