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As a widely discussed topic in Western academic circles, post-productionism has become an important background for studying China's three agricultural problems. By carefully analyzing the origin, development, and practice of post-productionism, this study reveals the conceptual connotations and research status of post-productionism, which has theoretical value and practical significance for the study of China's three agricultural problems. The research through the research method of philology and the analysis of CiteSpace, the representative foreign core journals in Wed of Science with the theme of "post-productionism" were selected and summarized on the basis of data analysis.The research draws the following results: 1) This paper introduces the origin of post-productionism and summarizes the existing problems of agricultural production, rural ecology, and farmers' survival during the crisis, as well as the social expectation of the linear transformation of productionism to post-productionism. 2) On the basis of comprehensive research on the concept of "post-productionism," the conceptual domain of post-productionism is constructed from the four aspects of farmer's role, agricultural policy, farming technology and environmental impact, and three stages of conceptual research are proposed: the opposite stage, the coexistence stage and the subjective perspective stage, and compares it with the concept of "multifunctional agriculture," and points out the value of "paradigm shift" represented by post-productionism. 3) Comparing the post-productive rural practices of developed and developing countries, analyzing the common factors and regional differences between the post-productive rural practices of developed countries, sorting out the "post-productive phenomenon" of developing countries, and explaining the research objectives and necessity. On this basis, The paper further responds to the key question whether there is consistency between the "de-agriculturalization" in developing countries and the "post-productionism" in developed countries, and points out that there are some differences between the two in development stage, concept difference, connotation extension, expression form and fundamental value orientation. The conclusions are as following: 1) The development concept of Post-productionism is a positive response to productionism crises. Under the integration of common concepts, its global development path reflects the characteristics of "time-space difference" and "nonlinear transformation." Productionism and post-productionism are not "opposites," but will coexist and influence each other as two types of agricultural and rural management systems for a long time. Attention should also be paid to the fundamental role of change at the microactor level. 2) In relation to specific national conditions, the enlightenment of post-productionism in China's three rural problems is that we should pay attention to the research and judgment and grasp the opportunity of rural development, combine the overall promotion with key breakthroughs, continue to adhere to industrial revitalization as the goal, build a solid foundation for agricultural development, focus on promoting the development of secondary and tertiary industries, and focus on industrial integration. It is also necessary to deeply explore the multidimensional value of rural space, enrich the spatial attributes of rural areas, and promote rural development and urban-rural integration.

BackgroundA person's rate of aging has important implications for his/her risk of death and disease; thus, quantifying aging using observable characteristics has important applications for clinical, basic, and observational research. Based on routine clinical chemistry biomarkers, we previously developed a novel aging measure, Phenotypic Age, representing the expected age within the population that corresponds to a person's estimated mortality risk. The aim of this study was to assess its applicability for differentiating risk for a variety of health outcomes within diverse subpopulations that include healthy and unhealthy groups, distinct age groups, and persons with various race/ethnic, socioeconomic, and health behavior characteristics.Methods and findingsPhenotypic Age was calculated based on a linear combination of chronological age and 9 multi-system clinical chemistry biomarkers in accordance with our previously established method. We also estimated Phenotypic Age Acceleration (PhenoAgeAccel), which represents Phenotypic Age after accounting for chronological age (i.e., whether a person appears older [positive value] or younger [negative value] than expected, physiologically). All analyses were conducted using NHANES IV (1999-2010, an independent sample from that originally used to develop the measure). Our analytic sample consisted of 11,432 adults aged 20-84 years and 185 oldest-old adults top-coded at age 85 years. We observed a total of 1,012 deaths, ascertained over 12.6 years of follow-up (based on National Death Index data through December 31, 2011). Proportional hazard models and receiver operating characteristic curves were used to evaluate all-cause and cause-specific mortality predictions. Overall, participants with more diseases had older Phenotypic Age. For instance, among young adults, those with 1 disease were 0.2 years older phenotypically than disease-free persons, and those with 2 or 3 diseases were about 0.6 years older phenotypically. After adjusting for chronological age and sex, Phenotypic Age was significantly associated with all-cause mortality and cause-specific mortality (with the exception of cerebrovascular disease mortality). Results for all-cause mortality were robust to stratifications by age, race/ethnicity, education, disease count, and health behaviors. Further, Phenotypic Age was associated with mortality among seemingly healthy participants-defined as those who reported being disease-free and who had normal BMI-as well as among oldest-old adults, even after adjustment for disease prevalence. The main limitation of this study was the lack of longitudinal data on Phenotypic Age and disease incidence.ConclusionsIn a nationally representative US adult population, Phenotypic Age was associated with mortality even after adjusting for chronological age. Overall, this association was robust across different stratifications, particularly by age, disease count, health behaviors, and cause of death. We also observed a strong association between Phenotypic Age and the disease count an individual had. These findings suggest that this new aging measure may serve as a useful tool to facilitate identification of at-risk individuals and evaluation of the efficacy of interventions, and may also facilitate investigation into potential biological mechanisms of aging. Nevertheless, further evaluation in other cohorts is needed.

