Cross-neutralizing antibodies bind a SARS-CoV-2 cryptic site and resist to circulating variants

The emergence of numerous variants of SARS-CoV-2, the causative agent of COVID-19, has presented new challenges to the global efforts to control the still ravaging COVID-19 pandemic. Here, we obtain two cross-neutralizing antibodies (7D6 and 6D6) that target Sarbecoviruses’ receptor binding domain (RBD) with sub-picomolar affinities and potently neutralize authentic SARS-CoV-2. Crystal structures show that both antibodies bind a cryptic site different from that recognized by existing antibodies and highly conserved across Sarbecovirus isolates. Binding of these two antibodies to the RBD clashes with the adjacent N-terminal domain and disrupts the viral spike. Significantly, both antibodies confer good mutation resistance to the currently circulating SARS-CoV-2 variants. Thus, our results have direct relevance to public health as options for passive antibody therapeutics and even active prophylactics, and can also inform the design of pan-sarbecovirus vaccines.

Feature-Based Test Focus Selection Technique Using Classes Connections Weight

Testing could cost more than fifty percent of all development cost, particularly integration testing consumes around eighty percent of testing cost. Integration testing aims to discover errors in the connections among classes which are collaborate and communicate in order to provide specific services. Though, testing all connections among classes is impractical because of the cost, effort and time constraints. Test focus selection might help testers to concentrate on the main and vital connections among classes which it could be the most error prone ones. The authors proposed approach amalgamates the static and dynamic analysis in order to detect, trace, and weight the connections among classes through method level communications. Their approach harnessed an open source tracing tool (MUTT). The MUTT allows them to return all the methods in all classes that have been called respecting to any specific feature which has triggered by the system user. The experimental results reveal how the proposed approach achieves good mutation testing score on the systems under study.

SELF-ORGANIZATION OF AGING IN A MODIFIED PENNA MODEL

The Penna model for biological aging is modified so that the fertility of each individual is determined by means of the number of activated mutations at that time. A new concept of "good" mutation, which makes an individual to mature enough to reproduce, is introduced. It is assumed that each individual can reproduce only during adulthood, which is determined by the number of activated mutations. The results of Monte Carlo calculations using the modified model show that the ranges of the reproductive age are broadened as time goes by, thus showing self-organization in the biological aging to the direction of the maximum self-conservation. In addition, the population, the survival rate, and the average life span were calculated and analyzed by changing the number of new mutations at birth. It is observed that the higher is the considered number of new mutations at birth, the shorter is the obtained average life span. The mortality functions are also calculated and they showed the exponential increase in adulthood, satisfying the Gompertz law.