High altitude environments challenge the physiology of mammals mainly due to hypoxic conditions. As a response to this, many mammals show molecular adaptations e.g. by mutations in the genes encoding the hemoglobins. This can result in an increase O2 affinity (and thereby a leftshifted O2 dissociation curve) which will enhance the pulmonary loading of O2. However, the shift can also hamper the O2 unloading process to the metabolic tissue. To elucidate this trade-off between loading and unloading a numerical computer model was made in the program Insigt maker based on the organism, deer mouse (Peromyscus maniculatus). The numerical solution was calculated by the Runge-Kutta method. A time step (dt) of 0.00001 was chosen, as the results were independent of dt at this value. This was tested by investigating the residual values of an interval of dt around 0.00001. The model showed how the trade-off is expressed at different altitudes and how the degree of advantages/disadvantages varies. Based on the model it can be suggested that the trade-off is beneficial, when the hypoxia is severe at high altitude. This is consistent with previously findings. 

 

If I could understand the logic in how '+' and '-' arrows work in this diagram, I would be thrilled! This would solve a lot of confusion I experience with many other diagrams. If I understand it here, I can understand it there.

REvisited Causal loop diagram based on PhenomenalWorld article on Non-Hegemony (Multilateralism) via my Gemini interaction using Gene Bellinger's AI prompts  Originally to provide an overarching structure to simplify the previous messy insight aggregate of multiple lower scale elements based on Fadhel Kaboub's substack blog entries esp BRICS  Multipolar world

A shifting the burden structure occurs when there are different ways to address a situation. With one approach being easier, faster, and requiring fewer resources, which do you think gets pursued? The problem is that taking the easier path ensures one will have to take the easier path repeatedly, and makes it harder to pursue the long-term better solution. To jest kopia modelu stworzonego przez Gene Bellinger

Questo modello ripercorre le dinamiche nella fiaba di  Esopo "Al lupo al lupo!"

Testingin...

WIP Book Summary 

WIP Cloned insight (Fig 3.1 from Jorgen Randers book 2052 a Global Forecast for the Next Forty Years) with Fidel Kaboub MMT Prespective CLD kumu added ALso AI based work at Gene's brain link  and Colonial origins (Why Nations Fail Critique paper (also via brain link) Continued top down integration at insight

We want to know the estimated projected Earnings before tax of a 5 year project. For this we have modeled Sales Growth, Cost of goods sold and Admin. expenses applying sensitivity testing by using probability distributions.

Causal loop diagram summary based on Richard Southwell's video Category Theory For Beginners: Open Dynamical Systems and my Gemini interaction Feb2026 using Gene Bellinger's AI prompts

William T. Powers insight that animate behavior is the purposeful control of perception. Behavior exists to control perceptual signals; internal reference signals are purposes. Continual reorganization of ends and means in the face of obstacles is Learning.

Examples of macroanalysis relevant to representing causal mechanisms linking genes to diseases from darden2018 MecCog article and 2017 Product to Process article