Search

BackgroundIncreasing evidence suggests that pregnancy loss can lead to negative emotional outcomes, such as anxiety and depression, for women. However, limited knowledge exists regarding the long-term risk of mental disorders among individuals who have experienced pregnancy loss.ObjectiveTo investigate the associations between pregnancy loss and the risk of common mental disorders.MethodsIn the UK Biobank, a total of 218,990 women without any mental disorder at baseline were enrolled between 2006 and 2010 and followed until October 2022. Information on the history of pregnancy loss was obtained through self-reported questionnaires at baseline. Cox proportional hazard regression models were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between pregnancy loss and common mental disorders.ResultsDuring a median follow-up time of 13.36 years, there were 26,930 incident cases of common mental disorders. Incidence rates of common mental disorders were elevated among women with a history of stillbirth (HR 1.15, 95% CI: 1.07–1.23), miscarriage (HR 1.06, 95% CI: 1.02–1.10), or pregnancy termination (HR 1.21, 95% CI: 1.17–1.25) compared to those without such experiences. Furthermore, the risk of common mental disorders significantly increased in women with two or more miscarriages (HR 1.14, 95% CI: 1.08–1.19) or two or more pregnancy terminations (HR 1.39, 95% CI: 1.30–1.48).ConclusionsPregnancy loss is associated with an increased risk of common mental disorders in women later in life. These findings may contribute to the enhancement of long-term monitoring and prevention of common mental disorders for women with such a history.

Abstract Neural Process (NP) fully combines the advantages of neural network and Gaussian Process (GP) to provide an efficient method for solving regression problems. Nonetheless, limited by the dimensionality of the latent variable, NP has difficulty fitting the observed data completely and predicting the targets perfectly. To remedy these drawbacks, the authors propose a concise and effective improvement of the latent path of NP, which the authors term Multi‐Latent Variables Neural Process (MLNP). MLNP samples multiple latent variables and integrates the representations corresponding to the latent variables in the decoder with adaptive weights. MLNP inherits the desirable property of linear computation scales of NP and learns the approximate distribution over objective functions from contexts more flexibly and accurately. By applying MLNP to 1‐D regression, real‐world image completion, which can be seen as a 2‐D regression task, the authors demonstrate its significant improvement in the accuracy of prediction and contexts fitting capability compared with NP. Through ablation experiments, the authors also verify that the number of latent variables has a great impact on the prediction accuracy and fitting capability of MLNP. Moreover, the authors also analyze the roles played by different latent variables in reconstructing images.

Abstract Forest expansion has been observed in China over the past decades, but the typically applied coarse resolution satellite data does not reveal spatial details about China’s forest transition. By using three decades of satellite observations at a 30-m spatial resolution, we reveal here the complex spatiotemporal patterns of individual forest stands forming the forest return history of southern China. We calculate forest age, forest densification rates, and annual forest fragmentation and show that the observed forest area surge around 2010 is a result of trees planted after 2000 that formed dense forests about a decade later. We document that old forests in the 1980s were mostly fragmented into scattered patches located on mountain tops, but forests rapidly expanded downhill by 729,540 km2 and alleviated the clear-cut and logging pressure from old forests. Our study provides a detailed documentation of forest densification and expansion for a country that had been largely deforested three decades ago.

Abstract Searching for new physics (NP) is one of the areas of high-energy physics that requires the most processing of large amounts of data. At the same time, quantum computing has huge potential advantages when dealing with large amounts of data. The principal component analysis (PCA) algorithm may be one of the bridges connecting these two aspects. On the one hand, it can be used for anomaly detection, and on the other hand, there are corresponding quantum algorithms for PCA. In this paper, we investigate how to use PCA to search for NP. Taking the example of anomalous quartic gauge couplings in the tri-photon process at muon colliders, we find that PCA can be used to search for NP. Compared with the traditional event selection strategy, the expected constraints on the operator coefficients obtained by PCA based event selection strategy are even better.

