thescientist--2020-10-29--New Genome Sequences Reveal Undescribed African Migration

A lack of ethnic diversity in global genome databases has long been a source of discussion in the scientific community .
Africans in particular are underrepresented in these datasets , limiting the conclusions that can be drawn about human health and disease on the continent .
Now , groups are looking to buck the trend by diversifying genomics research .
A new study published in Nature yesterday ( October 28 ) and conducted through Human Heredity and Health in Africa ( H3Africa ) , a consortium devoted to increasing African representation in genetics research , uncovered 3 million new genetic @ @ @ @ @ @ @ studies of African genomes reported to date .
The research team performed whole-genome sequencing analyses of 426 individuals that represent 50 ethnolinguistic groups , including previously unsampled populations , to explore the breadth of genomic diversity across Africa .
The Scientist spoke with University of the Witwatersrand geneticists Zané Lombard and Ananyo Choudhury and Baylor College of Medicine geneticist Neil Hanchard , authors of the study , about the results ’ most pressing takeaways .
The Scientist : Why is it so crucial to capture the genetic diversity found on the continent ?
@ @ @ @ @ @ @ questions about population migration and population history from a genomics perspective if we have data on different ethnolinguistic groups .
There ’ s the population genetics aspect , but I think a lot of us are also interested in health research and precision medicine , and from that aspect , the more you know about the populations that you work in and know more about the genomic diversity in patient populations as well as research participants , it informs the kind of conclusions that you can draw from the data and from the @ @ @ @ @ @ @ It really just informs and gives you a context from which to understand the genomics of African participants .
TS : Can you walk me through how you conducted your study ?
ZL : It really started off with engaging with different principal investigators and participants across the [ H3Africa ] consortium and asking whether there were DNA samples available that were properly consented for this kind of research we would be doing—whole genome sequencing and depositing our data into a public repository of data .
We aggregated what we had available , @ @ @ @ @ @ @ National Human Genome Research Institute ] to have the sequencing done at the Baylor Sequencing Center .
After that , we got together a group of African scientists from the consortium who then worked on different aspects of the data analysis .
Neil Hanchard : We were very deliberate in trying to look for populations that hadn ’ t been surveyed previously .
That is one of the big drivers for some other more interesting results that we found .
TS : Give me an overview of what you found .
Ananyo Choudhury @ @ @ @ @ @ @ discovered was around three million novel variants in these [ approximately ] three hundred samples , which was quite substantial .
We always knew that there is a lot of variation in Africans , but there has been quite a few genome sequencing projects in the last three to four years , so we thought it was getting to saturation .
One of our questions was : Are we exhausting that possibility of discovering novel variants ?
Our study kind of shows that ’ s not the case .
There is still a @ @ @ @ @ @ @ into geographic areas that have been underrepresented traditionally—you can still discover a lot of novel variants .
In terms of population genetics , we discovered a migration that was never known .
It was a trans-Sahelian migration that moved into central Nigeria .
The timing of the migration into Nigeria didn ’ t correspond to any of the previous known migrations .
We also kind of identified the root of the Bantu migration to southern Africa—that ’ s a very hotly debated topic not only in genetics , but also in linguistics .
@ @ @ @ @ @ @ migration into Nigeria was particularly interesting because Nigeria has been very well sampled at the genome level , and here we were finding a fairly large Nigerian group that had marked differences from the traditional groups that have been utilized .
Those groups are often utilized as proxies for West Africa or sometimes for all of Africa , but they may not even be great proxies for Nigeria .
The Bantu migration is this major migration of languages across the continent and so being able to fill in that part of human history @ @ @ @ @ @ @ forward .
ZL : From a health information and point of view , we also showed that there were 62 new loci that we found to be under positive selection and that gives you an idea of how our genome interacts with environments and how environmental forces like viral infections , et cetera , can drive genomic variation .
One other thing that I think is really fascinating is variants that were previously shown to be likely pathogenic were actually observed quite commonly in some of the population groups that we looked at @ @ @ @ @ @ @ those variants , because of how frequently they occur in these populations , are probably are not having the kind of pathogenic impact that they previously were predicted to have .
That kind of information helps us to better inform how we translate this information into health-related and disease-related information .
TS : In your opinion , what was the most surprising finding of your study ?
NH : It was really the breadth of diversity .
I think that oftentimes in the field there ’ s this view of Africa in a @ @ @ @ @ @ @ heartening to see the breadth of diversity across the continent and how genomic diversity interplays with things like ancestry and culture and really shows the rich tapestry that ’ s across the continent .
TS : How might these findings change how researchers think about biomedical research in African populations ?
ZL : I think it helps inform precision medicine and medical decision-making .
From a medical perspective , for instance , if you find a variant in a patient with a very rare disease and from a computational point of view it @ @ @ @ @ @ @ disease-causing but you don ’ t have any data out there to confirm this , adding data like frequency data really helps clinicians to make informed decisions .
AC : In Nigeria , if you travel 500 kilometers , there are two populations who have very distinct genetic makeups .
[ We had a similar finding in ] Uganda where you have two populations , and you can see from the genetic data that they have very distinct susceptibility to disease .
So , putting together a very granular map like a hyper-resolution @ @ @ @ @ @ @ map is important .
[ Our findings show that ] you can not just take a country as proxy and say , “ Okay , we are done.
” NH : One of the really important things to convey is that this was work done in Africa by Africans .
We collaborated with individual groups that are in the US or the UK , but this [ study ] is really rooted in Africa .
Editor ’ s note : The interview was edited for brevity .

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JENNIFER KOSIG
A lack of ethnic diversity in global genome databases has long been a source of discussion in the scientific community. Africans in particular are underrepresented in these datasets, limiting the conclusions that can be drawn about human health and disease on the continent. Now, groups are looking to buck the trend by diversifying genomics research.
The Bantu migration is this major migration of languages across the continent, and so being able to fill in that part of human history and migration is also a big step forward. —Neil Hanchard, Baylor College of Medicine
A new study published in Nature yesterday (October 28) and conducted through Human Heredity and Health in Africa (H3Africa), a consortium devoted to increasing African representation in genetics research, uncovered 3 million new genetic variants in one of the most extensive studies of African genomes reported to date.
The research team performed whole-genome sequencing analyses of 426 individuals that represent 50 ethnolinguistic groups, including previously unsampled populations, to explore the breadth of genomic diversity across Africa.
The Scientist spoke with University of the Witwatersrand geneticists Zané Lombard and Ananyo Choudhury and Baylor College of Medicine geneticist Neil Hanchard, authors of the study, about the results’ most pressing takeaways.
The Scientist: Why is it so crucial to capture the genetic diversity found on the continent?
Zané Lombard: We can ask interesting questions about population migration and population history from a genomics perspective if we have data on different ethnolinguistic groups. There’s the population genetics aspect, but I think a lot of us are also interested in health research and precision medicine, and from that aspect, the more you know about the populations that you work in and know more about the genomic diversity in patient populations as well as research participants, it informs the kind of conclusions that you can draw from the data and from the types of information that you pull. It really just informs and gives you a context from which to understand the genomics of African participants.
From left to right: Neil Hanchard, Baylor College of Medicine; Ananyo Choudhury, University of the Witwatersrand; Zané Lombard, University of the Witwatersrand BCM Creative Services; Sydney Brenner Institute for Molecular Bioscience; National Health Laboratory Service
TS: Can you walk me through how you conducted your study?
ZL: It really started off with engaging with different principal investigators and participants across the [H3Africa] consortium and asking whether there were DNA samples available that were properly consented for this kind of research we would be doing—whole genome sequencing and depositing our data into a public repository of data.
We aggregated what we had available, and funding was provided by the [National Human Genome Research Institute] to have the sequencing done at the Baylor Sequencing Center. After that, we got together a group of African scientists from the consortium who then worked on different aspects of the data analysis.
Neil Hanchard: We were very deliberate in trying to look for populations that hadn’t been surveyed previously. That is one of the big drivers for some other more interesting results that we found.
TS: Give me an overview of what you found.
Ananyo Choudhury: The first interesting thing that we discovered was around three million novel variants in these [approximately] three hundred samples, which was quite substantial. We always knew that there is a lot of variation in Africans, but there has been quite a few genome sequencing projects in the last three to four years, so we thought it was getting to saturation. One of our questions was: Are we exhausting that possibility of discovering novel variants? Our study kind of shows that’s not the case. There is still a lot of potential if you are looking into geographic areas that have been underrepresented traditionally—you can still discover a lot of novel variants.
In terms of population genetics, we discovered a migration that was never known. It was a trans-Sahelian migration that moved into central Nigeria. The timing of the migration into Nigeria didn’t correspond to any of the previous known migrations. We also kind of identified the root of the Bantu migration to southern Africa—that’s a very hotly debated topic not only in genetics, but also in linguistics.
It was really heartening to see the breadth of diversity across the continent and how genomic diversity interplays with things like ancestry and culture and really shows the rich tapestry that’s across the continent. —Neil Hanchard, Baylor College of Medicine
NH: The finding of that trans-Sahelian migration into Nigeria was particularly interesting because Nigeria has been very well sampled at the genome level, and here we were finding a fairly large Nigerian group that had marked differences from the traditional groups that have been utilized. Those groups are often utilized as proxies for West Africa or sometimes for all of Africa, but they may not even be great proxies for Nigeria. The Bantu migration is this major migration of languages across the continent and so being able to fill in that part of human history and migration is also a big step forward.
ZL: From a health information and point of view, we also showed that there were 62 new loci that we found to be under positive selection , and that gives you an idea of how our genome interacts with environments and how environmental forces like viral infections, et cetera, can drive genomic variation. One other thing that I think is really fascinating is variants that were previously shown to be likely pathogenic were actually observed quite commonly in some of the population groups that we looked at. What this tells us is that those variants, because of how frequently they occur in these populations, are probably are not having the kind of pathogenic impact that they previously were predicted to have. That kind of information helps us to better inform how we translate this information into health-related and disease-related information.
TS: In your opinion, what was the most surprising finding of your study?
NH: It was really the breadth of diversity. I think that oftentimes in the field there’s this view of Africa in a very singular way. It was really heartening to see the breadth of diversity across the continent and how genomic diversity interplays with things like ancestry and culture and really shows the rich tapestry that’s across the continent.
TS: How might these findings change how researchers think about biomedical research in African populations?
ZL: I think it helps inform precision medicine and medical decision-making. From a medical perspective, for instance, if you find a variant in a patient with a very rare disease and from a computational point of view it’s predicted to be pathogenic or disease-causing , but you don’t have any data out there to confirm this, adding data like frequency data really helps clinicians to make informed decisions.
AC: In Nigeria, if you travel 500 kilometers, there are two populations who have very distinct genetic makeups. [We had a similar finding in] Uganda where you have two populations, and you can see from the genetic data that they have very distinct susceptibility to disease. So, putting together a very granular map like a hyper-resolution genetic map as well as a phenotype map is important. [Our findings show that] you cannot just take a country as proxy and say, “Okay, we are done.”
NH: One of the really important things to convey is that this was work done in Africa by Africans. We collaborated with individual groups that are in the US or the UK, but this [study] is really rooted in Africa.
A. Choudhury et al., “High-depth African genomes inform human migration and health,” Nature, doi:10.1038/s41586-020-2859-7, 2020.
Editor’s note: The interview was edited for brevity.


