Impact of fish consumption on all-cause mortality in older people with and without dementia: a community-based cohort study

BACKGROUND Increased fish consumption reduces the risk of dementia. However, it is unknown whether fish consumption reduced all-cause mortality in people with dementia. The purpose of the study is to investigate the association of fish consumption with all-cause mortality in older people with dementia versus those without dementia. METHODS Using a standard method of the Geriatric Mental State, we interviewed 4165 participants aged ≥ 60 years who were randomly recruited from five provinces in China during 2007-2009 to collect the baseline data of socio-demography, disease risk factors, histories of disease, and details of dietary intakes, and diagnosed dementia (n = 406). They were followed up for vital status until 2012. RESULTS The cohort follow-up documented 329 deaths; 61 were in participants with dementia (55.3 per 1000 person-years) and 224 were those without dementia (22.3). In all participants, the risk of all-cause mortality was reduced with fish intake at " ≥ twice a week" (multivariate-adjusted hazard ratio 0.58, 95% CI 0.34-0.96) and at "once a week or less" (0.79, 0.53-1.18) compared to "never eat" over the past two years. In participants without baseline dementia, the corresponding HRs for all-cause mortality were 0.57 (0.33-0.98) and 0.85 (0.55-1.31), while in participants with dementia were 1.36 (0.28-6.60) and 1.05 (0.30-3.66), respectively. CONCLUSION This study reveals that consumption of fish in older age reduced all-cause mortality in older people without dementia, but not in people with dementia. Fish intake should be increased in older people in general, prior to the development of dementia in the hope of preventing dementia and prolonging life

Effects of Self-Esteem on the Association between Negative Life Events and Suicidal Ideation in Adolescents

Negative life events (NLEs) increase the risk of suicidal ideation (SI) in adolescents. However, it is not known whether the association between NLEs and SI can be moderated by self-esteem and varies with gender. The aim of the current paper was to examine gender differences in the association of SI with NLEs in adolescents, and assess the effects of self-esteem on the association and their gender variations. We conducted a school-based health survey in 15 schools in China between November 2013 and January 2014. A total of 9704 participants aged 11–19 years had sociodemographic data reported and self-esteem (Rosenberg self-esteem scale), NLEs, and SI measured. Multivariate-adjusted logistic regression was used to calculate the odds ratio (OR) of having SI in relation to NLEs. Increased risk of SI was significantly associated with NLEs (adjusted OR 2.19, 95%CI 1.94–2.47), showing no gender differences (in females 2.38, 2.02–2.80, in males 1.96, 1.64–2.36, respectively). The association was stronger in adolescents with high esteem (2.93, 2.34–3.68) than those with low esteem (2.00, 1.65–2.42) (ORs ratio 1.47, p = 0.012). The matched figures in females were 3.66 (2.69–4.99) and 2.08 (1.61–2.70) (1.76, p = 0.006), while in males these figures were 2.27(1.62–3.19) and 1.89 (1.41–2.53) (1.20, p = 0.422), respectively. Self-esteem had moderate effects on the association between NLEs and SI in adolescents, mainly in females. NLEs, self-esteem, and gender need to be incorporated into future intervention programs to prevent SI in adolescents

Patterns of adverse childhood experiences and suicidal behaviors in adolescents: A four-province study in China

This is an accepted manuscript of a paper published by Elsevier on 23/02/2021, available online at: https://doi.org/10.1016/j.jad.2021.02.045 The accepted manuscript of the publication may differ from the final published version.Background:: Adverse childhood experiences (ACEs) are associated with increased suicidal behaviors in adolescents and most research has been restricted to certain types of or cumulative exposure to ACEs. Few studies have examined the association between patterns of ACEs and suicidal behaviors. Objective:: To identify the contributions of type and pattern of exposure to ACEs to suicidal behaviors and their gender differences among middle school students in China. Methods:: A school-based health survey was conducted in four provinces in China between 2017 and 2018. 14 500 students aged 10–20 years completed standard questionnaires, to record details of ACEs, suicide ideation, suicide plan, and suicide attempt. Results:: Latent class analysis indicated four distinct patterns of ACEs exposure: high ACEs (6.3%), high abuse and neglect (21.4%), high neglect (45.5%), and low ACEs (26.8%). Logistic analyses showed that, compared with low ACEs, the high ACEs were more likely to report suicidal behaviors. No gender differences were found in the independent effects of ACEs type or pattern on suicidal behaviors, except for the emotional neglect associated with suicidal behaviors in girls than boys. Limitations:: The study was cross-sectional and used self-reported questionnaires. Thus, it is difficult to establish a causal relationship between patterns of ACEs and suicidal behaviors. Conclusion:: Our findings addressed the need for a comprehensive consideration of ACEs in preventive healthcare work to identify children exposed to the most problematic ACE patterns. The study provided the evidence of targeted intervention to preempt the emergence of suicide behavior in at-risk students in adolescents.Funding for the project was provided by National Natural Science Foundation of China (82073576 & 81773453).Published versio

Impacts of heart disease, depression and their combination on all-cause mortality in older people: A rural community-based cohort study in China

