regional banks participating in the program.
Stakeholder #1: regional (priv) banks
17 Matching Annotations
- Aug 2020
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www.japantimes.co.jp www.japantimes.co.jp
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$280 billion to small businesses
stakeholder #2: SMEs
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BOJ expanded a loan plan created in March and pledged to pay financial institutions a 0.1 percent interest for borrowing money and lending it out to companies.
is the loan plan part of monetary policy? discount rate?
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journals.plos.org journals.plos.org
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Monte Carlo simulations
much of their report is based on monte carlo simulations... seems quite complex
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The fact that the mean is clearly shifted to the left of the vertical line in Figure 5 indicates that the lung to regional lymph node connection for lung cancer is less significant, statistically, than for other cancer types. A possible anatomical explanation for this left shift could be the fact that regional lymph nodes, for lung cancer, are located very close to the lung itself, compared with their typical distance away from other primary tumor locations. Because of this unusually close proximity, regional lymph nodes could easily have been mistakingly considered as part of the lung in some of the autopsies in the series, effectively reducing the significance of the lung to regional lymph node connection
INTERPRETATION!!!
I think what they mean by statistically important means how close the bars are to the normal distribution line. e.g if it's 'shifted left', the left side aligns more closely? Just a guess but i can't see how else they would consider something right or left shifted
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lung cancer network conditioned on our initial guess averaged over 1000 training sessions.
i can also find the initial state using this method/directly from secondary data if there's any? anyways this is pretty trivial, just makes it more realistic
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edge values (transition probabilities) are best thought of as random variables which are (approximately) normally distributed.
this is the crux/major assumption, need to find a way to understand this and justify it
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lymph nodes, bone, kidney, and lung
strong self-seeders are the above organs. I'm assuming this is going from 2nd to 3rd site. e.g start from lungs, move to organs above, which leads to the highest chance of spreading to a 3rd site.
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one of the goals of future studies will be to compare the models obtained for different cancer types
extension. basically applicable to all tumour types woohoo!
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this data offers no particular information on the time history of the disease for the population or for individual patients - only the long-time metastatic distribution in a population of patients, where long-time is associated with end of life, a timescale that varies significantly from patient to patient
limitation? remember that constructing bar graphs does NOT give info on time since it varies with different patients.
long-time is associated with death, which basically means when equilibrium is reached then everyone dies(..?)
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autopsy analysis in [6] in which metastatic distributions in a population of 3827 deceased cancer victims were analyzed
this was their original source of data, they didnt collect it themselves. the theories are transferable
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focus on specific genotypes or phenotypes, or by more refined modeling of the correlations between the trapping of a CTC at a specific site, and the probability of secondary tumor growth at that location.
possible extensions
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probabilistic description of metastatic progression from primary neoplasm to metastatic sites; hence, we provide a quantitative framework for charting the time-evolution of cancer progression along with a stochastic description of the complex interactions of these cells with the organ microenvironment.
AIM!!
- probability of moving from primary to secondary sites
- charting time-evolution of cancer progression
- stochastic description of cell interaction(mathematical model that evolves over time in a probabilistic manner) description
2 and 3 seem similar to me at the moment
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CTCs play the role of seeds which detach from the primary tumor, disperse through the bloodstream, and get trapped at various distant sites (typically small blood vessels of organ tissues), then, if conditions are favorable, extravasate, form metastases, and subsequently colonize. The metastatic sites offer the soil for potential subsequent growth of secondary tumors.
The biological theory of metastasis
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One and two-step transition probabilities.
for 2nd step transition, does it take into account the first transition? probably not?? I have no idea but if i do figure it out then i can
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target’ distribution
is this the transition matrix?
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there's a steady state equilibrium?
so they used a bunch of data to find a transition matrix, which is equivalent to the steady state equilibrium
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