thescientist--2020-10-29--First Responders: SARS-CoV-2 and the Immune System

The immune system fights SARS-CoV-2 infection , but it also causes serious clinical symptoms when it rages out of control @ @ @ @ @ The Scientist , Angela Rasmussen and Ya-Chi Ho will discuss how the immune system reacts @ @ @ @ @ responses are beneficial or deleterious , and the mechanisms by which immune responses determine COVID-19 @ @ @ @ @ • The nature of the host response to SARS-CoV-2 Angela Rasmussen , PhD Associate Research @ @ @ @ @ Immunity Columbia University School of Public Health

FREE Webinar
Friday, November 20, 2020
2:30 - 4:00 PM, Eastern Standard Time
Register Now
The immune system fights SARS-CoV-2 infection, but it also causes serious clinical symptoms when it rages out of control. In this webinar from The Scientist, Angela Rasmussen and Ya-Chi Ho will discuss how the immune system reacts to SARS-CoV-2, whether these responses are beneficial or deleterious, and the mechanisms by which immune responses determine COVID-19 severity and patient outcomes.
Topics to be covered
The nature of the host response to SARS-CoV-2
Understanding adaptive immune responses to SARS-CoV-2 using high-dimensional single-cell profiling
Register Now
Meet the Speakers:
Angela Rasmussen, PhD
Affiliate
Center for Global Health Science and Security
Georgetown University
Ya-Chi Ho, MD, PhD
Assistant Professor
Department of Microbial Pathogenesis
Yale School of Medicine


thescientist--2020-10-30--Trials Unlikely to Show if COVID-19 Vaccine Prevents Severe Cases

Nearly 50 vaccines to protect against COVID-19 are speeding through clinical trials at an unprecedented pace .
These studies are designed to test the vaccines ’ safety and efficacy , but a review published in The Lancet earlier this week ( October 27 ) outlines the challenges in determining whether a vaccine candidate really is efficacious .
The authors of the review note that there are many definitions of efficacious—reducing the likelihood of developing severe symptoms is one , for instance , reducing the number of deaths is another .
Presently , the ongoing clinical trials are mainly designed to @ @ @ @ @ @ @ of a coronavirus infection , but knowing whether a vaccine can prevent people from developing severe COVID-19 or dying is a long way off .
Vaccine developers have not produced evidence of such a benefit yet , rather , a number of them have reported evidence of an immune response in recipients , data that do not necessarily mean the vaccine will prevent infection , the authors write .
The Scientist spoke with University of Oxford infectious disease specialist and coauthor Susanne Hodgson , who ’ s working on the UK clinical trials @ @ @ @ @ @ @ the timeline for generating evidence of vaccines ’ effect on morbidity and mortality rates .
The Scientist : All eyes are currently on the vaccines undergoing clinical trials .
What kind of evidence does a clinical trial show and not show ?
Susanne Hodgson : COVID vaccines could work in a number of ways , for example , it could prevent you getting infected in the first place , it could reduce the likelihood of you developing severe disease , reduce the likelihood of you transmitting infection to other people , or do @ @ @ @ @ @ @ .
And the hope is in Phase 3 clinical trials that we would start to get some data to tell us if the candidate COVID vaccine is actually able to do any of those three .
At the moment , unfortunately , because transmission rates are quite high , vaccine developers seem to be quite confident that we should get a measure of whether a COVID vaccine can prevent infection .
However , because death and severe disease from COVID are a relatively rare consequence of infection , it ’ s unlikely that @ @ @ @ @ @ @ be statistically powered to provide data to answer , ‘ Do these vaccines prevent severe disease ?
’ TS : Why is that an important distinction ?
SH : One of the reasons we wrote the article is because people often ask me , ‘ Will this vaccine work ?
’ That seems such a simple question , and it ’ s so complex .
There are so many caveats to answer : What do we mean when we say a vaccine ‘ works ?
’ Does it mean it prevents diseases and @ @ @ @ @ @ @ prevent transmission ?
And I think the most important vaccine that we need is one that prevents severe disease and death because that is what causes the most burden in our hospitals and of course loss of life .
That ’ s what we ’ re all aiming for .
That ’ s not to say that [ the clinical trials ] are not going to give us very important information—the safety data and an indication about whether they prevent infection , which of course are very important .
But what ’ s @ @ @ @ @ @ @ be deployed , given to a very large population on the basis of the clinical trial data , and only then , when a large number of people have had the vaccine , will we actually know if the vaccine prevents severe disease and or death .
TS : Will these vaccines need to be monitored after approval , and why ?
SH : There were already very clear protocols in place and infrastructure to monitor vaccines which are deployed and licensed in population .
A COVID vaccine would be no different to @ @ @ @ @ @ @ monitoring post licensure .
There would be ongoing , active monitoring of safety and side effects in COVID vaccines , so that if there were relatively rare side effects seen with the vaccine that only happened in one in many thousands of people we would still be able to get that information .
Post-licensure authorities will be very keen to look for any evidence of vaccine-associated enhancement of COVID .
This is individuals who ’ ve received a vaccine and then develop an immune response against COVID , who , when they then @ @ @ @ @ @ @ have a worse disease because of the vaccine-induced immune response .
The reason this is a concern is some animal studies of vaccines that have been developed for other coronaviruses such as MERS and SARS have demonstrated this phenomenon .
Fortunately , in the animal studies of COVID vaccines to date , this phenomenon hasn ’ t been seen , and there ’ s no suggestion yet to date that I ’ m aware of that this has been seen in humans in relation to COVID vaccines .
But of course , that @ @ @ @ @ @ @ be ongoing intense monitoring of safety post-deployment of a COVID vaccine .
TS : What makes it so challenging to determine if a vaccine is efficacious ?
SH : First , when you just decide whether a vaccine works enough , you need to decide what the definition of a vaccine working is .
As I mentioned before , a vaccine could reduce infection rates , transmission rates , or disease , and so for each of those outcomes you need to be very careful and understand how you will measure those outcomes @ @ @ @ @ @ @ on laboratory assays , for example , the real-time qPCR assay , which tests for the presence of antigen and confirms COVID testing .
As I ’ m sure a lot of people are familiar , this test is imperfect .
.
.
.
Other people have suggested perhaps using antibody levels as a way of telling if somebody has become infected or not , but as we ’ ve seen in a lot of the papers that are coming out , not everybody develops antibody levels , and if you do they @ @ @ @ @ @ @ long time .
And , of course , the many vaccines are aiming to induce antibodies , so you need to be able to distinguish vaccine-induced antibodies from those which are induced by infections specifically .
Clinical trials that are assessing efficacy take volunteers , typically will give half a COVID vaccine and half a placebo vaccine or a comparator , vaccinate them all , and then follow them up in the community , and you ’ re reliant on individuals in the trial meeting COVID in the community , becoming infected , @ @ @ @ @ @ @ in your test vaccine group compared to your placebo group .
If your vaccine is working , you would hope that statistically more people get COVID in your comparator group compared to your vaccine group .
Of course , that relies on ongoing transmission in the community , which is at odds with all the public health interventions that we ’ re trying to adhere , so paradoxically the better we are at controlling COVID transmission in the community , the longer it takes for us to get an efficacy read-out in terms @ @ @ @ @ @ @ TS : Given your background in vaccines , what gives you hope about the vaccine development process , looking forward to next year ?
SH : It ’ s the speed at which people are moving .
People [ are ] also publishing extremely quickly their data and sharing that , which is really encouraging .
I think all ethical bodies , regulatory bodies , and , internationally , people are very supportive of the vaccine development program for COVID .
Having worked on malaria vaccines for more than a decade now , @ @ @ @ @ @ @ COVID vaccine because of all the things I ’ ve just mentioned .
But I ’ m also a bit sanguine because we ’ ve seen so many malaria candidates that look promising in pre-clinical studies and in Phase 1 studies—you generate immune responses—but when it actually comes to the challenge study , which we use in malaria studies , it ’ s disappointing and you have to go right back again .
I ’ m hopeful and of course the large number of candidates you have the more likely you are to @ @ @ @ @ @ @ I think we need to just be realistic that this isn ’ t necessarily a matter of just , “ One more month , one more month , ” that seems to be presented in the media .
I just hope that the public should have faith in the vaccine development community—they ’ re a formidable professional group who have very stringent regulations that they ’ re adhering to and they ’ re prioritizing safety .
This is not about headlines and just getting a vaccine to meet a headline or a political @ @ @ @ @ @ @ working very hard and doing what they ’ ve been doing for many years , for many other pathogens , to get us a safe , efficacious vaccine as soon as we can .
That can ’ t be rushed , unfortunately—people are moving as quick as they can .
This is unprecedented pace of development , but it will only be ready when it ’ s safe and it actually works .
S.H .
Hodgson et al.
, “ What defines an efficacious COVID-19 vaccine ?
A review of the challenges assessing @ @ @ @ @ @ @ , ” The Lancet , doi:10.1016/S1473-3099 ( 20 ) 30773-8 , 2020 .
Editor ’ s note : The interview was edited for brevity .

