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. 

 

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. See also Archetypes.

Coping but not coping in health and social care: masking the reality of running organisations beyond safe design capacity. From Eric Wolstenholme Syst. Dyn. Rev. 23, 371–389, (2007) abstract. See also IM-1010 for unfolding CLD version. This is a cascade of swamping structures see gene's insight with video link

Summary of evolution and the modern synthesis with genetics. See also the more recent extended synthesis IM-2099

Radioaktiver Zerfall nach Beispiel des exponentiellen Zerfall-Modells von Gene Bellinger

Unfolding WIP of Eric Wolstenholme's explanation of hospital congestion from March 2022 Youtube video and online stella presentation. Use of cascading interlinked archetypes. See Kumu version,  early discharge boundaries IM for an earlier version and Generic Archetypes IM from Gene and Simpler Version IM

Go to Gene Bellinger's insight version with video link This common archetype of systems that include relapse or recidivism allows exploration of the unintended effects of increasing upstream capacity and swamping downstream capacity. The increase in the relapse rate eventually returns to swamp upstream capacity as well. A social welfare example, based on a TANF case study, from How Small System Dynamics Models Can Help the Policy Process. N. Ghaffarzadegan, J. Lyneis, GP Richardson. System Dynamics Review 27,1 (2011) 22-44 abstract Conference version here

Second Draft

NICOLE DESARIO 

AP BIOLOGY 

JUNE 2013


There are many factors that lead to an increased risk of osteoporosis later in life. Some of these risks are congenital; fixed risks that were acquired during fetal development. Other risks are created or reduced by an individual depending on their lifestyle; which make them unfixed variables. 

Definition: OSTEOPOROSIS (Also known as degenerative bone disease) - "is a disease of bones that leads to an increased risk of fracture. In osteoporosis, the bone mineral density (BMD) of an individual is reduced, bone micro-architecture deteriorates, and the amount and variety of proteins in bone and variety of proteins in bone are altered. Osteoporosis is defined by the World Health Organization as a bone mineral density of 2.5 standard deviations or more below the mean peak bone mass (average of young healthy adults)."

NON-MODIFIABLE RISK FACTORS (Explained)

Age: Increased age increases likelihood of developing osteoporosis

Sex: Females are more likely to experience osteoporosis fragility fractures

Race: Osteoporosis is more common in people of European and Asian decent

Frame: Thin-framed individuals do not stress their bones as much as heavier-set individuals, and therefore do not have as "thick" bones, and are more likely to develop fragile bones (osteoporosis) 

Family history: 30 genes are linked to development of osteoporosis, so an individual can be anywhere between 25 and 80% more likely to develop osteoporosis if it exists in the family. (my mother has it, so I am very likely to develop it if I don't actively make the efforts to protect my bones from degenerating over time.)

Insufficient Prenatal Care: During development in the womb if a fetus does not receive appropriate nutrition, it may develop malnutrition-related deficiency diseases.

(POTENTIALLY) MODIFIABLE RISK FACTORS (Explained)

Smoking/Drinking: Excessive use could lead to increased risk because alcohol use decreases your ability to absorb nutrients. It interferes with the absorption of calcium and Vit D (stomach, pancreas and liver affected). Alcohol also kills osteoblasts, the bone-making cells. It also increases bone-damaging hormones cortisol and parathyroid hormone 

Medication Use: Some medications increase risk of osteoporosis however discontinuing use of said medications is often impossible, and therefore the modifiable risk is non-modifiable at times.

Dietary Habits: Majority of bone development happens before an individual reaches the age of 20, so if dietary requirements of calcium, vitamin D, and phosphorus are insufficient, there will be a greater chance of osteoporosis later in life. 

Hormone Levels: In females, estrogen deficiency following menopause or oophorectomy is correlated with rapid reduction in bone mineral density, while in men, a decrease in testosterone levels has a comparable (but less pronounced) effect.

Sedentary Lifestyle: Staying active and stressing your bones decreases chances of osteoporosis because it encourages osteoblastic activity, if an individual is extremely sedentary, (coupled with a thin frame possibly) they are very susceptible to osteoporosis, and should consider getting active. Also, an individual with more sun exposure absorbs more Vit D.

Fractures: Increased breakage of bones creates weak points where BMD cannot recover to what it was prior to the fracture. Individuals should stay out of fights, reduce falling, and avoid clumsy behavior.

Model is created by Gene Bellinger. Original model can be accessed from https://insightmaker.com/insight/2249/Adjusting-the-Shower-SFD.

Level of biological organization linking cell level division and population level evolution

Regulación de la expresión en múltiples niveles de un gen hipotético de oxidoreductasa. Parametros; H2O2: moleculas iniciales de H2O2; Tasa Oxidativa: velocidad a la que la celula produce nuevo H2O2, Mutación TR1 y ProtS: Activa las mutaciones en estos genes (ver panel de información); miARN: activa la regulación por miARNs.

Summary of the Bumpus 2025 paper and 2024 video Wilmer Leal - Towards a Unified Theory of Time-Varying Data using Gene Bellinger's gemini prompts in a Jan2026 interaction. Paper featured in Nate Osgood's 2025 usask course on Sheaves and Applied Category THeory

Model in support of an article being written about the relationship between investment and austerity. See Version 2

See also:
* Inv vs Aust Sim [IM-2736]
* Inv & Output 1 [IM-2740]
* Inv & Output 2 [IM-2741]

Components of behaviour organised into domains, from NIMH Research Domain Criteria website and BMC paper and 2013 series on current controversies in psychiatry.

New Learning tend to reduce Outdated Thinking, Communicating & Learning though our Outdated Thinking, Communicating & Learning inhibits new learning. The question is then how do we break this cycle. Adopted from "An Introduction to Systems Thinking with STELLA" by Barry Richmond.

Since the mid-eighteenth century, new jobs have emerged then vanished throughout successive revolutions (Industrial, Technological, Digital) due to a combination of globalisation and automation. Now a fourth revolution (the joining of technologies like artificial intelligence, gene editing, and advanced robotics) threatens 4.4 million knowledge work jobs in the UK (“Transformed by AI – how generative artificial intelligence could affect work in the UK”, IPPR, March 2024).