356 Matching Annotations
  1. Last 7 days
    1. Discussion, revision and decision


      Discussion and Revision


      Author

      Reviewer 1 (Takehiko Ogawa, Yokohama City University, ogawa@yokohama-cu.ac.jp ):

      We had already suggested in the "Limitations" section on page 10 of the manuscript-PDF testis transplantation experiments to test biological functionality for future studies: https://www.biorxiv.org/content/10.1101/2021.10.12.464060v1.full.pdf

      However, these experiments will require new cell lines, new funding, more resources, more logistics, and new ethics approval which we are considering for future studies. We believe that the observations described in our manuscript are valuable to the scientific community and are a basis to conduct further studies.


      Reviewer 2 (Dr. Pradeep G Kumar, Rajiv Gandhi Centre for Biotechnology, and kumarp@rgcb.res.in )

      There are no line numbers in the manuscript-PDF but the pages are numbered: https://www.biorxiv.org/content/10.1101/2021.10.12.464060v1.full.pdf Since the pages of the manuscript-PDF are numbered, we think that citation of the manuscript is unproblematic. The text of the manuscript has been checked by multiple experienced authors and found not to require the suggested changes.


      Reviewer (Takehiko Ogawa)

      I think that authors admit the limit of the study, which I think is serious. I think the paper has its own value with limitation as almost always in most cases. I would like to take “verified with reservation” as my final decision.


      Decision

      Takehiko Ogawa: Verified with reservations

      Pradeep G Kumar: Verified manuscript

      Verified with reservations

    1. Discussion, revision and decision


      Revision


      Author

      The objective of the paper was not to address a research question but to report on a more recent set of PubMed retractions due to insufficient/old information available in the papers published on this subject(PubMed, not WoS retracted articles). One of the initial objectives was to analyze the dynamic of image related retractions(a relatively new subject), a subject for which the information is at least scarce if non existing. We have also studied the impact of retracted research via two citations databases (Google Scholar and Dimensions) and tried to represent the variability of this impact when the author country is being considered. At this time, the paper is the second biggest serie of PubMed retracted articles.

      There was an error from our part, thank you very much for pointing this.

      We have revised the article and added the informations related to previous research on this subject. Thank you so much for this suggestion.

    1. Discussion, revision and decision


      Discussion and Revision


      Author response

      We would like to thank the reviewers for their valuable comments. Below we provide pointwise response and the changes made in the revised manuscript.

      To Dr. Jyotsnamayee Sabat

      Pt-13: I want to know how the representative sequences were selected for different states. Is it based on no. of sequences submitted or positivity rate of a particular region?

      All the Indian isolates available in GISAID for the period 27th Jan – 27th May 2020 were download and considered for analysis. NO state-wise selection was done.

      To Dr Parvin Abraham

      Pt-12: The dataset is only from 27th Jan – 27th May 2020. Maybe they can include more Numbers.

      The period of data collection was restricted to 27th Jan – 27th May 2020 to basically understand the variations observed across different states of the country during the early phase of pandemic. Also, we are interested in assessing the impact of lockdown in containing the spread of COVID19 and state-specific subclusters, if any.

      To Hurng-Yi Wang:

      Pt-13: Agarwal and Parekh analyzed 685 SARS-CoV-2 isolates collected during 27th Jan - 27th May 2020 from India and described the distribution of virus strains and mutations across the country. While the information might be valuable to some local readers, the results are mainly descriptive and the data are a bit out of date. In addition, I have the following comments.

      The period of data collection is restricted to 27th Jan – 27th May 2020 to basically understand the variations observed across different states of the country during the early phase of pandemic. Also, we are interested in assessing the impact of lockdown in containing the spread of COVID19 and state-specific subclusters, if any.

      1. Some details of the methods are lacking. For example, the MUpro provides two methods, it is necessary to specify which method was used in the analysis. The confidence score of each prediction should also be provided. Besides, some results from I-Mutant and MUpro were conflicting, the authors may want to discuss the discrepancy.

        In the revised manuscript we give the sign of DDG predicted using the tools I-Mutant2.0 and the MUpro along with the respective confidence scores. In I-Mutant2.0, the sign of protein stability change predicted and reliability index (which provides confidence to the prediction) are now incorporated in Table-1. Similarly, the sign change and confidence scores given by MUpro on using SVM and NN based models have been incorporated. We expect all the models to give same results, except in cases where the predictions may be hard to make. This has now been explicitly mentioned in the Materials and Methods section: “In I-Mutant2.0, the sign of DDG is based on SVM classifier, and the associated confidence score is given by the reliability index. On the other hand, MUpro provides sign change prediction using two models, one SVM-based and the other using Neural Networks. InTable-1, the predicted sign of DDG by I-Mutant2.0 and MuPRO along with the respective confidence scores is reported.”

      2. The “Analysis of the Mutational Profile of Indian Isolates” should be moved to Materials and Methods.

        There indeed was some redundancy in the information available in the Materials and Methods section and in the section “Analysis of the Mutational Profile of Indian Isolates”. We have now edited the Materials and Methods section appropriately and deleted the para under the above- mentioned section.

      3. The authors provided lengthy discussion about the effect of each mutation in some lineages, such as 20A and I/A3i. However, as these mutations are tightly linked, the effect of each individual mutation is difficult to access. It is possible that some of the mutations are just hitchhikers. They may want to address this alternative point.

        For 20A we define the haplotype comprising four co-occurring mutations D614G, C241T, C3037T, and C14408T. Similarly, six co-occurring mutations C6312A, C13730T, C23929T, C28311T, C6310A (S2015R) and C19524T are shown to be associated with subclade I/A3i. Together as a set, these are useful in identifying clusters or group of isolates with similar mutational profile. However, those that are non-synonymous mutations are likely to have some individual impact on the overall stability of the respective protein. And so, we have presented both these results. To address this point, we have added a sentence at the end of Materials and Methods section and is reproduced below: “While we report individual effects of mutations on protein stability, some of the mutations in a haplotype may not be under natural selection and are just hitchhiking mutations.”

      4. Several figures are confusing and lack detail. The diversity plots of Figure 3 and Figure 8 are hard to be precisely compared to the mutations that occurred among different plots. Phylogenetic trees, as well as their figure legends, are confusing, especially Figure 9 and Figure 10. For Figure 9, it is impossible to tell which mutation site had changed from C to T. For Figure 10, spots depicted in yellow are both position 29827 A>T and position 29830 G>T, green spot only notes as G, but A29827 is not mentioned in the figure. Furthermore, the mutation position of blue spot C cannot be found.

        We have now redrawn the diversity plots in Figure 3 and Figure 8, (labelled Figure 2 and Figure 4, respectively, in the revised manuscript) and are shown below. We have introduced horizontal lines to show the height of the divergence line at variant positions discussed in the manuscript, and these are also marked with the same colour in corresponding subplots for comparison.

      In the revised manuscript, Figures 9 and 10 are now Supplementary Figures 2c and 2d respectively. The new figure legends are: Supplementary Figure 2: The sequences carrying the mutations a) C5700A b) C23929T c) C18877T d) G29830T are depicted in yellow colour. Figure 10 (now Supplementary Figure 2(d)) is now re-plotted, and we have removed the blue dot corresponding to ‘C’ since no samples from India had this variation.

      1. Figure 9 and Figure 10 were not mentioned inside the text.

        It has now been added in the manuscript: Supplementary Figure 2(c) – On Pg-9, in the first line under the heading “Identification of novel subclade I/GJ-20A and unique mutations in Maharashtra”. Supplementary Figure 2(d) – On Pg-11, in the last paragraph under the heading “Identification of novel subclade I/GJ-20A and unique mutations in Maharashtra”.

      2. The Top 10 mutations in PCA analysis are the mutations in 20A and I/A3i. It is reasonable to observed a clear association of the clusters with the clades. It is not clear, however, how these distribution correlate with lockdown, contact tracing and quarantine measures.

        From Supplementary Figure 1 clade 20A (shown in ‘Green’) is predominantly observed in Gujarat (178/201) and the distribution of clade 19A (shown in ‘Blue’) is high in Telangana (75/97), followed by Delhi (55/76), Maharashtra (31/80), and Tamil Nadu (19/34). Four mutations, C6312A, C13730T, C23929T, and C28311T are reported to be associated with subclade I/A3i, which is India-specific subclade of 19A. These co-occurring mutations are found in ~32% of Indian samples sequenced (till 31st May 2020). Only 5 isolates of this subclade were observed after May in India with the last one dated 13th June 2020 (according to data available in Nextstrain). This indicates that the spread of subclade I/A3i had been largely contained during lockdown with efforts of contact tracing and quarantining the infected individuals. Also, Telangana and Delhi isolates cluster together due to shared I/A3i mutations, primarily due to the Tablighi Jamaat congregation that occurred just before lockdown was announced. Similarly, clade 20A defining mutations were observed to occur in ~ 90% of Gujarat samples. Due to the countrywide lockdown from 25th March 2020, this clade and its sub-clusters were localized in the state, defined by Gujarat-specific mutations, e.g., I/GJ-20A.


      Hurng-Yi Wang:

      I agree to change to Verified manuscript.


      Decision

      Verified manuscript

      Dr. Abraham: Verified manuscript

      Dr. Sabat: Verified manuscript

      Dr. Wang: Verified manuscript

  2. Jan 2022
  3. Dec 2021
    1. Discussion, revision and decision


      Discussion and Revision


      Author response

      We would like to thank the reviewers for their valuable comments. Below we provide pointwise response and the changes made in the revised manuscript.

      To Dr. Jyotsnamayee Sabat

      Pt-13: I want to know how the representative sequences were selected for different states. Is it based on no. of sequences submitted or positivity rate of a particular region?

      All the Indian isolates available in GISAID for the period 27th Jan – 27th May 2020 were download and considered for analysis. NO state-wise selection was done.

      To Dr Parvin Abraham

      Pt-12: The dataset is only from 27th Jan – 27th May 2020. Maybe they can include more Numbers.

      The period of data collection was restricted to 27th Jan – 27th May 2020 to basically understand the variations observed across different states of the country during the early phase of pandemic. Also, we are interested in assessing the impact of lockdown in containing the spread of COVID19 and state-specific subclusters, if any.

      To Hurng-Yi Wang:

      Pt-13: Agarwal and Parekh analyzed 685 SARS-CoV-2 isolates collected during 27th Jan - 27th May 2020 from India and described the distribution of virus strains and mutations across the country. While the information might be valuable to some local readers, the results are mainly descriptive and the data are a bit out of date. In addition, I have the following comments.

      The period of data collection is restricted to 27th Jan – 27th May 2020 to basically understand the variations observed across different states of the country during the early phase of pandemic. Also, we are interested in assessing the impact of lockdown in containing the spread of COVID19 and state-specific subclusters, if any.

      1. Some details of the methods are lacking. For example, the MUpro provides two methods, it is necessary to specify which method was used in the analysis. The confidence score of each prediction should also be provided. Besides, some results from I-Mutant and MUpro were conflicting, the authors may want to discuss the discrepancy.

        In the revised manuscript we give the sign of DDG predicted using the tools I-Mutant2.0 and the MUpro along with the respective confidence scores. In I-Mutant2.0, the sign of protein stability change predicted and reliability index (which provides confidence to the prediction) are now incorporated in Table-1. Similarly, the sign change and confidence scores given by MUpro on using SVM and NN based models have been incorporated. We expect all the models to give same results, except in cases where the predictions may be hard to make. This has now been explicitly mentioned in the Materials and Methods section: “In I-Mutant2.0, the sign of DDG is based on SVM classifier, and the associated confidence score is given by the reliability index. On the other hand, MUpro provides sign change prediction using two models, one SVM-based and the other using Neural Networks. InTable-1, the predicted sign of DDG by I-Mutant2.0 and MuPRO along with the respective confidence scores is reported.”

      2. The “Analysis of the Mutational Profile of Indian Isolates” should be moved to Materials and Methods.

        There indeed was some redundancy in the information available in the Materials and Methods section and in the section “Analysis of the Mutational Profile of Indian Isolates”. We have now edited the Materials and Methods section appropriately and deleted the para under the above- mentioned section.

      3. The authors provided lengthy discussion about the effect of each mutation in some lineages, such as 20A and I/A3i. However, as these mutations are tightly linked, the effect of each individual mutation is difficult to access. It is possible that some of the mutations are just hitchhikers. They may want to address this alternative point.

