11/24/23: Happy Thanksgiving!

 The Human Race Into Space Requires Kidneys 

  

          A research and discussion group             

Agenda and Minutes

Image: Ice near entrance of Lava River Cave, Flagstaff, AZ, Summer 2022:

 

1. Updates/announcements/status reports

  • Anyone?

2. Reading and discussion

  • Current readings on kidneys or space (or both), depending on interest and attendance.
    • Current kidney reading: The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation, Tolstyak et al., 2021, https://www.mdpi.com/2076-3417/11/21/10380. We got up to section 2 and can start there next time.
    • Evaluate some new kidney related readings listed below.
 
 
  • Space readings. Here are some possible readings in order from highest to lowest score.
    • 9/29/23: We checked https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5, evaluating it at 4 1/3.
    • 9/8/23: We could do part of: "Understanding Statistics and Experimental Design," https://link.springer.com/book/10.1007/978-3-030-03499-3. Chapter "Meta-Analysis" was evaluated at 4.
    • 9/8/23: We once started: https://www.newthingsunderthesun.com/pub/4xnyepnn/release/9. We read up to "In their paper, firms use these technologies to produce a fixed amount of output every period." Working through more was evaluated at 4.
    • 9/8/23: We could do this from the "talks" section of planet4589: https://planet4589.org/talks. The ones we looked at were all voted at 3 1/3.
    • Attendees could suggest possible readings/viewings between now and next time, or we could take time now for each person to search for things and report back.
  • Kidney articles, some evaluated for future reading and some yet to be evaluated.
    • Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study, Jesper Kers*, Roman D Bülow*, Barbara M Klinkhammer, Gerben E Breimer, Francesco Fontana, Adeyemi Adefidipe Abiola, Rianne Hofstraat, Garry L Corthals, Hessel Peters-Sengers, Sonja Djudjaj, Saskia von Stillfried, David L Hölscher, Tobias T Pieters, Arjan D van Zuilen, Frederike J Bemelman, Azam S Nurmohamed, Maarten Naesens, Joris J T H Roelofs, Sandrine Florquin, Jürgen Floege, Tri Q Nguyen, Jakob N Kather†, Peter Boor†, https://pubmed.ncbi.nlm.nih.gov/34794930/. 9/1/23 vote was 3 5/6.
    • Artificial intelligence and kidney transplantation, Nurhan Seyahi, Seyda Gul Ozcan, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290997. 9/15/23 vote was 4.0. 
    • TC wrote: Authors Zhang et al. (2020) conducted a similar study for clear cell renal cell carcinoma survival prediction, however they worked with publicly available genetic data from the Genomic Data Commons Data Portal (National Cancer Institute, n.d.) and the Cancer Genome Atlas (National Center for Biotechnology Information, n.d.). Using this data as well as other clinical prognostic parameters, the authors identified five prognostic genes that they believe are better survival predictors of clear cell renal cell carcinoma than then TNM system. Considering that the authors only explored public data, there are potentially additional genes or non-publicly available variables that could be explored. [Reference:] Zhang, Z., Lin, E., Zhuang, H., Xie, L., Feng, X., Liu, J., & Yu, Y. (2020). Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma. Cancer Cell International, 20(1), 27. https://doi.org/10.1186/s12935-020-1113-6
    • Long-Term Time Series Forecasting and Updates on Survival Analysis of Glioblastoma Multiforme: A 1975–2018 Population-Based Study, https://doi.org/10.1159/000522611
    • These describe a method for using recent lifetime data, which is the same problem that TC is working on: (1) Brenner, H., Gefeller, O., & Hakulinen, T. (2004). Period analysis for ‘up-to-date’ cancer survival data. European Journal of Cancer, 40(3), 326–335. https://doi.org/10.1016/j.ejca.2003.10.013. (2) Brenner, H., & Hakulinen, T. (2006). Up-to-date and Precise Estimates of Cancer Patient Survival: Model-based Period Analysis. American Journal of Epidemiology, 164(7), 689–696. https://doi.org/10.1093/aje/kwj243
    • TC wrote: "On the other hand, authors Solé et al. (2014) investigate the dynamics of technological innovation and the conditions under which it may lead to explosive growth or enter a linear regime. It develops a generalized model of technological evolution, focusing on two crucial properties: the number of previous technologies required to create new innovations and the rate at which technology ages. The study explores two different models of combinatorial growth, one involving long-range memory and the availability of old inventions for new innovations, and the other with aging having a characteristic time scale" 
      • Ref.: Solé, R. V., Amor, D. R., & Valverde, S. (2014). On singularities in combination-driven models of technological innovation (arXiv:1407.6890). arXiv. https://doi.org/10.48550/arXiv.1407.6890
  • Here are some generic questions about articles (and videos):
    • What is the source?
    • What is the most significant advance in the human knowledge presented in the paper?
    • Why is that advance important?
    • What important questions arise from the paper for future research?
    • What important questions would it be nice if the paper answered, but does not answer?
    • What does the paper present that is novel (no one else has provided that before)?
    • What is the relevance of the paper to our satellite research goals?
    • Questions from the group?


