The findings are published in Psychological Science, a journal of the Association for Psychological Science.

Researchers led by Jana Berkessel of the University of Mannheim in Germany collected archival data on more than 3.4 million people living in the United States and United Kingdom. They found evidence that obesity tends to spur lighter medical and psychological harms when those who struggle with the disorder feel less conspicuous.

“For me, this means that at least some of the adverse consequences of obesity appear socially constructed and, thus, can be reduced,” Berkessel said.

The personal and societal toll of obesity is far-reaching. According to the World Health Organization, the global prevalence of obesity nearly tripled between 1975 and 2021. In the U.S. alone, health care costs related to obesity total roughly $147 billion, according to government figures. Research shows that compared with people without obesity, individuals living with obesity have higher unemployment rates, fewer friends, and poorer physical and mental health. They also face prejudice and discrimination.

But obesity rates vary between countries, states, provinces, and other regional divisions. In some parts of the U.S., roughly half of the population lives with obesity, while obesity rates in other regions are as low as 5%.

Berkessel and her colleagues theorized that the harsh effects of obesity vary based on the prevalence of obesity in a given region.

“It is quite easily imaginable that persons with obesity in regions with low obesity rates stick out much more, and therefore will have very different social experiences on an everyday basis,” said Berkessel, who studies the effects of social context on our well-being.

The researchers examined three large datasets of people living in thousands of U.S. counties and hundreds of U.K. districts. Those data included information on participants’ weight, height, and area of residence, as well as social, health, and economic outcomes. They used a Body Mass Index (BMI) of 30 or higher as a marker of obesity. (Medical professionals consider a healthy BMI to range from 18.5 to 24.9).

In one U.S. dataset, the researchers found obesity rates to be above average in the Midwest, the South, and along parts of the East Coast, and below average in New England, Florida, and the Western states. In a U.K. dataset, they found high obesity rates in Central and Northern parts of the country, particularly in Southern Wales. The lowest rates were found in the nation’s southern region, including London.

Berkessel and her team found that, overall, participants with obesity reported more relationship, economic, and health disadvantages compared with participants without obesity. But they also found that those living in low-obesity regions were significantly more likely to be unemployed — and to report suboptimal health compared to their counterparts in high-obesity areas.

The research team also examined U.S. data that included participants’ self-reported attitudes toward people’s weight. They found that weight bias seems to be lowest in areas with high rates of obesity, which might explain why people with obesity in those areas are less likely to be single and report poor health compared to those in areas with high weight bias.

Regardless of the regional differences around weight bias, public health experts should emphasize the importance of reducing obesity because of its health risks, the researchers concluded.

Berkessel’s co-authors included Jochen E. Gebauer of University of Mannheim and the University of Copenhagen, Tobias Ebert of the University of St. Gallen, and Peter J. Rentfrow of the University of Cambridge.

Gender inequalities in health-related quality of life are generally few and small, even in large surveys. Yet many generic measures limit assessment to quality of life overall and its physical and psychological dimensions, while overlooking internationally important environmental, social, and spiritual quality of life. To overcome this limitation, Skevington and colleagues collected data using four surveys of 17,608 adults living in 43 cultures worldwide. The researchers analyzed data encompassing six quality of life domains: physical, psychological, independence, social, environmental, and spiritual.

The results showed that environmental quality of life explained a substantial 46% of women’s overall quality of life and health, and home environment contributed the most to this result. In addition, women younger than 45 years reported the poorest quality of life on every domain. After the age of 45 years, all domains except physical quality of life increased to very good, and high levels were sustained beyond 75 years of age, especially environmental quality of life.

According to the authors, environmental actions that young adults take to draw public attention to climate change may be motivated by their poorer environmental quality of life. Very good environmental quality of life of older women may provide reason for them to work toward retaining this valued feature for future generations. This could be the topic of future research, as the data for this study was collected before it was widely appreciated that the effects of climate change and biodiversity loss would depend on changing human behavior.

In the meantime, the findings underscore the importance of assessing environmental, social, and spiritual quality of life to fully understand women’s quality of life and health. Moreover, information from this study could be used for the timely implementation of interventions to enhance the quality of life of young and older women.

