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The massive increase in the amount of greenhouse gases in the atmosphere, especially carbon dioxide (CO2), has had a significant impact on the global climate. Research has revealed that lakes play an important role in the global carbon cycle and that they can shift between the roles of carbon sources and sinks. This study used Landsat satellite images to analyze the spatiotemporal characteristics and factors influencing the CO2 changes in Chagan Lake in China. We conducted six water sampling campaigns at Chagan Lake in 2020–2021 and determined the partial pressure of carbon dioxide (pCO2) from 110 water samples. Landsat surface reflectance was matched with water sampling events within ±7 days of satellite overpasses, yielding 75 matched pairs. A regression analysis indicated strong associations between pCO2 and both the band difference model of the near-infrared band and green band (Band 5-Band 3, R2 = 0.83, RMSE = 27.55 μatm) and the exponential model [((exp(b3) − exp(b5))2/(exp(b3) + exp(b5))2, R2 = 0.82, RMSE = 27.99 μatm]. A comparison between the performances of a linear regression model and a machine learning model found that the XGBoost model had the highest fitting accuracy (R2 = 0.94, RMSE = 16.86 μatm). We used Landsat/OLI images acquired mainly in 2021 to map pCO2 in Chagan Lake during the ice-free period. The pCO2 in the surface water of Chagan Lake showed considerable spatiotemporal variability within a range of 0–200 μatm. pCO2 also showed significant seasonal variations, with the lowest and highest mean values in autumn (30–50 μatm) and summer (120–150 μatm), respectively. Spatially, the pCO2 values in the southeast of Chagan Lake were higher than those in the northwest. The CO2 fluxes were calculated based on the pCO2 and ranged from −3.69 to −2.42 mmol/m2/d, indicating that Chagan Lake was absorbing CO2 (i.e., it was a weak carbon sink). Temperature, chlorophyll a, total suspended matter, and turbidity were found to have reinforcing effects on the overall trend of pCO2, while the Secchi disk depth was negatively correlated with pCO2. The results of this study provide valuable insights for assessing the role of lakes in the carbon cycle in the context of climate change. [ABSTRACT FROM AUTHOR]

The CPC Central Committee and the State Council attach great importance to agricultural scientific and technological innovation. The "bottleneck" problem in agriculture has become a national strategy. Collaborative innovation is an effective way to solve the "bottleneck" problem. It faces the main battlefield of modern agricultural construction, the resolution of major problems facing the development of agricultural industry and the breakthrough of core technology. The construction and development of agriculture and rural areas put higher demands on scientific and technological innovation. As an important organizational form of agricultural science and technology innovation, collaborative innovation will play a pivotal role in the construction of innovative countries in China, and it is also of great significance for the construction of modern agricultural research institutes. Starting from the policy background of building agricultural and rural collaborative innovation infrastructure and the research status at home and abroad, this paper deeply analyzed the bottleneck problems faced by China' s agricultural scientific and technological innovation, such as agricultural scientific research big data, high-performance computing, original algorithm model, data application innovation and data security protection, and built an emerging agricultural collaborative innovation infrastructure of "data" + "algorithm" + "computing power" based on advanced information technologies such as artificial intelligence (AI), blockchain, cloud computing and big data. Furthermore, a collaborative innovation platform for agricultural and rural scientific research was constructed to serve the interdisciplinary integration. The platform simplified and speed up the iteration and flow of AI models and data, and implemented large-scale heterogeneous computing resource scheduling technology, multi-model data fusion method and friendly development support environment. The platform was dedicated to assist researchers in different disciplines to deeply integrate data, models, algorithms and computing power resource management. It created an out-of-the-box interactive cloud development environment, which was applied to the teaching, scientific research, scientific data sets and algorithm research and other scenarios, and formed an ecological closed-loop of artificial intelligence integration with multiple disciplines. It was of great strategic significance to enhance the original innovation ability in China's agricultural and rural fields, realize the leap in key fields, ensure the long-term development of science and technology, and transform from a large agricultural country to an agricultural power. [ABSTRACT FROM AUTHOR]

