Author’s information (optional)
Url Link
The hyperlink to my paper’s website.
Methods
- Paste quoted text from the Methods section of the article on the next line. Do not include quotation marks or a bullet mark:
To investigate the multivariate relationship between urban living environment and psychiatric symptoms, we conducted multivariate analyses using sCCA with the sgcca.wrapper function of the mixOmics R package based on our previous work.
- Write your translation on the next line:
Researchers used a special computer analysis method to look for patterns between where people live and their mental health symptoms. They compared many environmental factors such as pollution and traffic with things like anxiety and depression to see if they were connected.
Introduction
- Paste quoted text from the Introduction of the article on the next line. Do not include quotation marks:
We aim to understand what combinations of environmental factors are most relevant for these psychiatric symptoms, and how within these combinations each single factor contributes to the risk or resilience of mental health symptoms.
- Write your translation on the next line:
Scientists still do not fully know how living in cities affects people’s mental health. They wanted to study how things like crowded buildings, green spaces, and stress in cities may increase or decrease problems like anxiety and depression.
Results
- Paste quoted text from the Results section in the article on the next line. Do not include quotation marks:
Our results indicate that the affective symptom group was positively correlated with an environment profile dominated by high levels of social deprivation and air pollution.
- Write your translation on the next line:
People who lived in areas with more poverty and more air pollution were more likely to experience symptoms related to depression, such as feeling sad, lonely, tired, or unmotivated.
Discussion
- Paste quoted text from the Discussion section of the article on the next line. Do not include quotation marks:
The quantification of the contribution of each environmental factor to brain and psychiatric symptoms and their interplay in an urban-living environment could potentially aid in targeting and prioritizing future public health interventions.
- Write your translation on the next line:
If scientists understand how things in cities, such as pollution, green spaces, noise, and crowded buildings, affect people’s brains and mental health, they may be able to create better programs and city plans to help protect people’s mental health in the future.
Future Directions
- What future research should follow up on this work?
Future research should study people over a longer time to better understand if living in certain city environments actually causes mental health problems. Scientists should also study different countries and groups of people and look for ways cities can be designed to help improve mental health.
Difficult Material
- What did you not understand about this paper that someone else may be able to help you with? Or, if you understood everything, what did you find most challenging to understand?
One thing I did not fully understand was how the researchers used the complicated computer analyses and genetic testing methods, like sCCA and GWAS, to connect city environments with brain changes and mental health symptoms. I would want someone to explain those scientific methods in a simpler way.
BIOL 1593 – Assignment 2 Worksheet
Your Name:
URL link:
· Paste the hyperlink to the website containing the original posting about the paper – NOT to the scientific paper itself (either the public posting webpage, or the Moodle forum webpage):
https://moodle.tru.ca/mod/forum/discuss.php?d=1193021
Additional Translation:
· From which section of the paper is this passage?
Methods
· Paste quoted text on the next line. Do not include quotation marks or a bullet mark:
To investigate the multivariate relationship between urban living environment and psychiatric symptoms, we conducted multivariate analyses using sCCA with the sgcca.wrapper function of the mixOmics R package based on our previous work63. The analysis design was carried out as follows: (1) the full dataset was randomly split into training and test datasets. The training dataset consisted of 90% of the data, while the testing set consisted of the remaining 10%; (2) the training dataset was then randomly split into 100 resamples.
· Write your translation on the next line:
The researchers employed a statistical technique called sparse canonical correlation analysis (SCCA) to simultaneously search for associations among several mental health symptoms and several factors related to city living. They divided their data into 2 sets: 90% of the participants were used to detect patterns (training set), and 10% were used to check whether the patterns were accurate (test set). To make sure that their results weren’t a coincidence, they did this 100 times with a different random split.
Additional Future Directions:
· What future research do you think should follow up on this work?
Future studies should examine how altering a person’s built environment, such as from a highly deprived, low green space area to an area with more green space and cleaner air, can result in changes in brain structure and mental health symptoms over time. In addition, studies should be conducted to see if the effects on the environment are reversible. What about the brain volume differences in an individual who grew up in an impoverished urban neighborhood, but as an adult migrated to a green neighborhood? Do the effects of early urban exposure persist, or do they reverse?
Difficult Material (from original poster or subsequent student):
· What did the previous poster state was difficult to understand? (Please copy and paste their statement here):
One thing I did not fully understand was how the researchers used complicated computer analyses and genetic testing methods, like sCCA and GWAS, to connect city environments with brain changes and mental health symptoms. I would want someone to explain those scientific methods in a simpler way.
· Please try to explain the difficult materials to the original poster, as best as you can. (This is where you can help them understand what they found difficult.)
Imagine that sCCA is like a detective who is trying to solve a mystery with lots of clues all at once. Suppose that you have 50 distinct clues (environmental factors such as pollution, traffic, parks, etc.) and 20 distinct outcomes (mental health symptoms, such as anxiety, depression, tiredness, etc.). sCCA considers all of the clues, not each one individually, but which combination of clues best predicts which combination of outcomes. It is equivalent to discovering that “high pollution + lots of traffic + few parks” goes hand in hand with “feeling sad + tired + lonely.
GWAS is similar to scanning through a person’s entire instruction manual (DNA sequence) page by page, identifying specific spelling differences that occur more frequently in individuals who display certain mental health symptoms. The researchers then determine if the same differences in response to the environment exist for individuals with different genetic spellings. This can help them to see why some people are more sensitive to the effects of urban living than others – gene variation could make them more susceptible to environmental stress.
New Difficult Material (according to you):
· What did you not understand about this paper that someone else can help with? If you understood everything, then what did you find most challenging to understand?
The part that I had the most trouble with was the “projection deflation” procedure outlined on page 15 of the paper (close to the end of Methods). After they identified the first (affective symptoms) urban environmental profile, they deflated it, or projected it out of the way, before looking for the second (anxiety symptoms) profile. There is something about this statistical removal that I don’t fully understand, and it may be that the effect of the first profile is removed, causing the following profiles (anxiety, emotional instability) to look weak or less important than they actually are in the real world. For instance, if an environmental factor is associated with both affective and anxiety symptoms, is it being deflated from the anxiety analysis? It would be helpful for someone with more statistics expertise to explain how this approach could result in the suppression of important “shared” effects across groups of mental health symptoms.