Search for Soft Unclustered Energy Patterns in Proton-Proton Collisions at 13 TeV.

The first search for soft unclustered energy patterns (SUEPs) is performed using an integrated luminosity of 138  fb^{-1} of proton-proton collision data at sqrt[s]=13  TeV, collected in 2016-2018 by the CMS detector at the LHC. Such SUEPs are predicted by hidden valley models with a new, confining force with a large 't Hooft coupling. In events with boosted topologies, selected by high-threshold hadronic triggers, the multiplicity and sphericity of clustered tracks are used to reject the background from standard model quantum chromodynamics.

Adoptive transfer of membrane-restricted IL-12-TCR T cells promotes antigen spreading and elimination of antigen-negative tumor variants.

Background: Adoptive T-cell therapy has demonstrated clinical activity in B-cell malignancies, offering hope for its application to a broad spectrum of cancers. However, a significant portion of patients with solid tumors experience primary or secondary resistance to this treatment modality. Target antigen loss resulting either from non-uniform antigen expression or defects in antigen processing and presentation machinery is one well-characterized resistance mechanism.

Search for the Z Boson Decay to ττμμ in Proton-Proton Collisions at sqrt[s]=13 TeV.

The first search for the Z boson decay to ττμμ at the CERN LHC is presented, based on data collected by the CMS experiment at the LHC in proton-proton collisions at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 138  fb^{-1}. The data are compatible with the predicted background. For the first time, an upper limit at the 95% confidence level of 6.

Crystal structure validation of verinurad proton-detected ultra-fast MAS NMR and machine learning.

The recent development of ultra-fast magic-angle spinning (MAS) (>100 kHz) provides new opportunities for structural characterization in solids. Here, we use NMR crystallography to validate the structure of verinurad, a microcrystalline active pharmaceutical ingredient. To do this, we take advantage of H resolution improvement at ultra-fast MAS and use solely H-detected experiments and machine learning methods to assign all the experimental proton and carbon chemical shifts.