Media Analysis - Science topic

Good question

Vania and Hayder - great ideas.

I was going to propose also the use of AI in smart healthcare as a way to achieve quality personalized health service, while maximizing human personnel use and lowering hospital costs:

https://healthcareweekly.com/artificial-intelligence-in-healthcare/

https://www.smartcity.press/first-smart-medical-district/

The topic has a bright future, but is still in a nascent form. I do not know how much quantitative analysis can you perform in this context.

In terms of already available data that you can work with, out of all the topics you have enumerated, perhaps crowdsourced cartography (e.g. OpenStreetMap, efficient use of water/ land resources in agriculture) provides the best shot.

A good response from Karl. Direct quotation directly signifies that they are not 'your words' Matthias. On the other hand - don't be over-sensitive to the situation. Anyone reading/marking your work/thesis will know the context of it. Social research works best when it doesn't censure - and 'stays true' to the social reality and accurately represents the social actors/players within. So, for me, ensure that your opening chapter clearly defines context - and contains some sort of 'disclaimer'. As well as direct quotation - you can add emphasis to words or terms i.e. italics. Again - explain early on that emphasis means you are using them 'in context' because, not to, would not accurately represent the topic context.

I note your point about 'reproducing racisms' - but I don't see it that way. It is already there. You are merely 'representing' what is already known to exist. As long as you are presenting in an objective, distant, neutral and balanced manner (and not intending to 'fuel any flames) - then you can't be accused of 'reproducing'.

dear roberto, i am looking also for the same information. but you have to specify your target population,

women, men, adults, infants

Cited from the article "Determination of trans-arachidonic acid isomers in human blood plasma." published 2004 in Anal. Biochem.: "The mean concentration of TAA in human plasma was 20.2 ng/ml. The levels of isomers were 12.48 +/- 1.28, 2.75 +/- 0.39, and 4.99 +/- 0.74ng/ml for 5E-AA + 11E-AA, 8E-AA, and 14E-AA, respectively." Values were from 16 adults, eight males and eight females. Good luck!

According to this paper (Table 3, C20:4)

it is 393 ± 119 µM in plasma.

Hi Holger,

My two cents:

- "rvest" (https://cran.r-project.org/web/packages/rvest/rvest.pdf and https://www.reed.edu/data-at-reed/resources/R/rvest.html) and/or "XML" (http://www.omegahat.net/RSXML/shortIntro.pdf) for webscraping.

- Still in webscraping territory, "RSelenium" (https://www.rdocumentation.org/packages/RSelenium/versions/1.7.1 and http://johndharrison.github.io/RSOCRUG/) for dealing with "dynamic" websites (e.g. you need to interact with the website for it to generate the data you want).

- "tm" (https://cran.r-project.org/web/packages/tm/vignettes/tm.pdf), "quanteda" (https://cran.r-project.org/web/packages/quanteda/vignettes/quickstart.html), or "tidytext" (https://cran.r-project.org/web/packages/tidytext/vignettes/tidytext.html and their great open-source book: https://www.tidytextmining.com/) for text-mining

- As for machine learning, there are plenty. Almost as many as there are algorithms. A nice general library, with several algorithms and simple sintax, is offered by the "caret" package (http://topepo.github.io/caret/index.html).

Hope it helps.

Best,

José

If you don't use serum-free media as Alexey indicates, serum proteins and serum-derived exosomes overwhelm the amount of protein coming from your cells. Rather than using acetone precipitation, we concentrated protein by filter centrifugation using low MWCO Amicon concentrators. Using either method, you should calculate protein concentration in the media prior to concentrating, and in the case of filter centrifugation, after concentrating as well. If you're comparing across conditions or samples, you should compare based on the protein from equivalent initial volume of media, not equivalent amount of protein when running MS analysis.

Keep in mind that the amount of protein you obtain will be extremely low. In our studies, 48 hrs of cell culture in serum-free conditions yielded low ng per mL concentrations of protein (15-40 ng/mL, depending on experimental condition), a range that clearly requires concentration prior to MS analysis.

Dear Patrick,

As Henning does, I also frequently resort to LexisNexis and Factiva as the broadest aggregator sources (be cautious with group files, though, composition changes over time).

Not sure whether they are still in this market, but GaleCengage also sold various newspaper archive solutions to academic libraries. All three make batch downloading artifically thorny, however, and react allergically to frequently repeated search queries (be cautious with scraping!).

Dependig on what exactly you want to do, linguists might offer helpful ressources as their development of language reference corpora often also makes use of major newspapers (German example: http://www1.ids-mannheim.de/kl/projekte/korpora/)

Finally the NCCR Democracy in Zurich has built up quite a database on European newspapers but I am not sure about pre-selection and whether this is in the public domain...

Hi Oscar, 

I am using RPMI medium for culturing the hibridoma, but the production is not very good and I am not able to grow the cells in the CELLine. I am trying other options such DMEM +10% FBS or new serum-free media in order to increase the protein production and simplify/optimize the protocol.

Thank you for your answer!

You may be interested in looking at Symplur which provides a service for healthcare social media hashtags

At the very least it will give you an idea of what is happening in this particular space!

Martin Hawksey's TAGS is a great tool for harvesting tweets. It's simple to use and data is sent to a Google spreadsheet which can be downloaded as .xlsx 

It also has in built visualizations so you can see, for example, numbers of interactions and who has interacted with whom. 

I've written a little about my experience using it for the ascilite2014 conference.

I hope this helps.

Dear Leonard,

I fear there is no perfect answer to this, but I would recommend to consider one of the following options:

a) take a large sample of tweets and look for geo tagging information to get a rough estimate (although geo / location information is rarely used explicitly om Twitter).

b) do text analysis on the tweet content in order to determine whether its from Zibabwe or not

c) run ad hoc visual analytics using tweetping.net over a time slice of at least some hours - this might give you at least a rough idea of how many people are active on Twitter in Zimbabwe.

d) more elusive alternatives might include starting a crowdsourcing survey ...

Kind regards

Christian