© 2020 The Authors. Published by BMJ. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://bmjopen.bmj.com/content/10/12/e038341Objective To assess the impact of heart disease (HD) combined with depression on all-cause mortality in older people living in the community. Design A population-based cohort study. Participants We examined the data of 1429 participants aged ≥60 years recruited in rural areas in Anhui province, China. Using a standard method of interview, we documented all types of HD diagnosed by doctors and used the validated Geriatric Mental Status-Automated Geriatric Examination for Computer Assisted Taxonomy algorithm to diagnose any depression for each participant at baseline in 2003. The participants were followed up for 8 years to identify vital status. Measurements We sought to examine all-cause mortality rates among participants with HD only, depression only and then their combination compared with those without these diseases using multivariate adjusted Cox regression models. Results 385 deaths occurred in the cohort follow-up. Participants with baseline HD (n=91) had a significantly higher mortality (64.9 per 1000 person-years) than those without HD (42.9). In comparison to those without HD and depression, multivariate adjusted HRs for mortality in the groups of participants who had HD only, depression only and both HD and depression were 1.46 (95% CI 0.98 to 2.17), 1.79 (95% CI 1.28 to 2.48) and 2.59 (95% CI 1.12 to 5.98), respectively. Conclusion Older people with both HD and depression in China had significantly increased all-cause mortality compared with those with HD or depression only, and without either condition. Psychological interventions should be taken into consideration for older people and those with HD living in the community to improve surviving outcome.Published versio

Impact of fish consumption on all-cause mortality in older people with and without dementia: a community-based cohort study

© 2022 The Authors. Published by Springer. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1007/s00394-022-02887-yBackground: Increased fish consumption reduces the risk of dementia. However, it is unknown whether fish consumption reduced all-cause mortality in people with dementia. The purpose of the study is to investigate the association of fish consumption with all-cause mortality in older people with dementia versus those without dementia. Methods: Using a standard method of the Geriatric Mental State, we interviewed 4165 participants aged ≥ 60 years who were randomly recruited from five provinces in China during 2007–2009 to collect the baseline data of socio-demography, disease risk factors, histories of disease, and details of dietary intakes, and diagnosed dementia (n = 406). They were followed up for vital status until 2012. Results: The cohort follow-up documented 329 deaths; 61 were in participants with dementia (55.3 per 1000 person-years) and 224 were those without dementia (22.3). In all participants, the risk of all-cause mortality was reduced with fish intake at “ ≥ twice a week” (multivariate-adjusted hazard ratio 0.58, 95% CI 0.34–0.96) and at “once a week or less” (0.79, 0.53–1.18) compared to “never eat” over the past two years. In participants without baseline dementia, the corresponding HRs for all-cause mortality were 0.57 (0.33–0.98) and 0.85 (0.55–1.31), while in participants with dementia were 1.36 (0.28–6.60) and 1.05 (0.30–3.66), respectively. Conclusion: This study reveals that consumption of fish in older age reduced all-cause mortality in older people without dementia, but not in people with dementia. Fish intake should be increased in older people in general, prior to the development of dementia in the hope of preventing dementia and prolonging life.The data collection of the five provinces’ cohort study was funded by the BUPA Foundation (Grants Nos. 45NOV06, and TBF-M09-05) and Alzheimer’s Research UK (Grant No. ART/PPG2007B/2). The data management and the final work of the manuscript were supported by the Research fund of Anhui Medical University, China (Grant No. 2021xkjT049). Professor Ruoling Chen and Dr James J Tang thank an EU H2020 MSCA Fellowship (Grant No. DEMAIRPO-799247) to investigate the risk of dementia in relation to air pollution mediated by fish intake.Published versio

Role of corticotropin-releasing hormone in the impact of chronic stress during pregnancy on inducing depression in male offspring mice

This is an accepted manuscript of an article published by Elsevier in Brain Research on 30/07/2020, available online: https://doi.org/10.1016/j.brainres.2020.147029 The accepted version of the publication may differ from the final published version.This work was supported by the National Natural Science Foundation of China (grant no. 81773452).Published versio

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state‐of‐the‐art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water and natural hazard management are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined

Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model

This case study uses a long short-term memory (LSTM)-based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM-based model is compared with the autoregressive integrated moving average (ARIMA) model, the support vector regression (SVR) model, and the random forests (RF) model based on data with time resolutions ranging from 15 min to 24 h. Additionally, this paper investigates the performance of the LSTM-based model in predicting multiple successive data points. Results show that the LSTM-based model can offer predictions with improved accuracy than the other models when dealing with data with high time resolutions, data points with abrupt changes, and data with a relatively high uncertainty level. It is also observed that the LSTM-based model exhibits the best performance in predicting multiple successive water demands with high time resolutions. In addition, the inclusion of external parameters (e.g., temperature) cannot enhance the performance of the LSTM-based model, but it can improve ARIMAX's prediction ability (ARIMAX is the ARIMA with variables). These observations provide additional and improved evaluations regarding the LSTM-based models used for short-term urban water demand forecasting, thereby enabling their wider adoption in practical applications. </p

Automated Counting Grains on the Rice Panicle Based on Deep Learning Method

Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions

Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model

This case study uses a long short-term memory (LSTM)-based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM-based model is compared with the autoregressive integrated moving average (ARIMA) model, the support vector regression (SVR) model, and the random forests (RF) model based on data with time resolutions ranging from 15 min to 24 h. Additionally, this paper investigates the performance of the LSTM-based model in predicting multiple successive data points. Results show that the LSTM-based model can offer predictions with improved accuracy than the other models when dealing with data with high time resolutions, data points with abrupt changes, and data with a relatively high uncertainty level. It is also observed that the LSTM-based model exhibits the best performance in predicting multiple successive water demands with high time resolutions. In addition, the inclusion of external parameters (e.g., temperature) cannot enhance the performance of the LSTM-based model, but it can improve ARIMAX's prediction ability (ARIMAX is the ARIMA with variables). These observations provide additional and improved evaluations regarding the LSTM-based models used for short-term urban water demand forecasting, thereby enabling their wider adoption in practical applications. Accepted Author ManuscriptSanitary Engineerin