Microbial responses to inorganic nutrient amendment overridden by warming: Consequences on soil carbon stability.

Eutrophication and climate warming, induced by anthropogenic activities, are simultaneously occurring worldwide and jointly affecting soil carbon stability. Therefore, it is of great interest to examine whether and how they interactively affect soil microbial community, a major soil carbon driver. Here, we showed that climate warming, simulated by southward transferring Mollisol soil in agricultural ecosystems from the cold temperate climate zone (N) to warm temperate climate (C) and subtropical climate zone (S), decreased soil organic matter (SOM) by 6%-12%. In contrast, amendment with nitrogen, phosphorus and potassium enhanced plant biomass by 97% and SOM by 6% at the N site, thus stimulating copiotrophic taxa but reducing oligotrophic taxa in relative abundance. However, microbial responses to nutrient amendment were overridden by soil transfer in that nutrient amendment had little effect at the C site but increased recalcitrant carbon-degrading fungal Agaricomycetes and Microbotryomycetes taxa derived from Basidiomycota by 4-17 folds and recalcitrant carbon-degrading genes by 23%-40% at the S site, implying a possible priming effect. Consequently, SOM at the S site was not increased by nutrient amendment despite increased plant biomass by 108%. Collectively, we demonstrate that soil transfer to warmer regions overrides microbial responses to nutrient amendment and weakens soil carbon sequestration

Relations between global city connectivity of the primary city and the size national economy

Some scholars emphasize the global cities network and suggest the declining of the national power. On the contrary, many studies insist on the role of the national economy on global cities. However, there is no specific model to show this relation and no evidence to conform which factor at national level impact this connectivity. The aim of this paper is to set up a specific model to illustrate the relationship between the national economic size and global cities connectivity, and to find the factor at national level impacting on world city connectivity. Bootstrap regression is adopted to set up the model for the relation. The results reveal that the national economic size has significant effectiveness on the global city connectivity with logarithmic function. This finding gives an explicit approach to clarify the idea of 'glocal' states with the combination of global city connectivity and national urban system

Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?

Recent breakthroughs in pre-trained code models, such as CodeBERT and Codex, have shown their superior performance in various downstream tasks. The correctness and unambiguity of API usage among these code models are crucial for achieving desirable program functionalities, requiring them to learn various API fully qualified names structurally and semantically. Recent studies reveal that even state-of-the-art pre-trained code models struggle with suggesting the correct APIs during code generation. However, the reasons for such poor API usage performance are barely investigated. To address this challenge, we propose using knowledge probing as a means of interpreting code models, which uses cloze-style tests to measure the knowledge stored in models. Our comprehensive study examines a code model's capability of understanding API fully qualified names from two different perspectives: API call and API import. Specifically, we reveal that current code models struggle with understanding API names, with pre-training strategies significantly affecting the quality of API name learning. We demonstrate that natural language context can assist code models in locating Python API names and generalize Python API name knowledge to unseen data. Our findings provide insights into the limitations and capabilities of current pre-trained code models, and suggest that incorporating API structure into the pre-training process can improve automated API usage and code representations. This work provides significance for advancing code intelligence practices and direction for future studies. All experiment results, data and source code used in this work are available at \url{https://doi.org/10.5281/zenodo.7902072}

Socioecological influencers of health-promoting lifestyles in Chinese: a preliminary survey using convenient samples

BackgroundHealthy lifestyles are considered important means to reduce the burden of diseases. This cross-sectional study was conducted based on the Ecological Model of Health Behavior (EMHB) to analyze the factors associated with the health-promoting lifestyles of Chinese residents.MethodsWe carried out a cross-sectional investigation in July 2023. Our investigated factors included social-demographic characteristics (including sex, age, education level, employment status, marital status, personal monthly income, and daily behavioral habits [which were measured by a questionnaire)], health literacy [which was measured by the Chinese version of the Health Literacy Scale Short-Form scale (HLS-SF12)], and family health [which was measured by the Chinese version of the Short-Form of the Family Health Scale (FHS-SF)]. Our outcome was health promoting lifestyle, which was measured by a revised version of Health Promoting Lifestyle Profile-II (HPLP-IIR). Data were analyzed using stepwise regression.ResultsA total of 1,402 participants were enrolled. Higher scores of HLS-SF12 (β = 0.467), having regular exercise (β = 0.212), and regular physical examination (β = 0.088) were associated with better health-prompting lifestyles. However, older age (≥60 years) (β = −0.046), drinking (β = −0.066), and sleeping time (5–6 h/day) (β = −0.048) were associated lower levels of health-prompting lifestyles. Living with family (β = 0.077), FHS-SF (β = 0.104), and married (β = −0.077) were significant influencers. Unemployed (β = −0.048), receiving retirement pay (β = −0.053), and economic support provided by parents (β = 0.094) were associated with better health-prompting lifestyles. There were multiple influencing factors of the six dimensions of the HPLP-IIR. Our findings indicate that community residents with higher health literacy, better family health, and health-related behaviors tend to have better health-promoting lifestyles.ConclusionOur findings have confirmed the complex impacts of social-ecological factors on health-promoting lifestyles, which may help policy makers with health-promotion strategies making and also help researchers to control for confounding in study design

