A study of the relationship between rear-of-wheel particle emissions and close-proximity tire/road noise of a passenger car.

Non-exhaust particulate emissions and road traffic noise will continue to constitute significant threats to the environment and human health during the 21st century. In the EU alone, >60 million adults are subjected to unhealthy levels of vehicle noise, while over 200,000 premature deaths are caused by chronic exposure to excessive concentrations of fine particles, with road traffic being a key source. Although these pollutants share common sources and can affect the same targets, studies have often treated their emissions separately.

Neoantigen-specific CD8 T cells with high structural avidity preferentially reside in and eliminate tumors.

The success of cancer immunotherapy depends in part on the strength of antigen recognition by T cells. Here, we characterize the T cell receptor (TCR) functional (antigen sensitivity) and structural (monomeric pMHC-TCR off-rates) avidities of 371 CD8 T cell clones specific for neoantigens, tumor-associated antigens (TAAs) or viral antigens isolated from tumors or blood of patients and healthy donors. T cells from tumors exhibit stronger functional and structural avidity than their blood counterparts.

TCR sequencing and cloning methods for repertoire analysis and isolation of tumor-reactive TCRs.

T cell receptor (TCR) technologies, including repertoire analyses and T cell engineering, are increasingly important in the clinical management of cellular immunity in cancer, transplantation, and other immune diseases. However, sensitive and reliable methods for repertoire analyses and TCR cloning are still lacking. Here, we report on SEQTR, a high-throughput approach to analyze human and mouse repertoires that is more sensitive, reproducible, and accurate as compared with commonly used assays, and thus more reliably captures the complexity of blood and tumor TCR repertoires.

Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8 T-cell epitopes.

The recognition of pathogen or cancer-specific epitopes by CD8 T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions.

A differential process mining analysis of COVID-19 management for cancer patients.

During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death.