Pandemic prevention consortium announces new leadership team

STOP Spillover is strengthening our capacity to reduce the risks of emerging pathogens

Business Announcement

TUFTS UNIVERSITY

 

IMAGE: 

HELLEN AMUGUNI, ASSOCIATE PROFESSOR OF INFECTIOUS DISEASE AND GLOBAL HEALTH AT THE CUMMINGS SCHOOL OF VETERINARY MEDICINE AT TUFTS UNIVERSITY, IS NAMED PROJECT DIRECTOR FOR STOP SPILLOVER.

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CREDIT: ALONSO NICHOLS/TUFTS UNIVERSITY

Recognizing the many milestones it has reached in recent months, Strategies to Prevent Spillover, or STOP Spillover, a project funded by the U.S. Agency for International Development (USAID) and led by Tufts University, has announced that the interim leadership team that was put in place in March 2023 will take on a permanent role for the next two years of the project.

Hellen Amuguni, an associate professor in the Department of Infectious Disease and Global Health at Cummings School of Veterinary Medicine, is the new project director. The co-deputy directors are Felicia Nutter, director of the International Veterinary Medicine Program at Cummings School, and Jonathon Gass, an assistant professor of infectious disease epidemiology at the School of Medicine. (Amuguni and Nutter have secondary appointments at Tufts University School of Medicine, and Gass has a secondary appointment at Cummings School.)

“We are entering the fourth year of STOP Spillover on a high note, and our vision for the project remains clear,” says Amuguni. “Our focus is to build capacity and prepare countries to identify high-risk interfaces, control zoonotic diseases at their source before they become epidemics or pandemics, and develop interventions that reduce risks of exposure in human populations. We are privileged to work closely with amazing country teams and government counterparts as well as our consortium partners who bring expertise in wildlife health, infectious diseases, social and behavior change.”

At least 75 percent of emerging and re-emerging infectious diseases of humans—including Ebola, Nipah virus, and zoonotic avian flu—have an animal origin. Chances are that when the next illness like COVID-19 emerges to threaten global health, it will originate in animals before it passes to humans, a process known as spillover. STOP Spillover aims to keep that tipping point from happening, or at least mitigate the dangerous effects.

“STOP Spillover has achieved so much in its third year thanks to these directors, who have been working with stakeholders in key countries in Africa and Asia to find ways to decrease the risks of harmful viral pathogens that jump—or spill over—from animals to humans,” said Caroline Genco, Tufts’ provost and senior vice president, who is also an immunologist. “Through this important work, our expert researchers and community partners demonstrate our shared commitment to One Health as a way of mitigating the significant global risk represented by zoonotic disease spillover.”

Leading a Global Consortium of Regional Partners

Begun in late 2020, STOP Spillover has so far partnered with colleagues in Bangladesh, Cambodia, Côte d’Ivoire, Liberia, Uganda, Viet Nam, and Sierra Leone to strengthen country capacities to reduce the risks of zoonotic diseases, or those that move between animals and humans. Teams of experts collaborate to develop country- and locality-specific research studies and interventions to reduce risks associated with selected viral zoonotic pathogens and to prevent their spread.

USAID administers the U.S. foreign assistance program providing economic and humanitarian assistance in more than 80 countries worldwide. For this project, Tufts leads a global consortium of partners with cross-disciplinary experience and regional knowledge.

From the outset, this consortium of experts in human, animal, and environmental health has been heavily focused on engagement, working with stakeholders at the national, regional, and local levels to reduce risks of exposure to and mitigate the spread of selected zoonotic viral pathogens, including coronaviruses, filoviruses (Ebola and Marburg viruses), avian influenza, and Lassa virus, among others.

Protecting Health and Providing Financial Stability

On the ground at the local level—in places such as wildlife farms in Dong Nai province, Vietnam, and wild animal meat markets in Kenema, Sierra Leone—community-led workshops have provided important data about the interactions humans have with wild and domestic animals in these settings, as well as the barriers they see to behavior changes that reduce spillover risk.

Gass, who recently visited wildlife farms in Viet Nam with its in-country team, said that STOP Spillover is filling major gaps in understanding the spillover ecosystem, which will improve conditions for both animals and humans.

Gass noted that wildlife farmers, government officials, and other stakeholders are very interested in working together to increase biosafety. “Farming practices are critical for the financial livelihoods of farmers and their families,” he said. “When outbreaks occur on farms and the animals either die or need to be culled, this has serious financial repercussions. STOP Spillover’s interventions will not only protect health but also provide increased financial stability via risk reduction.”