ABOVE: © ISTOCK.COM,
GEBER86
Nearly 50 vaccines to protect against COVID-19 are speeding through clinical trials at an unprecedented pace. These studies are designed to test the vaccines’ safety and efficacy, but a review published in The Lancet earlier this week (October 27) outlines the challenges in determining whether a vaccine candidate really is efficacious.
The authors of the review note that there are many definitions of efficacious—reducing the likelihood of developing severe symptoms is one, for instance, reducing the number of deaths is another. Presently, the ongoing clinical trials are mainly designed to determine if recipients have a reduced risk of a coronavirus infection, but knowing whether a vaccine can prevent people from developing severe COVID-19 or dying is a long way off. Vaccine developers have not produced evidence of such a benefit yet, rather, a number of them have reported evidence of an immune response in recipients, data that do not necessarily mean the vaccine will prevent infection, the authors write.
The Scientist spoke with University of Oxford infectious disease specialist and coauthor Susanne Hodgson, who’s working on the UK clinical trials of an AstraZeneca COVID-19 vaccine, about the timeline for generating evidence of vaccines’ effect on morbidity and mortality rates.
The Scientist: All eyes are currently on the vaccines undergoing clinical trials. What kind of evidence does a clinical trial show and not show?
Susanne Hodgson: COVID vaccines could work in a number of ways, for example, it could prevent you getting infected in the first place, it could reduce the likelihood of you developing severe disease, reduce the likelihood of you transmitting infection to other people, or do a combination of all three of those. And the hope is in Phase 3 clinical trials that we would start to get some data to tell us if the candidate COVID vaccine is actually able to do any of those three.
At the moment, unfortunately, because transmission rates are quite high, vaccine developers seem to be quite confident that we should get a measure of whether a COVID vaccine can prevent infection. However, because death and severe disease from COVID are a relatively rare consequence of infection, it’s unlikely that our clinical trials that are ongoing will be statistically powered to provide data to answer, ‘Do these vaccines prevent severe disease?’
Susanne Hodgson, University of Oxford HANNAH VEALE
TS: Why is that an important distinction?
SH: One of the reasons we wrote the article is because people often ask me, ‘Will this vaccine work?’ That seems such a simple question, and it’s so complex. There are so many caveats to answer: What do we mean when we say a vaccine ‘works?’ Does it mean it prevents diseases and infection, or does it mean to prevent transmission? And I think the most important vaccine that we need is one that prevents severe disease and death because that is what causes the most burden in our hospitals and of course loss of life. That’s what we’re all aiming for.
That’s not to say that [the clinical trials] are not going to give us very important information—the safety data and an indication about whether they prevent infection, which of course are very important. But what’s most likely is that a vaccine will be deployed, given to a very large population on the basis of the clinical trial data, and only then, when a large number of people have had the vaccine, will we actually know if the vaccine prevents severe disease and or death.
TS: Will these vaccines need to be monitored after approval, and why?
SH: There were already very clear protocols in place and infrastructure to monitor vaccines which are deployed and licensed in population. A COVID vaccine would be no different to existing vaccines and in terms of its monitoring post licensure. There would be ongoing, active monitoring of safety and side effects in COVID vaccines, so that if there were relatively rare side effects seen with the vaccine that only happened in one in many thousands of people we would still be able to get that information.
Post-licensure authorities will be very keen to look for any evidence of vaccine-associated enhancement of COVID. This is individuals who’ve received a vaccine and then develop an immune response against COVID, who, when they then encounter COVID in the community, actually have a worse disease because of the vaccine-induced immune response. The reason this is a concern is some animal studies of vaccines that have been developed for other coronaviruses such as MERS and SARS have demonstrated this phenomenon. Fortunately, in the animal studies of COVID vaccines to date, this phenomenon hasn’t been seen, and there’s no suggestion yet to date that I’m aware of that this has been seen in humans in relation to COVID vaccines. But of course, that’s a reason why there will be ongoing intense monitoring of safety post-deployment of a COVID vaccine.
TS: What makes it so challenging to determine if a vaccine is efficacious?
SH: First, when you just decide whether a vaccine works enough, you need to decide what the definition of a vaccine working is. As I mentioned before, a vaccine could reduce infection rates, transmission rates, or disease, and so for each of those outcomes you need to be very careful and understand how you will measure those outcomes. A lot of those outcomes rely on laboratory assays, for example, the real-time qPCR assay, which tests for the presence of antigen and confirms COVID testing. As I’m sure a lot of people are familiar, this test is imperfect. . . . Other people have suggested perhaps using antibody levels as a way of telling if somebody has become infected or not, but as we’ve seen in a lot of the papers that are coming out, not everybody develops antibody levels, and if you do they don’t stay elevated for a long time. And, of course, the many vaccines are aiming to induce antibodies, so you need to be able to distinguish vaccine-induced antibodies from those which are induced by infections specifically.
Clinical trials that are assessing efficacy take volunteers, typically will give half a COVID vaccine and half a placebo vaccine or a comparator, vaccinate them all, and then follow them up in the community, and you’re reliant on individuals in the trial meeting COVID in the community, becoming infected, and then you compare the infection rates in your test vaccine group compared to your placebo group. If your vaccine is working, you would hope that statistically more people get COVID in your comparator group compared to your vaccine group. Of course, that relies on ongoing transmission in the community, which is at odds with all the public health interventions that we’re trying to adhere, so paradoxically the better we are at controlling COVID transmission in the community, the longer it takes for us to get an efficacy read-out in terms of infection from these clinical trials.
TS: Given your background in vaccines, what gives you hope about the vaccine development process, looking forward to next year?
SH: It’s the speed at which people are moving. People [are] also publishing extremely quickly their data and sharing that, which is really encouraging. I think all ethical bodies, regulatory bodies, and, internationally, people are very supportive of the vaccine development program for COVID.
Having worked on malaria vaccines for more than a decade now, I am optimistic with regards for a COVID vaccine because of all the things I’ve just mentioned. But I’m also a bit sanguine because we’ve seen so many malaria candidates that look promising in pre-clinical studies and in Phase 1 studies—you generate immune responses—but when it actually comes to the challenge study, which we use in malaria studies, it’s disappointing and you have to go right back again. I’m hopeful and of course the large number of candidates you have the more likely you are to get some that will work, but I think we need to just be realistic that this isn’t necessarily a matter of just, “One more month, one more month,” that seems to be presented in the media.
I just hope that the public should have faith in the vaccine development community—they’re a formidable professional group who have very stringent regulations that they’re adhering to and they’re prioritizing safety. This is not about headlines and just getting a vaccine to meet a headline or a political deadline. This is an international group working very hard and doing what they’ve been doing for many years, for many other pathogens, to get us a safe, efficacious vaccine as soon as we can. That can’t be rushed, unfortunately—people are moving as quick as they can. This is unprecedented pace of development, but it will only be ready when it’s safe and it actually works.
S.H. Hodgson et al., “What defines an efficacious COVID-19 vaccine? A review of the challenges assessing the clinical efficacy of vaccines against SARS-CoV-2,” The Lancet, doi:10.1016/S1473-3099(20)30773-8, 2020.
Editor’s note: The interview was edited for brevity.