No comments:

11/17/23: Reliability and failure functions; kidney reading

 The Human Race Into Space Requires Kidneys 

  

          A research and discussion group             

Agenda and Minutes

Kidney diagram

1. Updates/announcements/status reports

2. Reading and discussion

  • Current readings on kidneys or space (or both), depending on interest and attendance.
    • Current kidney reading: The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation, Tolstyak et al., 2021, https://www.mdpi.com/2076-3417/11/21/10380. We got up to section 2 and can start there next time.
The meeting ended here.
    • Evaluate some new kidney related readings listed below.
  • Space readings. Here are some possible readings in order from highest to lowest score.
    • 9/29/23: We checked https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 and evaluated it at 4.5.
    • 9/29/23: We checked https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5, evaluating it at 4 1/3.
    • 9/8/23: We could do part of: "Understanding Statistics and Experimental Design," https://link.springer.com/book/10.1007/978-3-030-03499-3. Chapter "Meta-Analysis" was evaluated at 4.
    • 9/8/23: We once started: https://www.newthingsunderthesun.com/pub/4xnyepnn/release/9. We read up to "In their paper, firms use these technologies to produce a fixed amount of output every period." Working through more was evaluated at 4.
    • 9/8/23: We could do this from the "talks" section of planet4589: https://planet4589.org/talks. The ones we looked at were all voted at 3 1/3.
    • Attendees could suggest possible readings/viewings between now and next time, or we could take time now for each person to search for things and report back.
  • Kidney articles, some evaluated for future reading and some yet to be evaluated.
    • Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study, Jesper Kers*, Roman D Bülow*, Barbara M Klinkhammer, Gerben E Breimer, Francesco Fontana, Adeyemi Adefidipe Abiola, Rianne Hofstraat, Garry L Corthals, Hessel Peters-Sengers, Sonja Djudjaj, Saskia von Stillfried, David L Hölscher, Tobias T Pieters, Arjan D van Zuilen, Frederike J Bemelman, Azam S Nurmohamed, Maarten Naesens, Joris J T H Roelofs, Sandrine Florquin, Jürgen Floege, Tri Q Nguyen, Jakob N Kather†, Peter Boor†, https://pubmed.ncbi.nlm.nih.gov/34794930/. 9/1/23 vote was 3 5/6.
    • Artificial intelligence and kidney transplantation, Nurhan Seyahi, Seyda Gul Ozcan, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290997. 9/15/23 vote was 4.0. 
    • TC wrote: Authors Zhang et al. (2020) conducted a similar study for clear cell renal cell carcinoma survival prediction, however they worked with publicly available genetic data from the Genomic Data Commons Data Portal (National Cancer Institute, n.d.) and the Cancer Genome Atlas (National Center for Biotechnology Information, n.d.). Using this data as well as other clinical prognostic parameters, the authors identified five prognostic genes that they believe are better survival predictors of clear cell renal cell carcinoma than then TNM system. Considering that the authors only explored public data, there are potentially additional genes or non-publicly available variables that could be explored. [Reference:] Zhang, Z., Lin, E., Zhuang, H., Xie, L., Feng, X., Liu, J., & Yu, Y. (2020). Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma. Cancer Cell International, 20(1), 27. https://doi.org/10.1186/s12935-020-1113-6
    • Long-Term Time Series Forecasting and Updates on Survival Analysis of Glioblastoma Multiforme: A 1975–2018 Population-Based Study, https://doi.org/10.1159/000522611
    • These describe a method for using recent lifetime data, which is the same problem that TC is working on: (1) Brenner, H., Gefeller, O., & Hakulinen, T. (2004). Period analysis for ‘up-to-date’ cancer survival data. European Journal of Cancer, 40(3), 326–335. https://doi.org/10.1016/j.ejca.2003.10.013. (2) Brenner, H., & Hakulinen, T. (2006). Up-to-date and Precise Estimates of Cancer Patient Survival: Model-based Period Analysis. American Journal of Epidemiology, 164(7), 689–696. https://doi.org/10.1093/aje/kwj243
    • TC wrote: "On the other hand, authors Solé et al. (2014) investigate the dynamics of technological innovation and the conditions under which it may lead to explosive growth or enter a linear regime. It develops a generalized model of technological evolution, focusing on two crucial properties: the number of previous technologies required to create new innovations and the rate at which technology ages. The study explores two different models of combinatorial growth, one involving long-range memory and the availability of old inventions for new innovations, and the other with aging having a characteristic time scale" 
      • Ref.: Solé, R. V., Amor, D. R., & Valverde, S. (2014). On singularities in combination-driven models of technological innovation (arXiv:1407.6890). arXiv. https://doi.org/10.48550/arXiv.1407.6890
  • Here are some generic questions about articles (and videos):
    • What is the source?
    • What is the most significant advance in the human knowledge presented in the paper?
    • Why is that advance important?
    • What important questions arise from the paper for future research?
    • What important questions would it be nice if the paper answered, but does not answer?
    • What does the paper present that is novel (no one else has provided that before)?
    • What is the relevance of the paper to our satellite research goals?
    • Questions from the group?


