Introducing the Health and Healthcare in Gender Diverse Communities Collection


We are delighted to announce our Collection on Health and Healthcare in Gender Diverse Communities, curated by our Guest Editors Dr. Asa Radix, Dr. Ayden Scheim, and Dr. Jae Sevelius. The collection includes a diverse group of articles investigating influences on mental and physical health, experiences accessing healthcare and engaging with the healthcare system, and the impacts of violence, discrimination, and stigma on health and wellbeing within gender diverse communities around the world. Additional articles will be added to the Collection as they become available, so be sure to keep checking back for the newest research.

Here, Drs. Sevelius and Scheim share their thoughts on this crucial area of research.

What recent developments or emerging trends in the field do you find most interesting or exciting?

JS: It is absolutely critical that we continue to advance the science around transgender children and youth. This science is imperative to inform advocacy for policies that support our young people and provide access to life-saving treatment, especially in this era of proposed treatment bans and myths around ‘desistance’. Further, learning more about how best to support trans people in their youth can help to prevent some of the persistent mental and physical health disparities we see among trans adults.

AS: I’m excited by the changing scientific and organizational leadership in the field, with trans health research increasingly led by trans people. This is not simply a matter of representation for its own sake — I think community knowledge and relationships can be leveraged to improve the rigour, relevance, and reach of our research. I also see growing topical and regional diversity in trans health research. Like cisgender people, trans people live everywhere in the world, grow older, and form families, and so improving the health of trans populations requires a holistic and global approach.

From your perspective, what are the biggest challenges faced by researchers working with and within gender diverse communities? Do you have any advice for effectively overcoming these challenges?

JS: As an intervention scientist working in close collaboration with trans communities, some of the biggest challenges are structural. The priorities of the funders drive the science, and the funding mechanisms and timelines often do not account for the incredible investment of time and funds required to get community-engaged science right. To be successful and relevant, our intervention research needs to be led by trans people themselves. Due to social marginalization, this work is the first formal job many of the trans people I work with have had, which means there is significant training and support required to ensure our teams are successful and thriving professionally.

AS: Although trans health research increasingly involves trans people in leadership roles, those trans people are too often those who (like me) benefit from structural racism and discrimination. It is vital that researchers attend to differences in power and life experience within trans and gender diverse communities. Ideally, they would use community-based participatory research approaches to forge research partnerships that build power and resources of trans individuals and organizations from marginalized backgrounds.

Why is open access publication important in this field?

JS: Among the many reasons open access is important, one tremendous benefit is ensuring that health care providers who are treating trans patients have access to the most current and relevant science, enabling them to make more informed treatment decisions. Further, because taxpayers fund the majority of our research, they should have free access to the results of our work.

AS: As anyone plugged into trans Twitter can tell you, trans advocates actively engage with research being published on trans health and use that research in their advocacy, from educating families to pursuing legal challenges. Among the many reasons for OA, making research findings accessible for community advocates is a key priority for me.

About the Guest Editors:

Asa Radix is the Senior Director of Research and Education at the Callen-Lorde Community Health Center and a Clinical Associate Professor and the NYU  Grossman School of Medicine.

Ayden Scheim is an Assistant Professor of Epidemiology and Biostatistics at Drexel University.

Jae Sevelius is an Associate Professor of Medicine at the University of California, San Francisco, Co-Director of the Center for AIDS Prevention Studies (CAPS), Co-Director of the CAPS Developmental Core, and PI and co-founder of the Center of Excellence for Transgender Health.

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From Collecting Sediment Cores in Iceland to Mentoring Students: The Busy Life of an Early Career Researcher


Have you ever wondered what life as an early career researcher entails?

From managing a lab group and mentoring PhD students, to applying for funding and leading intensive fieldwork campaigns, scientists in the early stages of their career do it all.

Here, we chat with Dr. Margit Simon from NORCE Climate and Bjerknes Centre for Climate Research and recent PLOS ONE author to find out more about her exciting work.

PLOS: Your recent work, published in PLOS ONE, investigated stable isotope data from a new marine core collected off of Iceland – how did using data with such a high temporal resolution (1-2 years) impact what we know about water mass changes?

MS: Marine sediment cores that have such high-resolution are still a quite rare finding globally. For that specific area, it was a new finding that the upper core section – the youngest sediment part – could resolve the historic time interval so well. Mostly, that is only possible with schlerochronological records, of which there are a few around Iceland actually. We found a good correspondence with the measured phosphate concentrations within the water column – a comparison only possible because we have such high temporal resolution. Stable carbon isotopes in planktonic foraminifera are influenced by a variety of factors and are normally not so easy to interpret. By constraining the influences on the carbon isotopes by comparing to modern measurements, we were able to detect an intermittent 30-year cycle over the entire time series length, that is likely reflecting the ocean response to atmospheric variability, presumably the East Atlantic Pattern. That was not known or found before in that area.

PLOS: Has your data highlighted changes in climate over the past 150 years? What impact have these changes had on ocean variability?

MS: What I was intrigued to see was the long-term trend in benthic δ18O, a proxy recording the water mass properties in the intermediate waters at that location. It suggests that Atlantic-derived waters are expanding their core within the water column, from the subsurface into deeper intermediate depths, towards the present day. That there is greater Atlantic-derived water mass influence in the surface waters offshore of NW Iceland over the past 150 years is well known by now. However, until now, we did not know that this process is also influencing the deeper realms in the water column contemporaneously. That was a new finding.

PLOS: What are some of the challenges of being an Early Career Researcher? Do you feel that these are mitigated by the specific opportunities for ECRs?

MS: Well, securing funding short-term and long-term for my position itself, but also for my research activities is challenging, as the field becomes more and more competitive. Basic research has to be very innovative and impactful to get funding these days. Hence, I am wondering how sustainable the system is over time. I would wish for some more basic funding security or baseline funding in the private research institute section in Norway.

Image credit: Margit Simon

PLOS: You’ve done fieldwork in a number of exciting locations – from Iceland all the way to Southern Africa. Do you have a favorite location? Were there any sampling campaigns that were particularly challenging?

MS: They were all very special and exciting. Despite the Greenland Ice sheet probably being the most ‘exotic’ one that I have been to, my favourite place remains Africa, or specifically South Africa. The most challenging sampling campaign was in Mozambique as part of a wider trip from Zambia to South Africa with the aim to collect modern day river sediments.

PLOS: Field work in many research areas has been delayed or postponed in 2020 due to the Covid-19 pandemic. Were your fieldwork plans affected? And if so, how did you regroup?

MS: I was part of a marine sediment coring campaign offshore South Africa in the beginning of 2020 and retrospectively, I am very happy that we managed to do everything as planned. How little did we know then what was coming! Parallelly on land in South Africa, my project partners did field work, field experiments and excavated archaeological sites that had to be stopped due to COVID-19. This affected me in the sense that I could not get the samples I had hoped for, and we will need to postpone that to approximately Nov/Dec. of this year (2021). It is obviously still unclear if then we can operate again with a kind of normalcy.

PLOS: Now that you have PhD students of your own, is there a particular strategy you take in mentoring them? How do you prepare them for to be Early Career Researchers themselves?

MS: Well, I don’t have a rocket science strategy in place, but I think it is important to be there for them for questions, reviewing and to bounce ideas. I think nothing is worse than when you don´t have someone that you can frequently go to and ventilate ideas and perhaps also frustration. I think when you are in your PhD yourself you might underestimate the value of someone actually taking the time to read your work and give thoughtful feedback back. I think further down the line of your career path that becomes rarer and you think back on those times where your supervisor always gave comments.

PLOS: The University of Bergen and NORCE are hubs of scientific research – how has being in such a diverse group of expertise helped your own work? Do you find yourself collaborating with people in different fields from your own?

MS: Definitely. Before moving to Norway and becoming a part of the Bjerknes Centre for Climate Research, I worked in smaller groups that are more specialised in one field. That is, of course for your own work, very beneficial. However, I recognised that the centre here and the diverse groups and topics really offer new opportunities to merge and reach out and broaden your topic. I have very much benefited and used that platform for my science ever since.

PLOS: What new projects do you have on the horizon?

MS: As much as I am fascinated by the ocean and reconstructing its past variability on various timescales, I am excited about my new project ideas that aim to reveal past climate information from land or specifically from South Africa itself. What is new is that I target specifically archaeological cave sites where we can extract environmental information from the same layer that the material culture information comes from. Key behavioural innovations emerged among Homo sapiens in South Africa around 120 ka ago and the drivers of this development remains debated. One hypothesis is centred around climate changes.

PLOS: As you know, PLOS ONE is an open access journal, and is devoted to promoting open science. We would be curious to know your thoughts and opinions on open access and/or open data and the importance of these concepts for researchers, particularly early career scientists.

MS: I think both are extremely important especially for ECRs, for different reasons.

It might be rather difficult if you are an “unknown scientist” to get access to data if that is not stored at an open access source. That might of course also cost you more time and delay the activity you are working on, while a more known scientist might have asked the same question and might have gotten the data already the next day. Especially currently, during COVID-19 times, universities are conducting more and more data synthesis projects as e.g., master’s projects for students since laboratories are closed. Hence, it is of vital importance to have access without barriers to this. I think data storage facilities like PANGEA are crucial and I think the movement in the community in the last years to use these platforms more and more is great and should be pursued. In this respect, I also appreciate and used recently myself the opportunity to publish data sets only in peer-reviewed journals. It ensures good quality control on the data published but does not force one to interpret the data. Still, one can gain credit for the work. Importantly, data such as this is also available to the community that otherwise might have been hidden in a drawer.

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Author Interview: Sacha Gómez Moñivas on student learning despite COVID-19 confinement


Using prior academic years as a control group, Sacha Gómez Moñivas and a group of fellow teachers and researchers found that despite the confinement caused by COVID-19, the learning habits of students became more continuous and ultimately led to better scores during assessments. Their study “Influence of COVID-19 confinement on students’ performance in higher education” was one of the highest viewed PLOS publications of 2020 with over 150,000 views. Read our interview with Sacha about his team’s initial response to the surprising results, the importance of providing details to replicate a study and the difficulties in collecting data on student learning.

Would you say this study is outside the scope of your normal research? How did you get involved in this study and why do you believe this research is important?

Our main research line since 2015 is related to new learning methodologies. Within this topic, we study in detail distant learning, among others. When the COVID-19 pandemic forced most of the students stay at home and change their learning strategies, we were completely prepared for this scenario because, by that time, we had already developed different tools and methods of distance learning already applied in our subjects.

We were involved in this study by analyzing and comparing the huge amounts of data obtained in previous years in our pilot experiences applying distant learning with the new data obtained during the COVID-19 pandemic. We were following the same research line as before, but in a new scenario.

This research is important because it is related to the Susta​inable Development Goal 4 of UNESCO. More specifically, this research helps us understand the impact of COVID-19 in education and students’ capability to change their learning strategies. It is also important because COVID-19 pandemic has many specific factors that can interact with the previously detected relevant characteristics of distant learning. For example, does student motivation behave in the same way in the pandemic as in a traditional distance learning setting?

I want to send an optimistic message in this case. We have demonstrated that, even in this very difficult situation, students and teachers were able to adapt their strategies in the learning process successfully

Read Sacha’s article

Did you find the results to be generally surprising, or were they relatively in line with your expectations?

Some results were in line with our expectations since, ultimately, distant learning is distant learning. For example, the limited access to technology by the students is a problem that was well-known before. Of course, it also appeared in the COVID-19 confinement. The problems that appear when preparing assessment tools are indeed also present in the pandemic.

There are, however, other elements that appeared and were a huge surprise. For example, the improvement in students’ performance was unbelievable. We spent a lot of time trying to justify it with arguments related to fraudulent behaviors, such as cheating or copying in different forms. For that reason, we discarded many subjects where we considered that we could not fully exclude the possibility of cheating. After that, we still had three subjects where we could be sure that only confinement was related to the increase in students’ performance.

Your Results state that “the new learning methodology is the main reason for the change in students’ performance during the confinement.” How important is it for leadership bodies at institutions and schools to provide teachers with resources to properly implement new teaching practices adapted for less face-to-face interactions?

It is crucial. The first step for a good teaching practice is having a good communication between teachers and students. If that fails, everything fails. In distant learning, teachers should have good multimedia resources and connectivity, at least. If not, it does not matter the amount of material developed by the teacher or how good the teacher is when explaining a lesson. I have seen a lot of very good attempts of developing new and very well-organized online courses that failed at the very beginning due to not having the adequate resources.

I note that you opted to publish a preprint when you initially submitted this paper for review, and that you published your peer review history alongside your PLOS ONE publication. What led you to these decisions and how important is scientific transparency to you?

We believe that scientific advances must follow FATE principles: fairness, accountability, transparency and ethics. Transparency is, actually, a key factor in the scientific method itself. If a scientific result must be replicable, it should include all details about experimental procedures, materials, etc. Obviously, transparency is a must. In the case of scientific publications, the whole peer review history is very important for two reasons. First, it demonstrates that the article followed a rigorous peer review process. Second, it gives valuable information about the questions raised by the reviewers and how they were answered by the authors, which could lead in additional criticism by the readers, which can be also valuable.

Do you think your study could be easily reproduced in other parts of the world by other researchers interested in using your methodologies, or were there specific pre-existing conditions that allowed for this study to take place? How helpful would it be to have data from classrooms in other parts of the world?

The bigger problem is getting data. There are many factors that must be considered. Because of potential cheating by the students when working at home, we had to discard 80% of our data to be sure that this did not influencing in the study. This is the first and maybe more important problem, but there are others. For example, researchers must also take into account the differences between countries in the sense that different countries faced the pandemic with varying levels of confinement. This is important because conclusions should be related to those conditions.

At the very beginning, when we did our study, not many groups had the opportunity to collect and analyze reliable data. Now, there are more and more very interesting studies from many different countries. Soon we will have enough data to get conclusions about the success of different strategies, which will be very helpful for planning distant learning at all levels in the future.

If the general public were to take one lesson from your study, what should that be?

I want to send an optimistic message in this case. We have demonstrated that, even in this very difficult situation, students and teachers were able to adapt their strategies in the learning process successfully. We are going through some very difficult times, but we have been able to adapt and we must have the courage and energy to continue fighting until we overcome this pandemic.

Thank you to Sacha and his research team for their important work and taking the time to answer these questions. Their work was founded by CRUE, CSIC and Banco Santander.

Citation

Gonzalez T, de la Rubia MA, Hincz KP, Comas-Lopez M, Subirats L, Fort S, et al. (2020) Influence of COVID-19 confinement on students’ performance in higher education. PLoS ONE 15(10): e0239490. https://doi.org/10.1371/journal.pone.0239490

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Infectious disease modeling in a time of COVID-19 – PLOS ONE authors’ perspectives – Part 2

In February 2020, PLOS published a Collection entitled “Mathematical Modeling of Infectious Disease Dynamics” which includes papers from PLOS ONE, PLOS Biology and PLOS Computational Biology, on a variety of topics relevant to the modeling of infectious diseases, such as disease spread, vaccination strategies and parameter estimation. As the world grappled with the effects of COVID-19 this year, the importance of accurate infectious disease modeling has become apparent. We therefore invited a few authors featured in the Collection to give their perspectives on their research during this global pandemic. We caught up with Verrah Otiende (independent researcher, Pan African University Institute of Basic Sciences Technology and Innovation), Lauren White (USAID), Jess Liebig (CSIRO) and Johnny Whitman (The Ohio State University) to hear their reflections on this collection and the time that has passed.

In this second blog post of two, we hear from Jess Liebig and Johnny Whitman, who discuss the modeling of human movement, the assumptions that go into creating a model, the virtue of simpler models, and the importance of understanding under-reporting in disease modeling.

What is your research focused on currently?

JL: Since September 2017 I am part of CSIRO’s DiMeMo (Disease Networks and Mobility) team. The aim of DiNeMo is to understand how human infectious diseases might arrive and spread in Australia. We analyse various sources of data and identify patterns of people movement both internationally and domestically in order to forecast the risk of disease spread. Initially I worked on modelling dengue importations via air travel. However, since the beginning of the pandemic the focus of my work has shifted to COVID-19. I am currently studying the effects of international travel restrictions on COVID-19 importation risk. The results of this study shed light onto how many importations a country can expect when opening its borders and can guide authorities in making decisions.

JW: My research is currently split between two main thrusts: the first is a collaboration with Battelle Memorial Institute, working on comparisons of codon usage in certain classes of proteins. The second is investigating methods of identifying parameters in biological signaling networks using the supercomputing cluster at Nationwide Children’s Hospital in Columbus, Ohio. Finally, I am finishing my PhD this spring semester and my current research for that deals with the design and verification of biological circuits for intracellular signaling, as well as developing methods to coarse-grain out complicated host-virus interactions in simulations of dendritic and epithelial cells.

We analyse various sources of data and identify patterns of people movement both internationally and domestically in order to forecast the risk of disease spread.

Jess Liebig

What do you think are the lessons we can learn from the research in your field which will help us to better model infectious diseases in the future?

JL: We need high quality datasets to accurately model the spread of infectious diseases. In reality, the datasets that are accessible for researchers are often biased, incomplete and erroneous. While the process of data collection can be tedious and expensive it can add much value to the research community when done in an organised and purposeful manner.

JW: A trend in current modeling is to hyperfocus on fitting parameters in a model in order to precisely match available data; with advances in artificial intelligence and neural networks, researchers are quick to use these overparameterization models to get very good fits to the data. I would argue that we should instead focus on identifying important qualitative features of data or populations – a difficult and careful human process – and implementing simpler models around these features. To be concrete, if a complex model of American COVID-19 cases from January to May fits the data extremely well, but offers 500 parameters to change to predict future behavior, it is very difficult to make any form of meaningful prediction or understanding of what the model is actually saying about the underlying population, whereas a simpler model with directly interpretable parameters may perform worse quantitatively, but be much more expressive overall.

I think the pandemic has (or should have) focused researchers more on making observations in real populations and taking note of how real behavior patterns can make fundamental difference in model predictions.

Johnny Whitman

Have your motivations, direction or the way you conduct or disseminate your research changed in 2020 as a consequence of the COVID-19 pandemic, either for yourself or the field as a whole?

JL: My work is motivated by several studies that have shown that the structure of the global air transport network as well as the increasing volume of international travellers has contributed to the large-scale spread of infectious diseases. The COVID-19 pandemic is an unpalatable reminder of human movement being able to rapidly spread a disease across the globe. While the motivation and direction of my work has been reinforced as a consequence of the pandemic, there have been changes to the way I disseminate my research. With travel restrictions and lockdowns in place, conferences, research meetings etc. have moved online, giving rise to new challenges. For example, it can be more difficult to clearly communicate your ideas to collaborators in a teleconference as opposed to a face-to-face meeting. What I find particularly challenging is to give online presentations where you cannot see the reaction of your audience.

JW: I think the pandemic has (or should have) focused researchers more on making observations in real populations and taking note of how real behavior patterns can make fundamental difference in model predictions. A simple example is a very good group at the University of Illinois put together an intricate and well-thought out model, which ultimately failed. The failure was due to not including the possibility that a contagious individual who knew they were contagious would continue to be social. Clearly, they are not at fault for using a rational actor assumption, but the lesson is that we should always remain grounded in the people and phenomenon we model if we hope to make any progress.

It is very important to understand what exactly these assumptions are and how they affect the results of the modelling study. Any conclusions have to be drawn carefully, taking into consideration the set of assumptions that were made.

Lauren White

If there was one thing you wished that the general public understood better about modeling infectious diseases, what would that be?

JL: Naturally, when modelling the spread of infectious disease (or any other process), scientists have to make certain assumptions due to incomplete data and knowledge gaps. It is very important to understand what exactly these assumptions are and how they affect the results of the modelling study. Any conclusions have to be drawn carefully, taking into consideration the set of assumptions that were made.

JW: Partially due to the manner in which models are presented to the public and also how researchers have positioned their work, I think that the public believes that models are intended to exactly predict the course of a disease. Rather, I wish we collectively understood the role of modeling more as a probe into the possibilities of a system; I would never trust a model to truly predict the number of COVID cases, but they can give us the possibilities of recurrent infection waves, how the dynamics depend on observable parameters like recovery time and incubation period, and other broad qualitative features that can influence public health decisions. A more technical wish would be that the public understood model predictions in the same sense that they understand weather predictions; most complex systems modeling is stochastic in some sense, so I would prefer that reporting on modeling emphasized the possibilities of events more than definitive statements. We’ve seen public support unnecessarily erode due to unrealized model predictions, and I think this could be avoided if communication was clearer.

Are there any unanswered research questions in this field that you would really like to see us make progress on?

JL: A key ingredient to modelling the spread of infectious disease is the incidence rate. Unfortunately, the incidence of most infectious diseases is under-estimated, which is due to under-reporting and under-ascertainment. Under-reporting refers to positive disease cases not being reported, for example due to mis-diagnosis. Under-ascertainment occurs when infected individuals do not report to a health professional, for example due to the absence of symptoms. Reporting and ascertainment rates vary across time and space and depend on the disease itself. A model that requires incidence rates as input can only be accurate if we have a good understanding of the level of under-estimation surrounding the incidence rates. Unfortunately, current techniques for determining the level of under-estimation are time consuming, expensive and often biased.

JW: The physics background in me would like to see a more general study of disease modeling in the spirit of field theory models; due to the much simpler nature of interactions in theoretical physics problems, we have done a careful and systematic investigation of how essentially every class of interaction type affects the macroscopic behavior of the model, e.g. if there is some symmetry, what types of particles are allowed, if this interaction is strong, it suppresses that behavior. I would like to see a similar-minded effort in disease modeling, so that researchers in this community build up a common base of tools and understanding. As it stands, the field is so fragmented in terminology and approach that it is difficult to quickly agree about what the setup of a problem is, much less the implications of the model.

About the authors:


Jess Liebig: Jessica Liebig is a postdoctoral fellow at the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia’s national science agency. She received a BSc(Hons) and a PhD in Applied Mathematics from RMIT University in 2013 and 2017, respectively. Her primary research interest lies in the area of network science and is directed towards the study of infectious disease spread. She is part of CSIRO’s Disease Networks and Mobility (DiNeMo) project, an interdisciplinary research initiative that aims to understand how human infectious diseases might arrive and spread in Australia. As part of her work she identifies patterns of people movement, both internationally and domestically, to forecast the risk of disease spread.


Johnny Whitman: John Whitman graduated from the University of Illinois in 2016, and is currently finishing his PhD in Physics at The Ohio State University with Prof. Ciriyam Jayaprakash. His research interests include stochastic modeling of systems at all scales, from intracellular signaling pathways to large scale population epidemiological modeling. He is most interested in problems which exhibit some form of complexity, since he really enjoys scientific programming and visualization/animation of processes.

Disclaimer: Views expressed by contributors are solely those of individual contributors, and not necessarily those of PLOS.

Featured Image : Spencer J. Fox, CC0

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Infectious disease modeling in a time of COVID-19 – PLOS ONE authors’ perspectives

In February 2020, PLOS published a Collection entitled “Mathematical Modeling of Infectious Disease Dynamics” which includes papers from PLOS ONE, PLOS Biology and PLOS Computational Biology, on a variety of topics relevant to the modeling of infectious disease, such as disease spread, vaccination strategies and parameter estimation. As the world grappled with the effects of COVID-19 this year, the importance of accurate infectious disease modeling has become apparent. We therefore invited a few authors  featured in the Collection to give their perspectives on their research during this global pandemic. We caught up with Verrah Otiende (independent researcher, Pan African University Institute of Basic Sciences Technology and Innovation), Lauren White (USAID), Jess Liebig (CSIRO) and Johnny Whitman (The Ohio State University) to hear their reflections on this collection and the time that has passed.

In this first blog post of a set of two, we hear from Verrah Otiende and Lauren White, who discuss the modeling of other infectious diseases such as HIV and TB during the COVID-19 pandemic, the importance of good data, the increasing focus of incorporating human behavior in disease models, and more. Please check back in a couple of weeks for the next installment of this blog post series.

What is your research focused on currently?

VO: Currently, I am independently researching the spatiotemporal patterns of successful TB treatment outcomes for HIV co-infected cases in Kenya. The motivation of this study is mainly the convergence of TB and HIV epidemics that threatens the management of TB treatment. This is evidenced by various spatial studies that have described how HIV co-infection propagates unsuccessful TB treatment outcomes. I am using the Bayesian Hierarchical Modeling approach to generate the estimates for each of the 47 counties of Kenya. These estimates will help identify the high-risk counties with successful TB treatment outcomes and deliberately prioritize other counties with an increased risk of unsuccessful treatment outcomes.

I believe that we will continue to improve disease models as we learn more about the ways that individual contact patterns, behaviors, and immune responses affect epidemics.

Lauren White

LW: I am a quantitative disease ecologist interested in developing and improving mathematical models of disease to assist in prediction and prevention of emerging and zoonotic infectious diseases in the context of rapidly changing, human-impacted environments. The overall objective of my research is to explore the effects of heterogeneity in behavioral and immune competence on disease modeling predictions within and across populations. I use mathematical modelling approaches, integrated with empirical data, to explore three different types of heterogeneity that can alter individual transmission rates: (i) within-host heterogeneity; (ii) contact heterogeneity and group structure within populations; and (iii) spatial heterogeneity across landscapes. My work also has broader implications for understanding human disease risk within the One Health framework, which includes human, animal, and environmental health.

What do you think are the lessons we can learn from the research in your field which will help us to better model infectious diseases in the future?

VO: Applying Bayesian algorithms to modeling multiple related infectious diseases is critical for quantifying both the joint and disease-specific risk estimates. The flexibility and informative outputs of Bayesian Hierarchical Models play a key role in clustering the geographical risk areas over a given time period. This would further provide additional insights towards the collaborative monitoring of the diseases and facilitate the comparative benefit obtained across the disease populations.

LW: Before this year, “superspreader” was considered a technical term, but COVID-19 has really highlighted the role of individual behavior in community spread.  I believe that we will continue to improve disease models as we learn more about the ways that individual contact patterns, behaviors, and immune responses affect epidemics. These are still very open questions, especially for less-studied livestock and wildlife, host-pathogen systems.

It is critical not to ignore other life-threatening infectious diseases while working towards managing COVID-19.

Verrah Otiende

Have your motivations, direction or the way you conduct or disseminate your research changed in 2020 as a consequence of the COVID-19 pandemic, either for yourself or the field as a whole?

VO: I am still enthusiastic about conducting and disseminating research work on infectious diseases. The direction has changed as a consequence of the COVID-19 pandemic, especially during dissemination. But the most positive effect of this change was reaching a wider audience virtually than I have ever thought of.

On case notifications, my worry is on underreporting and data capture processes of other infectious diseases since most efforts have been directed towards controlling and preventing the spread of COVID-19. Probably the non-pharmaceutical practices like physical distancing and lockdowns have kept some infectious diseases from spreading for now but there is still a vacuum for certain diseases to rebound and spread which could have much more severe consequences to millions of humans for a very long time. It is critical not to ignore other life-threatening infectious diseases while working towards managing COVID-19.

LW: I have just recently started a position through the AAAS Science and Technology Policy Fellowship program. This means that I am spending less time researching questions around COVID-19 directly but learning a lot more about program planning and implementation, as well as the effects of COVID-19 on other public health efforts like epidemic control for HIV/AIDS. This is an important career opportunity for me to see what makes science actionable and useful for stakeholders, policymakers, and other end users.

Disease models are only as good as the information or data that we put into them—often times in new situations we end up using “best guesses.”

Lauren White

If there was one thing you wished that the general public understood better about modeling infectious diseases, what would that be?

VO: Modeling the joint dynamics of infectious diseases and human behavior is fundamental in understanding and quantifying the risks and effects associated with their global spread.

LW: COVID-19 has highlighted some confusion in how disease models are used for decision making. Disease models come in many types, but especially those that aim to predict or forecast the future function as thought experiments, not as written-in-stone prophecies. Disease models are only as good as the information or data that we put into them—often times in new situations we end up using “best guesses.” As our information and estimates improve, so can the accuracy of our models. This is not, by default, bad science; it simply reflects an iterative process.

It is also important to note that sometimes models can show as the worst case or “do nothing” scenario. Again, such an outcome is not a forgone conclusion. Public health interventions can help us do better. So better outcomes are not necessarily a failure of modeling or an overreaction to an epidemic, rather they are an indication that we, as a society, are doing something right.

Are there any unanswered research questions in this field that you would really like to see us make progress on?

VO: Numerous unanswered research questions would be of interest to progress on. A quick one that comes to my mind would be incorporating human behavior in the spatiotemporal joint modeling of infectious diseases to understand the possible effects of such behavior. This would require rich behavioural datasets and developing unsupervised ML algorithms to automate and predict the risks of joint infections over spatial and temporal dimensions.

LW: There will always be more to discover with regards to infectious diseases, but I actually think that the most pressing question is how we, as a scientific community, will do a better job in this current crisis and during future epidemics. I have faith that we will be able to answer research questions as they arise, and in fact, we have increased our understanding of a completely novel pathogen incredibly quickly. But we need to think more critically about how we are communicating results and making our work actionable: How do we maintain and build trust in a climate where scientific expertise itself is controversial? How can we better engage with the communities that we live in and serve? Are we communicating results thoughtfully and responsibly? These are by no means “new” or “novel” research questions, but COVID-19 has starkly highlighted their importance. 

About the authors:


Verrah Otiende: My name is Verrah Otiende and I am a statistician and an ML enthusiast with proven expertise in data governance concepts and using Big Data platforms to efficiently store and manage large amounts of data. I am an independent researcher and currently working on building, evaluating, and integrating predictive models on infectious disease case notifications using unsupervised ML algorithms to optimize intervention options and public health decisions. Besides infectious disease modeling, I am also working on the Named Entity Recognition (NER) datasets to build translation models for African languages through the MASAKHANE research initiative for Natural Language Processing (NLP).


Lauren White: Dr. Lauren White is a first year AAAS Science and Technology Policy Fellow at the Office of HIV/AIDS in USAID. Dr. White has a background in infectious disease modeling and epidemiology with an interest in the intersections of human, animal, and environmental health. Most recently, she worked as a post-doctoral research fellow at the National Socio-Environmental Synthesis Center (SESYNC) at the University of Maryland. Dr. White finished her Ph.D. in 2018 at the University of Minnesota in the Department of Ecology, Evolution & Behavior.

Disclaimer: Views expressed by contributors are solely those of individual contributors, and not necessarily those of PLOS.

Disclaimer from Lauren White: The views in this interview are those of the author and do not necessarily represent the views of USAID, PEPFAR, or the United States Government.

Featured Image : Spencer J. Fox, CC0

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A river of new ecological data – An Interview with the Guest Editors of our Freshwater Ecosystems Call for Papers

Freshwater ecosystems provide important services to human societies, such as water, food, regulation of hydrological extremes, pollutant attenuation, and carbon sequestration. As freshwater systems are under pressure from human activity and climate change, a more complete understanding of these systems is needed to respond to the environmental changes associated with these processes.

Here Prof Kirsten Seestern Christoffersen and Dr Ben Abbott, Guest Editors of PLOS ONE Call for Papers on Freshwater Ecosystems, share their thoughts on the present and future of freshwater science research.

What are the most interesting scientific advances in freshwater science recently?

KSC: I would say it is the enormous amount of data that is becoming available as we apply more and more continuous recording data loggers with sensitive sensors, drones, unmanned vehicles, all sort of cameras, fast running analytic instruments – and that these great things are also becoming more and more affordable. Because of these advances, it is possible to get data, photos and live videos for almost any part of the World, from the deepest lakes and the permanently ice-covered lakes to boiling mud-holes. And then, it follows from these advances mentioned above that these great challenges require computer power to handle, analyse and store these large amounts of data. So, it is no longer a question of how to get enough data but rather how to manage the wealth of data that we can produce.

BA: Our capacity to measure parameters in more ways has greatly expanded over the past two decades. This opens up the possibility for new spatiotemporal analyses to move beyond just calculating concentrations and loads to understanding the mechanisms driving ecosystem function across the terrestrial-aquatic gradient. The combination of traditional physicochemical parameters with metrics of ecological community and remotely-sensed watershed characteristics is really exciting.

And, on the other hand, what are the main challenges freshwater ecosystems will face in the near future?

KSC: Here I would say all the “usual challenges”: climate change, biodiversity crisis, eutrophication (still an issue despite it has been a problem for many years now). One thing that we really need to do is to establish what the baseline conditions are especially for freshwater ecosystems that have not yet been affected too much – like the freshwaters in the Arctic and alpine regions.

BA: This flood of new data represents a challenge in itself. More numbers do not automatically translate into greater understanding. We need new approaches to extract meaningful patterns and attribute those signals to ecological processes, especially human disturbance. Another challenge is that many of our long-term monitoring stations are at risk because of changes in funding priorities. We need to leverage these long-term data sources and figure out ways to better integrate across sites.

What new approaches are needed to respond to these challenges?

KSC: Awareness, political will and resources.

BA: See my last two responses.

What are your main research interests? What do you consider to be your biggest accomplishment in your career so far?

KSC: My main interests these years are understanding how Arctic freshwater ecosystems are organised under different (natural) environmental conditions and identifying the drivers and stressors that rule the biota. This might be the key elements to understand the uniqueness of pristine ecosystems and also to be able to predict their changes.

BA: There are still two million people who die every year from polluted water. Many more than that are affected by chronic or acute disease associated with exposure to pollutants. At the same time, aquatic ecosystems around the world are experiencing huge declines in biomass and biodiversity. We need to improve global water governance and ensure access to clean water for all people and ecosystems. The biggest accomplishment of my career has been the privilege of working with students, researchers, and water managers who are striving to address these global water crises.

What advice would you give to early-career freshwater researchers that want to make a difference?

KSC: It will be to follow your interests and go for the things that you think is important; if you can’t really get yourself into an enthusiastic mode when doing your research, you should maybe change horse. In other words, don’t necessary follow the main stream and where the money is often good – but follow your sense for what really matters. Another go advice is to talk with other scientists but not only those that are close to you (physically and thematically)!

BA: The distinction between basic and applied research is really counterproductive. Any good research has applications, and we should be seeking to share the relevant information we discover with all interested parties. As an early-career researcher myself, I frequently ask myself, how relevant and important is the work I am doing? Are there other issues or problems that I could be contributing to in a meaningful way? In this time of accelerating consumption and restructuring of human activity, the world needs high-quality information more than ever.

***

PLOS ONE has an open Call for Papers on Freshwater Ecosystems. Researchers working on freshwater ecology are encouraged to submit their work before January 8, 2021.

***

About our Guest Editors:

Kirsten Seestern Christoffersen

Kirsten Seestern Christoffersen is Professor of Freshwater Ecology at the University of Copenhagen

Ben Abbott

Ben Abbott is an Assistant Professor at Brigham Young University

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Open science and cognitive psychology: An interview with Guest Editor Nivedita Mani and Mariella Paul

Nivi is Professor at University of Göttingen, Germany where she leads the “Psychology of Language” research group at the Georg-Elias-Müller Institute for Psychology. Her work examines the factors underlying word learning and recognition in young children and views word learning as the result of a dynamic mutual interaction between the environment and the learner. She is also one of the Guest Editors of an ongoing PLOS ONE Call for Papers in developmental cognitive psychology in collaboration with the Center for Open Science. This Call has a particular emphasis on reproducibility, transparency in reporting, and pre-registration.

Prof. Dr. Nivedita Mani

Mariella is a postdoctoral researcher in Nivi’s department. She is interested in how children’s interests shape their word learning, which she investigates using several methods, including EEG, online studies, and meta-analytic approaches. Mariella was one of the co-founders of the Open Science initiative at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany, where she did her PhD, and was awarded an eLife Community Ambassadorship to promote open science.

Mariella Paul

I asked them about their views on how open science affects and shapes their research and their field.

Can you tell me about your interest in open science?

MP: The first time I heard about open science and the replication crisis was during a conference I attended during my Master’s, but I only really got into it during my PhD, when I learned much more about it through academic Twitter and started to apply it to my own research. I think the ideas around open science appealed to me as a (then very) early career researcher (ECR) because they were how I, perhaps idealistically, thought science should be done. I have heard the same sentiment from bachelor’s (or undergrad) students when giving lectures about open science practices: “Why wasn’t it always done like this?”. After learning bits and pieces from Twitter and podcasts, such as ReproducibiliTea and the Black Goat, I got in touch with other ECRs at my institute and we founded an open science initiative, organized workshops for our colleagues and ourselves to learn more about open science, and eventually even started our own ReproducibiliTea journal club, where we read and discuss papers about different open science practices.

NM: My interest in open science is relatively recent. I am quite late to the party and my invitation is by virtue of the people in my lab who keep finding better ways to do science. My interest is driven by the fact that the small steps towards transparency and best practice that we take in successive projects not only makes us more confident of the results we report but also makes us calmer in planning projects. What I find interesting and quite marvelous actually, is that this trend towards greater transparency in research and reporting is being spearheaded by young researchers. That’s really amazing to me, because, as a tenured Professor, that next publication – and lingering difficulties associated with publishing null results – is not going to impact my next paycheck but it might well impact the future prospects of the young researchers who are leading this change, who nevertheless weigh doing science well equally with getting cool results!    

How does transparency in reporting affect your own research?

MP: My PhD consisted largely of conceptual replications, that is, I replicated studies previously done with infants and adults with young children. Directly building on previous studies has clearly illustrated the need for transparent reporting for me – because only with transparent reporting and shared materials one can hope to conduct a close replication. Therefore, for my own research, I aim to report my methods as transparently as possible, to make the lives of future researchers wanting to run replications or meta-analyses easier.

Photo by Markus Spiske on Unsplash

NM: I think the best thing to say for it is that it is frees you. There is, on the one hand, more acceptance these days for the publication of null results, but also, more importantly, greater appreciation for the scientific process rather than the scientific result. This makes it a much more relaxing climate to be a researcher in, since you don’t need to find that perfect result, you need only to document that you went about looking for evidence of that effect in an appropriate manner. This makes you more conscious of critically evaluating your methods prior to testing while leaving you rather calm about the result of your manipulation. So for instance in my group, we now routinely write up the Introduction, Methods and Planned analyses of a paper before we start testing. This makes us think much more about what it is we are actually testing, what we plan to analyze, whether we can conduct the analyses we hope to, and whether that analyses actually tests the hypotheses under consideration. I think this way of planning studies not only makes us methodologically rigorous but also makes us more likely to actually find meaningful effects.

Why do you think pre-registration matters in developmental cognitive psychology?

MP: I think pre-registration can be valuable for any confirmatory study, by adding transparency early during the research process, and by decreasing researchers’ analytic flexibility. In developmental cognitive psychology in particular, we deal with unique issues. For example, when working with infants and young children, data collection and drop-outs require special attention. Pre-registration can help us set some of the parameters around these issues beforehand, for example by pre-specifying transparent data-peeking and planning a correction for sequential testing. I work a lot with EEG, where we additionally have a myriad of analytic decisions to make in how to preprocess the data. Also here, pre-registration can decrease researchers’ analytic flexibility and reduce bias by making these decisions before seeing the data.

NM: Developmental research is plagued with many of the issues in cognitive science, unfortunately amplified by difficulties with regards to access to participant pools (babies are more difficult to recruit relative to undergraduate students) and resulting issues in sample size, shorter attention spans of participants (leading to shorter and less well-powered experiments) as well as greater variance in infant responding. Thinking more carefully about the study and what you actually have adequate power to do – as one is forced to with a preregistration – may help us avoid costly mistakes of running under-powered studies that eventually lead to inconclusive results. From a pragmatic point of view, preregistration, in particular, helps us to better motivate analyses choices that may be questioned later in the process – so in a recent review of a paper, we were asked why we chose a particular exclusion criterion. We did not preregister this analysis (it’s a relatively old study that is only now seeing the light of day) but based this exclusion criterion on previous work – had we preregistered this, it would have been easier for us to justify our choice of this particular exclusion criterion. As it stands now, I can see that a skeptical reviewer may be inclined to believe our choice of this exclusion criterion is post-hoc.

How does the field of developmental cognitive psychology differ now compared to 10-15 years ago, and has open science played a role in that?

MP: I have only been in the field for a few years, but even in that time, I think open science has played a role in the development of the field. For example, large-scale replication efforts such as the ManyBabies project help us better understand central findings in our field, such as infants’ preference for speech presented in a child-directed manner. Similarly, platforms such as Wordbank – an open database of children’s vocabulary – and MetaLab –an interactive tool for meta-analysis in cognitive development – are now available for everyone to run their own studies on large-scale data.

there is greater acceptance of such “failed” experiments these days and this is to a large extent due to our increased appreciation for the scientific process (including open science practices) rather than the result.

NM: To be really honest, on a personal level, I am rather shamefaced about the practices that I believed acceptable 10 years ago. For instance, 10 years ago, I posted on social media that my “failed” experiments folder was 1.5 times larger than my “successful” experiments folder. Back then, it didn’t occur to me that the failed experiments folder (null results to be precise) was as important as the published successful experiments folder – and indeed, they were not failures, because they were providing us valuable information about potential contexts in which we do not find evidence for particular effects. However, now, there is greater acceptance of such “failed” experiments these days and this is to a large extent due to our increased appreciation for the scientific process (including open science practices) rather than the result. At the same time, there is greater emphasis on correct reporting of results, which I belatedly realize, I have been on the wrong side of, by not reporting aspects of the analyses that were important to interpretation of the results. I think this is changing too, with greater awareness of what we need to report when it comes to reporting the analyses we perform.  

What do you see as the greatest challenges for the field going forward?

MP: I think with the current development of the field and psychology in general, there are many challenges as well as opportunities. For many, including myself, one of the most direct challenges recently has been the restrictions on data collection due to the pandemic. With studies in the lab, as we know them, not having been possible (or only to a very limited degree) for over half a year now, many projects needed to be delayed, and we have been forced to rethink our way of planning new experiments. However, this unique situation also offers the possibility to conduct studies that we perhaps usually would not have thought of. For example, meta-analyses of previous studies in the literature can be conducted even when the lab is closed, and so can online-studies, of course. Also, the time away from the lab can be used to get started on new open science practices. For example, a registered report can be written and submitted so that the stage 1 protocol [i.e., a Registered Report Protocol at PLOS ONE] is already accepted by the time testing can be resumed.

NM: We seem to have achieved greater understanding of the requirements of good science, but I do worry about the extent to which we can implement these requirements. How can we run well-powered studies in developmental research, given restrictions on access to population pools and infant attention span? Cross-laboratory efforts (like the ManyBabies projects or a recent project on the effect of the Covid-19 lockdown on language development that I am involved in) here may be the way forward, allowing us to pool resources across laboratories. Equally, we are looking more deeply into sequential Bayesian designs, that may potentially allow us to get around some of the problems I have mentioned (sample size, power, inconclusive results). In general, I think we need to get more inventive about how to continue doing good developmental research.

At the same time, I don’t know if we really know how to analyze our data. In asking the more critical questions that the field is asking these days, I don’t really see one correct answer – and unfortunately, I don’t feel qualified to choose one answer over another. Again, I think greater transparency in research reporting helps here, because I get to post my data and my analyses and the results that I obtained with these analyses. This allows someone else to look through my data and analyze it differently to see if the pattern holds. Having said that, I don’t also think we are where we could be with regards to this solution – at least, I know my group isn’t – with regards to how well we archive our data and how transparent it is for others to use. That is definitely going to be one of the challenges we will face going forward.

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Physical forces at the interface with biology and chemistry: a conversation with Kerstin Blank and Matthew Harrington

 

Cell behaviour, tissue formation/regulation, physiology and disease are all influenced by cellular mechanics and physical forces. The field of mechanobiology has for a long time striven to fully understand how these forces affect biological and cellular processes, as well as developing new analytical techniques. At the same time, the properties of advanced smart materials, such as self-healing, self-reporting and responsive polymers, have been determined by a complex interplay between the thermodynamics, kinetics and mechanics of dynamic bonding strategies. These are tightly connected to the field of mechanochemistry, which aims to elucidate and harness molecular level design principles and translate these to the bulk material level as emergent properties. At this interface between disciplines lies an emerging and exciting research area that has been strongly facilitated by the collaboration of physicists, chemists, engineers, materials scientists, and biologists.

We had the pleasure of speaking to Kerstin Blank and Matthew Harrington, who have been working on how mechanical forces influence biological systems, molecules and responsive biomaterials, about their views of the field and the recent ‘Multiscale Mechanochemistry and Mechanobiology’ conference of which PLOS ONE was one of the proud sponsors.

 

How did you first become interested in this topic?

 

Kerstin: When I started in this field in 2000, I was mostly impressed by the technical possibilities. I was working with Hermann Gaub, one of the leaders in single-molecule force spectroscopy. I found it fascinating that we could stretch a single biological molecule and observe its response. I did ask myself sometimes if this was just something that physicists like to play with or if one could solve biomedically relevant questions with this approach. Now, almost 20 years later, it has become very evident that a large number of biological systems are regulated by mechanical forces in many different ways.

 

Matt: My educational background was primarily in biology and biochemistry, but I became fascinated with the capacity of certain biological materials to exhibit self-healing responses in the absence of living cells. I reasoned that this must arise from specific chemical and physical design principles in the material building blocks themselves, and I became obsessed with figuring out how this works. This led me to the self-healing materials community, which was largely populated with chemists and materials engineers, but not so many biologists. When I began to see that many of the same principles at play in synthetic self-healing materials were present in nature, and that in some cases nature was going well beyond the state of the art in synthetic self-healing materials, I realized the enormous potential at the interface of mechanobiology and mechanochemistry. I haven’t looked back since.

 

Which areas are you most excited about?

 

Kerstin: I find it very intriguing how cells utilize mechanical information from their environment and then feed it into intracellular biochemical signalling cascades. Understanding these mechanosensing and mechanotransduction processes requires knowledge of the cellular players and their interactions. But to develop the complete picture, we also need to investigate how cells interact with their extracellular environment. This also involves understanding the microscopic and macroscopic mechanical properties of the extracellular environment. I am highly excited about the development of molecular force sensors that convert mechanical force into a fluorescent signal. This allows for the localized detection of cell traction forces and, in the future, will also enable us to visualize force propagation inside materials that mimic the natural extracellular matrix.

 

Matt: I am currently most excited about understanding how and why nature uses different transient interactions to control the fabrication and viscoelastic mechanical responses of biopolymeric materials and the potential this has for the development of sustainable advanced polymers of the future. Recent discoveries in the field clearly show that in contrast to traditional polymers, living organisms commonly use specific supramolecular interactions based on dynamic bonds (e.g. hydrogen bonding, metal coordination or pi-cation interactions) to guide the self-assembly and mechanical properties of protein-based materials. The thermodynamic and kinetic properties of these labile bonds enable a certain dynamicity and responsiveness in these building blocks that provides potential inspiration for environmentally friendly materials processing and active/tuneable material properties. These concepts are already being adapted in a number of exciting bio-inspired polymers.

 

What progress has the field made in the last years?

 

Kerstin: It is now well-established that cells are able to sense and respond to the elastic and viscoelastic properties of the material they grow in. We have also learned a lot about how the mechanical signal is converted into biochemical signalling on the intracellular side. This is a direct result of many new technological developments, including the molecular force sensors described above. It is further a result of the increasing development of extracellular matrix mimics with well-defined and tuneable mechanical properties and microstructures.

 

Matt: Due to recent technological advances it is becoming possible to link specific aspects of mechanical material responses directly to structural features at multiple length scales. The better we understand these structure-property relationships, the better we can optimize the material response. This provides an intimate feedback loop that has enabled major breakthroughs in the fields of active matter, including self-healing and self-reporting polymers.

 

What is the real-world impact?

 

Kerstin: It is widely accepted that mechanical information plays a key role in stem cell differentiation. It has further been shown that mutated cells, e.g. in cancer or cardiovascular diseases, have different mechanical properties and show alterations in processing mechanical information. Understanding the origin of these changes and being able to interfere with them will have direct impact in disease diagnostics and treatment. Engineering materials with molecularly controlled structures and mechanical properties will further enable the community to direct stem cell differentiation in a more defined manner for applications in tissue engineering and regenerative medicine.

 

Matt: Aside from biomedical impacts, the insights gained from understanding the structure-function relationships defining the mechanical response of molecules are also extremely relevant for the development and sustainable fabrication of next generation advanced polymers. Given the global threat of petroleum-based plastics processing and disposal, this is an extremely important aspect of the research in this field.