Single-cell Secrets

Scientists have realized that the billions of cells that make up the human body are more distinct than previously anticipated @ @ @ @ @ , researchers understand how a given cell type operates and appreciate its underlying complexity , @ @ @ @ @ cellular subtypes .
Download this ebook from The Scientist 's Creative Services team to learn @ @ @ @ @ new evidence for neuron production in adults • Reveals why COVID-19 is more severe in @ @

Scientists have realized that the billions of cells that make up the human body are more distinct than previously anticipated. With new single-cell technologies, researchers understand how a given cell type operates and appreciate its underlying complexity, including genetic patterns and novel cellular subtypes.
Download this ebook from The Scientist's Creative Services team to learn how single-cell analysis


thescientist--2020-10-20--The Scientist's LabTalk Podcast - Episode 2

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Information you provide will be held in confidence and will be shared with the sponsoring vendor(s) of this podcast. We may use the information to contact you about your account and to let you know about related programs and products; you may opt-out at any time. This allows The Scientist to keep these podcasts free of charge for our readers.


thescientist--2020-10-13--Shifting Space and Time: How Harnessing Our Most Powerful Cells is Changing Immune Medicine

Immune medicines hold promise as therapies for debilitating disorders such as cancer , infectious diseases , and inflammatory diseases .
@ @ @ @ @ , researchers gain insights into which patients will respond to various immunotherapies .
To accelerate @ @ @ @ @ , scientists use applications that decrease the space and time needed for assays that target @ @ @ @ @ this event brought to you by IsoPlexis , industry and academic leaders will describe how @ @ @ @ @ subsets shift conceptions about how humans mount effective immune responses , including which cells orchestrate @ @ @ @ @ will discuss technological advancements that enable researchers to use functional proteomes to detect powerful , @ @ @ @ @ .
Additionally , the speakers will highlight applications for harnessing these powerful cell subsets to @ @ @ @ @ .
Attend our upcoming summit & register for a chance to receive a Google Cardboard @ @ @ @ @ immersive VR experience : https : //info.isoplexis.com/vr-goggle-giveaway Rong Fan , PhD Associate Professor of Biomedical @ @ @ @ @ Applied Science Yale University

Shifting Space and Time
How harnessing our most powerful cells is changing immune medicine
Immune medicines hold promise as therapies for debilitating disorders such as cancer, infectious diseases, and inflammatory diseases. By studying immune cell subsets, researchers gain insights into which patients will respond to various immunotherapies. To accelerate the timelines for finding cures, scientists use applications that decrease the space and time needed for assays that target single immune cells.
In this event brought to you by IsoPlexis, industry and academic leaders describe how data from small immune cell subsets shift conceptions about how humans mount effective immune responses, including which cells orchestrate a balanced attack. They discuss technological advancements that enable researchers to use functional proteomes to detect powerful, highly active immune cell subsets. Additionally, the speakers highlight applications for harnessing these powerful cell subsets to advance cell and gene therapies.


thescientist--2020-10-31--DNA Evidence Shows Ancient Humans and Dogs Migrated Together

The landscape of what early relationships between canines and humans looked like is now becoming clearer , as a study published in Science on October 30 uses DNA evidence to explain migrational partnerships between dogs and humans going back to the end of the last ice age , around 11,000 years ago .
To understand how ancient hunter-gatherer societies might have associated with early dogs , a research team analyzed the genomes from the remains of ancient dogs representing 27 species .
The time when the dogs lived spanned from the last ice age to about 800 years ago .
@ @ @ @ @ @ @ genomes from ancient humans from the same time period and regions .
Trying to marry this history of dog evolution with that of humans is a tall order .
“ It ’ s like you have an ancient text in two different languages , and you ’ re looking to see how both languages have changed over time , ” Pontus Skoglund , a paleogenomicist at the Francis Crick Institute and a coauthor of the study , tells Science .
By examining shifts in each species ’ genomes depending on time and location @ @ @ @ @ @ @ the researchers were able to find patterns of how they changed together over time , painting a picture of shared global migration .
While there is archaeological evidence of domestication , this study is the first to use genomics to show those relationships through time and illustrate migrating in tandem .
The researchers also found that , essentially , all the dog breeds that we know , ranging from the tiny Chihuahua to a large St. Bernard , stem from five groups of dogs that existed 11,000 years ago .
Because they were @ @ @ @ @ @ @ team suggests that the initial domestication of canines and subsequent artificial selection for particular traits had initially occurred much earlier , supporting predictions that domestication occurred at least 15,000 years ago .
At that time in Early Europe , there were many types of dogs .
That changed roughly 5,000 years ago when there was a bottleneck and only two of the five groups propagated all of the canine diversity on the continent that followed .
Genetic traces of these European dogs were also found in breeds associated with distant locations , such @ @ @ @ @ @ @ from Siberia .
The researchers also report that while dogs and wolves were interbreeding , there is tremendous evidence for gene flow from dogs to wolves , but not much of the reverse .
This suggests , they say , that humans did not prefer dogs that had a lot of wolf DNA .
The results of this study don ’ t tell the full story of domestication and there are some cases where the stories in the genomes diverged , indicating that the humans went on without dogs at certain points , @ @ @ @ @ @ @ also raise questions about the nature of the human-dog relationship , such as whether they were companions , trade fodder , or something else , the authors suggest .
“ The fact that we see sometimes the history doesn ’ t match , could mean people would move around while leaving their dogs behind , or having preferences for other types of dogs , ” Skoglund tells The Guardian , “ but it could also be due to dog dynamics.
” “ Perhaps the most compelling conclusions relate to their comparison of human-dog @ @ @ @ @ @ @ canine geneticist at the National Human Genome Research Institute who was not involved in the study , tells The Guardian .
“ The identification of events where dogs moved between different human groups , or humans dispersed without dogs provides a granularity that we have not previously had in addressing [ questions of domestication origins ] .
”

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The landscape of what early relationships between canines and humans looked like is now becoming clearer, as a study published in Science on October 30 uses DNA evidence to explain migrational partnerships between dogs and humans going back to the end of the last ice age, around 11,000 years ago.
To understand how ancient hunter-gatherer societies might have associated with early dogs, a research team analyzed the genomes from the remains of ancient dogs representing 27 species. The time when the dogs lived spanned from the last ice age to about 800 years ago. These sequences were then compared to the genomes from ancient humans from the same time period and regions.
Trying to marry this history of dog evolution with that of humans is a tall order. “It’s like you have an ancient text in two different languages, and you’re looking to see how both languages have changed over time,” Pontus Skoglund, a paleogenomicist at the Francis Crick Institute and a coauthor of the study, tells Science.
By examining shifts in each species’ genomes depending on time and location as they adapted to new surroundings, the researchers were able to find patterns of how they changed together over time, painting a picture of shared global migration. While there is archaeological evidence of domestication, this study is the first to use genomics to show those relationships through time and illustrate migrating in tandem.
The researchers also found that, essentially, all the dog breeds that we know, ranging from the tiny Chihuahua to a large St. Bernard, stem from five groups of dogs that existed 11,000 years ago. Because they were distinctly dogs, not wolves, the team suggests that the initial domestication of canines and subsequent artificial selection for particular traits had initially occurred much earlier, supporting predictions that domestication occurred at least 15,000 years ago.
At that time in Early Europe, there were many types of dogs. That changed roughly 5,000 years ago when there was a bottleneck and only two of the five groups propagated all of the canine diversity on the continent that followed. Genetic traces of these European dogs were also found in breeds associated with distant locations, such as Chihuahuas from ancient Mexico and huskies from Siberia.
The researchers also report that while dogs and wolves were interbreeding, there is tremendous evidence for gene flow from dogs to wolves, but not much of the reverse. This suggests, they say, that humans did not prefer dogs that had a lot of wolf DNA.
The results of this study don’t tell the full story of domestication and there are some cases where the stories in the genomes diverged, indicating that the humans went on without dogs at certain points, the authors say. These timeline discrepancies also raise questions about the nature of the human-dog relationship, such as whether they were companions, trade fodder, or something else, the authors suggest.
“The fact that we see sometimes the history doesn’t match, could mean people would move around while leaving their dogs behind, or having preferences for other types of dogs,” Skoglund tells The Guardian, “but it could also be due to dog dynamics.”
“Perhaps the most compelling conclusions relate to their comparison of human-dog genomes,” Elaine Ostrander, a canine geneticist at the National Human Genome Research Institute who was not involved in the study, tells The Guardian. “The identification of events where dogs moved between different human groups, or humans dispersed without dogs provides a granularity that we have not previously had in addressing [questions of domestication origins].”


thescientist--2020-11-01--Book Excerpt from Every Life is On Fire

Book Excerpt from Every Life is On Fire

In an environment that looks random , but isn ’ t , it becomes particularly important to ask what it would mean behaviorally for a clump of driven matter to exhibit an emergent adaptation behavior in the presence of a subtle influence .
If inexorable physical principles are biasing the gradual exploration of shape space , so that things have a specialized matching to a pattern whose nonrandomness is hard to discern , that means the dynamics of the matter in question has had to “ figure out ” something difficult about how to detect the pattern .
If we @ @ @ @ @ @ @ the pattern ourselves , either we would have had to engage in some form of intuition or abstract mathematical manipulation , or we would have had to write a computer program that could use numerical calculation to substitute for those activities .
In any event , whether human or artificial , we ’ d be inclined to say some kind of intelligence was being brought to bear .
Here , the question that suddenly confronts us is whether , in the absence of any brain , equation , or algorithm , an emergently @ @ @ @ @ @ @ said to be doing something called computing—or better yet , learning .
Posing the question might be likened to asking whether a pond that refreezes after being partly melted is healing or repairing itself .
Clearly , for such primitive examples , language like this is loaded and misleading—and the same goes for saying that when a ball rolls down a hillside it is “ learning ” to lower its altitude .
The case we are not considering , though , in leveling that critique is one where optimizing one ’ s relationship @ @ @ @ @ @ @ generic kind of task , and in which the environment is assumed to have a regularity that is not at all obvious .
When a collection of matter we have built with our own ingenuity to be a computer behaves in a way that spits out an accurate prediction of a complex signal , we call that computation .
If a bunch of particles whose structure we did not design nevertheless engages in behavior that is just as behaviorally successful with respect to the task of prediction , we may well want to @ @ @ @ @ @ @ But if so , we still have to acknowledge that it is doing something just as useful as what computation does .
Consider , for example , the work of Weishun Zhong , David Schwab , and Arvind Murugan carried out at the University of Chicago , where they simulated the equilibrium self-assembly of many grayscale-shaded particles whose interparticle forces had been designed to arrange the particles as pixels in multiple recognizable photographic images ( such as a picture of Einstein ) .
The authors demonstrated that when such a soup of sticky @ @ @ @ @ @ @ remnants of the original image ( through some patterned , but static , external forces ) , the collective was able to reconstruct the whole image by leveraging what little information the fragments contained to select the matching image state and assemble into it .
From one perspective , the physics of what went on in this study was always only a very high-dimensional version of a ball rolling downhill ( with the fragmentary image clues acting as a way of tilting the energy landscape to help the ball roll the right way @ @ @ @ @ @ @ , remembering and reconstructing an image sounds like something useful that we make software do for us .
Moving away from thermal equilibrium , in principle , should only expand the playbook available to us in terms of the types of behaviors physically allowed to occur in the system .
This possibility immediately raises the question of whether self-organizing , nonequilibrium , many-particle systems might be coaxed into doing various other things that computer programs are good for .
Musings such as these may sound far-fetched , but there are both theoretical and @ @ @ @ @ @ @ .
In simulation , we can study the behavior of “ messy ” many-body systems that we subject to forcing patterns much subtler than simple oscillations , and the results so far show significant promise .
Random spin glasses are materials built out of collections of atoms arrayed together in such a way that they interact with their neighbors like tiny bar magnets .
Each atom has a north-south arrow that results from the behavior of its electrons , and any one arrow can feel the influence of the other arrows of neighboring @ @ @ @ @ @ @ is that there can be disorder in how the arrows couple : one pair of neighbors might feel a force trying to align their arrows north to north , whereas another pair might be trying to anti-align and match up north to south .
In a big jumble of such atoms , it ’ s very hard to satisfy all the forces at once , because the same atom might be pointing up as the result of the influence of one neighbor , but this could be against the direction of a force @ @ @ @ @ @ @ , the story of when a given arrow flips , as the system tries to lower its energy , can be complicated and unpredictable .
Experiments on spin glasses have already shown that they exhibit intriguing memory effects : if you cool one down with an external bath whose gradually dropping temperature plateaus for a while at a few different temperatures you have chosen , then the magnetic properties of the material are detectably different at those same chosen temperatures when you warm things back up , as though an echo of the @ @ @ @ @ @ @ of all the arrows .
MIT mathematics graduate student Jacob Gold and I picked up on this idea and simulated a random spin glass with a “ barcoded ” environment that varied over time .
In addition to the forces acting between atoms , roughly half the atoms were made to feel external forces randomly pushing some arrows up and others down .
After a fixed duration , a new set of random forces was generated ( i.e.
, a new barcode ) , and the configuration of the arrows in the simulated @ @ @ @ @ @ @ .
If the simulation always keeps showing the atoms new random barcodes pushing some arrows up and others down , then the behavior is largely indistinguishable from random thermal fluctuations at a higher temperature than the ambient environment—in other words , it just acts like friction is heating it up .
However , things get remarkably different when a limited number of barcodes get shuffled into a deck and then reused over and over again .
At each moment , the current barcode is randomly selected without any particular order , but over @ @ @ @ @ @ @ of barcodes recurs .
The first—now perhaps unsurprising—thing to notice is that the rate of energy absorption drops over time .
Though the barcoded forces initially “ look ” random to the material , eventually it adapts to the pattern and finds a configuration that absorbs less energy from its environment .
What happens , though , if we suddenly introduce a new barcode , never seen before in the history of the system dynamics ?
Naturally , the rate of energy absorption spikes upward , because the system has not yet adapted @ @ @ @ @ @ @ environmental state as a possibility .
Let us recap : A messy collection of atoms gets shown a bunch of different forces from the environment , each described with its own unique barcode .
Its energy absorption adapts and drops , and now will spike if we poke the system in a new way in which it has not yet been poked .
Inadvertently , perhaps , we have built what one might call a novelty detector .
The state of the system after it has adapted embodies an implicit prediction about the @ @ @ @ @ @ @ see this , this , or this , but not that .
In essence , then , the mere fact that these atoms have been poked in a patterned way leads them into a state that appears to have learned to tell the precise difference between something familiar and something new and unexpected .
And , interestingly , if we were to design a computer program to make that kind of judgment for us , we might be inclined to call it machine learning .
The past decade has seen tremendous growth in @ @ @ @ @ @ @ technologies that exhibit flexible capability for discovering accurate ways of computationally modeling complex relationships previously thought to be exclusively the province of the human mind .
Facial recognition and language processing are two of the most famous examples , but the common principle in many of these applications is that a long list of numerical parameters describing some way of mapping computational inputs to outputs are trained using large data sets ( whether pictures of faces , a corpus of text , or whatever else ) .
In practice , this means that @ @ @ @ @ @ @ through a very high-dimensional parameter space in order to find a special and exceptional choice of parameters that will enable the computational model to exhibit high-quality matching to the data used for training .
This search is typically carried out by programming the parameter coordinates to traverse partly random but biased paths that tend to go downhill over time in a “ landscape ” whose altitude is defined by some measure of error generated by using the current parameters to map inputs to outputs .
If any of this sounds familiar , it @ @ @ @ @ @ @ of this kind and the mechanism for dissipative adaptation we described in the previous chapter are numerous and significant .
An assemblage of many particles has a shape or structure that is described by a high-dimensional space of possible arrangements , much the same way that a machine-learning training algorithm has a high-dimensional space of modeling parameters that determine the way it maps inputs to outputs .
A machine learner is trying , over time , to change its parameters so as to reduce an error function that measures how badly it models @ @ @ @ @ @ @ driven mechanical system that is gradually reducing its rate of energy absorption does so by intricately changing its shape so as to modify the way it responds to and moves with the external forcing .
Moreover , just as a machine learner maps observed inputs to calculated outputs , a driven mechanical system maps the input of external forces into the outputs of dynamical behavior .
Does this mean we want to say that all driven , many-particle assemblies are doing a form of machine learning ?
Probably not , unless we expand @ @ @ @ @ @ @ However , in a side-by-side comparison , it is apparent that the mathematical structure of what is going on in each case is similar on multiple scores , which suggests there may be an as-yet-unexplored spectrum of possibilities that could lie somewhere in between these two poles , and look like an evolving jumble of building blocks that compute something useful as they disperse and reassemble .
Excerpted from Every Life Is on Fire : How Thermodynamics Explains the Origins of Living Things by Jeremy England .
Copyright © 2020 by Jeremy England @ @ @ @ @ @