The program has formed local expert working groups to identify places where spillover is most likely to occur and to design risk-reduction interventions. In Liberia, for example, STOP Spillover is conducting research to understand Lassa virus distribution in rodent reservoir hosts both within what is considered the “Lassa belt” and beyond. Working with the Ministry of Health, National Public Health Lab, the Ministry of Agriculture, and local communities, teams are collecting and testing samples from the African soft-furred mouse and other rodents for the presence of Lassa virus RNA (an indication of infection) within and outside of the Lassa belt.

The documentation of the true distribution of Lassa virus in reservoir hosts will allow the country to better understand the risks to humans, develop more effective rodent control strategies, and inform future research, policy, and public health measures.

Technology and Space Redesign for Biosafety

At live bird markets in Dhaka, Bangladesh, where the threat of highly pathogenic avian influenza is a concern, efforts are underway to develop a coordinated and sustainable platform for pathogen surveillance and data sharing. A mobile application has been developed, enabling the public to report sick and dead poultry as well as sudden febrile illness among market vendors. Moreover, the STOP Spillover team is working with public health experts and engineers to redesign market spaces so that biosafety is optimized, and consumer and vendor health protected.

In Côte d’Ivoire, Cambodia, and Liberia, teams have been trained to safely collect samples for surveillance of wastewater and liquid waste effluent, with potential testing for multiple zoonotic viruses. The aim is to create a surveillance system that can act as an “early warning system” for potential spillover events.

The program exemplifies the One Health concept: the interconnection of human, animal, and environmental health. “STOP Spillover continues the longstanding work of Tufts University, mainstreaming One Health approaches to address complex, globally important health problems, including zoonotic diseases,” said Felicia Nutter.

“Humans make choices every day that impact our health, the health of other animals, and the ecosystems and environments that we all share. Our current work empowers people to make more informed choices that safeguard our shared health,” said Nutter.

 

For relationship maintenance, accurate perception of partner’s behavior is key

Peer-Reviewed Publication

UNIVERSITY OF ILLINOIS COLLEGE OF AGRICULTURAL, CONSUMER AND ENVIRONMENTAL SCIENCES

URBANA, Ill. – Married couples and long-term romantic partners typically engage in a variety of behaviors that sustain and nourish the relationship. These actions promote higher levels of commitment, which benefits couples’ physical and psychological health. A new study from the University of Illinois Urbana-Champaign looks at how such relationship maintenance behaviors interact with satisfaction and commitment.

“Relationship maintenance is a well-established measure of couple behavior. In our study, we measured it with five main categories, which are positivity, openness, assurances, use of social networks, and sharing tasks,” said Yifan Hu, a doctoral student in the Department of Human Development and Family Studies (HDFS), part of the  College of Agricultural, Consumer and Environmental Sciences at U. of I.

“Relationship maintenance is usually studied on an individual level. But two partners work together to maintain the relationship. Each person contributes, and each person also perceives the efforts their partner is making. We wanted to look at both individual and interactive (or couple-level) relationship processes,” she added.

The researchers analyzed data from 192 heterosexual married couples. Each partner completed an online survey separately. Participants reported their own relationship maintenance behaviors over the past two weeks, as well as their perception of the partner's behaviors. The surveys also included questions measuring relationship satisfaction and commitment.

The results contained some unexpected findings, as there were few direct effects of relationship maintenance behaviors on commitment. However, relationship satisfaction appeared as a moderating factor between relationship maintenance and commitment. In other words, higher levels of satisfaction led to a more positive assessment of the partner’s behavior, which strengthened commitment.

“Generally, we found people were relatively accurate about their partner’s maintenance behaviors. We also found that it is better to have accurate perception when you are highly satisfied. If you are less satisfied, accurately perceiving your partner’s efforts may not be positive. And your partner’s accuracy in perceiving your behavior may make you feel worse, because they are aware you may not be doing that much for the relationship,” Hu said.

“When a stressful event happens, a couple that is less satisfied with each other may be more likely to react negatively than a couple with higher relationship satisfaction,” she added.

Another unexpected finding was that similarity in relationship maintenance behaviors was negatively correlated with wives’ level of commitment. Studies have shown that similarity in personality traits, values, and attitudes enhance relationship satisfaction. However, for relationship maintenance strategies, complementary approaches may be more beneficial.

