Ambient air pollution is an established risk factor for premature mortality from chronic cardiovascular, respiratory and metabolic diseases, while evidence on neurodegenerative diseases and psychiatric disorders remains limited. We examined the association between long-term exposure to air pollution and mortality from dementia, psychiatric disorders, and suicide in seven European cohorts. Within the multicenter project 'Effects of Low-Level Air Pollution: A Study in Europe' (ELAPSE), we pooled data from seven European cohorts from six countries. Based on the residential addresses, annual mean levels of fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), black carbon (BC), ozone (O₃), and 8 PM_2.5 components were estimated using Europe-wide hybrid land-use regression models. We applied stratified Cox proportional hazard models to investigate the associations between air pollution and mortality from dementia, psychiatric disorders, and suicide. Of 271,720 participants, 900 died from dementia, 241 from psychiatric disorders, and 164 from suicide, during a mean follow-up of 19.7 years. In fully adjusted models, we observed positive associations of NO₂ (hazard ratio [HR] = 1.38; 95 % confidence interval [CI]: 1.13, 1.70 per 10 µg/m³), PM_2.5 (HR = 1.29; 95 % CI: 0.98, 1.71 per 5 µg/m³), and BC (HR = 1.37; 95 % CI: 1.11, 1.69 per 0.5 × 10^-5/m) with psychiatric disorders mortality, as well as with suicide (NO₂: HR = 1.13 [95 % CI: 0.92, 1.38]; PM_2.5: HR = 1.19 [95 % CI: 0.76, 1.87]; BC: HR = 1.08 [95 % CI: 0.87, 1.35]), and no association with dementia mortality. We did not detect any positive associations of O₃ and 8 PM_2.5 components with any of the three mortality outcomes. Long-term exposure to NO₂, PM_2.5, and BC may lead to premature mortality from psychiatric disorders and suicide.

The mechanisms whereby ambient air pollution and temperature changes promote cardiac events remain incompletely described. Seventy-three nonsmoking healthy adults (mean age 23.3, SD 5.4 years) were followed with up to four repeated visits across 15 months in Beijing in 2014–2016. Biomarkers relevant to myocardial damage (high-sensitivity cardiac troponin I [hs-cTnI]), inflammation (growth differentiation factor-15 [GDF-15]), and oxidative stress (8-hydroxy-2′-deoxyguanosine [8-OHdG]) were measured at each visit, while ambient air pollution and temperature were monitored throughout the study. Linear mixed-effects models coupled with distributed lag nonlinear models were used to assess the impacts of each exposure measure on study outcomes. During follow-up, average daily concentrations of fine particulate matter and outdoor temperature were 62.9 µg/m³ (8.1–331.0 µg/m³) and 10.1 °C (−6.5°C to 29.5°C). Serum hs-cTnI levels were detectable in 18.2% of blood samples, with 27.4% of individuals having ≥1 detectable values. Higher levels of ambient particulates and gaseous pollutants (per interquartile range) up to 14 days before clinical visits were associated with significant alterations in hs-cTnI levels of 22.9% (95% CI, 6.4, 39.4) to 154.7% (95% CI, 94.4, 215.1). These changes were accompanied by elevations of circulating GDF-15 and urinary 8-OHdG levels. Both low (5th percentile, −2.5 °C) and high (95th percentile, 24.8°C) outdoor temperatures, with breakpoint at ~13.0°C as the reference level, were also associated with elevations of hs-cTnI levels. Short-term exposure to ambient air pollution and temperature was associated with cardiac troponin, a biomarker of myocardial damage, along with increased inflammation and oxidative stress responses. These findings extend our understanding of the biological mechanisms linking pervasive environmental exposure to adverse cardiac events.

The linear Doppler effect has been widely used to detect the translational motion of objects. However, it suffers difficulties in measuring the angular motion of a rotating target. In recent years, the rotational Doppler effect based on a vortex beam has been helpful to solve the problem of rotational measurement and has attracted extensive attention in remote sensing. This paper expounds the theoretical and experimental basis of the rotational Doppler effect and briefly summarizes its development for the detection of macro and micro targets. Specifically, the properties and analysis methods of a rotational Doppler shift when the vortex beam is misaligned with the rotation axis are described in detail. In addition, the existing problems and further developments in rotation detection based on the rotational Doppler effect are discussed.

Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance. Typical MTL methods are jointly trained with the complete multitude of ground-truths for all tasks simultaneously. However, one single dataset may not contain the annotations for each task of interest. To address this issue, we propose the Semi-supervised Multi-Task Learning (SemiMTL) method to leverage the available supervisory signals from different datasets, particularly for semantic segmentation and depth estimation tasks. To this end, we design an adversarial learning scheme in our semi-supervised training by leveraging unlabeled data to optimize all the task branches simultaneously and accomplish all tasks across datasets with partial annotations. We further present a domain-aware discriminator structure with various alignment formulations to mitigate the domain discrepancy issue among datasets. Finally, we demonstrate the effectiveness of the proposed method to learn across different datasets on challenging street view and remote sensing benchmarks.