Profiling in healthcare is closely linked to personalized medicine and allows for better tailored decisions rather than decisions based on average characteristics. Possible applications include diagnostic decision-making and selection of biomarker-led therapies, predictions, resistance detection, disease surveillance, risk assessment, detection of recurrence, and early detection (WJst, 2010). The biggest challenges of big data-based profiling arise from the derivation of knowledge that leads to certain ethically and legally sensitive decisions: this can lead to non-transparent decision-making and even unfair treatment and discrimination of individuals. “Profiling can perpetuate existing stereotypes and social segregation. It can also lock a person into a certain category and limit them to their proposed preferences. This may affect their freedom to choose, for example, certain products or services such as books, music or news feeds. It can lead to inaccurate predictions, denial of services and goods and, in some cases, unjustified discrimination” (European Commission, 2018a). The relationship between population dynamics and invariances of frequency (or rate) distributions could be made a little more explicit: “By definition, big data in healthcare refers to electronic health records that are so large and complex that they are difficult (if not impossible) to manage with traditional software and/or hardware.” (Rubin and Desser, 2008). On the one hand, many of the promises related to this large and complex data have not yet been realized; On the other hand, there are fears of increasing misuse of this data. Both of these aspects are consequences of complexity, making it impossible to assess in advance the achievable benefits and abuses. Therefore, discussing big data challenges carries a high risk of bias projection, and it is important to reduce this risk by presenting and reviewing relevant legal terms and frameworks. To understand common frequency patterns, the generic form of probability distributions plus the preserved average frequency is sufficient. The general theory includes the cases between the Zipf and Log Series endpoints and provides a general framework for analyzing widespread abundance patterns.
for n ≥ 1. If we limit the average frequency, 〈n〉, in terms of this distribution, then bioabundancy director and city councillor Sue Roberts said that “democracy is dead” when the government “prevented it from rejecting the hated plan.” Bioabundance actively supports and cooperates with the Climate and Ecology Emergency Bill Alliance. Lead: Sally Mears Although only 20-30% of judicial reviews were successful in planning, lawyers felt Bioabundance had a reasonable record. Diversity, productivity, abundance, and biomass in ecology are loosely analogous to state variables in thermodynamics, such as pressure, volume, temperature, and the number of moles of a gas reservoir. In thermodynamics, there is a universal relationship between state variables, also called the equation of state, in the form of the ideal gas law: PVâ=ânRT. Equations of state are common in physics and chemistry and are derived from fundamental theory, but such framing was lacking in macroecological studies of ecosystems. A successful equation of state derived from ecological theory would deepen our understanding of ecology, predict diversity or productivity from knowledge of other state variables at the system level, and possibly improve the application of ecological theory to conservation and restoration.9 The conclusion of the document needs to be broadened. As a reader, I need a more detailed explanation of how the “simple invariant structure” approach of common probability distributions is revealed. Finally, we should have more intuition about how maintaining the average frequency leads to results. In addition, it would be good to have more knowledge about what would make a system have more or less proportional processes acting on it. New technologies continue to democratize, decentralize and disrupt production, offering the possibility that scarcity is a thing of the past for many industries.
We call these technologies abundance. But our economy and legal institutions are based on scarcity. This is a groundbreaking action by Bioabundanz and our last chance to put our environment ahead of the profit of homebuilders in South Oxfordshire. In thermodynamics, it has proved useful to distinguish between the micro-plane and macro-plane descriptions of the system, and then to maximize the entropy of Shannon`s information10,11 to derive micro-level phenomena from constraints imposed by state variables at the macro level. For example, the Boltzmann distribution of molecular kinetic energies can be derived from knowledge of the total energy of the system and the number of molecules. When we extend this concept to ecology, we take variables at the micro level such as the metabolic rates (varepsilon,) of individuals and the abundances, n, of species within an ecological community composed, for example, of plants, arthropods or mammals. We take state variables at the macro level such as the total number of species, S, the total number of individuals, N, in the community, and the total metabolic rate, E, of all individuals in a given area A. An application of MaxEnt then leads to the theory of maximum entropy of ecology (METE)12,13,14, which we use to derive an equation of state. What I think would improve the manuscript is greater contact with other methods to derive the same frequency distributions and a broader discussion of boundaries. Various other semi-empirical relationships between biomass, species richness, abundance and productivity or metabolic rate have also been proposed5,7,8. In addition, attempts have been made to link models in macroecology by looking for associations between hypothetical power-law exponents used to characterize different scale relationships between, for example, abundance, body size, and spatial distribution.19,20 None of these efforts has led to the desired broadly applicable unification of state variables in ecology. For this reviewer, the importance of the article lies not so much in the technical results (which, given the premises set out in equation 1 and the arguments preceding equation 2, directly, as in the implications of algebra, but more in the broader questions it raises about observation in general.
In particular, as with the previous articles on related topics that Frank wrote, this article gives a principled way of inferring observed generic relationships on rank and frequency that are independent of the physical details of the system being studied. One possible answer to this type of result is to see it as a consequence of how the argument is constructed in the premises, but I think the way the different parameters are related to physical properties (albeit in a generic and therefore somewhat abstract way) shows that there is something more fundamental at work here than the mathematical hand. Imagine, for example, how strange the universe would seem (to us, here and now) if the information constraints on probability distributions were not invariant to the affine transformation; for example, the Poisson distribution described random numbers of small numbers of small things, but not small numbers of large things. Just as understanding what it means for small random counts to follow a Poisson distribution contributes to the understanding of a data set, it is not a trivial thing (nor a piece of pure phenomenology) for researchers to be able to use the general relationship that Frank derived here to understand the observed rank frequency relationships relative to the scale in which the underlying processes express measurable phenomena.