 

What are the challenges and future developments of the field?

 

Kerstin: At this moment, we usually try to relate the macroscopic material properties (measured in the lab) with the microscopic environment that cells sense. In my view, we are missing a key piece of information. We need to understand how the macroscopic properties of a material emerge from its molecular composition, topography and hierarchical structure. In combination, all these parameters determine the mechanical properties of a material and, more importantly, what the cells ‘see’. In fact, this is not only key for the development of new extracellular matrix mimics. The same questions need to be answered for understanding how nature assembles a wide range of structural and functional materials with outstanding properties, such as spider silk, cellulose composites and nacre. Here, I see a great potential for future collaboration between disciplines.

 

Matt: There are enormous challenges on the bio-inspiration side of the field involved with transferring design principles extracted from biological materials into synthetic systems. Biology is inherently complex, so there is a common tendency to distil the extracted concept to a single functional group or concept, while often there are collective effects that are lost by this more reductionist approach. On the biological side, a key challenge is ascertaining which are the relevant design principles. On the bio-inspired side, there are challenges in finding appropriate synthetic analogues to mimic the chemical and structural complexity of the natural system. Overcoming this barrier requires cross-disciplinary communication and feedback and is an extremely exciting and active area in our field.

 

Why and when did you decide to organize a conference on this topic?

 

Kerstin & Matt: While both working at the Max Planck Institute of Colloids and Interfaces, we quickly realized that the cell biophysics, biomaterials, mechanochemistry and soft matter communities are all interested in very similar questions while using similar methods and theoretical models; however, we had the impression that they hardly interact with each other. We thought of ways to change this and organizing a conference was clearly one way to do it. The first conference with the topic ‘Multiscale Mechanochemistry and Mechanobiology: from molecular mechanisms to smart materials’ took place in Berlin in 2017. When bringing this idea forward in our respective communities, we immediately realized that we hit a nerve. Now that the conference has taken place for the second time in Montreal in 2019, we really got the feeling that we are starting to create a community around this topic. There will be another follow up conference from August 23-25, 2021 in Berlin (@mcb2021Berlin).

 

What are the most interesting and representative papers published in PLOS ONE in this field?

 

Kerstin: The paper ‘Monodisperse measurement of the biotin-streptavidin interaction strength in a well-defined pulling geometry’, published by Sedlak et al., is a highly interesting contribution to the field of single-molecule force spectroscopy, which was also presented at the conference. This work highlights the methodological developments in single-molecule force spectroscopy since its very early days. The authors from the Gaub lab have re-measured the well-known streptavidin-biotin interaction, now with a very high level of control over the molecular setup. It clearly shows how far the field has come and also that protein engineering, bioconjugation chemistry, instrumentation development and data analysis all need to go hand in hand to obtain clear and unambiguous experimental results. Clearly, considering a defined molecular setup is not only crucial for this kind of measurement but also for the development of biomimetic materials with controlled mechanical properties.

 

Sedlak SM, Bauer MS, Kluger C, Schendel LC, Milles LF, Pippig DA, et al. (2017) Monodisperse measurement of the biotin-streptavidin interaction strength in a well-defined pulling geometry. PLoS ONE 12(12): e0188722, https://doi.org/10.1371/journal.pone.0188722 

 

Matt: Accurately detecting and measuring the mechanical forces at play inside living cells is one of the key challenges in the field of mechanobiology, given the small size and dynamic nature of the intracellular environment. However, this information is extremely important for understanding the role of mechanics in regulating cellular functions such as growth, differentiation and proliferation, as well as disease states. In the ‘Nuclei deformation reveals pressure distributions in 3D cell clusters’ paper from the Ehrlicher group, the authors address this challenge by using fluorescently labelled proteins in the cell nucleus coupled with confocal microscopy to measure compressive pressures within cells and cell clusters. Using this methodology, they explored the effect of cell number and shape of multicellular clusters on the internal compressive pressure within cells, providing potentially important insights for cellular signalling and function. These studies have potential applications in both in vitro and in vivo models, and provide a relatively simple methodology for acquiring intracellular mechanical data.

 

Khavari A, Ehrlicher AJ (2019) Nuclei deformation reveals pressure distributions in 3D cell clusters. PLoS ONE 14(9): e0221753, https://doi.org/10.1371/journal.pone.0221753

 

 Other PLOS ONE representative papers:

 

  • Huth S, Sindt S, Selhuber-Unkel C (2019) Automated analysis of soft hydrogel microindentation: Impact of various indentation parameters on the measurement of Young’s modulus. PLoS ONE 14(8): e0220281, https://doi.org/10.1371/journal.pone.0220281
  • Taufalele PV, VanderBurgh JA, Muñoz A, Zanotelli MR, Reinhart-King CA (2019) Fiber alignment drives changes in architectural and mechanical features in collagen matrices. PLoS ONE 14(5): e0216537. https://doi.org/10.1371/journal.pone.0216537
  • Wheelwright M, Win Z, Mikkila JL, Amen KY, Alford PW, Metzger JM (2018) Investigation of human iPSC-derived cardiac myocyte functional maturation by single cell traction force microscopy. PLoS ONE 13(4): e0194909. https://doi.org/10.1371/journal.pone.0194909
  • Opell BD, Clouse ME, Andrews SF (2018) Elastic modulus and toughness of orb spider glycoprotein glue. PLoS ONE 13(5): e0196972. https://doi.org/10.1371/journal.pone.0196972
  • Yalak G, Shiu J-Y, Schoen I, Mitsi M, Vogel V (2019) Phosphorylated fibronectin enhances cell attachment and upregulates mechanical cell functions. PLoS ONE 14(7): e0218893. https://doi.org/10.1371/journal.pone.0218893

 

Kerstin Blank studied Biotechnology at the University of Applied Sciences in Jena and obtained her PhD in Biophysics under the supervision of Prof Hermann Gaub at Ludwig-Maximilians Universität in Munich. After two postdocs at the Université de Strasbourg and the Katholieke Universiteit Leuven, she became an Assistant Professor at Radboud University in Nijmegen in 2009. In 2014, she moved to the Max Planck Institute of Colloids and Interfaces where she holds the position of a Max Planck Research Group Leader. Her research interests combine biochemistry and single molecule biophysics with the goal of developing molecular force sensors for biological and materials science applications.

 

Matthew J. Harrington is Canada Research Chair in Green Chemistry and assistant professor in Chemistry at McGill University since 2017. He received his PhD in the lab of J. Herbert Waite from the University of California, Santa Barbara. Afterwards, he was a Humboldt postdoctoral fellow and then research group leader at the Max Planck Institute of Colloids and Interfaces in the Department of Biomaterials. His research interests are focused on understanding biochemical structure-function relationships and fabrication processes of biopolymeric materials and translating extracted design principles for production of sustainable, advanced materials.

 

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It’s the little things- An interview with the Guest Editors of our Microbial Ecology of Changing Environments Call for Papers

PLOS ONE has an open Call for Papers on the Microbial Ecology of Changing Environments, with selected submissions to be featured in an upcoming Collection. We aim to highlight a range of interdisciplinary articles showcasing