“We found that similarity in behaviors might not be helpful for promoting interactive relationship maintenance. A possible explanation could be that if partners are too similar in their approach, they have a smaller repertoire of coping behaviors,” Hu explained.

“When partners are dealing with stressors, they need to work in concert, but using different strategies may be helpful. For example, one partner can use positivity and assurances, while the other can use social networks. They can be mindful of trying to have a larger skill set for relationship maintenance behaviors,” she suggested.

Brian Ogolsky, professor in HDFS, is co-author on the paper. “Our study aligns with existing literature showing that relationship maintenance enactment and satisfaction are related to commitment,” he said. “At the same time, we found that most relationship maintenance processes at the individual level are associated with commitment only when moderated by satisfaction, which underscores the complexity of couple dynamics.”

The paper, "The role of individual- and interactive-level relationship maintenance on married couples' commitment," is published in Personal Relationships [doi.org/10.1111/pere.12517]. Authors include Yifan Hu, Brian Ogolsky, and Laura Stafford.

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JOURNAL

Personal Relationships

DOI

10.1111/pere.12517 

METHOD OF RESEARCH

Survey

SUBJECT OF RESEARCH

People

ARTICLE TITLE

The role of individual- and interactive-level relationship maintenance on married couples' commitment

 

Keeping a human in the loop: Managing the ethics of AI in medicine

Peer-Reviewed Publication

UNIVERSITY OF ROCHESTER MEDICAL CENTER

Artificial intelligence (AI)—of ChatGPT fame—is increasingly used in medicine to improve diagnosis and treatment of diseases, and to avoid unnecessary screening for patients. But AI medical devices could also harm patients and worsen health inequities if they are not designed, tested, and used with care, according to an international task force that included a University of Rochester Medical Center bioethicist.

Jonathan Herington, PhD, was a member of the AI Task Force of the Society for Nuclear Medicine and Medical Imaging, which laid out recommendations on how to ethically develop and use AI medical devices in two papers published in the Journal of Nuclear Medicine. In short, the task force called for increased transparency about the accuracy and limits of AI and outlined ways to ensure all people have access to AI medical devices that work for them—regardless of their race, ethnicity, gender, or wealth.  

While the burden of proper design and testing falls to AI developers, health care providers are ultimately responsible for properly using AI and shouldn’t rely too heavily on AI predictions when making patient care decisions.

“There should always be a human in the loop,” said Herington, who is assistant professor of Health Humanities and Bioethics at URMC and was one of three bioethicists added to the task force in 2021. “Clinicians should use AI as an input into their own decision making, rather than replacing their decision making.”

This requires that doctors truly understand how a given AI medical device is intended to be used, how well it performs at that task, and any limitations—and they must pass that knowledge on to their patients. Doctors must weigh the relative risks of false positives versus false negatives for a given situation, all while taking structural inequities into account.

When using an AI system to identify probable tumors in PET scans, for example, health care providers must know how well the system performs at identifying this specific type of tumor in patients of the same sex, race, ethnicity, etc., as the patient in question.

“What that means for the developers of these systems is that they need to be very transparent,” said Herington.

According to the task force, it’s up to the AI developers to make accurate information about their medical device’s intended use, clinical performance, and limitations readily available to users. One way they recommend doing that is to build alerts right into the device or system that informs users about the degree of uncertainty of the AI’s predictions. That might look like heat maps on cancer scans that show whether areas are more or less likely to be cancerous.

To minimize that uncertainty, developers must carefully define the data they use to train and test their AI models, and should use clinically relevant criteria to evaluate the model’s performance. It’s not enough to simply validate algorithms used by a device or system. AI medical devices should be tested in so-called “silent trials”, meaning their performance would be evaluated by researchers on real patients in real time, but their predictions would not be available to the health care provider or applied to clinical decision making.

Developers should also design AI models to be useful and accurate in all contexts in which they will be deployed.

“A concern is that these high-tech, expensive systems would be deployed in really high-resource hospitals, and improve outcomes for relatively well-advantaged patients, while patients in under-resourced or rural hospitals wouldn't have access to them—or would have access to systems that make their care worse because they weren’t designed for them,” said Herington.

