Evaluating exposure to vehicle pollutants using physics-informed immersive reality models.

Major health risks and chronic diseases are caused by exposure to unregulated particle pollutants from road, tyre and brake sources. Here, we use large-eddy simulations to identify local exposure to these harmful pollutants and build a physics-informed immersive reality experience to communicate outcomes with the general public for health guidance. Our analysis reveals that exposure to non-exhaust pollution is greatest at the end of braking phases, when deceleration rates are above 3 m s, diminishes to background levels for pedestrians located 1.

The Detection of Invisible Abnormal Metabolism in the FDG-PET Images of Patients With Anti-LGI1 Encephalitis by Machine Learning.

Objective: This study aims to detect the invisible metabolic abnormality in PET images of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis using a multivariate cross-classification method.

Methods: Participants were divided into two groups, namely, the training cohort and the testing cohort. The training cohort included 17 healthy participants and 17 patients with anti-LGI1 encephalitis whose metabolic abnormality was able to be visibly detected in both the medial temporal lobe and the basal ganglia in their PET images [completely detectable (CD) patients].

The Impacts of Obstructive Sleep Apnea Severity on Brain White Matter Integrity and Cognitive Functions in Children: A Diffusion Tensor Imaging Study.

Objective: To investigate the impacts of obstructive sleep apnea (OSA) on white matter (WM) integrity and cognitive functions of pediatric patients with different levels of OSA severity.

Methods: Fifty-eight children with OSA and thirty-four healthy controls (HC) were recruited. All participants underwent diffusion tensor imaging (DTI) examination, polysomnography (PSG), and neurocognitive assessments.

Identification of a Transcriptionally Forward α Gene and Two υ Genes within the Pigeon () IgH Gene Locus.

Compared with mammals, the bird Ig genetic system relies on gene conversion to create an Ab repertoire, with inversion of the IgA-encoding gene and very few cases of Ig subclass diversification. Although gene conversion has been studied intensively, class-switch recombination, a mechanism by which the IgH C region is exchanged, has rarely been investigated in birds. In this study, based on the published genome of pigeon () and high-throughput transcriptome sequencing of immune-related tissues, we identified a transcriptionally forward α gene and found that the pigeon IgH gene locus is arranged as μ-α-υ1-υ2.