Recently historians and philosophers of science have been interested in the role of statistics and probability in investigating population variation. The focus is typically on investigators applying statistics and probability to explain large scale phenomenon that arise out of the collective behavior of numerous and varied individuals. The case studies that inform this work come mostly from molecular physics and 20th century genetical versions of evolutionary theory. Charles Darwin's work on evolution is rarely mentioned in this context except to point out his shortcomings-he made evolutionary theory "ripe" for statistical investigations, but he was not a statistical thinker. But this is a mistake, Darwin was a statistical thinker. In this essay I describe two instances where Darwin utilized statistical methods to investigate evolution. In the light of these cases, we ought to revise our views about Darwin's scientific methodology, in particular, how he came to develop his ideas about evolution and about the nature of his "population thinking". Furthermore, Darwin's cases provide us with an expanded view about what constitutes "statistical thinking" in the biological sciences. In the examples we will find Darwin using statistical measures of type frequencies to detect large scale ensemble effects, confirm hypotheses by comparing between expected and observed averages, and applying the astronomer's law of error to explain evolutionary trends.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.shpsa.2022.08.005 | DOI Listing |
Behav Brain Funct
January 2025
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
Gene-environment interactions in the postnatal period have a long-term impact on neurodevelopment. To effectively assess neurodevelopment in the mouse, we developed a behavioural pipeline that incorporates several validated behavioural tests to measure translationally relevant milestones of behaviour in mice. The behavioral phenotype of 1060 wild type and genetically-modified mice was examined followed by structural brain imaging at 4 weeks of age.
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, 0909, Australia.
This study presents a novel privacy-preserving self-supervised (SSL) framework for COVID-19 classification from lung CT scans, utilizing federated learning (FL) enhanced with Paillier homomorphic encryption (PHE) to prevent third-party attacks during training. The FL-SSL based framework employs two publicly available lung CT scan datasets which are considered as labeled and an unlabeled dataset. The unlabeled dataset is split into three subsets which are assumed to be collected from three hospitals.
View Article and Find Full Text PDFFront Immunol
December 2024
Department of Life Science Frontiers, Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan.
Age-associated differences in the effect of repetitive vaccination, particularly on memory T-cell and B-cell responses, remain unclear. While older adults (aged ≥65 years) exhibited enhanced IgG responses following COVID-19 mRNA booster vaccination, they produced fewer spike-specific circulating follicular helper T cells-1 than younger adults. Similarly, the cytotoxic CD8 T-cell response remained diminished with reduced PD-1 expression even after booster vaccination compared with that in younger adults, suggesting impaired memory T-cell activation in older adults.
View Article and Find Full Text PDFBMC Pregnancy Childbirth
December 2024
Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia.
Background: PTB increases the risk of health problems such as chronic renal disease and diabetes in later life and adverse impacts are inversely correlated with gestational age at birth. Rates of PTB in the Northern Territory (NT) of Australia are amongst the highest nationally and globally, with First Nations babies most affected. This study assessed the magnitude and potential drivers of intergenerational PTB recurrence in the NT.
View Article and Find Full Text PDFJ Theor Biol
February 2025
Institute of Evolution, Centre for Ecological Research, 1121 Budapest, Hungary; Center for the Conceptual Foundations of Science, Parmenides Foundation, 82343 Pöcking, Germany. Electronic address:
Building on the algorithmic equivalence between finite population replicator dynamics and particle filtering based approximation of Bayesian inference, we design a computational model to demonstrate the emergence of Darwinian evolution over representational units when collectives of units are selected to infer statistics of high-dimensional combinatorial environments. The non-Darwinian starting point is two units undergoing a few cycles of noisy, selection-dependent information transmission, corresponding to a serial (one comparison per cycle), non-cumulative process without heredity. Selection for accurate Bayesian inference at the collective level induces an adaptive path to the emergence of Darwinian evolution within the collectives, capable of maintaining and iteratively improving upon complex combinatorial information.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!