No comments:

11/10/23: Project updates; Weibull discussion

 The Human Race Into Space Requires Kidneys 

  

          A research and discussion group             

Agenda and Minutes

Demonstrators protesting Pluto's reclassification from planet to dwarf planet (Lowell Observatory, Summer 2022)

1. Updates/announcements/status reports

  • PT. Presented graphs showing behavior of the LSTM.
  • TC. EG has a lot of data, including genomics, so he may have data that we need. There is potential for synergy.
  • EG. (Power BI and Tableau useful? Domestic, currently.)
  • NMIMS project: "Good evening sir, there was some confusion among the group on how are we estimating the lifetime based on the distribution.
    We were able to calculate the parameters for the different distributions based on the half life calculated for different years but we are not clear as to what x is in the distribution. And if we input the values of the parameters and x (which we don't know what to put yet) into the distribution what we would get is half-life? That is, is f(x) our half-life?"

 
 
 
 
 
 
 


(The meeting ended here)

2. Reading and discussion

  • Current readings on kidneys or space (or both), depending on interest and attendance.
    • Current kidney reading: The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation, Tolstyak et al., 2021, https://www.mdpi.com/2076-3417/11/21/10380. We previously got up to "The main objectives of this study are to use the Kapplan-Meier [sic] method and machine learning" and can start with that next time we do this article.
    • Evaluate some new kidney related readings listed below.
  • Space readings. Here are some possible readings in order from highest to lowest score.
    • 9/29/23: We checked https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 and evaluated it at 4.5.
    • 9/29/23: We checked https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5, evaluating it at 4 1/3.
    • 9/8/23: We could do part of: "Understanding Statistics and Experimental Design," https://link.springer.com/book/10.1007/978-3-030-03499-3. Chapter "Meta-Analysis" was evaluated at 4.
    • 9/8/23: We once started: https://www.newthingsunderthesun.com/pub/4xnyepnn/release/9. We read up to "In their paper, firms use these technologies to produce a fixed amount of output every period." Working through more was evaluated at 4.
    • 9/8/23: We could do this from the "talks" section of planet4589: https://planet4589.org/talks. The ones we looked at were all voted at 3 1/3.
    • Attendees could suggest possible readings/viewings between now and next time, or we could take time now for each person to search for things and report back.
  • Kidney articles, some evaluated for future reading and some yet to be evaluated.
    • Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study, Jesper Kers*, Roman D Bülow*, Barbara M Klinkhammer, Gerben E Breimer, Francesco Fontana, Adeyemi Adefidipe Abiola, Rianne Hofstraat, Garry L Corthals, Hessel Peters-Sengers, Sonja Djudjaj, Saskia von Stillfried, David L Hölscher, Tobias T Pieters, Arjan D van Zuilen, Frederike J Bemelman, Azam S Nurmohamed, Maarten Naesens, Joris J T H Roelofs, Sandrine Florquin, Jürgen Floege, Tri Q Nguyen, Jakob N Kather†, Peter Boor†, https://pubmed.ncbi.nlm.nih.gov/34794930/. 9/1/23 vote was 3 5/6.
    • Artificial intelligence and kidney transplantation, Nurhan Seyahi, Seyda Gul Ozcan, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290997. 9/15/23 vote was 4.0. 
