AKU Overview

Submitted by msgordon on Sat, 10/21/2017 - 13:42

Alkaptonuria, also known as black urine disease or black bone disease is a genetic disorder that prevents the body from processing the amino acids phenylalanine and tyrosine. The disease is caused by a mutation in the HGD gene which codes for homogentisate 1,2 dioxygenase(HGD), an enzyme that degrades homogentisate acid(HGA). The lack of a functioning HGD enzyme results in a buildup of homogentisate acid and its subsequent oxidation products, all of which result in the deposition of ochronotic pigment in cartilage and other connective tissues. While the disease primarily affects cartilage and osseous tissue, Alkaptonuria patients often have heart valve and kidney involvement as well.  The valves of alkaptonuria patients become calcified and continued buildup of calcified tissue may result in stenosis -- the narrowing of blood vessels. Additionally, those affected also see a stark increase in kidney stone formation. Although the disease is not lethal, it greatly reduces quality of life and renders joint replacement surgeries an inevitability.

Homogentisate 1,2 dioxygenase function and AKU phenotypic variability

Submitted by msgordon on Sat, 10/21/2017 - 13:33

The role of a normal, functioning HGD protein is to degrade homogentisate acid into 4-Maleylacetoacetate, however AKU-causing mutations in the HGD gene impede this function. The adenonsine insertion mutation prevents proper mRNA splicing while the 817C-->T mutation results in a truncated protein that is failed to be exported. Generally there is no phenotypic variability between AKU patients in the common sense. Instead, the variation in presentation differs not by individual, but by age. As an example, dark urine and dark ear cerumen is reported as a severe symptom across all ages, but osteoarthritis is only reported as a truly severe symptom in patients 50 or older. This phenomenon can most likely be attributed to the continued buildup of homogentisate acid in the body as patients age.

Comm 118 Study Guide Exam 2

Submitted by samihaalam on Fri, 10/20/2017 - 21:58

1. Comm as:

  • primary social process
    • persons in conversation
    • look at what people do in conversations to understand people, religion, politics, ethniticities, etc
  • formative
    • of meanings
    • of selves in socieities
  • face-to-face
    • prototype of communication - all other forms of comm compared to this, based off this
    • counter to idea that electronic texts are more important
  • interactive
    • dialogic 
  • meaningful
    • counter to idea of simple transmission of ideas
  • cultural
    • particular expressive system 
    • counter to idea that comm is the same, everywhere

2. Meaning-making

  • role of terms, phrases
    • used as symbols
  • forms-sequences
    • sqeuences of actions
  • terms and forms = premises of meanings
  • Brad Hall!
    • on a bus: "escuse me, would you mind moving over so I can sit?" vs. "move over."
  • Blackfeet people!
    • Northern Montana
    • old and young speak the language
    • not just about how much Blackfeet "blood" you have - also about how you act, the traditions

Cell and Molec Notes 10/17

Submitted by samihaalam on Fri, 10/20/2017 - 19:16

Fig 1:

  • co-localization = where things overlap in cell
  • used pulse-chase methods!

MC vs PC:

  • MC = Manders overlap coefficient 
    • # voxels with above-threshold fluorescence in 2 channels / # voxels with above-threshold fluorescence for 1 of the 2 channels 
    • MC of LDLR with clathrin = how much LDLR-pos voxels that also contain clathrin
  • PC = Pearson product-momentum correlation coefficient
    • normalized covariance in fluorescent intensities b/w 2 channels 
    • range from -1 to 1
    • 1 = 1:1 correspondence in intensities
    • 0 = 2 intensities uncorrelated
    • -1 = 2 intensities are inversely correlated
    • expect to see covariance if 2 components related stoichiometrically
  • changes in MC and PC indicate change in association
    • when 2 components begin to interact, MC and PC both rise
    • when 2 components being to dissociate, MC and PC both decrease 
  • PC better measure of dissociation than MC

A:

  • co-localization of LDL with lipoprotein
  • 4°C = temp where things can bind, but not necessarily be taken into the cell
  • MC LwR = MC of LDL with LDL receptor (LDLR)
  • MC RwL = MC of LDLR with LDL
  • PC of LDLR with LDL
  • → why no PC of LDL with LDLR?
  • → why does MC LwR differ from MC RwL? 
    • because one case is how much LDL positive stuff also contains receptor; other is how much receptor positive stuf contains LDL; different things!