        For 20A we define the haplotype comprising four co-occurring mutations D614G, C241T, C3037T, and C14408T. Similarly, six co-occurring mutations C6312A, C13730T, C23929T, C28311T, C6310A (S2015R) and C19524T are shown to be associated with subclade I/A3i. Together as a set, these are useful in identifying clusters or group of isolates with similar mutational profile. However, those that are non-synonymous mutations are likely to have some individual impact on the overall stability of the respective protein. And so, we have presented both these results. To address this point, we have added a sentence at the end of Materials and Methods section and is reproduced below: “While we report individual effects of mutations on protein stability, some of the mutations in a haplotype may not be under natural selection and are just hitchhiking mutations.”

      4. Several figures are confusing and lack detail. The diversity plots of Figure 3 and Figure 8 are hard to be precisely compared to the mutations that occurred among different plots. Phylogenetic trees, as well as their figure legends, are confusing, especially Figure 9 and Figure 10. For Figure 9, it is impossible to tell which mutation site had changed from C to T. For Figure 10, spots depicted in yellow are both position 29827 A>T and position 29830 G>T, green spot only notes as G, but A29827 is not mentioned in the figure. Furthermore, the mutation position of blue spot C cannot be found.

        We have now redrawn the diversity plots in Figure 3 and Figure 8, (labelled Figure 2 and Figure 4, respectively, in the revised manuscript) and are shown below. We have introduced horizontal lines to show the height of the divergence line at variant positions discussed in the manuscript, and these are also marked with the same colour in corresponding subplots for comparison.

      In the revised manuscript, Figures 9 and 10 are now Supplementary Figures 2c and 2d respectively. The new figure legends are: Supplementary Figure 2: The sequences carrying the mutations a) C5700A b) C23929T c) C18877T d) G29830T are depicted in yellow colour. Figure 10 (now Supplementary Figure 2(d)) is now re-plotted, and we have removed the blue dot corresponding to ‘C’ since no samples from India had this variation.

      1. Figure 9 and Figure 10 were not mentioned inside the text.

        It has now been added in the manuscript: Supplementary Figure 2(c) – On Pg-9, in the first line under the heading “Identification of novel subclade I/GJ-20A and unique mutations in Maharashtra”. Supplementary Figure 2(d) – On Pg-11, in the last paragraph under the heading “Identification of novel subclade I/GJ-20A and unique mutations in Maharashtra”.

      2. The Top 10 mutations in PCA analysis are the mutations in 20A and I/A3i. It is reasonable to observed a clear association of the clusters with the clades. It is not clear, however, how these distribution correlate with lockdown, contact tracing and quarantine measures.

        From Supplementary Figure 1 clade 20A (shown in ‘Green’) is predominantly observed in Gujarat (178/201) and the distribution of clade 19A (shown in ‘Blue’) is high in Telangana (75/97), followed by Delhi (55/76), Maharashtra (31/80), and Tamil Nadu (19/34). Four mutations, C6312A, C13730T, C23929T, and C28311T are reported to be associated with subclade I/A3i, which is India-specific subclade of 19A. These co-occurring mutations are found in ~32% of Indian samples sequenced (till 31st May 2020). Only 5 isolates of this subclade were observed after May in India with the last one dated 13th June 2020 (according to data available in Nextstrain). This indicates that the spread of subclade I/A3i had been largely contained during lockdown with efforts of contact tracing and quarantining the infected individuals. Also, Telangana and Delhi isolates cluster together due to shared I/A3i mutations, primarily due to the Tablighi Jamaat congregation that occurred just before lockdown was announced. Similarly, clade 20A defining mutations were observed to occur in ~ 90% of Gujarat samples. Due to the countrywide lockdown from 25th March 2020, this clade and its sub-clusters were localized in the state, defined by Gujarat-specific mutations, e.g., I/GJ-20A.


      Hurng-Yi Wang:

      I agree to change to Verified manuscript.


      Decision

      Verified manuscript

      Dr. Abraham: Verified manuscript

      Dr. Sabat: Verified manuscript

      Dr. Wang: Verified manuscript

    1. Discussion, revision and decision


      Discussion and Revision


      Author

      Reviewer 1 (Takehiko Ogawa, Yokohama City University, ogawa@yokohama-cu.ac.jp ):

      We had already suggested in the "Limitations" section on page 10 of the manuscript-PDF testis transplantation experiments to test biological functionality for future studies: https://www.biorxiv.org/content/10.1101/2021.10.12.464060v1.full.pdf

      However, these experiments will require new cell lines, new funding, more resources, more logistics, and new ethics approval which we are considering for future studies. We believe that the observations described in our manuscript are valuable to the scientific community and are a basis to conduct further studies.


      Reviewer 2 (Dr. Pradeep G Kumar, Rajiv Gandhi Centre for Biotechnology, and kumarp@rgcb.res.in )

      There are no line numbers in the manuscript-PDF but the pages are numbered: https://www.biorxiv.org/content/10.1101/2021.10.12.464060v1.full.pdf Since the pages of the manuscript-PDF are numbered, we think that citation of the manuscript is unproblematic. The text of the manuscript has been checked by multiple experienced authors and found not to require the suggested changes.


      Reviewer (Takehiko Ogawa)

      I think that authors admit the limit of the study, which I think is serious. I think the paper has its own value with limitation as almost always in most cases. I would like to take “verified with reservation” as my final decision.


      Decision

      Takehiko Ogawa: Verified with reservations

      Pradeep G Kumar: Verified manuscript

      Verified with reservations

  4. Nov 2021
    1. Discussion, revision and decision


      Revision


      Author

      The objective of the paper was not to address a research question but to report on a more recent set of PubMed retractions due to insufficient/old information available in the papers published on this subject(PubMed, not WoS retracted articles). One of the initial objectives was to analyze the dynamic of image related retractions(a relatively new subject), a subject for which the information is at least scarce if non existing. We have also studied the impact of retracted research via two citations databases (Google Scholar and Dimensions) and tried to represent the variability of this impact when the author country is being considered. At this time, the paper is the second biggest serie of PubMed retracted articles.

      There was an error from our part, thank you very much for pointing this.

      We have revised the article and added the informations related to previous research on this subject. Thank you so much for this suggestion.

    1. Many decisions are reversible, two-way doors. Those decisions can use a light-weight process. Most decisions should probably be made with somewhere around 70 percent of the information you wish you had. Some decisions are consequential and irreversible or nearly irreversible -- one-way doors -- and these decisions must be made methodically, carefully, slowly, with great deliberation and consultation. If you walk through and don't like what you see on the other side, you can't get back to where you were before. But most decisions aren't like that -- they are changeable, reversible -- they're two-way doors. If you've made a suboptimal two-way door decision, you don't have to live with the consequences for that long. You can reopen the door and go back through.

      Reversible decisions can be made with less information / certainty

    1. ReconfigBehSci. (2021, October 30). Does there maybe need to be more distinction between points raised for discussion and any actual decision? Without knowing about votes etc., it’s maybe a bit strong to say ‘JCVI wanted x...’? I’ve sat on many bodies with minutes documenting positions I disagreed with [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1454488759785897987

    1. Dr. Thomas Wilckens. (2021, October 31). JCVI facing calls from within for greater transparency over decision-making https://buff.ly/3GwVqCZ JCVI has been criticised for failing to publish detailed minutes, modelling and analysis behind its decision to advise vaccinating all over-16s in Britain #covid19 #coronavirus https://t.co/nWbnvci7LI [Tweet]. @Thomas_Wilckens. https://twitter.com/Thomas_Wilckens/status/1454798820156530689

  5. Oct 2021
    1. “Speed kills.” If you are able to be nimble, assess the ever-changing environment, and adapt quickly, you’ll always carry the advantage over any opponents. Start applying the OODA Loop to your day-to-day decisions and watch what happens. You’ll start to notice things that you would have been oblivious to before. Before jumping to your first conclusion, you’ll pause to consider your biases, take in additional information, and be more thoughtful of consequences.

      In che modo si può applicare il modello OODA Loop nella vita quotidiana?

      Semplicemente applicando ad ogni nostra decisione le fasi previste dal modello, rendendo questo processo una abitudine riusciremo ad essere sempre più veloci nell'eseguirlo e questo ci darà la velocità necessaria per sopravvivere e vincere.

    1. Team syntegrity and democratic group decision making: theory and practice

      Team Syntegrity

      Stafford Beer created Team Syntegrity as a methodology for social interaction that predisposes participants towards shared agreement among varied and sometimes conflicting interests, without compromising the legitimate claims and integrity of those interests. This paper outlines the methodology and the underlying philosophy, describing several applications in a variety of countries and contexts, indicating why such an approach causes us to re-think more traditional approaches to group decision processes, and relating Team Syntegrity to other systems approaches.