Currently, AI medical devices are being trained on datasets in which Latino and Black patients are underrepresented, meaning the devices are less likely to make accurate predictions for patients from these groups. In order to avoid deepening health inequities, developers must ensure their AI models are calibrated for all racial and gender groups by training them with datasets that represent all of the populations the medical device or system will ultimately serve.

Though these recommendations were developed with a focus on nuclear medicine and medical imaging, Herington believes they can and should be applied to AI medical devices broadly.

“The systems are becoming ever more powerful all the time and the landscape is shifting really quickly,” said Herington. “We have a rapidly closing window to solidify our ethical and regulatory framework around these things.”

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JOURNAL

Journal of Nuclear Medicine

DOI

10.2967/jnumed.123.266080 

METHOD OF RESEARCH

Commentary/editorial

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation

ARTICLE PUBLICATION DATE

12-Oct-2023

COI STATEMENT

Melissa McCradden acknowledges funding from the SickKids Foundation pertaining to her role as the John and Melinda Thompson Director of AI in Medicine at the Hospital for Sick Children. Abhinav Jha acknowledges support from NIH R01EB031051-02S1. Sven Zuehlsdorff is a full-time employee of Siemens Medical Solutions USA, Inc. No other potential conflict of interest relevant to this article was reported.

To excel at engineering design, generative AI must learn to innovate, study finds

AI models that prioritize similarity falter when asked to design something completely new.

Peer-Reviewed Publication

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

 

IMAGE: 

MIT ENGINEERS TRAINED SEVERAL AI MODELS ON THOUSANDS OF BICYCLE FRAMES, SOURCED FROM A DATASET OF FULL BICYCLE DESIGNS, SHOWN HERE COLOR-CODED BY BIKE STYLE.

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CREDIT: COURTESY OF FAEZ AHMED, LYLE REGENWETTER, ET AL.

ChatGPT and other deep generative models are proving to be uncanny mimics. These AI supermodels can churn out poems, finish symphonies, and create new videos and images by automatically learning from millions of examples of previous works. These enormously powerful and versatile tools excel at generating new content that resembles everything they’ve seen before. 

But as MIT engineers say in a new study, similarity isn’t enough if you want to truly innovate in engineering tasks. 

“Deep generative models (DGMs) are very promising, but also inherently flawed,” says study author Lyle Regenwetter, a mechanical engineering graduate student at MIT. “The objective of these models is to mimic a dataset. But as engineers and designers, we often don’t want to create a design that’s already out there.”

He and his colleagues make the case that if mechanical engineers want help from AI to generate novel ideas and designs, they will have to first refocus those models beyond “statistical similarity.” 

“The performance of a lot of these models is explicitly tied to how statistically similar a generated sample is to what the model has already seen,” says co-author Faez Ahmed, assistant professor of mechanical engineering at MIT. “But in design, being different could be important if you want to innovate.”

In their study, Ahmed and Regenwetter reveal the pitfalls of deep generative models when they are tasked with solving engineering design problems. In a case study of bicycle frame design, the team shows that these models end up generating new frames that mimic previous designs but falter on engineering performance and requirements. 

When the researchers presented the same bicycle frame problem to DGMs that they specifically designed with engineering-focused objectives, rather than only statistical similarity, these models produced more innovative, higher-performing frames. 

The team’s results show that similarity-focused AI models don’t quite translate when applied to engineering problems. But, as the researchers also highlight in their study, with some careful planning of task-appropriate metrics, AI models could be an effective design “co-pilot.” 

“This is about how AI can help engineers be better and faster at creating innovative products,” Ahmed says. “To do that, we have to first understand the requirements. This is one step in that direction.”

The team’s new study appeared recently online, and will be in the December print edition of the journal Computer Aided Design. The research is a collaboration between computer scientists at MIT-IBM Watson AI Lab and mechanical engineers in MIT’s DeCoDe Lab. The study’s co-authors include Akash Srivastava and Dan Gutreund at the MIT-IBM Watson AI Lab.

Framing a problem

As Ahmed and Regenwetter write, DGMs are “powerful learners, boasting unparalleled ability” to process huge amounts of data. DGM is a broad term for any machine-learning model that is trained to learn distribution of data and then use that to generate new, statistically similar content. The enormously popular ChatGPT is one type of deep generative model known as a large language model, or LLM, which incorporates natural language processing capabilities into the model to enable the app to generate realistic imagery and speech in response to conversational queries. Other popular models for image generation include DALL-E and Stable Diffusion.

