The lived experience of family members of older people who have died by suicide in rural China

Aim The aim of the study is to provide insight and facilitate a deeper understanding of family members who have experienced their older family member's suicide. Design Interpretative phenomenological analysis (IPA) study. Methods Semi-structured individual interviews with five family members of older people who died by suicide recruited from a rural area of Shanxi Province, China. Smith's (2009) six steps of IPA was used for data analysis. Results Three main themes emerged from the study: (a) Initial psychological reactions; (b) Long-term life effects; (c) Social attitudes. The study shows how the family members of older people who died by suicide have experienced stigmatization and felt largely ignored. A suicide event poses a challenge to the future living quality of the family members. The study also highlights that it is necessary to pay attention to families of older people who died by suicide and providing support is required to improve the quality of life of these family members in rural China. Conclusion The study adds to the understanding of the lived experience of family members of older people who died by suicide in less economically developed rural areas. Patient or Public Contribution Patients and the public were not involved in the design, conduct or reporting of this study. Participants of this study helped with recruitment via snowball sampling

Risk Measuring of Internet Financial Structural Products Based on Garch-EVT-Copula

Internet structured financial products quickly occupied the market, however, ordinary investors cannot identify its risks because of complex product design. In this paper, Garch-EVT-Copula is used to scale the market risk of these products and quantify the extreme market risk through the Extreme Value Theory, Copula function and VaR model. After introducing our model, this paper uses the method to measure the risk of Internet structured financial products on the platform with an example, and provide scientific decision-making basis for the risk management of Internet financial products

Reduced neural responses to reward reflect anhedonia and inattention: an ERP study

An inhibited neural response to reward is typical of clinical depression and can predict an individual's overall depressive symptoms. However, the mechanism underlying this are unclear. Previous studies have found that anhedonia and inattention may mediate the relationship between reward sensitivity and depressive symptoms. Therefore, this study aimed to verify the relationship between reward sensitivity and overall depressive symptoms in a depressive tendency sample as well as to explore the mechanism underlying the ability of neural responses to reward to predict overall depressive symptoms via a mediation model. Sixty-four participants (33 with depressive tendencies and 31 without; dichotomized by BDI-II) finished simple gambling tasks while their event-related potential components (ERPs) were recorded and compared. Linear regression was conducted to verify the predictive effect of ERPs on overall depressive symptoms. A multiple mediator model was used, with anhedonia and distractibility as mediators reward sensitivity and overall depressive symptoms. The amplitude of reward positivity (ΔRewP) was greater in healthy controls compared to those with depressive tendencies (p = 0.006). Both the gain-locked ERP component (b = − 1.183, p = 0.007) and the ΔRewP (b = − 0.991, p = 0.024) could significantly negatively predict overall depressive symptoms even after controlling for all anxiety symptoms. The indirect effects of anhedonia and distractibility were significant (both confidence intervals did not contain 0) while the direct effect of reward sensitivity on depressive symptom was not significant (lower confidence interval = − 0.320, upper confidence interval = 0.065). Individuals with depressive tendencies display impaired neural responses to reward compared to healthy controls and reduced individual neural responses to reward may reflect the different biotypes of depression such as anhedonia and inattention.publishedVersio

A novel square root adaptive unscented Kalman filter combined with variable forgetting factor recursive least square method for accurate state-of-charge estimation of lithium-ion batteries.