Background: Conflicting results have been reported on the association of dietary unsaturated fatty acids (UFAs) with longevity and cardiovascular health. Most previous studies have focused only on the amount of UFAs consumed, not the timing of intake. Methods: This prospective cohort study used data from 30,136 adults aged 18 years and older. Intakes of UFAs by meal time and types were assessed by a 24-h dietary recall for two days. The covariate-adjusted survey-weighted Cox proportional hazards models were performed to evaluate the associations of dietary total unsaturated fatty acid (TUFA), polyunsaturated fatty acid (PUFA), and monounsaturated fatty acid (MUFA) intakes throughout the day and three meals with mortality. Results: During a median of 10.0 years of follow-up, 4510 total deaths occurred. All-cause mortality decreased with increasing intakes at dinner of TUFA (HR: 0.87 [0.77–0.98]), PUFA (HR: 0.81 [0.73–0.91]), and MUFA (HR: 0.88 [0.77–0.99]). With an increased intake of PUFA at dinner, CVD mortality showed a decreasing trend. However, the inverted L-shaped non-linear trend in all-cause mortality was found with increasing intake at breakfast of TUFA (HR: 1.35 [1.17–1.57], Q3 vs. Q1), PUFA (HR: 1.30 [1.13–1.50]), and MUFA (HR: 1.28 [1.13–1.45]). Meanwhile, increased breakfast intake of UFAs was associated with increased CVD and heart disease mortality. Conclusions: Meal timing influences the association of UFAs with all-cause and CVD-related mortality.

Abstract Background Previous studies have shown that secondary metabolites of Bacillus subtilis strain Z15 (BS-Z15) are effective in treating fungal infections in mice. To evaluate whether it also modulates immune function in mice to exert antifungal effects, we investigated the effect of BS-Z15 secondary metabolites on both the innate and adaptive immune functions of mice, and explored its molecular mechanism through blood transcriptome analysis. Results The study showed that BS-Z15 secondary metabolites increased the number of monocytes and platelets in the blood, improved natural killer (NK) cell activity and phagocytosis of monocytes-macrophages, increased the conversion rate of lymphocytes in the spleen, the number of T lymphocytes and the antibody production capacity of mice, and increased the levels of Interferon gamma (IFN-γ), Interleukin-6 (IL-6), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) in plasma. The blood transcriptome analysis revealed 608 differentially expressed genes following treatment with BS-Z15 secondary metabolites, all of which were significantly enriched in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms for immune-related entries and pathways such as Tumor Necrosis Factor (TNF) and Toll-like receptor (TLR) signaling pathways, and upregulated expression levels of immune-related genes such as Complement 1q B chain (C1qb), Complement 4B (C4b), Tetracyclin Resistant (TCR) and Regulatory Factor X, 5 (RFX5). Conclusions BS-Z15 secondary metabolites were shown to enhance innate and adaptive immune function in mice, laying a theoretical foundation for its development and application in the field of immunity.

Both air pollution and physical inactivity contribute to the increased risk of incident chronic kidney disease (CKD). However, the detrimental effects of air pollution exposure could be augmented by an elevated intake of air pollutants during exercise. In the present study, we analyzed 367,978 participants who were CKD-free at baseline (2006–2010) based on the UK Biobank. Air pollutants included fine particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and nitrogen oxides (NOX). Physical activity (PA) was obtained by the self-reported questionnaire. Using Cox proportional hazards models, hazard ratios (HRs) for incident CKD related to air pollution, PA, and incident CKD were evaluated. During a median of 12.4 years of follow-up, 14,191 incident CKD events were documented. High PM2.5, PM10, NO2, and NOX increased CKD risks by 11 %, 15 %, 14 %, and 12 %, respectively, while moderate and high PA reduced CKD risks by 18 % and 22 %, respectively. Participants with high PA and low air pollution exposure had 29 %, 31 %, 30 %, and 30 % risks of incident CKD than those with low PA and high air pollution exposure for the four air pollutants, with multivariable-adjusted HRs of 0.71 (95 % confidence intervals [CI]: 0.65–0.76) for PM2.5, 0.69 (95 % CI: 0.64–0.75) for PM10, 0.70 (95 % CI: 0.64–0.75) for NO2, and 0.70 (95 % CI: 0.64–0.75) for NOX. No clear interactions were observed between each air pollutant exposure and PA (all P for interaction > 0.05). The findings that reducing air pollution exposure and increasing PA were both independently correlated with a diminished risk of incident CKD suggest that PA could be targeted to prevent CKD generally regardless of air pollution levels. Further research is needed in areas polluted moderately and severely to examine our findings.