Relativistic Heavy--Ion Collisions in the Dynamical String--Parton Model

We develop and extend the dynamical string parton model. This model, which is based on the salient features of QCD, uses classical Nambu-Got\=o strings with the endpoints identified as partons, an invariant string breaking model of the hadronization process, and interactions described as quark-quark interactions. In this work, the original model is extended to include a phenomenological quantization of the mass of the strings, an analytical technique for treating the incident nucleons as a distribution of string configurations determined by the experimentally measured structure function, the inclusion of the gluonic content of the nucleon through the introduction of purely gluonic strings, and the use of a hard parton-parton interaction taken from perturbative QCD combined with a phenomenological soft interaction. The limited number of parameters in the model are adjusted to $e^+e^-$ and $p$--$p$ data. Utilizing these parameters, the first calculations of the model for $p$--$A$ and $A$--$A$ collisions are presented and found to be in reasonable agreement with a broad set of data.Comment: 26 pages of text with 23 Postscript figures placed in tex

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease