Scientists create model to predict depression and anxiety using artificial intelligence and social media

A study by a group at the University of São Paulo reported in a scientific journal involved the construction of a database and models. Preliminary results are described in the article.

Peer-Reviewed Publication

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO

Researchers at the University of São Paulo (USP) in Brazil are using artificial intelligence (AI) and Twitter, one of the world’s largest social media platforms, to try to create anxiety and depression prediction models that could in future provide signs of these disorders before clinical diagnosis.

The study is reported in an article published in the journal Language Resources and Evaluation. 

Construction of a database, called SetembroBR, was the first step in the study. The name is a reference to Yellow September, an annual suicide awareness and prevention campaign, and also to the fact that data collection for the study began one day in September.

The second step is still in progress but has provided some preliminary findings, such as the possibility of detecting whether a person is likely to develop depression solely on the basis of their social media friends and followers, without taking their own posts into account.

The database compiled by the group contains information relating to a corpus of texts (in Portuguese) and the network of connections involving 3,900 Twitter users who reported having been diagnosed with or treated for mental health problems before the survey. The corpus includes all public tweets posted by these users individually (without retweets), for a total of some 47 million of these short texts.

“First, we collected timelines manually, analyzing tweets by some 19,000 users, equivalent to the population of a village or small town. We then used two datasets, one for users who reported being diagnosed with a mental health problem and another selected at random for control purposes. We wanted to distinguish between people with depression and the general population,” said Ivandre Paraboni, last author of the article and a professor at USP’s School of Arts, Sciences and Humanities (EACH).

The study also collected tweets from friends and followers, in accordance with the observation that people with mental health problems tend to follow certain accounts, such as discussion forums, influencers and celebrities who publicly acknowledge their depression. “These people are attracted to each other. They have shared interests,” said Paraboni, who is a researcher with the Center for Artificial Intelligence (C4AI), an Engineering Research Center (ERC) established by FAPESP and IBM Brazil at USP.

FAPESP also supported the project study via the project “Social media language analysis for early detection of mental health disorders”, led by Paraboni. 

Mental health disturbances, including depression and anxiety, are a growing global concern. The World Health Organization (WHO) estimated on the basis of 2021 data that 3.8% of the world population, or some 280 million people, were affected by depression.

WHO also estimated an increase of 25% in global prevalence of these mental health problems during the COVID-19 pandemic. The tweets were collected for the study during this period.

In a recent survey by the Brazilian Health Ministry involving 784,000 participants, 11.3% said they had been diagnosed with depression. Most were women.

According to previous research, mental health problems are often reflected by the language used by the sufferers. This finding has led to a considerable number of studies involving natural language processing (NLP), with a focus on depression, anxiety and bipolar disorder, among others. However, most of these studies analyze texts in English and do not always match the profile of most Brazilians.

Models

The researchers pre-processed the corpus to remove hashtags, URLs, emoticons and non-standard characters while maintaining the original texts. They then deployed deep learning, an AI technique that teaches computers to process data in a way inspired by the human brain, to create four text classifiers and word embeddings (context-dependent mathematical representations of relations between words) using models based on bidirectional encoder representations from transformers (BERT), a machine learning algorithm for NLP. These models correspond to a neural network that learns contexts and meanings by monitoring sequential data relationships, such as words in a sentence.

The training input consisted of a sample of 200 tweets selected at random from each user. The parameters were defined by executing cross-validation of the training data five times and calculating the average result.

The conclusion was that BERT performed best in terms of predicting depression and anxiety, with a statistically significant difference between it and LogReg, the next best option. Because the models analyzed sequences of words and complete sentences, it was possible to observe that people with depression, for example, tended to write about subjects connected to themselves, using verbs and phrases in the first person, as well as topics such as death, crisis and psychology.

“The signs of depression that can be detected during a visit to the doctor aren’t necessarily the same as the ones that appear on social media,” Paraboni said. “For example, use of the first-person singular pronouns I and me was very evident, and in psychology this is considered a classic sign of depression. We also observed frequent use of the heart emoji by depressive users. This is widely felt to be a symbol of affection and love, but maybe psychologists haven’t yet characterized it as such.”