    • TC wrote: Authors Zhang et al. (2020) conducted a similar study for clear cell renal cell carcinoma survival prediction, however they worked with publicly available genetic data from the Genomic Data Commons Data Portal (National Cancer Institute, n.d.) and the Cancer Genome Atlas (National Center for Biotechnology Information, n.d.). Using this data as well as other clinical prognostic parameters, the authors identified five prognostic genes that they believe are better survival predictors of clear cell renal cell carcinoma than then TNM system. Considering that the authors only explored public data, there are potentially additional genes or non-publicly available variables that could be explored. [Reference:] Zhang, Z., Lin, E., Zhuang, H., Xie, L., Feng, X., Liu, J., & Yu, Y. (2020). Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma. Cancer Cell International, 20(1), 27. https://doi.org/10.1186/s12935-020-1113-6
    • Long-Term Time Series Forecasting and Updates on Survival Analysis of Glioblastoma Multiforme: A 1975–2018 Population-Based Study, https://doi.org/10.1159/000522611
    • These describe a method for using recent lifetime data, which is the same problem that TC is working on: (1) Brenner, H., Gefeller, O., & Hakulinen, T. (2004). Period analysis for ‘up-to-date’ cancer survival data. European Journal of Cancer, 40(3), 326–335. https://doi.org/10.1016/j.ejca.2003.10.013. (2) Brenner, H., & Hakulinen, T. (2006). Up-to-date and Precise Estimates of Cancer Patient Survival: Model-based Period Analysis. American Journal of Epidemiology, 164(7), 689–696. https://doi.org/10.1093/aje/kwj243
    • TC wrote: "On the other hand, authors Solé et al. (2014) investigate the dynamics of technological innovation and the conditions under which it may lead to explosive growth or enter a linear regime. It develops a generalized model of technological evolution, focusing on two crucial properties: the number of previous technologies required to create new innovations and the rate at which technology ages. The study explores two different models of combinatorial growth, one involving long-range memory and the availability of old inventions for new innovations, and the other with aging having a characteristic time scale" 
      • Ref.: Solé, R. V., Amor, D. R., & Valverde, S. (2014). On singularities in combination-driven models of technological innovation (arXiv:1407.6890). arXiv. https://doi.org/10.48550/arXiv.1407.6890
  • Here are some generic questions about articles (and videos):
    • What is the source?
    • What is the most significant advance in the human knowledge presented in the paper?
    • Why is that advance important?
    • What important questions arise from the paper for future research?
    • What important questions would it be nice if the paper answered, but does not answer?
    • What does the paper present that is novel (no one else has provided that before)?
    • What is the relevance of the paper to our satellite research goals?
    • Questions from the group?


No comments:

11/3/23: Readings and Status Updates

Agenda and Minutes

Lavacicles on the roof of Lava River Cave, Flagstaff, AZ (Summer 2022). (There is another cave of the same name in Oregon.)

 

 

 

 

 

 

For a different image of a lava flow cave, see https://www.universetoday.com/164015/a-collapsed-martian-lava-chamber-seen-from-space

1. Updates/announcements/status reports

  • HA is session chair at the OAS meeting next week (Math, Computer Science and Statistics).
  • EG: Senior data scientist with knowledge of large language models needed. Hands on work. 
  • NMIMS project - they have exams now

