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This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support—and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified—both positive.

Single-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome.

This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support—and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified—both positive.

Single-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome.

Single-cell sequencing provides a new level of granularity in studying the heterogeneous nature of cancer cells. For some cancers, this heterogeneity is the result of copy number changes of genes within the cellular genomes. The ability to accurately determine such copy number changes is critical in tracing and understanding tumorigenesis. Current single-cell genome sequencing methodologies infer copy numbers based on statistical approaches followed by rounding decimal numbers to integer values. Such methodologies are sample dependent, have varying calling sensitivities which heavily depend on the sample’s ploidy and are sensitive to noise in sequencing data. In this paper we have demonstrated the concept of integer-counting by using a novel bioinformatic algorithm built on our library construction chemistry in order to detect the discrete nature of the genome.