The authors add: “For women, the effect of the environment, in particular, on their quality of life is substantial. This includes things like their home conditions, financial resources, and environmental health including pollution levels.”

Prior research suggests that Americans in their early 20s may be less happy, on average, than at other points in their lives. Meanwhile, a growing percentage of young adults are not in long-term romantic relationships, and researchers are increasingly studying single people as a distinct group, without conventional comparisons to coupled people.

However, few studies have focused on distinct categories of single people, such as younger adults. To better understand these individuals’ experiences, Walsh and colleagues analyzed online survey data from 1,073 single American adults aged 18 to 24.

The survey included questions assessing participants’ overall happiness as well five predictors of happiness: satisfaction with family, satisfaction with friends, self-esteem, neuroticism, and extraversion. To analyze participants’ answers, the researchers applied latent profile analysis, a research approach that assumes individuals fall into diverse subgroups within a population, instead of assuming a more homogeneous population, as traditional approaches often do.

The research team found that the heterogeneity of the young, single adults in their dataset was best represented by dividing them into five subgroups, or profiles, each with distinctive combinations of the five measured predictors, and each corresponding to a different level of happiness.

For instance, people in profile 1 were happiest and had favorable levels of all five predictors, including high friendship satisfaction and low neuroticism. Meanwhile, people in profile 5, who were least happy, had unfavorable levels of all five predictors. Higher scores on some of the five predictors appeared to offset lower scores on others, with friendship satisfaction being particularly strongly linked to participants’ happiness.

On the basis of their findings, the researchers suggest that young, single adults might benefit from deliberately creating meaningful, long-term friendships. However, they note that further research is needed to clarify any cause-effect relationship between happiness and the five predictors they studied.

The authors add: “One of the standout findings from our study is how deeply friendships shape happiness for single emerging adults. We found that singles who were satisfied with their friendships tended to be happy with their lives, while those dissatisfied with their friendships were less happy. In short, the quality of your friendships is a key factor for your well-being, especially if you’re single.”

This reliance on cloud computing poses significant security risks, particularly in areas like health care, where hospitals may be hesitant to use AI tools to analyze confidential patient data due to privacy concerns.

To tackle this pressing issue, MIT researchers have developed a security protocol that leverages the quantum properties of light to guarantee that data sent to and from a cloud server remain secure during deep-learning computations.

By encoding data into the laser light used in fiber optic communications systems, the protocol exploits the fundamental principles of quantum mechanics, making it impossible for attackers to copy or intercept the information without detection.

Moreover, the technique guarantees security without compromising the accuracy of the deep-learning models. In tests, the researcher demonstrated that their protocol could maintain 96 percent accuracy while ensuring robust security measures.

“Deep learning models like GPT-4 have unprecedented capabilities but require massive computational resources. Our protocol enables users to harness these powerful models without compromising the privacy of their data or the proprietary nature of the models themselves,” says Kfir Sulimany, an MIT postdoc in the Research Laboratory for Electronics (RLE) and lead author of a paper on this security protocol.

Sulimany is joined on the paper by Sri Krishna Vadlamani, an MIT postdoc; Ryan Hamerly, a former postdoc now at NTT Research, Inc.; Prahlad Iyengar, an electrical engineering and computer science (EECS) graduate student; and senior author Dirk Englund, a professor in EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE. The research was recently presented at Annual Conference on Quantum Cryptography.

A two-way street for security in deep learning

The cloud-based computation scenario the researchers focused on involves two parties — a client that has confidential data, like medical images, and a central server that controls a deep learning model.

The client wants to use the deep-learning model to make a prediction, such as whether a patient has cancer based on medical images, without revealing information about the patient.

In this scenario, sensitive data must be sent to generate a prediction. However, during the process the patient data must remain secure.

Also, the server does not want to reveal any parts of the proprietary model that a company like OpenAI spent years and millions of dollars building.

“Both parties have something they want to hide,” adds Vadlamani.

In digital computation, a bad actor could easily copy the data sent from the server or the client.

