Trajectories of psychological adaptation to Chinese older internal migrants: a latent transition model.

OBJECTIVES: Little is known about the heterogeneity and transitions between psychological adaptation patterns in Chinese older internal migrants. This study addressed two questions: (a) Do distinct patterns of psychological adaptation exist among Chinese older internal migrants? (b) If so, what factors predict different trajectories? METHOD: The study drew on two waves of data and involved 405 older internal migrants into Nanjing, China. First, a latent transition analysis was performed to visualize the different patterns of psychological adaptation. Second, an ecological model of resilience was used to identify the factors explaining the differences between adaptation patterns. RESULTS: Three main trajectories of psychological adaptation among Chinese older internal migrants over time were: recovery, stability and deterioration. Adaptation trajectories were associated with age, gender, length of stay, psychological resilience, self-esteem, family support, social participation, and living with a spouse. CONCLUSION: Chinese older internal migrants undertake heterogeneous psychological adaptation trajectories, and their positive adaptation is closely associated with coping resources. Our data may provide references for the identification of vulnerable older internal migrants, as well as the making of targeted interventions.

Depression and Anxiety among Migrant Older Adults during the COVID-19 Pandemic in China: Network Analysis of Continuous Cross-Sectional Data.

Many Chinese migrant older adults are more prone to mental health problems due to their "migrant" status. During the COVID-19 pandemic, restrictions on their mobility exacerbated these conditions. Mental health is a crucial dimension of healthy aging. Network analysis offers a novel method for exploring interactions between mental health problems at the symptom level. This study employs network analysis to examine the interactions between comorbid depressive and anxiety symptoms across different stages of the COVID-19 pandemic. Surveys were conducted from September 2019 to January 2020 (T1), September 2020 to January 2021 (T2), and September 2021 onwards (T3). Depression and anxiety symptoms were measured by the Patient Health Questionnaire-9 (PHQ-9) and the Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Expected Influence (EI) and Bridge Expected Influence (Bridge EI) were used to identify central and bridge symptoms in the network. Network stability and accuracy tests were performed. Among the Chinese migrant older adults, the anxiety prevalence was 18.50% at T1, 21.11% at T2, and 9.38% at T3. The prevalence of depression was 26.95% at T1, 55.44% at T2, and 60.24% at T3. The primary central symptoms included 'Afraid something will happen' (A2), 'Irritability' (A6), 'Panic' (A7), 'Feeling of worthlessness' (D6), 'Anhedonia' (D1), and 'Feeling of fear' (A5). The major bridge symptoms included 'Feeling of fear' (A5), 'Panic' (A7), 'Irritability' (A6), 'Fatigue' (D4), 'Anhedonia' (D1), and 'Depressed or sad mood' (D2). Differences in network structure were observed across the periods. The network analysis further revealed the evolving relationships between central and bridge symptoms over time, highlighting the importance of targeted intervention strategies for central and bridge symptoms of comorbid depression and anxiety at different periods.

Multimodal adversarial representation learning for breast cancer prognosis prediction.

With the increasing incidence of breast cancer, accurate prognosis prediction of breast cancer patients is a key issue in current cancer research, and it is also of great significance for patients' psychological rehabilitation and assisting clinical decision-making. Many studies that integrate data from different heterogeneous modalities such as gene expression profile, clinical data, and copy number alteration, have achieved greater success than those with only one modality in prognostic prediction. However, many of these approaches that exist fail to dramatically reduce the modality gap by aligning multimodal distributions. Therefore, it is crucial to develop a method that fully considers a modality-invariant embedding space to effectively integrate multimodal data. In this study, to reduce the modality gap, we propose a multimodal data adversarial representation framework (MDAR) to reduce the modal heterogeneity by translating source modalities into distributions for the target modality. Additionally, we apply reconstruction and classification losses to embedding space to further constrain it. Then, we design a multi-scale bilinear convolutional neural network (MS-B-CNN) for uni-modality to improve the feature expression ability. In addition, the embedding space generates predictions as stacked feature inputs to the extremely randomized trees classifier. With 10-fold cross-validation, our results show that the proposed adversarial representation learning improves prognostic performance. A comparative study of this method and other existing methods on the METABRIC (1980 patients) dataset showed that Matthews correlation coefficient (Mcc) was significantly enhanced by 7.4% in the prognosis prediction of breast cancer patients.

Investigating wine astringency profiles by characterizing tannin fractions in Cabernet Sauvignon wines and model wines.

Contribution of various phenols on wine astringency profiles was far from clear explanations. To effectively describe wine astringency profiles and determined the function of tannins/matrix (pH and ethanol), multiple chemical analyses combined RATA (Rate-all-that-apply) sensory method were applied in Cabernet Sauvignon and model wines. Results showed that polymeric flavanols determined the bulk of wine astringency intensity, oligomeric tannins enriched the smoothness and periodontium astringency, and monomeric phenol enhanced overall astringency intensity through synergistic effect. Astringency balance was effectively quantification, and its potential correlation relationship with epicatechin extension subunit (0.83) and fluorescence peak shift (0.75) cannot be ignored. The astringency profiles of condensed tannins with anthocyanins were enhanced. Low-pH (from 3.8 to 3.0) enhanced astringency by increasing the tannins affinity to proteins, while ethanol (from 10.0 % âˆ¼ 15.0 %) decreased the hydrophobicity bond between tannins-protein interaction. This paper provided new insights to explain wine astringency profiles and a reference for astringency modification during winemaking.