Infectious Diseases
Lyle D. Broemeling in Bayesian Analysis of Infectious Diseases, 2021
Microorganisms play the survival game very well indeed, because they adapt far more rapidly than humans as the scene shifts in front of them. Among humans each generation is approximately 20 years, but for bacteria a generation ranges from 20–30 min, and in the case of viruses, a generation is much smaller. Richard Krause, former director of the National Institute of Allergy and Infectious Disease (NIAID) calls this rapidity a millennium in a fortnight. The current director of this agency is Dr. Fauci, who appears with President Trump on the daily briefings of the Coronavirus Task Force. An important aspect of evolution is natural selection, the process by which genetically adapted individuals leave more offspring and in the process transmit more favorable characteristics, and thus operate far more efficiently in the setting of microorganisms. Since these aggregate in large numbers, bacteria and viruses do support a considerable variety in their communities, including mutations that may be expressed when the environment changes. A thimble can contain a billion bacteria, can disappear on Tuesday, and appear in the same quantity on Wednesday.
Life and Its Information
David E. H. Jones in Why Are We Conscious?, 2017
My rather old natural history books tend to ignore such internal chemistry. Their ideal reader, I feel, would be examining a dead specimen. Many geologists classify and date rocks by the fossils they find in them: shells and bones and suchlike (very tiny creatures in the rock might require a microscope). The type and abundance of the extant creatures changes greatly over time—which is itself evidence that evolution has occurred. My books give a lot of detail about e.g. the skeleton of a specimen, the number and the nature of its teeth, and what other species it might have been related to. And indeed, biology advances by amassing huge numbers of such physical details. As with all science, it is ultimately based on careful observations, and not at all on the state of mind of whoever made those observations. It is largely by studying such observations that Darwin built the evidence to buttress his mighty theory of evolution. This asserts that while species seem immutable in the short term (so that, for example, lions always beget more lions) they can vary in the long term—which Darwin felt was many millions of years. His argument, supported by many biological facts, was that each act of sexual reproduction gave offspring which varied slightly. The variations which improved the species had a better chance of surviving and having offspring in their turn (the so-called survival of the fittest). So the species slowly changed. On a long enough time scale, all living things may have evolved from just one ancestor.
Cosmology and Quantum Biology
Jim Lynch in What Is Life and How Might It Be Sustained?, 2023
In June 2021, the London Institute of Mathematical Sciences compiled a list of the 23 most important mathematical questions of our time. At the top of the list was Theory of Everything. Will this be resolved by string theory, loop quantum gravity, or something new? Also included in the list was Thermodynamics of Life. According to Darwin’s theory, evolution is the result of mutation, selection, and inheritance, but from a physical perspective, we do not understand how life got started in the first place. What is the thermodynamic basis for emergent self-selection and adaptation of which biology is just one instance? Can it be used to create digital artificial life? Theory of Immortality. Ageing is ascribed to the accumulation of errors – an inevitable consequence of the increase of disorder. But mounting experimental evidence suggests that ageing is not a fact of life. Is it a thermodynamics necessity, or is it instead favoured by natural selection? Can we mathematically describe the pros and cons of ageing? Is it possible to slow or even stop it?
The Prosthetic Penis and the Trans Penis: Changing Representations of and Cultural Discourses About the Penis
Published in Studies in Gender and Sexuality, 2020
Evolutionary theory is a related scientific discourse arriving on the scene late or evolving late (pun intended). After a long silence following Darwin, theorists attempted to explain and affirm the importance of the size increase that occurred between chimpanzees, prehominids, and hominids. Some went so far as to make it the founding moment of our evolution when males stood on their hind legs to make their penises maximally visible as a mating signal for females. Or, as Geoffrey Miller (2000) put it, “size mattered,” at least in the evolutionary past. But after that phase of evolution was completed, as with the medical discourse, there was another discourse of reassurance since the meaningful comparison became men to chimpanzees (prehominids to hominids), who had small, inflexible penises. The comparison was not one man compared to another man. From that evolutionary perspective, in a sense all men were large in comparison to their ancestors. But predictably, the cultural importance of comparing men to each other continued and still continues.
On the life and work of Korbinian Brodmann (1868–1918)
Published in Journal of the History of the Neurosciences, 2019
Thomas Mueller, Uta Kanis-Seyfried
Brodmann and his contemporaries also used the term localization for the morphological part of a topical brain research in the cerebral cortex—that is, for the mapping of the brain. Theodor Meynert (1833–1892) had recognized functional differences of individual regions of the cortex and demanded the description of individual “cortical organs.” Oskar Vogt’s Berlin Institute immediately tied in with Meynert’s cortex organology. Whereas Oskar Vogt and Cécile Vogt studied the myeloarchitectural structure (i.e., the nerve fibers), Brodmann focused on the cellular structure (i.e., cytoarchitectonics). Research to date had described brain convolutions and convolution complexes. A uniform nomenclature was missing. Brodmann’s aim was to obtain a complete picture of the cortex construction and its local modifications in all its parts, and possibly to arrive at a topographical–localizational structure of the cortex surface that could also be used by the clinic. He had realized that the creation of these foundations for more precise topographical–localizational research needed to be preceded by improved information on the principles of cerebral cortex organization: by a comparative anatomical analysis of human and mammal brains. An important prerequisite for this approach was the theory of evolution by Charles Darwin (1809–1882).
An insight into technology diffusion of tractor through Weibull growth model
Published in Journal of Applied Statistics, 2018
Bishal Gurung, K. N. Singh, Ravindra Singh Shekhawat, Md Yeasin
To this end, a very efficient and powerful stochastic optimization technique, viz. GA technique first conceived by John Holland in 1975, can be employed. GA methodology, a computational method to find optimal solution, is capable of rectifying the above limitations. In GA, the parameters form a string, where each string represents a solution to the problem. An objective payoff function is used to measure how well a particular solution solves the problem. Improved solutions are subsequently evolved within a population of chromosomes over a number of generations. The GA algorithm optimizes a problem by iteratively trying to improve a candidate solution with respect to an objective function. GA simulates the evolution of living organisms, where the fittest individual dominates over the weaker ones, by mimicking the biological mechanism of evolution. It combines Darwin’s principle of ‘natural selection’ and ‘survival of the fittest’ with computer-constructed evolution mechanism to select better offspring from the original population. In the GA-literature, mainly two types of procedures are available, viz. Binary-coded GA (BCGA) and Real-coded GA (RCGA). In the former, the coded parameters form a string (chromosome), where each string represents a solution to the problem. Unlike the classical search and optimization methods, GA begins its search with a random set of solutions instead of just one solution. So, GA not only overcomes the traps of local optimization, but also reduces much computational time to find optimum solution [8].
Related Knowledge Centers
- Biodiversity
- Genetic Drift
- Heredity
- Morphology
- Phenotype
- Phenotypic Trait
- Physiology
- Heritability
- Natural Selection
- Fitness