Because of their ability to learn from data and generate realistic samples, DGMs have been increasingly applied in multiple engineering domains. Designers have used deep generative models to draft new aircraft frames, metamaterial designs, and optimal geometries for bridges and cars. But for the most part, the models have mimicked existing designs, without improving the performance on existing designs.

“Designers who are working with DGMs are sort of missing this cherry on top, which is adjusting the model’s training objective to focus on the design requirements,” Regenwetter says. “So, people end up generating designs that are very similar to the dataset.”

In the new study, he outlines the main pitfalls in applying DGMs to engineering tasks, and shows that the fundamental objective of standard DGMs does not take into account specific design requirements. To illustrate this, the team invokes a simple case of bicycle frame design and demonstrates that problems can crop up as early as the initial learning phase. As a model learns from thousands of existing bike frames of various sizes and shapes, it might consider two frames of similar dimensions to have similar performance, when in fact a small disconnect in one frame — too small to register as a significant difference in statistical similarity metrics — makes the frame much weaker than the other, visually similar frame. 

Beyond “vanilla”

The researchers carried the bicycle example forward to see what designs a DGM would actually generate after having learned from existing designs. They first tested a conventional “vanilla” generative adversarial network, or GAN — a model that has widely been used in image and text synthesis, and is tuned simply to generate statistically similar content. They trained the model on a dataset of thousands of bicycle frames, including commercially manufactured designs and less conventional, one-off frames designed by hobbyists. 

Once the model learned from the data, the researchers asked it to generate hundreds of new bike frames. The model produced realistic designs that resembled existing frames. But none of the designs showed significant improvement in performance, and some were even a bit inferior, with heavier, less structurally sound frames. 

The team then carried out the same test with two other DGMs that were specifically designed for engineering tasks. The first model is one that Ahmed previously developed to generate high-performing airfoil designs. He built this model to prioritize statistical similarity as well as functional performance. When applied to the bike frame task, this model generated realistic designs that also were lighter and stronger than existing designs. But it also produced physically “invalid” frames, with components that didn’t quite fit or overlapped in physically impossible ways. 

“We saw designs that were significantly better than the dataset, but also designs that were geometrically incompatible because the model wasn’t focused on meeting design constraints,” Regenwetter says. 

The last model the team tested was one that Regenwetter built to generate new geometric structures. This model was designed with the same priorities as the previous models, with the added ingredient of design constraints, and prioritizing physically viable frames, for instance, with no disconnections or overlapping bars. This last model produced the highest-performing designs, that were also physically feasible. 

“We found that when a model goes beyond statistical similarity, it can come up with designs that are better than the ones that are already out there,” Ahmed says. “It’s a proof of what AI can do, if it is explicitly trained on a design task.”

For instance, if DGMs can be built with other priorities, such as performance, design constraints, and novelty, Ahmed foresees “numerous engineering fields, such as molecular design and civil infrastructure, would greatly benefit. By shedding light on the potential pitfalls of relying solely on statistical similarity, we hope to inspire new pathways and strategies in generative AI applications outside multimedia.”

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Written by Jennifer Chu, MIT News

Paper: “Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design”

https://www.sciencedirect.com/science/article/abs/pii/S0010448523001410

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JOURNAL

Computer-Aided Design

DOI

10.1016/j.cad.2023.103609 

ARTICLE TITLE

“Beyond Statistical Similarity: Rethinking Metrics for Deep Geerative Models in Engineering Design”

 

High pregnancy weight gain tied to higher risk of death in the following decades

Peer-Reviewed Publication

UNIVERSITY OF PENNSYLVANIA SCHOOL OF MEDICINE

Pregnant people who gained more than the now-recommended amount of weight had a higher risk of death from heart disease or diabetes in the decades that followed, according to new analysis of 50 years of data published in The Lancet and led by researchers from the Perelman School of Medicine at the University of Pennsylvania. The group studied a large national data set that stretched from when a person gave birth through the next five decades, assessing mortality rates to show the potential long-term effects of weight gain in pregnancy. Higher risk of death was found for all weight groups studied — including those defined as underweight, normal weight, or overweight prior to their pregnancies — but no increase in risk was uncovered among those who had been obese.

“We hope that this work leads to greater efforts to identify new, effective, and safe ways to support pregnant people in achieving a healthy weight gain,” said the study’s lead author, Stefanie Hinkle, PhD, an assistant professor of Epidemiology and Obstetrics and Gynecology at Penn. “We showed that gaining weight during pregnancy within the current guidelines may protect against possible negative impacts much later in life, and this builds upon evidence of the short-term benefits for both maternal health and the health of the baby.”