Quantum information, on the other hand, cannot be perfectly copied. The researchers leverage this property, known as the no-cloning principle, in their security protocol.

For the researchers’ protocol, the server encodes the weights of a deep neural network into an optical field using laser light.

A neural network is a deep-learning model that consists of layers of interconnected nodes, or neurons, that perform computation on data. The weights are the components of the model that do the mathematical operations on each input, one layer at a time. The output of one layer is fed into the next layer until the final layer generates a prediction.

The server transmits the network’s weights to the client, which implements operations to get a result based on their private data. The data remain shielded from the server.

At the same time, the security protocol allows the client to measure only one result, and it prevents the client from copying the weights because of the quantum nature of light.

Once the client feeds the first result into the next layer, the protocol is designed to cancel out the first layer so the client can’t learn anything else about the model.

“Instead of measuring all the incoming light from the server, the client only measures the light that is necessary to run the deep neural network and feed the result into the next layer. Then the client sends the residual light back to the server for security checks,” Sulimany explains.

Due to the no-cloning theorem, the client unavoidably applies tiny errors to the model while measuring its result. When the server receives the residual light from the client, the server can measure these errors to determine if any information was leaked. Importantly, this residual light is proven to not reveal the client data.

A practical protocol

Modern telecommunications equipment typically relies on optical fibers to transfer information because of the need to support massive bandwidth over long distances. Because this equipment already incorporates optical lasers, the researchers can encode data into light for their security protocol without any special hardware.

When they tested their approach, the researchers found that it could guarantee security for server and client while enabling the deep neural network to achieve 96 percent accuracy.

The tiny bit of information about the model that leaks when the client performs operations amounts to less than 10 percent of what an adversary would need to recover any hidden information. Working in the other direction, a malicious server could only obtain about 1 percent of the information it would need to steal the client’s data.

“You can be guaranteed that it is secure in both ways — from the client to the server and from the server to the client,” Sulimany says.

“A few years ago, when we developed our demonstration of distributed machine learning inference between MIT’s main campus and MIT Lincoln Laboratory, it dawned on me that we could do something entirely new to provide physical-layer security, building on years of quantum cryptography work that had also been shown on that testbed,” says Englund. “However, there were many deep theoretical challenges that had to be overcome to see if this prospect of privacy-guaranteed distributed machine learning could be realized. This didn’t become possible until Kfir joined our team, as Kfir uniquely understood the experimental as well as theory components to develop the unified framework underpinning this work.”

In the future, the researchers want to study how this protocol could be applied to a technique called federated learning, where multiple parties use their data to train a central deep-learning model. It could also be used in quantum operations, rather than the classical operations they studied for this work, which could provide advantages in both accuracy and security.

This work was supported, in part, by the Israeli Council for Higher Education and the Zuckerman STEM Leadership Program.

According to their new study in eBioMedicine, these mouse models are important for COVID-19 research because their cells were engineered to include two important human molecules that are involved in SARS-CoV-2 infection of human cells — and these humanized mice were generated on two different immunologic backgrounds. The new models can help shed light on how SARS-CoV-2 moves through the body and why different people experience wildly different COVID-19 symptoms.

“With these mouse models, we can model epidemiologically-relevant SARS-CoV-2 infection and vaccination settings, and we can study all relevant tissues (not just the blood) at different timepoints following infection and/or vaccination,” says LJI Professor Sujan Shresta, Ph.D., who co-led the research with LJI Histopathology Core Director Kenneth Kim, Dipl. ACVP, and the late Kurt Jarnagin, Ph.D., of Synbal, Inc.

Already, these new mouse models have helped scientists capture a clearer picture of how SARS-CoV-2 affects humans. They are also available to the wider COVID-19 research community.

“This work is part of LJI’s mission to contribute to pandemic preparedness around the world,” says Shresta.

Mouse models are a critical tool for understanding infection

Shresta’s lab is known for producing mouse models to study immune responses to infectious diseases such as dengue virus and Zika virus. In 2021, her laboratory partnered with Synbal, Inc., a preclinical biotechnology company based in San Diego, CA, to develop multi-gene, humanized mouse models for COVID-19 research. The project was also supported by Synbal CEO and LJI Board Member David R. Webb, Ph.D.

