Terminating the SARS-CoV-2 pandemic relies upon pan-global vaccination. Current vaccines elicit neutralizing antibody responses to the virus spike derived from early isolates. However, new strains have emerged with multiple mutations: P.1 from Brazil, B.1.351 from South Africa and B.1.1.7 from the UK (12, 10 and 9 changes in the spike respectively). All have mutations in the ACE2 binding site with P.1 and B.1.351 having a virtually identical triplet: E484K, K417N/T and N501Y, which we show confer similar increased affinity for ACE2. We show that, surprisingly, P.1 is significantly less resistant to naturally acquired or vaccine induced antibody responses than B.1.351 suggesting that changes outside the RBD impact neutralisation. Monoclonal antibody 222 neutralises all three variants despite interacting with two of the ACE2 binding site mutations, we explain this through structural analysis and use the 222 light chain to largely restore neutralization potency to a major class of public antibodies.

Highlights d Original strain convalescent and vaccine sera show reduced B.1.1.7 neutralization d N501Y enhances RBD: ACE2 binding affinity d N501Y compromises neutralization by many antibodies with public V-region IGHV3-53 d No widespread escape by B.1.1.7 was observed

Two genes, Idd-3 and Idd-4, that influence the onset of autoimmune type 1 diabetes in the nonobese diabetic mouse have been located on chromosomes 3 and 11, outside the chromosome 17 major histocompatibility complex. A genetic map of the mouse genome, analysed using the polymerase chain reaction, has been assembled specifically for the study. On the basis of comparative maps of the mouse and human genomes, the homologue of Idd-3 may reside on human chromosomes 1 or 4 and Idd-4 on chromosome 17.

Dissemination of systemic carcinoma to the brain continues to carry a poor prognosis. Knowledge of the metastatic patterns and limited survival associated with specific tumor types may be useful for guiding future therapeutic intervention.

We experimentally evaluate bagging and seven other randomization-based approaches to creating an ensemble of decision tree classifiers. Statistical tests were performed on experimental results from 57 publicly available data sets. When cross-validation comparisons were tested for statistical significance, the best method was statistically more accurate than bagging on only eight of the 57 data sets. Alternatively, examining the average ranks of the algorithms across the group of data sets, we find that boosting, random forests, and randomized trees are statistically significantly better than bagging. Because our results suggest that using an appropriate ensemble size is important, we introduce an algorithm that decides when a sufficient number of classifiers has been created for an ensemble. Our algorithm uses the out-of-bag error estimate, and is shown to result in an accurate ensemble for those methods that incorporate bagging into the construction of the ensemble.