Is childhood adversity associated with biological aging, and if so, does sex modify the association, and do lifestyle and mental health mediate the association? A lifespan analysis is conducted using data on 142 872 participants from the UK Biobank to address these questions. Childhood adversity is assessed through the online mental health questionnaire (2016), including physical neglect, physical abuse, emotional neglect, emotional abuse, sexual abuse, and a cumulative score. Biological aging is indicated by telomere length (TL) measured from leukocyte DNA using qPCR, and the shorter TL indicates accelerated biological aging; a lifestyle score is constructed using body mass index, physical activity, drinking, smoking, and diet; mental disorder is assessed using depression, anxiety, and insomnia at the baseline survey. The results reveal a sex‐specific association such that childhood adversity is associated with shorter TL in women after adjusting for covariates including polygenic risk score for TL, but not in men. Unhealthy lifestyle and mental disorder partially mediate the association in women. The proportions of indirect effects are largest for sexual and physical abuse. These findings highlight the importance of behavioral and psychological interventions in promoting healthy aging among women who experienced childhood adversity, particularly sexual and physical abuse. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disorder, and its strongest risk factor is aging. A few studies have explored the relationship between aging and AD, while the underlying mechanism remains unclear. We assembled data across multi‐omics (i.e., epigenetics, transcriptomics, and proteomics, based on frozen tissues from the dorsolateral prefrontal cortex) and neuropathological and clinical traits from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). Aging was assessed using six DNA methylation clocks (including the Horvath clock, Hannum clock, Levine clock, HorvathSkin clock, Lin clock, and Cortical clock) that capture mortality risk in literature. After accounting for age, we first identified a gene module (including 263 genes) that was related to the integrated aging measure of six clocks, as well as three neuropathological traits of AD (i.e., β‐amyloid, Tau tangles, and tangle density). Interestingly, among 20 key genes with top intramodular connectivity of the module, PBXIP1 was the only one that was significantly associated with all three neuropathological traits of AD at the protein level after Bonferroni correction. Furthermore, PBXIP1 was associated with the clinical diagnosis of AD in both ROSMAP and three independent datasets. Moreover, PBXIP1 may be related to AD through its role in astrocytes and hippocampal neurons, and the mTOR pathway. The results suggest the critical role of PBXIP1 in AD and support the potential and feasibility of using multi‐omics data to investigate mechanisms of complex diseases. However, more validations in different populations and experiments in vitro and in vivo are required in the future. [ABSTRACT FROM AUTHOR]

Background: Intrinsic capacity is the combination of individual physical and mental abilities, reflecting the aging degree of the older adults. However, the mechanisms and metabolic characteristics of the decline in intrinsic capacity are still unclear. Aims: To identify metabolic signatures and associated pathways of decline in intrinsic capacity based on the metabolite features. Methods: We recruited 70 participants aged 77.19 ± 8.31 years. The five domains of intrinsic capacity were assessed by Short Physical Performance Battery (for mobility), Montreal cognition assessment (for cognition), 30-Item Geriatric Depression Scale (for psychology), self-reported hearing/visual impairment (for sensory) and Nutritional risk screening (for vitality), respectively. The serum samples of participants were analyzed by liquid chromatography-mass spectrometry-based metabolomics, followed by metabolite set enrichment analysis and metabolic pathway analysis. Results: There were 50 participants with a decline in intrinsic capacity in at least one of the domains. A total of 349 metabolites were identified from their serum samples. Overall, 24 differential metabolites, 5 metabolite sets and 13 pathways were associated with the decline in intrinsic capacity. Discussion: Our results indicated that decline in intrinsic capacity had unique metabolomic profiles. Conclusion: The specific change of acyl carnitines was observed to be a feature of decline in intrinsic capacity. Dysregulation of the pentose phosphate pathway and of arginine and ornithine metabolism was strongly associated with the decline in intrinsic capacity. [ABSTRACT FROM AUTHOR]