Epidemic management and control through risk-dependent individual contact interventions

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.     

Polypeptide fold in the two metal clusters of metallothionein-2 by nuclear magnetic resonance in solution

The solution conformation of rabbit liver Cd27+-metallothionein-2 was determined by nuclear magnetic resonance (n.m.r.) and distance geometry. The n.m.r. data are based on complete sequence-specific resonance assignments for the polypeptide chain. This letter describes the global arrangement of the polypeptide chain, which forms two distinct domains containing metal clusters of three and four Cd ions, respectively.     

Sequential 1H NMR assignments of kistrin, a potent platelet aggregation inhibitor and glycoprotein IIb-IIIa antagonist.

Sequence-specific nuclear magnetic resonances assignments have been obtained for the protons of kistrin. Kistrin is a small naturally occurring snake venom protein that inhibits platelet aggregation by blocking the interaction of fibrinogen with the membrane-bound glycoprotein IIb-IIIa (GP IIb-IIIa), a receptor from the integrin family. Kistrin has an Arg-Gly-Asp sequence which is believed to form an adhesion recognition sequence that is essential for activity. Therefore, the interaction between kistrin and GP IIb-IIIa may provide important information on the motif used by integrins to recognize their target proteins which bear RGD sequences. Kistrin consists of 68 residues and contains six intramolecular disulfide bonds. Although one-third of the amide protons are protected from exchange with the solvent, there appears to be little or no regular secondary structure. The large number of NOE's between residues separated by two and three positions in the sequence indicates that the protein contains a large number of tightly packed loops. Along with the sequential assignments, this paper also discusses the construction and use of computerized data bases for manipulating NMR results. A strategy for computer-assisted sequential resonance using these data bases is also presented.