2. Reading and discussion

  • Current readings on kidneys or space (or both), depending on interest and attendance.
    • Our own article “Future Satellite lifetime prediction from the historical trend in satellite half-lives,” Journal of Systemics, Cybernetics and Informatics (2022), 20(3):40-45. We got up to the paragraph beginning "Figure 4 shows the half-life 5 year moving average curve." and can start there (depending on attendance).
    • Current kidney reading: The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation, Tolstyak et al., 2021, https://www.mdpi.com/2076-3417/11/21/10380. We previously got up to "The main objectives of this study are to use the Kapplan-Meier [sic] method and machine learning" and can start with that next time we do this article.
    • Evaluate some new kidney related readings listed below.
    • We started (and finished) a new space(-inspired) reading.
  • Space readings. Here are some possible readings in order from highest to lowest score.
    • 9/29/23: We checked https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62 and evaluated it at 4.5.
    • 9/29/23: We checked https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5, evaluating it at 4 1/3.
    • 9/8/23: We could do part of: "Understanding Statistics and Experimental Design," https://link.springer.com/book/10.1007/978-3-030-03499-3. Chapter "Meta-Analysis" was evaluated at 4.
    • 9/8/23: We once started: https://www.newthingsunderthesun.com/pub/4xnyepnn/release/9. We read up to "In their paper, firms use these technologies to produce a fixed amount of output every period." Working through more was evaluated at 4.
    • 9/8/23: We could do this from the "talks" section of planet4589: https://planet4589.org/talks. The ones we looked at were all voted at 3 1/3.
    • Attendees could suggest possible readings/viewings between now and next time, or we could take time now for each person to search for things and report back.
  • Kidney articles, some evaluated for future reading and some yet to be evaluated.
    • Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study, Jesper Kers*, Roman D Bülow*, Barbara M Klinkhammer, Gerben E Breimer, Francesco Fontana, Adeyemi Adefidipe Abiola, Rianne Hofstraat, Garry L Corthals, Hessel Peters-Sengers, Sonja Djudjaj, Saskia von Stillfried, David L Hölscher, Tobias T Pieters, Arjan D van Zuilen, Frederike J Bemelman, Azam S Nurmohamed, Maarten Naesens, Joris J T H Roelofs, Sandrine Florquin, Jürgen Floege, Tri Q Nguyen, Jakob N Kather†, Peter Boor†, https://pubmed.ncbi.nlm.nih.gov/34794930/. 9/1/23 vote was 3 5/6.
    • Artificial intelligence and kidney transplantation, Nurhan Seyahi, Seyda Gul Ozcan, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290997. 9/15/23 vote was 4.0. 
    • TC wrote: Authors Zhang et al. (2020) conducted a similar study for clear cell renal cell carcinoma survival prediction, however they worked with publicly available genetic data from the Genomic Data Commons Data Portal (National Cancer Institute, n.d.) and the Cancer Genome Atlas (National Center for Biotechnology Information, n.d.). Using this data as well as other clinical prognostic parameters, the authors identified five prognostic genes that they believe are better survival predictors of clear cell renal cell carcinoma than then TNM system. Considering that the authors only explored public data, there are potentially additional genes or non-publicly available variables that could be explored. [Reference:] Zhang, Z., Lin, E., Zhuang, H., Xie, L., Feng, X., Liu, J., & Yu, Y. (2020). Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma. Cancer Cell International, 20(1), 27. https://doi.org/10.1186/s12935-020-1113-6
    • Long-Term Time Series Forecasting and Updates on Survival Analysis of Glioblastoma Multiforme: A 1975–2018 Population-Based Study, https://doi.org/10.1159/000522611
    • These describe a method for using recent lifetime data, which is the same problem that TC is working on: (1) Brenner, H., Gefeller, O., & Hakulinen, T. (2004). Period analysis for ‘up-to-date’ cancer survival data. European Journal of Cancer, 40(3), 326–335. https://doi.org/10.1016/j.ejca.2003.10.013. (2) Brenner, H., & Hakulinen, T. (2006). Up-to-date and Precise Estimates of Cancer Patient Survival: Model-based Period Analysis. American Journal of Epidemiology, 164(7), 689–696. https://doi.org/10.1093/aje/kwj243
    • TC wrote: "On the other hand, authors Solé et al. (2014) investigate the dynamics of technological innovation and the conditions under which it may lead to explosive growth or enter a linear regime. It develops a generalized model of technological evolution, focusing on two crucial properties: the number of previous technologies required to create new innovations and the rate at which technology ages. The study explores two different models of combinatorial growth, one involving long-range memory and the availability of old inventions for new innovations, and the other with aging having a characteristic time scale" 
      • Ref.: Solé, R. V., Amor, D. R., & Valverde, S. (2014). On singularities in combination-driven models of technological innovation (arXiv:1407.6890). arXiv. https://doi.org/10.48550/arXiv.1407.6890
  • Here are some generic questions about articles (and videos):
    • What is the source?
    • What is the most significant advance in the human knowledge presented in the paper?
    • Why is that advance important?
    • What important questions arise from the paper for future research?
    • What important questions would it be nice if the paper answered, but does not answer?
    • What does the paper present that is novel (no one else has provided that before)?
    • What is the relevance of the paper to our satellite research goals?
    • Questions from the group?

No comments:
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5/17/24: Status update on AM & TE papers

   The Human Race Into Space Requires Kidneys, and Other Important Topics              A research and discussion group              Ag...