In an environment that looks random, but isn’t, it becomes particularly important to ask what it would mean behaviorally for a clump of driven matter to exhibit an emergent adaptation behavior in the presence of a subtle influence. If inexorable physical principles are biasing the gradual exploration of shape space, so that things have a specialized matching to a pattern whose nonrandomness is hard to discern, that means the dynamics of the matter in question has had to “figure out” something difficult about how to detect the pattern. If we had been given the task of discerning the pattern ourselves, either we would have had to engage in some form of intuition or abstract mathematical manipulation, or we would have had to write a computer program that could use numerical calculation to substitute for those activities. In any event, whether human or artificial, we’d be inclined to say some kind of intelligence was being brought to bear. Here, the question that suddenly confronts us is whether, in the absence of any brain, equation, or algorithm, an emergently fine-tuned assembly of particles can fairly be said to be doing something called computing—or better yet, learning.
Posing the question might be likened to asking whether a pond that refreezes after being partly melted is healing or repairing itself. Clearly, for such primitive examples, language like this is loaded and misleading—and the same goes for saying that when a ball rolls down a hillside it is “learning” to lower its altitude. The case we are not considering, though, in leveling that critique is one where optimizing one’s relationship to the environment is a much less generic kind of task, and in which the environment is assumed to have a regularity that is not at all obvious. When a collection of matter we have built with our own ingenuity to be a computer behaves in a way that spits out an accurate prediction of a complex signal, we call that computation. If a bunch of particles whose structure we did not design nevertheless engages in behavior that is just as behaviorally successful with respect to the task of prediction, we may well want to refrain from saying it is computing. But if so, we still have to acknowledge that it is doing something just as useful as what computation does.
Consider, for example, the work of Weishun Zhong, David Schwab, and Arvind Murugan carried out at the University of Chicago, where they simulated the equilibrium self-assembly of many grayscale-shaded particles whose interparticle forces had been designed to arrange the particles as pixels in multiple recognizable photographic images (such as a picture of Einstein). The authors demonstrated that when such a soup of sticky building blocks got exposed to fragmentary partial remnants of the original image (through some patterned, but static, external forces), the collective was able to reconstruct the whole image by leveraging what little information the fragments contained to select the matching image state and assemble into it. From one perspective, the physics of what went on in this study was always only a very high-dimensional version of a ball rolling downhill (with the fragmentary image clues acting as a way of tilting the energy landscape to help the ball roll the right way). Yet, from another perspective, remembering and reconstructing an image sounds like something useful that we make software do for us. Moving away from thermal equilibrium, in principle, should only expand the playbook available to us in terms of the types of behaviors physically allowed to occur in the system. This possibility immediately raises the question of whether self-organizing, nonequilibrium, many-particle systems might be coaxed into doing various other things that computer programs are good for.
Musings such as these may sound far-fetched, but there are both theoretical and empirical reasons to give them serious consideration. In simulation, we can study the behavior of “messy” many-body systems that we subject to forcing patterns much subtler than simple oscillations, and the results so far show significant promise. Random spin glasses are materials built out of collections of atoms arrayed together in such a way that they interact with their neighbors like tiny bar magnets. Each atom has a north-south arrow that results from the behavior of its electrons, and any one arrow can feel the influence of the other arrows of neighboring atoms. What makes these materials random is that there can be disorder in how the arrows couple: one pair of neighbors might feel a force trying to align their arrows north to north, whereas another pair might be trying to anti-align and match up north to south. In a big jumble of such atoms, it’s very hard to satisfy all the forces at once, because the same atom might be pointing up as the result of the influence of one neighbor, but this could be against the direction of a force being exerted by another neighbor. Accordingly, the story of when a given arrow flips, as the system tries to lower its energy, can be complicated and unpredictable.
Experiments on spin glasses have already shown that they exhibit intriguing memory effects: if you cool one down with an external bath whose gradually dropping temperature plateaus for a while at a few different temperatures you have chosen, then the magnetic properties of the material are detectably different at those same chosen temperatures when you warm things back up, as though an echo of the original treatment is stored in the configurations of all the arrows. MIT mathematics graduate student Jacob Gold and I picked up on this idea and simulated a random spin glass with a “barcoded” environment that varied over time. In addition to the forces acting between atoms, roughly half the atoms were made to feel external forces randomly pushing some arrows up and others down. After a fixed duration, a new set of random forces was generated (i.e., a new barcode), and the configuration of the arrows in the simulated material was allowed to evolve over time.
If the simulation always keeps showing the atoms new random barcodes pushing some arrows up and others down, then the behavior is largely indistinguishable from random thermal fluctuations at a higher temperature than the ambient environment—in other words, it just acts like friction is heating it up. However, things get remarkably different when a limited number of barcodes get shuffled into a deck and then reused over and over again. At each moment, the current barcode is randomly selected without any particular order, but over a longer stretch the same small set of barcodes recurs.
The first—now perhaps unsurprising—thing to notice is that the rate of energy absorption drops over time. Though the barcoded forces initially “look” random to the material, eventually it adapts to the pattern and finds a configuration that absorbs less energy from its environment. What happens, though, if we suddenly introduce a new barcode, never seen before in the history of the system dynamics? Naturally, the rate of energy absorption spikes upward, because the system has not yet adapted to a pattern that includes this novel environmental state as a possibility.
Let us recap: A messy collection of atoms gets shown a bunch of different forces from the environment, each described with its own unique barcode. Its energy absorption adapts and drops, and now will spike if we poke the system in a new way in which it has not yet been poked. Inadvertently, perhaps, we have built what one might call a novelty detector.
The state of the system after it has adapted embodies an implicit prediction about the future, namely: I expect to see this, this, or this, but not that. In essence, then, the mere fact that these atoms have been poked in a patterned way leads them into a state that appears to have learned to tell the precise difference between something familiar and something new and unexpected. And, interestingly, if we were to design a computer program to make that kind of judgment for us, we might be inclined to call it machine learning.
The past decade has seen tremendous growth in the power and diversity of so-called machine-learning technologies that exhibit flexible capability for discovering accurate ways of computationally modeling complex relationships previously thought to be exclusively the province of the human mind. Facial recognition and language processing are two of the most famous examples, but the common principle in many of these applications is that a long list of numerical parameters describing some way of mapping computational inputs to outputs are trained using large data sets (whether pictures of faces, a corpus of text, or whatever else). In practice, this means that a computer algorithm is trying to search through a very high-dimensional parameter space in order to find a special and exceptional choice of parameters that will enable the computational model to exhibit high-quality matching to the data used for training. This search is typically carried out by programming the parameter coordinates to traverse partly random but biased paths that tend to go downhill over time in a “landscape” whose altitude is defined by some measure of error generated by using the current parameters to map inputs to outputs.
If any of this sounds familiar, it is because the parallels between machine learning of this kind and the mechanism for dissipative adaptation we described in the previous chapter are numerous and significant. An assemblage of many particles has a shape or structure that is described by a high-dimensional space of possible arrangements, much the same way that a machine-learning training algorithm has a high-dimensional space of modeling parameters that determine the way it maps inputs to outputs. A machine learner is trying, over time, to change its parameters so as to reduce an error function that measures how badly it models relationships in the training data; a driven mechanical system that is gradually reducing its rate of energy absorption does so by intricately changing its shape so as to modify the way it responds to and moves with the external forcing. Moreover, just as a machine learner maps observed inputs to calculated outputs, a driven mechanical system maps the input of external forces into the outputs of dynamical behavior. Does this mean we want to say that all driven, many-particle assemblies are doing a form of machine learning? Probably not, unless we expand the definition of the term excessively. However, in a side-by-side comparison, it is apparent that the mathematical structure of what is going on in each case is similar on multiple scores, which suggests there may be an as-yet-unexplored spectrum of possibilities that could lie somewhere in between these two poles, and look like an evolving jumble of building blocks that compute something useful as they disperse and reassemble.
Excerpted from Every Life Is on Fire: How Thermodynamics Explains the Origins of Living Things by Jeremy England. Copyright © 2020 by Jeremy England. Available from Basic Books.


thescientist--2020-11-01--Scientists, Publishers Debate Paychecks for Peer Reviewers