As in their previous work showing links between complications in pregnancy and higher death rates in the following years, Hinkle and her colleagues — who included members of Penn’s departments of Biostatistics, Epidemiology and Informatics, and Obstetrics and Gynecology, as well as the Intramural Research Program of the National Institute of Child Health and Human Development — examined data from the Collaborative Perinatal Project. This project catalogued data from a racially diverse cohort of people who gave birth in the 1950s or 1960s and linked their records to mortality data that ran through 2016, approximately 50 years later. The researchers analyzed information from more than 45,000 people that included their body mass indices (BMI), weight changes over pregnancy, and compared these data to modern recommendations. Those numbers were then linked first to deaths of any cause, then to deaths by cardiovascular- or diabetes-related causes.

Modern recommendations for weight gain during pregnancy were set in 2009 and are linked directly to a person’s weight at the start of their pregnancy. They range from 28 to 40 pounds for people considered “underweight” by BMI standards to 11 to 20 pounds for those considered “obese.” In the present day, almost half of those who are pregnant gain more weight than recommended.

Approximately 39 percent of the people in the cohort had died by 2016, and the death rate increased in correlation with pre-pregnancy BMI — those with the lowest BMI died at a lower rate than those with the highest BMI.

Among those who were “underweight” before pregnancy but gained more than the (now) recommended amount of weight, the risk of death related to heart disease climbed by 84 percent. Among those considered to be of “normal” weight before their pregnancy (which was roughly two-thirds of the cohort), all-cause mortality rose by nine percent when they gained more weight than recommended, with their risk of heart disease-related death climbing by 20 percent. Finally, those considered “overweight” had a 12 percent increased risk of dying if they gained more weight than is now recommended, with a 12 percent increase in their risk of diabetes-related death.

The study found no correlation between high weight gain during pregnancy and subsequent deaths among those in the obese range. While their study wasn’t designed to look into that specific point, Hinkle said that it’s possible this group’s already-elevated death rate could have had a bearing on this finding.

Weight gain during pregnancy doesn’t happen in a vacuum, as health care access, nutrition, and stress can all play a significant factor in it. But now that they have a better picture of the long-term risks associated with unhealthy gains, Hinkle and her colleagues hope to find more that will help address the issue.

“We are committed to delving deeper into the various factors that can affect pregnant individuals' ability to achieve healthy weight gain during pregnancy,” Hinkle said. “Our team is dedicated to exploring the social, structural, biological, and individual aspects that play a role in this process.”

This study was funded by the Intramural Research Program of the Eunice Kennedy Shriver National Institutes of Child Health and Human Development.

Pregnant people who gained more than the now-recommended amount of weight had a higher risk of death from heart disease or diabetes in the decades that followed, according to new analysis of 50 years of data published in The Lancet and led by researchers from the Perelman School of Medicine at the University of Pennsylvania. The group studied a large national data set that stretched from when a person gave birth through the next five decades, assessing mortality rates to show the potential long-term effects of weight gain in pregnancy. Higher risk of death was found for all weight groups studied — including those defined as underweight, normal weight, or overweight prior to their pregnancies — but no increase in risk was uncovered among those who had been obese.

“We hope that this work leads to greater efforts to identify new, effective, and safe ways to support pregnant people in achieving a healthy weight gain,” said the study’s lead author, Stefanie Hinkle, PhD, an assistant professor of Epidemiology and Obstetrics and Gynecology at Penn. “We showed that gaining weight during pregnancy within the current guidelines may protect against possible negative impacts much later in life, and this builds upon evidence of the short-term benefits for both maternal health and the health of the baby.”

As in their previous work showing links between complications in pregnancy and higher death rates in the following years, Hinkle and her colleagues — who included members of Penn’s departments of Biostatistics, Epidemiology and Informatics, and Obstetrics and Gynecology, as well as the Intramural Research Program of the National Institute of Child Health and Human Development — examined data from the Collaborative Perinatal Project. This project catalogued data from a racially diverse cohort of people who gave birth in the 1950s or 1960s and linked their records to mortality data that ran through 2016, approximately 50 years later. The researchers analyzed information from more than 45,000 people that included their body mass indices (BMI), weight changes over pregnancy, and compared these data to modern recommendations. Those numbers were then linked first to deaths of any cause, then to deaths by cardiovascular- or diabetes-related causes.

