<p id="p00015">Leaf is one of the important organs of plants that facilitates the exchange of water and air with the surrounding environment. The morphological variation of leaves directly affect the physiological and biochemical processes of plants, which also reflects the adaptive strategies of plants to obtain resources. By focusing on several leaf morphological traits, including leaf size, leaf shape, leaf margin (with or without teeth) and leaf type (i.e. single vs. compound leaf), here, we reviewed the relevant research progresses in this field. We summarized the ecological functions of leaf morphological traits, identified their geographical distribution patterns, and explored the underlying environmental drivers, potential ecological interactions, and their effects on ecosystem functioning. We found that the current studies exploring the distribution and determinants of leaf size and leaf margin states mainly focused on single or specific taxon in local regions. Studies have also explored the genetic mechanisms of leaf morphology development. Leaf traits trade off with other functional traits, and their spatial variation is driven by both temperature and water availability. Leaf morphological traits, especially leaf size, influence water and nutrient cycling, reflect the response of communities to climate change, and can be scaled up to predict ecosystem primary productivity. Further studies should pay attention to combine new approaches to obtain unbiased data with high coverage, to explore the long-term adaptive evolution of leaf morphology, and to generalize the scaling in leaf morphology and its effect on ecosystem functioning. Leaf provides an important perspective to understand how plants respond and adapt to environmental changes. Studying leaf morphological traits provides insight into species fitness, community dynamics and ecosystem functioning, and also improves our understanding of the research progresses made in related fields, including plant community ecology and functional biogeography.</p>
In this work, we develop a distributed least-square approximation (DLSA) method that is able to solve a large family of regression problems (e.g., linear regression, logistic regression, and Cox’s model) on a distributed system. By approximating the local objective function using a local quadratic form, we are able to obtain a combined estimator by taking a weighted average of local estimators. The resulting estimator is proved to be statistically as efficient as the global estimator. Moreover, it requires only one round of communication. We further conduct a shrinkage estimation based on the DLSA estimation using an adaptive Lasso approach. The solution can be easily obtained by using the LARS algorithm on the master node. It is theoretically shown that the resulting estimator possesses the oracle property and is selection consistent by using a newly designed distributed Bayesian information criterion. The finite sample performance and computational efficiency are further illustrated by an extensive numerical study and an airline dataset. The airline dataset is 52 GB in size. The entire methodology has been implemented in Python for a de-facto standard Spark system. The proposed DLSA algorithm on the Spark system takes 26 min to obtain a logistic regression estimator, which is more efficient and memory friendly than conventional methods. Supplementary materials for this article are available online.
Background Air pollutants, particularly fine particulate matters (PM2.5) have been associated with mental disorder such as depression. Clean air policy (CAP, i.e., a series of emission-control actions) has been shown to reduce the public health burden of air pollutions. There were few studies on the health effects of CAP on mental health, particularly, in low-income and middle-income countries (LMICs). We investigated the association between a stringent CAP and depressive symptoms among general adults in China. Methods We used three waves (2011, 2013 and 2015) of the China Health and Retirement Longitudinal Study (CHARLS), a prospective nationwide cohort of the middle-aged and older population in China. We assessed exposure to PM2.5 through a satellite-retrieved dataset. We implemented a difference-in-differences (DID) approach, under the quasi-experimental framework of the temporal contrast between 2011 (before the CAP) and 2015 (after the CAP), to evaluate the effect of CAP on depressive symptoms. The association was further explored using a mixed-effects model of the three waves. To increase the interpretability, the estimated impact of PM2.5 was compared to that of aging, an established risk factor for depression. Findings Our analysis included 15,954 participants. In the DID model, we found a 10-µg/m3 reduction of PM2.5 concentration was associated with a 4.14% (95% CI: 0.41–8.00%) decrement in the depressive score. The estimate was similar to that from the mixed-effects model (3.63% [95% CI, 2.00–5.27%]). We also found improved air quality during 2011–2015 offset the negative impact from 5-years’ aging. Interpretation The findings suggest that implementing CAP may improve mental wellbeing of adults in China and other LMICs.
Having a risk analysis of harmful releases over mountainous terrains through wind tunnel experiment is a frontier problem in China. In this paper, a straight-flow wind tunnel is applied to simulate the atmospheric boundary layer and research the motion of high-sulfur gas released to atmosphere when accidental releases occur in a gathering station over the mountainous terrain. After an analysis of hourly concentration in the field accident for eight wind directions, experimental results reveal that nearby concentration fields are dominated by wind and far-field concentration distribution is dominated by topography, which leads to complete levels of consequence impact for the personnel risk inside and around the gathering station. Based on CFD techniques, a three-dimensional modelling was established in comparison with the wind tunnel experiment, which suggests that CFD prediction had underestimated the near-field gas concentration and the performance could not precisely match actual risks the gathering station causes to the mountainous terrain, which leads to a modified equation for numerical prediction. Instead of proposing a lower personnel risk evaluation obtained through the use of CFD techniques, the wind tunnel experiment offers a new choice for the consequence impact analysis for the petrochemical industry in China.
Background
Although the progress in global health initiatives has improved the availability of primary health care (PHC), unqualified healthcare remains a serious challenge in low- and middle-income countries, where PHC is often underutilized. This study examines factors associated with patients’ healthcare-seeking behaviors in rural Chin—seeking healthcare at village-level PHC providers, at higher-level health facilities, self-medicating, and refraining from seeking medical help. We focus on provider-side factors, including (1) the unobservable quality indicator, (2) the observable quality indicator, and (3) the observable signal indicator.
Methods
We analyzed 1578 episodes of healthcare-seeking behaviors of patients with diarrhea or cough/runny nose symptom from surveys conducted in July 2017 and January 2018 in 114 villages of the Yunnan province. We investigated the correlation between quality-related factors with patients’ healthcare-seeking behaviors by multinomial logit regression.
Results
We found that rural patients were insensitive to the unobservable quality of healthcare providers, as measured by standardized clinical vignettes, which might be attributable to the credence nature of PHC. The observable quality indicator, whether the clinician has received full-time junior college formal medical education, was associated with patients’ healthcare choices. Patients, however, were more likely to select healthcare based on the observable signal indicator, which was measured by the availability of medicines. Additionally, the observable signal indicator had no significant association with two quality indicators. Notably, socioeconomically-disadvantaged patients relied more on the village-level PHC, which emphasized the role of PHC in promoting the welfare of rural populations.
Conclusions
Our study found an inconsistency between objective quality of healthcare provided by providers and subjective quality perceived by patients. Patients could not identify the actual quality of PHC precisely, while they were more likely to make decisions based on the observable signal indicator. Therefore, the quality of PHC should be more observable to patients. This study not only supplements the literature on healthcare-seeking choices by examining four types of behaviors simultaneously but also clarifies rural patients’ perceptions of the quality of PHC for policy decision-making on increasing the utilization of PHC and improving the medical welfare of the vulnerable.