Automatically Improving Constraint Models in Savile Row through Associative-Commutative Common Subexpression Elimination

When solving a problem using constraint programming, constraint modelling is widely acknowledged as an important and difficult task. Even a constraint modelling expert may explore many models and spend considerable time modelling a single problem. Therefore any automated assistance in the area of constraint modelling is valuable. Common sub-expression elimination (CSE) is a type of constraint reformulation that has proved to be useful on a range of problems. In this paper we demonstrate the value of an extension of CSE called Associative-Commutative CSE (AC-CSE). This technique exploits the properties of associativity and commutativity of binary operators, for example in sum constraints. We present a new algorithm, X-CSE, that is able to choose from a larger palette of common subexpressions than previous approaches. We demonstrate substantial gains in performance using X-CSE. For example on BIBD we observed speed increases of more than 20 times compared to a standard model and that using X-CSE outperforms a sophisticated model from the literature. For Killer Sudoku we found that X-CSE can render some apparently difficult instances almost trivial to solve, and we observe speed increases up to 350 times. For BIBD and Killer Sudoku the common subexpressions are not present in the initial model: an important part of our methodology is reformulations at the preprocessing stage, to create the common subexpressions for X-CSE to exploit. In summary we show that X-CSE, combined with preprocessing and other reformulations, is a powerful technique for automated modelling of problems containing associative and commutative constraints

A cellular chemical probe targeting the chromodomains of Polycomb repressive complex 1

We report the design and characterization of UNC3866, a potent antagonist of the methyllysine (Kme) reading function of the Polycomb CBX and CDY families of chromodomains. Polycomb CBX proteins regulate gene expression by targeting Polycomb repressive complex 1 (PRC1) to sites of H3K27me3 via their chromodomains. UNC3866 binds the chromodomains of CBX4 and CBX7 most potently, with a K d of â ∼1/4100 nM for each, and is 6-to 18-fold selective as compared to seven other CBX and CDY chromodomains while being highly selective over >250 other protein targets. X-ray crystallography revealed that UNC3866's interactions with the CBX chromodomains closely mimic those of the methylated H3 tail. UNC4195, a biotinylated derivative of UNC3866, was used to demonstrate that UNC3866 engages intact PRC1 and that EED incorporation into PRC1 is isoform dependent in PC3 prostate cancer cells. Finally, UNC3866 inhibits PC3 cell proliferation, consistent with the known ability of CBX7 overexpression to confer a growth advantage, whereas UNC4219, a methylated negative control compound, has negligible effects

Erratum: Addendum: A cellular chemical probe targeting the chromodomains of Polycomb repressive complex 1 (Nature chemical biology (2016) 12 3 (180-187))

[No abstract available

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