Modern recommendations for weight gain during pregnancy were set in 2009 and are linked directly to a person’s weight at the start of their pregnancy. They range from 28 to 40 pounds for people considered “underweight” by BMI standards to 11 to 20 pounds for those considered “obese.” In the present day, almost half of those who are pregnant gain more weight than recommended.

Approximately 39 percent of the people in the cohort had died by 2016, and the death rate increased in correlation with pre-pregnancy BMI — those with the lowest BMI died at a lower rate than those with the highest BMI.

Among those who were “underweight” before pregnancy but gained more than the (now) recommended amount of weight, the risk of death related to heart disease climbed by 84 percent. Among those considered to be of “normal” weight before their pregnancy (which was roughly two-thirds of the cohort), all-cause mortality rose by nine percent when they gained more weight than recommended, with their risk of heart disease-related death climbing by 20 percent. Finally, those considered “overweight” had a 12 percent increased risk of dying if they gained more weight than is now recommended, with a 12 percent increase in their risk of diabetes-related death.

The study found no correlation between high weight gain during pregnancy and subsequent deaths among those in the obese range. While their study wasn’t designed to look into that specific point, Hinkle said that it’s possible this group’s already-elevated death rate could have had a bearing on this finding.

Weight gain during pregnancy doesn’t happen in a vacuum, as health care access, nutrition, and stress can all play a significant factor in it. But now that they have a better picture of the long-term risks associated with unhealthy gains, Hinkle and her colleagues hope to find more that will help address the issue.

“We are committed to delving deeper into the various factors that can affect pregnant individuals' ability to achieve healthy weight gain during pregnancy,” Hinkle said. “Our team is dedicated to exploring the social, structural, biological, and individual aspects that play a role in this process.”

This study was funded by the Intramural Research Program of the Eunice Kennedy Shriver National Institutes of Child Health and Human Development.

Pregnant people who gained more than the now-recommended amount of weight had a higher risk of death from heart disease or diabetes in the decades that followed, according to new analysis of 50 years of data published in The Lancet and led by researchers from the Perelman School of Medicine at the University of Pennsylvania. The group studied a large national data set that stretched from when a person gave birth through the next five decades, assessing mortality rates to show the potential long-term effects of weight gain in pregnancy. Higher risk of death was found for all weight groups studied — including those defined as underweight, normal weight, or overweight prior to their pregnancies — but no increase in risk was uncovered among those who had been obese.

“We hope that this work leads to greater efforts to identify new, effective, and safe ways to support pregnant people in achieving a healthy weight gain,” said the study’s lead author, Stefanie Hinkle, PhD, an assistant professor of Epidemiology and Obstetrics and Gynecology at Penn. “We showed that gaining weight during pregnancy within the current guidelines may protect against possible negative impacts much later in life, and this builds upon evidence of the short-term benefits for both maternal health and the health of the baby.”

As in their previous work showing links between complications in pregnancy and higher death rates in the following years, Hinkle and her colleagues — who included members of Penn’s departments of Biostatistics, Epidemiology and Informatics, and Obstetrics and Gynecology, as well as the Intramural Research Program of the National Institute of Child Health and Human Development — examined data from the Collaborative Perinatal Project. This project catalogued data from a racially diverse cohort of people who gave birth in the 1950s or 1960s and linked their records to mortality data that ran through 2016, approximately 50 years later. The researchers analyzed information from more than 45,000 people that included their body mass indices (BMI), weight changes over pregnancy, and compared these data to modern recommendations. Those numbers were then linked first to deaths of any cause, then to deaths by cardiovascular- or diabetes-related causes.

Modern recommendations for weight gain during pregnancy were set in 2009 and are linked directly to a person’s weight at the start of their pregnancy. They range from 28 to 40 pounds for people considered “underweight” by BMI standards to 11 to 20 pounds for those considered “obese.” In the present day, almost half of those who are pregnant gain more weight than recommended.

Approximately 39 percent of the people in the cohort had died by 2016, and the death rate increased in correlation with pre-pregnancy BMI — those with the lowest BMI died at a lower rate than those with the highest BMI.