Lithium-ion battery state-of-charge (SOC) serves as an important battery state parameter monitored by the battery management system (BMS), real-time and accurate estimation of the SOC is vital for safe, reasonable, and efficient use of the battery as well as the development of BMS technology. Taking the ternary lithium battery as the research object, based on the second-order RC equivalent circuit model, a variable forgetting factor least square method (VFFRLS) is used for parameter identification and a combination of the square root of covariance and noise statistics estimation techniques to estimate the SOC, to solve the problem of dispersion of the unscented Kalman filter and the error covariance tends to infinity with iterative calculation, thus ensuring the accuracy of SOC estimation. The feasibility and robustness of the algorithm and the battery state estimation strategy are verified under HPPC and BBDST conditions with maximum errors of 1.41% and 1.53%, respectively. The experimental results show that the combined algorithm of VFFRLS and SRAUKF has good robustness and stability, and has high accuracy in the SOC estimation of Li-ion batteries, which provides a reference for the research of lithium-ion batteries

LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation

Data augmentation has become a de facto component of deep learning-based medical image segmentation methods. Most data augmentation techniques used in medical imaging focus on spatial and intensity transformations to improve the diversity of training images. They are often designed at the image level, augmenting the full image, and do not pay attention to specific abnormalities within the image. Here, we present LesionMix, a novel and simple lesion-aware data augmentation method. It performs augmentation at the lesion level, increasing the diversity of lesion shape, location, intensity and load distribution, and allowing both lesion populating and inpainting. Experiments on different modalities and different lesion datasets, including four brain MR lesion datasets and one liver CT lesion dataset, demonstrate that LesionMix achieves promising performance in lesion image segmentation, outperforming several recent Mix-based data augmentation methods. The code will be released at https://github.com/dogabasaran/lesionmix.Comment: 13 pages, 5 figures, 4 tables, MICCAI DALI Workshop 202

Robust identification of gene-environment interactions for prognosis using a quantile partial correlation approach

Abstract(#br)Gene-environment (G-E) interactions have important implications for the etiology and progression of many complex diseases. Compared to continuous markers and categorical disease status, prognosis has been less investigated, with the additional challenges brought by the unique characteristics of survival outcomes. Most of the existing G-E interaction approaches for prognosis data share the limitation that they cannot accommodate long-tailed or contaminated outcomes. In this study, for prognosis data, we develop a robust G-E interaction identification approach using the censored quantile partial correlation (CQPCorr) technique. The proposed approach is built on the quantile regression technique (and hence has a solid statistical basis), uses weights to easily accommodate censoring, and adopts partial correlation to identify important interactions while properly controlling for the main genetic and environmental effects. In simulation, it outperforms multiple competitors with more accurate identification. In the analysis of TCGA data on lung cancer and melanoma, biologically sensible findings different from using the alternatives are made

What factors influence older people’s intention to enrol in nursing homes? A cross-sectional observational study in Shanghai, China

Objectives Given the increasing need of long-term care and the low occupancy rate of nursing homes in Shanghai, this study attempts to explore what factors influence older people’s intention to enrol in nursing homes. Design A cross-sectional observational study based on the theory of reasoned action was conducted. Survey data were collected from subjects during face-to-face interviews. Structural equation modelling was employed for data analysis. setting This study was conducted in six community health service centres in Shanghai, China. Two service centres were selected in urban, suburban and rural areas, respectively. Participants A total of 641 Shanghai residents aged over 60 were surveyed. results Structural equation modelling analysis showed that the research model fits the data well (χ2/df=2.948, Comparative Fit Index=0.972 and root mean squared error of approximation =0.055). Attitude (β=0.41, p<0.01), subjective norm (β=0.28, p<0.01) and value- added service (β=0.16, p<0.01) were directly associated with enrolment intention, explaining 32% of variance in intention. Attitude was significantly influenced by loneliness (β=−0.08, p<0.05), self-efficacy (β=0.32, p<0.01) and stigma (β=−0.24, p<0.01), while subjective norm was significantly influenced by life satisfaction (β=−0.15, p<0.01) and stigma (β=−0.43, p<0.01). Conclusions This study advances knowledge regarding the influencing factors of older people’s intention to enrol in nursing homes. It suggests that Chinese older persons’ perceived stigma has the strongest indirect effect on their intention to enrol in nursing homes. This is unique to the Chinese context and has practical implications for eldercare in China and other Asian countries with similar sociocultural contexts