Mixing and sharing of genes is essential for population diversity, which contributes to the resilience and, ultimately, the survival of animal and plant communities. However, sharing of genes is antithetical to individual fitness; hence gene mixing is threatened with extinction if selection rewards selfish (clonal) reproduction. To address this dilemma, mechanisms have evolved to enforce a mandate for gene sharing. In most metazoa, the functions of sex and reproduction are tightly entwined, presumably for the purpose of compelling the sharing of genes. In protists, the functions of sex (conjugation) and reproduction (amitosis) are separate. The mandate for gene sharing is enforced, instead, by a form of cellular senescence. Ciliates can reproduce clonally a few hundred times before they senesce and die. Conjugation resets their biological clock, restarting the cycle of clonal reproduction. The question how reproduction in metazoa came to be linked to sex has been explored in the past, but a fully satisfying account in terms of evolutionary ecology is lacking. The question how senescence in protozoa came to be linked to sex has not been addressed, and is the topic of the current study. I present herein two numerical simulations (IBMs) for the evolution of cellular senescence in ciliates. The results shed light not only on the evolution of senescence in higher life forms, but on more general questions concerning the plausibility of group selection.

During the pandemic, millions of Americans have become acquainted with the CDC because its reports and the data it collects affect their day-today lives. But the methodology used and even some of the data collected by CDC remain opaque to the public and even to epidemiologists. In this paper, we highlight areas in which CDC methodology might be improved and where greater transparency could lead to broad collaboration. (1) "Excess" deaths are routinely reported, but not "years of life lost", an easily-computed and more granular datum that is important for public policy. (2) What counts as an "excess death"? The method for computing the number of excess deaths does not include error bars and we show a substantial range of estimates is possible. (3) Pneumonia and influenza death data on different CDC pages is grossly contradictory. (4) The methodology for computing influenza deaths is not described in sufficient detail that an outside analyst might pursue the source of the discrepancy. (5) Guidelines for filling out death certificates have changed during the COVID-19 pandemic, preventing the comparison of 2020-21 death profiles with any previous year. We conclude with a series of explicit recommendations for greater consistency and transparency, and ultimately to make CDC data more useful to the public and epidemiologists and other scientists.

During the pandemic, millions of Americans have become acquainted with the CDC because its reports and the data it collects affect their day-today lives. But the methodology used and even some of the data collected by CDC remain opaque to the public and to the community of academic epidemiology. In this paper, we highlight areas in which CDC methodology might be improved and where greater transparency might lead to broad collaboration. (1) "Excess" deaths are routinely reported, but not "years of life lost", an easily-computed datum that is important for public policy. (2) What counts as an "excess death"? The method for computing the number of excess deaths does not include error bars and we show a substantial range of estimates is possible. (3) Pneumonia and influenza death data on different CDC pages is grossly contradictory. (4) The methodology for computing influenza deaths is not described in sufficient detail that an outside analyst might pursue the source of the discrepancy. (5) Guidelines for filling out death certificates have changed during the COVID-19 pandemic, preventing the comparison of 2020-21 death profiles with any previous year. We conclude with a series of explicit recommendations for greater consistency and transparency, and ultimately to make CDC data more useful to outside epidemiologists.