Shresta and Jarnagin collaborated to produce mice that express either human ACE2, human TMPRSS2, or both molecules in the C57BL/6 and BALB/c mouse genetic backgrounds. “Immunologists have found that these two genetic backgrounds in mice elicit different immune responses,” says Shresta.

As Shresta explains, having the flexibility to include the genes for one or both of these molecules in two different mouse genetic backgrounds gives scientists an opportunity to investigate two key areas. First, they can examine how each of these molecules contribute to infection with different SARS-CoV-2 variants. Second, they can study how the host’s genetic background might influence disease progression and immune response following infection with different variants.

Zooming into infected tissues

The researchers then took a closer look at how these models responded to actual SARS-CoV-2 infection. LJI Postdoctoral Fellow Shailendra Verma, Ph.D., worked in LJI’s High Containment (BSL-3) Facility to take tissue samples from the various mouse strains exposed to SARS-CoV-2.

“This work wouldn’t have been possible if we didn’t have a BSL-3 facility at LJI,” says Shresta, who has worked closely with LJI’s Department of Environmental Health and Safety to conduct several cutting-edge studies in the facility.

Next, Kim, a board-certified pathologist, examined the tissue samples and compared them to pathologic findings from humans with COVID-19.

Kim’s analysis showed signs of SARS-CoV-2 infection in the lungs, which are also the tissue most vulnerable to SARS-CoV-2 infection in humans. Kim could also see mouse immune cells responding to infection in a way that reflected the human immune response.

By characterizing these responses in the new mouse models, the researchers have established a foundation for understanding the immune heterogeneity — or wide range of immune responses — of SARS-CoV-2-induced disease.

“There’s no such thing as a perfect animal model, but our goal is always to make an animal model that recapitulates the human disease and immune response as much as possible,” says Shresta.

The new mouse models may prove valuable for studying responses to emerging SARS-CoV-2 variants and future coronaviruses with pandemic potential.

“Not only are these models useful for current COVID-19 studies, but if there should be another coronavirus pandemic — with a virus that utilizes the same ACE2 receptor and/or TMPRSS2 molecule for viral entry into human cells — then these mouse lines on two different genetic backgrounds will be ready,” says Kim.

Additional authors of the study, “Influence of Th1 versus Th2 immune bias on viral, pathological, and immunological dynamics in SARS-CoV-2 variant-infected human ACE2 knock-in mice,” include Erin Maule, Paolla B. A. Pinto, Chris Conner, Kristen Valentine, Dale O Cowley, Robyn Miller, Annie Elong Ngono, Linda Tran, Krithik Varghese, Rúbens Prince dos Santos Alves, Kathryn M. Hastie and Erica Ollmann Saphire.

This study was supported by the National Institutes of Health (grant U19 AI142790-02S1 and R44 AI157900), the GHR Foundation, the Arvin Gottlieb Charitable Foundation, the Overton family, and by a American Association of Immunologists Career Reentry Fellowship (FASB).

Dogs respond to human speech, even though they themselves cannot produce human sounds. To better understand how people and pups communicate, the scientists analyzed the vocal sounds of 30 dogs. They also analyzed the sounds of 27 humans across five languages speaking to other people, and 22 humans across those languages speaking to dogs. The scientists also used electroencephalography (EEG) to examine the brain responses to speech in humans and dogs.

Humans are much faster ‘talkers’ than dogs, the study showed, with a speech rate of about four syllables per second, while dogs bark, growl, woof, and whine at a rate of about two vocalizations per second. When talking to dogs, the humans slowed their speech to around three syllables per second. EEG signals of humans and canines showed that dogs’ neural responses to speech are focused on delta rhythms, while human responses to speech are focused on faster theta rhythms. The authors suggest that humans and dogs have different vocal processing systems, and that slowing down our speech when speaking to pets may have ultimately helped us better connect with them.

The authors add, “What’s further interesting, is that while dogs use slow rhythm to process speech and contrary to popular beliefs, they need both content and prosody to successfully comprehend it.”