Explore the vast range of titles available.

Climate models indicate that a warmer environment will increase low‐level moisture, potentially intensify extreme precipitation. However, its impact on different rainfall types remains unclear. Using satellite data, we examined changes in light (0‐95th percentile, ≤5.28 mm hr−1) and heavy (95‐100th percentile, >5.28 mm hr−1) precipitation in the tropics from 1998 to 2019. Our findings show a −9 ± 2% (23 ± 2%) change in heavy (light) rain intensity and a 13 ± 2% (−24 ± 1%) change in heavy (light) rain frequency. These changes link to warmer sea surface temperatures, increased atmospheric stability and water vapor, and weakened upward velocity. These insights shed light on how heavy and light precipitation patterns respond to changing climate, emphasizing the complexities within the hydrological cycle. Plain Language Summary The hydrologic cycle depends greatly on precipitation. With the expectation of climate warming causing substantial changes to the global water cycle, comprehending these shifts becomes pivotal in foreseeing how climate changes will affect society. Climate models suggest that as temperatures rise, low‐level moisture will increase. However, the specific impacts of this heightened moisture on the intensity and frequency of precipitation events remain uncertain. Analyzing data from the Tropical Rainfall Measuring Mission (1998–2019), we found notable changes: a 23 ± 2% increase in light rain intensity, a −9 ± 2 % decrease in heavy rain intensity, a 13 ± 2 % rise in heavy rain frequency, and a significant −24 ± 1% decrease in light rain frequency. These changes result diverse factors like warming oceans, stabilized atmospheric conditions, increased water vapor, and weakened upward velocity. This underscores the intricate interplay between different rainfall types and how they respond to the evolving climate. Key Points In the tropics from 1998 to 2019, heavy rain intensity decreased and light rain intensity increased In the tropics from 1998 to 2019, heavy rain frequency increased and light rain frequency decreased Rain shifting patterns linked to increased SST, atmospheric stability, and water vapor and decreased upward velocity

Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods. This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers. A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically. Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain. Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers' status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.

Callicarpalongissima has been used as a Yao folk medicine to treat arthritis for years in China, although its active anti-arthritic moieties have not been clarified so far. In this study, two natural phenolic diterpenoids with anti-rheumatoid arthritis (RA) effects, rosmanol and carnosol, isolated from the medicinal plant were reported on for the first time. In type II collagen-induced arthritis DBA/1 mice, both rosmanol (40 mg/kg/d) and carnosol (40 mg/kg/d) alone alleviated the RA symptoms, such as swelling, redness, and synovitis; decreased the arthritis index score; and downregulated the serum pro-inflammatory cytokine levels of interleukin 6 (IL-6), monocyte chemotactic protein 1 (MCP-1), and tumor necrosis factor α (TNF-α). Additionally, they blocked the activation of the Toll-like receptor 4 (TLR4)/nuclear factor κB (NF-κB)/c-Jun N-terminal kinase (JNK) and p38 mitogen-activated protein kinase (MAPK) pathways. Of particular interest was that when they were used in combination (20 mg/kg/d each), the anti-RA effect and inhibitory activity on the TLR4/NF-κB/MAPK pathway were significantly enhanced. The results demonstrated that rosmanol and carnosol synergistically alleviated RA by inhibiting inflammation through regulating the TLR4/NF-κB/MAPK pathway, meaning they have the potential to be developed into novel, safe natural combinations for the treatment of RA.

This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.

This paper leverages the Opinions of the State Council on Further Promoting the Reform of the Household Registration System as a quasi-natural experiment to examine the impact of rural migrant urbanization on local government expenditures in science and technology(S&T) and education.Employing a generalized difference-in-differences(generalized-DID) approach and panel data from 271 Chinese prefecture-level and above-level cities spanning 2003-2019, we find that household registration reform driven by this urbanization process significantly increased the absolute level as well as the fiscal share of S&T and education spending.Mechanism analyses indicate that rural migrant urbanization promotes these expenditures through two channels: increasing the overall local fiscal revenue and encouraging a greater proportion of the fiscal resources to be allocated to these sectors.The findings uncover the current evolving trends in fiscal resource allocation due to rural migrant urbanization and provide empirical evidence for optimizing the provision of public services and enhancing the potential of long-term economic development.

Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods. This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers. A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically. Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain. Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers' status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.

Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods. This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers. A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically. Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain. Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers' status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.

Although institutional pressures have huge strategic implications for organizational activities, this certainly does not mean that organizations under institutional pressures can improve environmental performance automatically. Institutional pressures are critical but not sufficient to affect environmental performance directly. Therefore, additional research is needed to explore the missing link between institutional pressures and environmental performance. Based on the “pressure-response-performance” framework, this study integrates perspectives of institutional theory and organizational learning to argue the mediating role of organizational learning in the relationship between institutional pressures and environmental performance. Data were collected via 268 valid questionnaires from construction firms located in Shanxi Province in central China. Hypotheses in the conceptual model were tested with structural equation modeling. Empirical results reveal that both coercive and mimetic pressures have significantly positive effects on organizational learning, whereas normative pressures have a non-significant effect on organizational learning. Besides that, organizational learning has a significantly positive effect on environmental performance. In addition, organizational learning partially mediates the relationship between coercive pressures and environmental performance and completely mediates the relationship between mimetic pressures and environmental performance. By exploring the mediating role of organizational learning, the article uncovers the missing link in the relationship between institutional pressures and environmental performance.

Due to the identification functionality, identity authentication is the first and primary security step in many information systems. There exist many works dedicated to giving secure identity authentication. However, most of the existing schemes suffer from at least one of the following problems: heavy account management, single point of failure, and privacy leakage. To tackle these challenges, we propose two blockchain-based identity authentication schemes in this paper. One is based on the famous Diffie-Hellman key exchange protocol and is efficient but with user-verifier interaction. The other utilizes the ring signature, which is non-interactive with a small computational cost. Besides the traditional security properties, such as unforgeability and identity anonymity, our proposed schemes can hold non-transferability, i.e., the verifier cannot prove the user’s identity authentication to any third party. At last, the extensive experimental results demonstrate that our proposals are practical and efficient.

The eigenvalue problem for the p-Laplace operator with Robin boundary condition is considered in this paper. A Faber-Krahn type inequality is proved. More precisely, it is shown that amongst all the domains of fixed volume, the ball has the smallest first eigenvalue.