Abstract Environmental hazards are an increasing concern due to the rapid pace of industrialization. Among these hazards, noise and carbon monoxide (CO) are common risk factors and have been shown to cause serious health problems. However, existing studies focused on the individual effects of noise and CO exposure and the combined effects of these two factors remain poorly understood. Our study aimed to examine the combined effects of noise and CO exposure on testicular function by constructing individual and combined exposure models. Our findings indicated that combined exposure to noise and CO was associated with a higher risk of testicular damage and male reproductive damage when compared to exposure alone. This was evidenced by poorer semen quality and more severe pathological damage to the testis. This combined exposure led to higher levels of oxidative stress and apoptosis in the testes, with bioinformatics analyses suggesting the signaling pathways involved in these responses. Specifically, activation of the P53 signaling pathway was found to contribute to the testicular damage caused by the combined exposure. Encouragingly, pterostilbene (PTE), a novel phytochemical, alleviated combined exposure‐induced testicular damage by reducing oxidative stress and germ cell apoptosis. Overall, we identified joint reproductive toxicity resulting from the exposure to noise and CO, and found that PTE is a promising potential treatment for injuries caused by these factors. The cover image is based on the Research Article Effects and possible mechanisms of combined exposure to noise and carbon monoxide on male reproductive system in rats by Yingqing Li et al., https://doi.org/10.1002/tox.23927 .
Abstract Simulating and predicting the urban land use change can provide deeper spatial insights into dynamics and sustainable developments of urban planning. This research takes the Shanghai‐Hangzhou Bay (SHB) agglomeration as a study area and selects natural, economic, social, and policy variables as restraint conditions. A cellular automaton (CA) model and the naive Bayes‐cellular automaton (NB‐CA) model are employed and compared to simulate the built‐up land in SHB. Results show that the NB‐CA model greatly improves the simulation accuracy of built‐up land compared to CA model. Specifically, the simulation accuracy of the NB‐CA model is 14.68%, 14.03%, 7.43%, 6.00%, 5.32%, and 2.65% higher than that of the traditional CA model in Shanghai, Hangzhou, Huzhou, Jiaxing, Ningbo, and Shaoxing, respectively. Among the four restraint conditions, the least influential variable is the natural variable and the most influential is the policy variable in Shanghai, Ningbo, and Shaoxing and the social variable in Hangzhou, Huzhou, and Jiaxing. It is the first usage of naive Bayes and CA to simulate built‐up expansion and this new combination method highlights the improvement of simulation accuracy. The naive Bayes technology implies that government policy is an unstable factor that can influence the simulation of built‐up land change. The methodology will be applicable to other regions experiencing rapid built‐up land expansion under government policy.
Food security is one of the main challenges facing humanity, and increasing crop yields is critical to meet the growing global food demand. Grain production in China has remarkably improved since its founding in 1949, but the growth remains uneven across regions. The goal of this research was to assess the determinants of the yields of three major food crops (maize, rice, and wheat) in China and the mechanism of their responses to increased crop yields. Boosted regression tree models were created to capture the linked complex predictor-response relationships between crop yields and individual explanatory variables using prefecture-level agricultural statistics in China during the period from 1952 to 2017. The results showed that technological inputs (e.g., fertilizers and electricity consumption) played a key role in the increase of crop yields, which explained 47%, 39%, and 62% of the variances in the yields of maize, rice, and wheat during the period 1952–2017, respectively. However, the contribution of technology became weaker over time, while the contribution of agro-environmental conditions and structural characteristics of administrative regions became stronger. Partial dependence plots indicated that encouraging higher technological inputs, accelerating large-scale grain production, shortening the urban-rural income gap, and improving the education of farmers were conductive to increasing crop yields. Overall, our results suggest that grain production policies aimed at increasing crop yields should better reflect the spatial heterogeneity of yield gaps; low-yield-gap regions should improve the utilization efficiency of water and fertilizer and formulate measures to inhibit non-grain production on cultivated land, while high-yield-gap regions should focus on improving technological inputs and promoting agricultural restructuring. This study provides deep insights into the processes of how individual explanatory variables affect crop yields, which is essential to develop differentiated grain production policies and provide valuable references for shortening yield gaps in high-yield-gap regions.
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