Global, Regional, National, and Local Burden of COVID‐19 With Inequality Analysis Across 920 Locations, 2020–2021

Although the COVID‐19 pandemic has profoundly reshaped global health systems, a comprehensive and standardized quantification of its direct health burden across multiple spatial scales, particularly during its initial and most severe phases in 2020 and 2021, has remained lacking. Here, we present the first global‐to‐local estimation of the direct COVID‐19 burden during this period based on a repeated cross‐sectional design and secondary analysis of population‐level data using the Global Burden of Disease (GBD) 2021 analytical framework. We evaluated key measures of health burden for each year separately and assessed temporal changes to capture evolving patterns and disparities across 920 locations spanning five spatial hierarchies: global, regional, national, subnational, and local. The analysis includes 204 countries and territories, 77 international regions (e.g., the Commonwealth), 20 subnational regions (e.g., North England), and 618 local units (e.g., London). By integrating coarse‐grained (e.g., global and regional) and fine‐grained (e.g., national and local) estimates, we identified substantial spatial and socioeconomic inequalities in incidence, prevalence, mortality, disability‐adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs). Both age‐standardized and age‐, sex‐, and location‐specific estimates were generated. Leveraging the robust epidemiological modeling capabilities of the GBD framework, along with inequality metrics, including the slope index of inequality (SII) and the concentration index of inequality (CII), we uncovered pronounced disparities not only across countries and regions but also within them. Our findings underscore that aggregated regional data could mask potentially substantial cross‐national disparities and national aggregates could similarly obscure subnational and local differences. This has profound implications for understanding the distribution of long COVID risk, improving pandemic preparedness, and guiding equitable public health policy. Ultimately, this study provides an unprecedented evidence base to inform global‐to‐local health system strengthening and support data‐driven equity‐focused responses to future public health emergencies

Identifying Potential Vulnerability to Long COVID Through Global‐to‐Local Inequalities in Years Lived With Disability Attributed to COVID‐19, 2020–2021, Across 920 Locations

The COVID‐19 pandemic has reshaped global health; however, the long‐term burden of long COVID remains poorly understood, especially in low‐ and middle‐income countries (LMICs), where limited surveillance and data gaps may obscure a substantial and sustained impact. Using the Global Burden of Disease (GBD) 2021 framework, we previously assessed the direct COVID‐19 burden—including incidence, prevalence, mortality, and disability‐adjusted life‐years (DALYs)—across 920 locations during 2020–2021. In this study, we focus on years lived with disability (YLDs), particularly in 2021, as a potential early indicator to identify locations and populations that may be at higher risk of long COVID burden in subsequent years (e.g., 2022–2023). We also examine patterns of inequality to highlight vulnerable groups. Our findings are consistent with multiple large‐scale studies on long COVID and suggest that YLDs may serve as a useful early proxy for ongoing burden. Importantly, we identify notably higher age‐standardized YLD rates in LMICs—especially in Sub‐Saharan Africa and in parts of South Asia and Eastern Europe. These areas, previously underexplored in long COVID research, might be particularly susceptible to its effects. Among the top 10 countries with the highest age‐standardized YLD rates in 2021, 80% fell within the low, low‐middle, and middle Socio‐demographic Index (SDI) categories. These high age‐standardized YLD rates may point to systemic vulnerabilities and entrenched structural health disparities, indicating a potential for considerable and enduring long COVID burden that could persist to the present day in the absence of targeted interventions. Furthermore, our inequality analysis underscores that while both advantaged and disadvantaged groups in LMICs require attention, the most disadvantaged groups warrant special focus due to their more severe resource constraints and restricted capacity for resilience‐building. Overall, this study supports calls for stronger surveillance, expanded access to rehabilitation, and better integration of long COVID care into universal health coverage. Continued GBD updates will be essential for monitoring trends and guiding responsive public health strategies

NTIRE 2022 Challenge on High Dynamic Range Imaging:Methods and Results

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under-or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).</p