Back in 2015 , academic publisher Dan Morgan , then of the University of California Press , took to Medium to argue that a foundational assumption of his industry—that experts should volunteer their time as peer reviewers—is flawed .
“ I am fairly sure I have never volunteered my time but then had a 3rd party charge a commercial , profit-generating price for it , ” he wrote .
“ [ W ] e all know that some publishers are getting very , very rich , while nothing tangible is making its way back to the primary volunteers making this @ @ @ @ @ @ @ , Morgan asserted , reviewers for commercial publishers should have a say over the use of some portion of the revenue from published papers .
As an example , he cited the model of an open access journal he ’ d cofounded the previous year , Collabra : Psychology .
Reviewers for that journal earn credits that give them control over a portion of the quarterly revenue from the article processing charges ( APCs ) charged to authors : they can choose to direct the money to an APC waiver fund , which @ @ @ @ @ @ @ funds budgeted for APCs to publish in the journal , or to their own institution ’ s budget for open access–related costs , or they can pocket the money themselves .
The idea was : “ Let ’ s test [ whether we can ] set up a system whereby the value that gets created in the scholarly publishing system could not just all go to publishers , but could also be hard-coded to circulate and return back to the academic community , ” explains Morgan , who left UC Press for PLOS @ @ @ @ @ @ @ a rare example of a journal that gives reviewers the option of monetary compensation .
Although the idea that peer reviewers should be paid for their work crops up periodically in online discussions and opinion articles , the Collabra : Psychology model hasn ’ t elicited interest among other scientific publishers who responded to The Scientist .
Still , there is a widespread sentiment among publishers and reviewers alike that some stronger form of peer reviewer recognition is needed—and some initiatives are taking steps in that direction .
Like Morgan , academics who @ @ @ @ @ @ @ least from commercial publishers—often cite the perceived unfairness of performing free labor in service of companies ’ profits .
“ If academic work is to be commodified and turned into a source of profit for shareholders and for the 1 percent of the publishing world , then we should give up our archaic notions of unpaid craft labor and insist on professional compensation for our expertise , just as doctors , lawyers , and accountants do , ” wrote George Mason University anthropologist Hugh Gusterson in a 2012 opinion article for The Chronicle @ @ @ @ @ @ @ to review as volunteers for nonprofit publishers , he argued , but should “ just say no ” to requests from for-profit publishers that don ’ t offer payment .
Similar arguments appear regularly on Twitter and elsewhere online .
“ Hey academic publishers !
Your profit margin is like 30 % , ” wrote urban planning researcher Natalie Osborne of Griffith University in Australia in a 2018 tweet .
“ If you want to ‘ recognise me ’ for peer review , pay me !
Pay everyone who reviews for you !
@ @ @ @ @ @ @ former academic James Heathers began a Twitter account dedicated to lobbying for peer review to be paid for like any commercial transaction .
“ I review academic literature during peer review for very , very large companies , and it is one of their revenue streams .
When I do this , I want them to pay me four hundred and fifty dollars .
No , I am not joking , ” Heathers wrote in the account ’ s first tweet .
This fairness argument resonates with Mick Watson , a computational biologist @ @ @ @ @ @ @ Roslin Institute , but he also cites more-pragmatic reasons for why he thinks publishers should pay for peer review .
In addition to peer review requests from journals , he receives offers to serve on funders ’ grant committees—work that he ’ s compensated for , he notes .
“ If I ’ ve only got so much time to spend on reviewing , then I ’ m going to prioritize the things that are going to bring in some money rather than the things that aren ’ t. ” Having served as @ @ @ @ @ @ @ , where he was tasked with seeing submitted papers through peer review , Watson knows he ’ s not alone in declining peer review requests due to competing priorities .
As an editor , “ you ’ re sending ten , twenty , thirty invites into the ether , ” he recalls .
“ And you just either hear nothing , or you get instant rejections back from people saying they won ’ t review the paper.
” Of eight scientific publishers contacted by The Scientist , only one , PLOS , expressed @ @ @ @ @ @ @ review .
“ We ’ d love to compensate our more than 9,000 Editorial Board members and more than 100,000 reviewers in a given year , but that scope is not sustainable for a publisher our size , ” the organization says in a statement .
PLOS is a nonprofit organization funded through authors ’ APCs , the statement notes .
Morgan says he doesn ’ t anticipate PLOS journals reconsidering such payments , absent an attitude shift in the industry as a whole .
Such a shift does not appear to be @ @ @ @ @ @ @ now .
Apart from PLOS and UC Press , none of the publishers contacted by The Scientist said they ’ d so much as considered paying peer reviewers .
Nancy Winchester , the Director of Publications for the American Society of Plant Biologists ( which publishes The Plant Cell and Plant Physiology ) , writes in an email to The Scientist , “ We have not considered paying peer reviewers .
This is not something that the Publications Committee and editorial boards anticipate taking up anytime soon ” —although she notes that reviewers @ @ @ @ @ @ @ memberships or merchandise .
The Science family of journals similarly has no plans to pay peer reviewers , a spokesperson wrote in a statement to The Scientist , adding , “ Thankfully for us all , many reviewers feel a sense of obligation to participate , and that is a tradition that has served science well.
” In its own statement , Springer Nature cited its efforts to improve the peer review process , such as a recent trial initiative to recognize reviewers in published papers , but did not address paid peer @ @ @ @ @ @ @ publisher of the Journal of Neuroscience and eNeuro ) declined to comment , and publishing giant Elsevier had not provided comment by the time this article went to print .
Despite the calls for change from Gusterson and others , it ’ s not clear that many academics support payments for peer reviewers either .
In 2018 , Publons , a site that enables researchers to track their publications and other activities in online profiles , conducted a survey of more than 15,000 researchers in the social and natural sciences and engineering , @ @ @ @ @ @ @ 17 percent selected “ cash or in-kind payments from journals ” as a factor that would make them more likely to accept review requests .
“ Discounts on publisher ’ s products or services ” was a factor for even fewer of the respondents , just 4.5 percent , while 11.6 percent selected “ personal access to journal content.
” ( Each respondent could choose up to two factors . )
In a separate question about respondents ’ reasons for serving as peer reviewers , the most popular answer was : “ It @ @ @ @ @ @ @ researcher , ” followed by “ I want to do my fair share/reciprocate for reviews of my work , ” then “ To keep up to date on the latest research trends in my field , ” and “ To ensure the quality and integrity of research published in my field.
” It ’ s similarly been UC Press ’ s experience that the potential for compensation isn ’ t a significant motivator for peer reviewers at Collabra : Psychology or Elementa : Science of the Anthropocene , which uses the same reviewer-payment @ @ @ @ @ @ @ the publisher ’ s Director of Publications and Open Access .
The editors of those journals “ didn ’ t feel that the opportunity to be paid as a peer reviewer was something that was motivating a lot of peer reviewers to work for either of the journals , ” he says .
There are other barriers that may help explain why paid peer review hasn ’ t caught on .
For one , the money would have to come from somewhere , and Stephen Heard , an ecology researcher at the University @ @ @ @ @ @ @ that publishers would pass the costs along to users rather than letting their profits take a hit .
“ Normally , if you make something more expensive to produce then that something gets more expensive , ” he says .
“ So presumably , if we paid reviewers , then either subscription rates or article processing fees would have to go up.
” Jason Hoyt , the CEO of open-access publisher PeerJ , estimates that paying peer reviewers would increase the overall costs of publishing papers by 10 percent to 20 percent “ @ @ @ @ @ @ @ even at a modest $ 50 to $ 100 per review .
“ I think people would prefer to have cheaper publishing , not more expensive publishing , ” he says .
There ’ s also a concern that offering money to peer reviewers could introduce real or perceived conflicts of interest ( COIs ) into the reviewing process .
For example , a journal might be able to influence a paid reviewer to recommend acceptance of a particular paper , suggests Aparna Hegde , an oncologist and researcher at the University of @ @ @ @ @ @ @ such conflicts would arise is “ an open question , ” although he acknowledges that the perception of COIs can be damaging even in the absence of any real influence .
Another difficulty is the administrative cost and effort in managing peer reviewer compensation .
At Collabra : Psychology , the amount of money each reviewer controls from the quarterly pot varies but is fairly small—around $ 50 , Morgan and van Rijn estimate .
Van Rijn says only about 15 percent of reviewers opt to take the cash themselves , and an @ @ @ @ @ @ @ open access at their own institutions , while the majority elect to support the waiver fund .
Because of the time and effort needed to administer the reviewer-directed pot of money , and because its model doesn ’ t seem to be a motivating factor for reviewers , UC Press is now phasing out its model of revenue-sharing , doing away with the reviewer-compensation option in favor of automatically directing a portion of each APC into the waiver fund , van Rijn says .
Despite general agreement that many reviewers are primarily motivated @ @ @ @ @ @ @ do see a need for more concrete rewards for peer reviewers , if not in the form of hard cash .
PeerJ offers a $ 200 discount on a future APC in exchange for each peer review , and Hoyt says 17 percent of submissions come from past reviewers .
Jon Tennant , an open science advocate who died earlier this year in a traffic accident , proposed that in lieu of paying individual researchers , publishers should include peer reviewing services in their agreements with academic institutions .
“ [ U ] @ @ @ @ @ @ @ provide a certain number of reviews for a publisher in exchange for a commensurate discount on subscriptions or [ the APCs charged by open access journals ] .
.
.
.
If publishers do not lower their costs and provide financial reimbursement , then universities can stop those publishers from exploiting their staff for free labour , ” he wrote in a preprint .
Others propose that employers and funders should formally consider peer review activities alongside research accomplishments and other factors when they make hiring , promotion , or grant decisions .
@ @ @ @ @ @ @ .
much more recognition in general by the community of the important work of reviewers is still needed , ” says Morgan .
Two organizations , Publons and ORCID , now enable journals to report peer reviews , so that researchers can include these contributions in online profiles of their work .
For Watson , an additional step needed to make this reporting a true incentive on par with direct payments from journals would be “ if the rest of the academic community—the hiring committees or promotional committees , the funders—all got on @ @ @ @ @ @ @ what , we ’ re going to pay attention to those review databases when we make our decisions.
’ ” Such a movement would be in keeping with what Ana Heredia , the regional director for Latin America at ORCID , sees as a trend toward more quantification in academia , such as study authors using standard categories to classify their contributions to a published paper .
( ORCID added peer review to its researcher profiles last year . )
“ Time is very short today , ” she says , so when @ @ @ @ @ @ @ types of work researchers are expected to do , “ you want to be recognized , at least .
”