Among those who were “underweight” before pregnancy but gained more than the (now) recommended amount of weight, the risk of death related to heart disease climbed by 84 percent. Among those considered to be of “normal” weight before their pregnancy (which was roughly two-thirds of the cohort), all-cause mortality rose by nine percent when they gained more weight than recommended, with their risk of heart disease-related death climbing by 20 percent. Finally, those considered “overweight” had a 12 percent increased risk of dying if they gained more weight than is now recommended, with a 12 percent increase in their risk of diabetes-related death.

The study found no correlation between high weight gain during pregnancy and subsequent deaths among those in the obese range. While their study wasn’t designed to look into that specific point, Hinkle said that it’s possible this group’s already-elevated death rate could have had a bearing on this finding.

Weight gain during pregnancy doesn’t happen in a vacuum, as health care access, nutrition, and stress can all play a significant factor in it. But now that they have a better picture of the long-term risks associated with unhealthy gains, Hinkle and her colleagues hope to find more that will help address the issue.

“We are committed to delving deeper into the various factors that can affect pregnant individuals' ability to achieve healthy weight gain during pregnancy,” Hinkle said. “Our team is dedicated to exploring the social, structural, biological, and individual aspects that play a role in this process.”

This study was funded by the Intramural Research Program of the Eunice Kennedy Shriver National Institutes of Child Health and Human Development.

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JOURNAL

The Lancet

 

Identifying the maker of an artwork by fingerprint examination

Researchers used micro-computed tomography to examine a Rijksmuseum statue and discovered the characteristics of the artist.

Peer-Reviewed Publication

CENTRUM WISKUNDE & INFORMATICA

 

IMAGE: 

IN THE FINE ARTS, IMPRESSIONS FOUND ON TERRACOTTA SCULPTURES IN MUSEUM COLLECTIONS ARE SCARCELY REPORTED AND NOT IN A SYSTEMATIC MANNER. IN A NEW STUDY PUBLISHED IN SCIENCE ADVANCES, RESEARCHERS PRESENT A PROCEDURE FOR SCANNING FINGERMARKS AND TOOLMARKS FOUND ON THE VISIBLE SURFACE AND INNER WALLS OF A TERRACOTTA SCULPTURE USING 3D MICRO COMPUTED TOMOGRAPHY, AS WELL AS METHODS FOR QUANTITATIVELY CHARACTERIZING THESE IMPRESSIONS.

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CREDIT: DZEMILA SERO

Dzemila Sero, now Migelien Gerritzen Fellow at the Rijksmuseum and former postdoc at the Centrum Wiskunde & Informatica, together with a team of researchers from the Rijksmuseum, Leiden and Cambridge University, examined the terracotta sculpture Study for a Hovering Putto attributed to Laurent Delvaux (1696 - 1778) and housed in the Rijksmuseum permanent collection.

The methodology and findings were published open access in Science Advances in a paper with title "Artist profiling using micro-CT scanning of a Rijksmuseum terracotta sculpture".

To acquire preserved impressions on the sculpture, researchers used the computed tomography machine located at the FleX-ray Lab.

Sero and her colleagues developed a pipeline to acquire preserved fingerprints and toolmarks on the visible surface of the statue, as well as on its voids hidden from view, using 3D micro-computed tomography. In addition, they implemented methods for quantitatively characterizing these impressions.

The authors estimated that the partial fingerprints of this specific piece of art belong to an adult male. This corresponds with the attribution of the model to Laurent Delvaux. Estimating the age cluster of an artist can be useful in those cases where the master was closely working with young pupils, and more information extracted from surviving marks can add value to artworks by supporting artistic attribution. 

Dzemila Sero initiated this research line when she was a postdoc in the Computational Imaging group at Centrum Wiskunde & Informatica and was part of the Impact4Art project.

The Impact4Art project, conceived by Joost Batenburg (project leader) and Erma Hermens, was financially supported by De Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) and The Netherlands Institute for Conservation+Art+Science+ (NICAS).

Sero later obtained a Migelien Gerritzen Fellowship at the Rijksmuseum tu run her own research project “Imaging patterns on terracotta sculptures”. She studies impressions left by artists on artworks from the Rijksmuseum collections, such as human prints, brush strokes and toolmarks, using high resolution 2D and 3D imaging and advanced computational methods.

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JOURNAL

Science Advances

DOI

10.1126/sciadv.adg6073 

METHOD OF RESEARCH

Imaging analysis

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Artist profiling using micro-CT scanning of a Rijksmuseum terracotta sculpture