Ecology Assignment 3

Submitted by hamacdonald on Fri, 10/20/2017 - 18:23

Lambda is gradually increasing over time as seen when graphed above. The population will continue to grow until some factor stops this growth, i.e carrying capacity is reached and resources run dry. Assuming the age specific survival rates do not change however, the population will continue to rise and either remain the same or decline after. The reason for this being, the survival rate for 2 and 3 year olds are both high, over 50 percent and cause an overall in crease in population for the next generation. 

Figure legend for R software data set

Submitted by tterrasi on Fri, 10/20/2017 - 15:24

Figure 1. In panel 1, the graph shows that the female GPA mode is around 3.0-3.5 and for male there is a bimodal distrubution around 1.5 and 3.5. In panel 2, there is no relationship between the amount of hours slept per week and GPA for males, and a weak positive association of hours slept per week and GPA in females. In panel 3, In both genders there is a strong positive association between hours studied per week and GPA. As the numbere of hours studied increases, so does the GPA. In panel 4, there seems to be no relationship between GPA and hours slept per week in males, and a moderate positive relationship between GPA and hours slept per week in females. In panel 5, there is both a bimodal distribution in males for hours slept per week is around 35 and 60 and females is also around 35 and 60 hours slept per week. In panel 6, the females have a positive association between hours studied per week and hours slept per week. The opposite is true for males in this panel. In panel 7, there is a positive association bewteen GPA and hours studied per week in both genders. As the GPA increases the hours studied also increases. In panel 8, there is a negative association between hours slept and hours studied per week for males, but a positive association for females. In panel 9, there is a unimodal distribution for hours studied per week in females around 7 and 10, and a bimodal distribution of hours studied per week in males around 5 and 8, and 9 and 10.

Figure legend for R software data set

Submitted by briangriffin on Fri, 10/20/2017 - 15:23

Figure 1. In panel 1, the graph shows that the female GPA mode is around 3.0-3.5 and for male there is a bimodal distrubution around 1.5 and 3.5. In panel 2, there is no relationship between the amount of hours slept per week and GPA for males, and a weak positive association of hours slept per week and GPA in females. In panel 3, In both genders there is a strong positive association between hours studied per week and GPA. As the numbere of hours studied increases, so does the GPA. In panel 4, there seems to be no relationship between GPA and hours slept per week in males, and a moderate positive relationship between GPA and hours slept per week in females. In panel 5, there is both a bimodal distribution in males for hours slept per week is around 35 and 60 and females is also around 35 and 60 hours slept per week. In panel 6, the females have a positive association between hours studied per week and hours slept per week. The opposite is true for males in this panel. In panel 7, there is a positive association bewteen GPA and hours studied per week in both genders. As the GPA increases the hours studied also increases. In panel 8, there is a negative association between hours slept and hours studied per week for males, but a positive association for females. In panel 9, there is a unimodal distribution for hours studied per week in females around 7 and 10, and a bimodal distribution of hours studied per week in males around 5 and 8, and 9 and 10.

Alabasta Data Set

dthaley's picture
Submitted by dthaley on Fri, 10/20/2017 - 15:21

Figure 1. Relationship between gender, hours slept, and hours studied. Two outliers were taken out of the data set to allow for a smoother result. Top row, first column from left: Shows the trend between GPA and gender. Second row, first column: GPA vs. sleep hours in scatter-plot form. Third row, first column: GPA as it correlates to hours studied. First row, Second column: hours slept compared GPA. Second row, second column: Trend between hours slept and gender. Third row, second column: Hours slept per week vs. hours studied per week. First row, third column: Hours studied per week vs. GPA. Second row, third column: Hours studied vs. hours slept. Third row, third column: Hours studied in terms of gender.

 

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