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Back in 2015, academic publisher Dan Morgan, then of the University of California Press, took to Medium to argue that a foundational assumption of his industry—that experts should volunteer their time as peer reviewers—is flawed. “I am fairly sure I have never volunteered my time but then had a 3rd party charge a commercial, profit-generating price for it,” he wrote. “[W]e all know that some publishers are getting very, very rich, while nothing tangible is making its way back to the primary volunteers making this happen.”
In return for their labor, Morgan asserted, reviewers for commercial publishers should have a say over the use of some portion of the revenue from published papers. As an example, he cited the model of an open access journal he’d cofounded the previous year, Collabra: Psychology. Reviewers for that journal earn credits that give them control over a portion of the quarterly revenue from the article processing charges (APCs) charged to authors: they can choose to direct the money to an APC waiver fund, which enables researchers who don’t have funds budgeted for APCs to publish in the journal, or to their own institution’s budget for open access–related costs, or they can pocket the money themselves.
Thankfully for us all, many reviewers feel a sense of obligation to participate, and that is a tradition that has served science well. —Science journals spokesperson
The idea was: “Let’s test [whether we can] set up a system whereby the value that gets created in the scholarly publishing system could not just all go to publishers, but could also be hard-coded to circulate and return back to the academic community,” explains Morgan, who left UC Press for PLOS in 2018.
Collabra: Psychology represents a rare example of a journal that gives reviewers the option of monetary compensation. Although the idea that peer reviewers should be paid for their work crops up periodically in online discussions and opinion articles, the Collabra: Psychology model hasn’t elicited interest among other scientific publishers who responded to The Scientist. Still, there is a widespread sentiment among publishers and reviewers alike that some stronger form of peer reviewer recognition is needed—and some initiatives are taking steps in that direction.
The case for peer reviewer compensation
Like Morgan, academics who think they should receive compensation for reviewing—at least from commercial publishers—often cite the perceived unfairness of performing free labor in service of companies’ profits. “If academic work is to be commodified and turned into a source of profit for shareholders and for the 1 percent of the publishing world, then we should give up our archaic notions of unpaid craft labor and insist on professional compensation for our expertise, just as doctors, lawyers, and accountants do,” wrote George Mason University anthropologist Hugh Gusterson in a 2012 opinion article for The Chronicle of Higher Education. Academics should continue to review as volunteers for nonprofit publishers, he argued, but should “just say no” to requests from for-profit publishers that don’t offer payment.
Similar arguments appear regularly on Twitter and elsewhere online. “Hey academic publishers! Your profit margin is like 30%,” wrote urban planning researcher Natalie Osborne of Griffith University in Australia in a 2018 tweet. “If you want to ‘recognise me’ for peer review, pay me! Pay everyone who reviews for you!” Earlier this year, consultant and former academic James Heathers began a Twitter account dedicated to lobbying for peer review to be paid for like any commercial transaction. “I review academic literature during peer review for very, very large companies, and it is one of their revenue streams. When I do this, I want them to pay me four hundred and fifty dollars. No, I am not joking,” Heathers wrote in the account’s first tweet.
This fairness argument resonates with Mick Watson, a computational biologist at the University of Edinburgh’s Roslin Institute, but he also cites more-pragmatic reasons for why he thinks publishers should pay for peer review. In addition to peer review requests from journals, he receives offers to serve on funders’ grant committees—work that he’s compensated for, he notes. “If I’ve only got so much time to spend on reviewing, then I’m going to prioritize the things that are going to bring in some money rather than the things that aren’t.” Having served as an unpaid academic editor at several journals, where he was tasked with seeing submitted papers through peer review, Watson knows he’s not alone in declining peer review requests due to competing priorities. As an editor, “you’re sending ten, twenty, thirty invites into the ether,” he recalls. “And you just either hear nothing, or you get instant rejections back from people saying they won’t review the paper.”
Reluctance from publishers
Of eight scientific publishers contacted by The Scientist, only one, PLOS, expressed enthusiasm for the concept of paid peer review. “We’d love to compensate our more than 9,000 Editorial Board members and more than 100,000 reviewers in a given year, but that scope is not sustainable for a publisher our size,” the organization says in a statement. PLOS is a nonprofit organization funded through authors’ APCs, the statement notes. Morgan says he doesn’t anticipate PLOS journals reconsidering such payments, absent an attitude shift in the industry as a whole.
Such a shift does not appear to be in the cards, at least for now. Apart from PLOS and UC Press, none of the publishers contacted by The Scientist said they’d so much as considered paying peer reviewers. Nancy Winchester, the Director of Publications for the American Society of Plant Biologists (which publishes The Plant Cell and Plant Physiology), writes in an email to The Scientist, “We have not considered paying peer reviewers. This is not something that the Publications Committee and editorial boards anticipate taking up anytime soon”—although she notes that reviewers earn points they can redeem for society memberships or merchandise.
Presumably, if we paid reviewers, then either subscription rates or article processing fees would have to go up. —Jason Hoyt, PeerJ
The Science family of journals similarly has no plans to pay peer reviewers, a spokesperson wrote in a statement to The Scientist, adding, “Thankfully for us all, many reviewers feel a sense of obligation to participate, and that is a tradition that has served science well.” In its own statement, Springer Nature cited its efforts to improve the peer review process, such as a recent trial initiative to recognize reviewers in published papers, but did not address paid peer review. The Society for Neuroscience (publisher of the Journal of Neuroscience and eNeuro) declined to comment, and publishing giant Elsevier had not provided comment by the time this article went to print.
Despite the calls for change from Gusterson and others, it’s not clear that many academics support payments for peer reviewers either. In 2018, Publons, a site that enables researchers to track their publications and other activities in online profiles, conducted a survey of more than 15,000 researchers in the social and natural sciences and engineering, and found that a little more than 17 percent selected “cash or in-kind payments from journals” as a factor that would make them more likely to accept review requests. “Discounts on publisher’s products or services” was a factor for even fewer of the respondents, just 4.5 percent, while 11.6 percent selected “personal access to journal content.” (Each respondent could choose up to two factors.) In a separate question about respondents’ reasons for serving as peer reviewers, the most popular answer was: “It is part of my job as a researcher,” followed by “I want to do my fair share/reciprocate for reviews of my work,” then “To keep up to date on the latest research trends in my field,” and “To ensure the quality and integrity of research published in my field.”
It’s similarly been UC Press’s experience that the potential for compensation isn’t a significant motivator for peer reviewers at Collabra: Psychology or Elementa: Science of the Anthropocene, which uses the same reviewer-payment model, says Erich van Rijn, the publisher’s Director of Publications and Open Access. The editors of those journals “didn’t feel that the opportunity to be paid as a peer reviewer was something that was motivating a lot of peer reviewers to work for either of the journals,” he says.
Practical challenges associated with paid review models
There are other barriers that may help explain why paid peer review hasn’t caught on. For one, the money would have to come from somewhere, and Stephen Heard, an ecology researcher at the University of New Brunswick in Canada, suspects that publishers would pass the costs along to users rather than letting their profits take a hit. “Normally, if you make something more expensive to produce then that something gets more expensive,” he says. “So presumably, if we paid reviewers, then either subscription rates or article processing fees would have to go up.” Jason Hoyt, the CEO of open-access publisher PeerJ, estimates that paying peer reviewers would increase the overall costs of publishing papers by 10 percent to 20 percent “for the entire publishing ecosystem,” even at a modest $50 to $100 per review. “I think people would prefer to have cheaper publishing, not more expensive publishing,” he says.
There’s also a concern that offering money to peer reviewers could introduce real or perceived conflicts of interest (COIs) into the reviewing process. For example, a journal might be able to influence a paid reviewer to recommend acceptance of a particular paper, suggests Aparna Hegde, an oncologist and researcher at the University of Alabama at Birmingham. Morgan says whether such conflicts would arise is “an open question,” although he acknowledges that the perception of COIs can be damaging even in the absence of any real influence.
Another difficulty is the administrative cost and effort in managing peer reviewer compensation. At Collabra: Psychology, the amount of money each reviewer controls from the quarterly pot varies but is fairly small—around $50, Morgan and van Rijn estimate. Van Rijn says only about 15 percent of reviewers opt to take the cash themselves, and an even smaller percentage direct it toward supporting open access at their own institutions, while the majority elect to support the waiver fund. Because of the time and effort needed to administer the reviewer-directed pot of money, and because its model doesn’t seem to be a motivating factor for reviewers, UC Press is now phasing out its model of revenue-sharing, doing away with the reviewer-compensation option in favor of automatically directing a portion of each APC into the waiver fund, van Rijn says.
What Changes Do Peer Reviewers Want to See? Although scientists often take on reviewing assignments for altruistic reasons, many would like more recognition for the work, according to a 2018 survey by the reviewing website Publons. Of nearly 12,000 people who responded to a question about incentives for reviewers, 85 percent agreed with the statement that “institutions should more explicitly require and recognize peer review contributions.” Asked in a separate question to choose up to two factors that would make them more likely to accept an invitation to peer review (shown below), close to half of respondents selected “more explicit recognition of peer review contributions from universities/funders.”
Alternatives to paid peer review
Despite general agreement that many reviewers are primarily motivated by altruism, some publishers and researchers do see a need for more concrete rewards for peer reviewers, if not in the form of hard cash. PeerJ offers a $200 discount on a future APC in exchange for each peer review, and Hoyt says 17 percent of submissions come from past reviewers. Jon Tennant, an open science advocate who died earlier this year in a traffic accident, proposed that in lieu of paying individual researchers, publishers should include peer reviewing services in their agreements with academic institutions. “[U]niversities could agree that their staff will provide a certain number of reviews for a publisher in exchange for a commensurate discount on subscriptions or [the APCs charged by open access journals]. . . . If publishers do not lower their costs and provide financial reimbursement, then universities can stop those publishers from exploiting their staff for free labour,” he wrote in a preprint.
Others propose that employers and funders should formally consider peer review activities alongside research accomplishments and other factors when they make hiring, promotion, or grant decisions. “I absolutely think that . . . much more recognition in general by the community of the important work of reviewers is still needed,” says Morgan. Two organizations, Publons and ORCID, now enable journals to report peer reviews, so that researchers can include these contributions in online profiles of their work. For Watson, an additional step needed to make this reporting a true incentive on par with direct payments from journals would be “if the rest of the academic community—the hiring committees or promotional committees, the funders—all got on board and said, ‘You know what, we’re going to pay attention to those review databases when we make our decisions.’”
Such a movement would be in keeping with what Ana Heredia, the regional director for Latin America at ORCID, sees as a trend toward more quantification in academia, such as study authors using standard categories to classify their contributions to a published paper. (ORCID added peer review to its researcher profiles last year.) “Time is very short today,” she says, so when it comes to peer review and other types of work researchers are expected to do, “you want to be recognized, at least.”