Background: China is currently making efforts to transform the current hospital-centric service delivery system to people-centred primary health care (PHC)-based delivery system, with service delivery organized around the health needs and expectations of people. To help direct China's PHC reform efforts, a profile of high-quality PHC from the public's perspective is required., Objectives: To profile high-quality PHC from the perspective of the Chinese public., Methods: Semistructured interviews were conducted in 6 provinces (Henan, Shandong, Zhejiang, Shaanxi, Shanxi, and Heilongjiang) in China. In total, 58 interviewees completed the recorded interview. For transcription, trained research assistant listened to the recording of the interviews, summarizing each 30-s segment in English. Next, thematic analysis was performed on the narrative summaries to identify thematic families., Results: Seven themes and 16 subthemes were generated from the analysis of our interview data. In order of their frequency, the interviewees expressed a high expectation for interpersonal communication and technical quality; followed by access, comprehensive care, cost, continuity, and coordination., Conclusions: Using qualitative data from 6 provinces in China, knowledge was generated to reveal the public's views and expectations for high-quality PHC. Our results confirm the urgent need for quality improvement efforts to improve patient experience and technical quality. The government also needs to further improve the delivery system and medical training programme to better meet public expectation in these areas, especially in establishing an innovative integrated primary care model, and strengthening interpersonal and clinical competency training for family doctors., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Primary health care plays an important role in providing populations with access to health care. However, it is currently facing unprecedented workforce shortages and high turnover worldwide., Objective: This study examined the relationship between organizational culture and turnover intention among primary care providers in China., Methods: A cross-sectional survey was administered in four large cities in China, Tianjin, Jinan, Shanghai, and Shenzhen, comprising 38 community health centers and 399 primary care providers. Organizational culture was measured using the Competing Value Framework model, which is divided into four culture types: group, development, hierarchy, and rational culture. Turnover intention was measured using one item assessing participants' intention to leave their current position in the following year. We compared the turnover intention among different organizational culture types using a Chi-square test, while the hierarchical logistic regression was used to examine the relationship between organizational culture and turnover intention., Results: The study found that 32% of primary care providers indicated an intention to leave. Primary care providers working in a hierarchical culture reported higher turnover intention (43.18%) compared with those in other cultures ( p  < 0.05). Hierarchical culture was a predictor of turnover intention (OR = 3.453, p  < 0.001), whereas rational culture had a negative effect on turnover intention (OR = 0.319, p  < 0.05)., Conclusions: Our findings inform organizational management strategies to retain a healthy workforce in primary health care.

Objective: Cognitive frailty (CF) is associated with adverse health outcomes, but different CF models have been proposed in the literature. We explored whether cognitive impairment (CoI) assessed by the Revised Hasegawa dementia scale (HDS-R) and physical frailty (PF) assessed by the modified Frailty Phenotype can be combined to predict risks of adverse outcomes in elderly Chinese subjects., Methods: The data collected in the Rugao Longevity and Aging Study were used. PF was assessed by the modified Frailty phenotype. CoI was defined as the lowest 20% of the HDS-R score. CF was defined as the co-existence of physical frailty and CoI, excluding those with severe CoI(HDR-S ≤ 10). The date and cause of death were acquired from the Death registry of Rugao's Civil Affairs Bureau. Disability was measured using the activities of daily living (ADL) scale., Results: The prevalence of CF was 2.9% at baseline. During 3-year follow-up, 130 (8.1%) of the 1607 elderly subjects died. The highest mortality risk was observed among subjects with CF (hazard ratio [HR] = 2.94, 95% confidence interval [CI] = 1.28-6.78). Fully adjusted survival plots showed that CF was associated with the highest risk for 3-year mortality. The incidence of ADL disability was 6.0% after 3 years. Compared with the robust and non-CoI elderly, CF was associated with a 10.48-fold (95% CI 2.98-36.80) higher risk of ADL disability., Conclusions: The CF model defined as a combination of HDS-R and Frail Phenotypes showed predictive validity for all-cause death and new-onset ADL disability 3 years later.