Last 2 pages of endocrinology

Endocrinology

• Mineralocorticoids (aldosterone, deoxycorticosterone) regulate sodium-potassium metabolism.
• The adrenal medulla produces epinephrine, which stimulates the "fight or flight" response.
• Norepinephrine stimulates reactions similar to epinephrine but causes more vasoconstriction.
• Without a working pancreas, death would occur.

Pancreas

• The pancreas produces insulin.
• Insulin stimulates glycogen formation and storage, and stimulates carbohydrate oxidation.
• Insulin inhibits the formation of new glucose.
• Glucose is important for brain function.
• Glucagon stimulates conversion of glycogen stored in the liver into glucose.

Kidneys

• Kidneys filter blood and excrete waste extracts in the form of uric acid and water (urine).
• Antidiuretics help the body retain water.
• Angiotensin II is an antidiuretic agent that controls thirst and water absorption.
• Renin is a hypertensive agent that controls blood pressure.
• "Diuretic" means to "pee urine".

Algorithmic Complexity

What?

• A way to describe the performance of an algorithm in terms of time or space, as a function of the input size.
• Time Complexity is the amount of time taken by an algorithm to run, relative to the length of the input
• Space Complexity is the amount of memory space taken by an algorithm to run, relative to the length of the input

Why?

• Allows comparison of different algorithms' efficiency.
• Helps predict how well an algorithm scales as the input size grows.
• Informs decisions about which algorithms to use for specific tasks.

How?

• Big O notation describes the asymptotic behavior of an algorithm.

Big O Notation

What?

• Describes the upper bound of an algorithm's time or space complexity.
• Describes the worst-case scenario for an algorithm's performance.
• "Big O" drops all terms except the highest order term.

Common Complexities

• Constant: O(1)
  • Example: Accessing an element in an array by index
• Logarithmic: O(log n)
  • Example: Binary search
• Linear: O(n)
  • Example: Searching for an element in a linked list
• Log-Linear: O(n log n)
  • Example: Merge sort
• Quadratic: O(n^2)
  • Example: Comparing each element in a list to every other one
• Cubic: O(n^3)
  • Example: Matrix Multiplication
• Exponential: O(2^n)
  • Example: Finding all subsets of a set
• Factorial: O(n!)
  • Example: Finding all permutations of a set
• Double Factorial: O(n!!)
  • Algorithms that exhibit double factorial time complexity are be highly inefficient and generally impractical

How to Determine Complexity

• Identify the input size - what variable represents the size of the input?
• Count the operations performed by the algorithm as a function of the input size.
• Simplify the expression by dropping all constant factors and lower-order terms.
• Express the simplified expression using Big O notation.

Example

• The time complexity of this sum_array algorithm is O(n), because the loop iterates over each element in the array once.

def sum_array(array):
    sum = 0 # O(1)
    for number in array: # O(n)
        sum += number # O(1)
    return sum # O(1)