      Shared by Kirby Urner in the Trimtab Book Club

  6. Sep 2021
    1. it’s time to reconsider that decision. Here are three reasons you might have waited to make the switch — and why those reasons are out of date in 2021.
  7. Aug 2021
  8. Jul 2021
    1. Short interview that covers the findings of a systematic review that aimed to identify the number of studies trying to replicate the outcomes of clinical decision support systems.

      Article being discussed in this piece: Coiera, E., & Tong, H. L. (2021). Replication studies in the clinical decision support literature-frequency, fidelity, and impact. Journal of the American Medical Informatics Association: JAMIA, ocab049. https://doi.org/10/gmb35n

    1. To the extentthat people accommodate themselves to the faceless inflexibility ofplatforms, they will become less and less capable of seeing thevirtues of institutions, on any scale. One consequence of thataccommodation will be an increasing impatience withrepresentative democracy, and an accompanying desire to replacepolitical institutions with platform-based decision making:referendums and plebiscites, conducted at as high a level as possible(national, or in the case of the European Union, transnational).Among other things, these trends will bring, in turn, theexploitation of communities and natural resources by people whowill never see or know anything about what they are exploiting. !escope of local action will therefore be diminished, and will comeunder increasing threat of what we might call, borrowing a phrasefrom Einstein, spooky action at a distance.

      This fits in line with my thesis to make corporations and especially corporate executives and owners be local, so that they can see the effect that their decisions are having.

  9. Jun 2021
    1. FOR THESE REASONS, THE COURT UNANIMOUSLY

      for these reasons, the court unanimously

    Tags

    Annotators

    1. your goal cannot be to follow orders in order to get a higher grade, instead you are free to listen, consider things, ignore ideas, or ask more honest questions of your readers. You are now free to make your own decisions on your writing. 

      Labor-based grading in writing allows students to listen and adjust to comments which gives them greater freedom and autonomy in both their learning process as well as their writing.

      Ideally, in a system like this, a shorter feedback loop of commentary and readjustment may also help to more carefully hone their skills versus potentially hitting a plateau after which it's more difficult to improve.

    1. V Shah, A. S., Gribben, C., Bishop, J., Hanlon, P., Caldwell, D., Wood, R., Reid, M., McMenamin, J., Goldberg, D., Stockton, D., Hutchinson, S., Robertson, C., McKeigue, P. M., Colhoun, H. M., & McAllister, D. A. (2021). Effect of vaccination on transmission of COVID-19: An observational study in healthcare workers and their households [Preprint]. Public and Global Health. https://doi.org/10.1101/2021.03.11.21253275

    1. better “decision hygiene” such as designating an observer for group decisions, to prevent common biases and noisy judgments. For example, they can ensure that participants in a team reach independent assessments before coming together as a group to aggregate their decisions.

      Approaches for decreasing noise in decision making

  10. May 2021
    1. Career decision making involves so much uncertainty that it’s easy to feel paralysed. Instead, make some hypotheses about which option is best, then identify key uncertainties: what information would most change your best guess?

      We tend to think that uncertainties can't be weighted in our decision-making, but we bet on uncertainties all the time. Rather than throw your hands up and say, "I don't have enough information to make a call", how can we think deliberately about what information would reduce the uncertainty?

    1. Dr Nisreen Alwan 🌻. (2021, March 14). Exactly a year ago we wrote this letter in the Times. We were gobsmacked! We just didn’t understand what the government was basing all its decisions on including stopping testing and the herd immunity by natural infection stuff. We wanted to see the evidence backing them. [Tweet]. @Dr2NisreenAlwan. https://twitter.com/Dr2NisreenAlwan/status/1371168531669258242

  11. Apr 2021
  12. Mar 2021
    1. Chapman, G. B., & Coups, E. J. (2006). Emotions and preventive health behavior: Worry, regret, and influenza vaccination. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 25(1), 82–90. https://doi.org/10.1037/0278-6133.25.1.82

    1. Baker, C. M., Campbell, P. T., Chades, I., Dean, A. J., Hester, S. M., Holden, M. H., McCaw, J. M., McVernon, J., Moss, R., Shearer, F. M., & Possingham, H. P. (2020). From climate change to pandemics: Decision science can help scientists have impact. ArXiv:2007.13261 [Physics]. http://arxiv.org/abs/2007.13261