All the collected texts were anonymized. “We published neither actual tweets nor users’ names. We took care to ensure that the students involved in the project didn’t have access to user data so as to protect people’s identity,” he said.

The researchers are now extending the database, refining their computational techniques and upgrading the models in order to see if they can produce a tool for future use in screening prospective sufferers from mental health problems and helping families and friends of young people at risk from depression and anxiety. 

Brazil ranks third among the countries that most consume social media in the world, according to a Comscore survey published in early March, behind India and Indonesia but ahead of the United States, Mexico and Argentina. Its 131.5 million users are online for 46 hours a month on average. The most widely used platforms are YouTube, Facebook, Instagram, TikTok, Kwai and Twitter, which recently changed its rules and began charging for certain services.

About São Paulo Research Foundation (FAPESP)

The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by awarding scholarships, fellowships and grants to investigators linked with higher education and research institutions in the State of São Paulo, Brazil. FAPESP is aware that the very best research can only be done by working with the best researchers internationally. Therefore, it has established partnerships with funding agencies, higher education, private companies, and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration. You can learn more about FAPESP at www.fapesp.br/en and visit FAPESP news agency at www.agencia.fapesp.br/en to keep updated with the latest scientific breakthroughs FAPESP helps achieve through its many programs, awards and research centers. You may also subscribe to FAPESP news agency at http://agencia.fapesp.br/subscribe.

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DOI

10.1007/s10579-022-09633-0 

ARTICLE TITLE

SetembroBR: a social media corpus for depression and anxiety disorder prediction

Table tennis brain teaser: Playing against robots makes our brains work harder

Brain scans taken during table tennis reveal differences in how we respond to human versus machine opponents

Peer-Reviewed Publication

UNIVERSITY OF FLORIDA

 

VIDEO: A PARTICIPANT PLAYS TABLE TENNIS AGAINST GRADUATE STUDENT AMANDA STUDNICKI WHILE HAVING HIS BRAIN IMAGED VIA AN EEG CAP. THE EXPERIMENT REVEALED BIG DIFFERENCES IN HOW OUR BRAINS RESPOND TO HUMAN AND MACHINE OPPONENTS DURING SPORTS. view more 

CREDIT: FRAZIER SPRINGFIELD

Captain of her high school tennis team and a four-year veteran of varsity tennis in college, Amanda Studnicki had been training for this moment for years.

All she had to do now was think small. Like ping pong small.

For weeks, Studnicki, a graduate student at the University of Florida, served and rallied against dozens of players on a table tennis court. Her opponents sported a science-fiction visage, a cap of electrodes streaming off their heads into a backpack as they played against either Studnicki or a ball-serving machine. That cyborg look was vital to Studnicki’s goal: to understand how our brains react to the intense demands of a high-speed sport like table tennis – and what difference a machine opponent makes.

Studnicki and her advisor, Daniel Ferris, discovered that the brains of table tennis players react very differently to human or machine opponents. Faced with the inscrutability of a ball machine, players’ brains scrambled themselves in anticipation of the next serve. While with the obvious cues that a human opponent was about to serve, their neurons hummed in unison, seemingly confident of their next move.

The findings have implications for sports training, suggesting that human opponents provide a realism that can’t be replaced with machine helpers. And as robots grow more common and sophisticated, understanding our brains’ response could help make our artificial companions more naturalistic.

“Robots are getting more ubiquitous. You have companies like Boston Dynamics that are building robots that can interact with humans and other companies that are building socially assistive robots that help the elderly,” said Ferris, a professor of biomedical engineering at UF. “Humans interacting with robots is going to be different than when they interact with other humans. Our long term goal is to try to understand how the brain reacts to these differences.”

Ferris’s lab has long studied the brain’s response to visual cues and motor tasks, like walking and running. He was looking to upgrade to studying complex, fast-paced action when Studnicki, with her tennis background, joined the research group. So the lab decided tennis was the perfect sport to address these questions with. But the oversized movements – especially high overhand serves – proved an obstacle to the burgeoning tech.

“So we literally scaled things down to table tennis and asked all the same questions we had for tennis before,” Ferris said. The researchers still had to compensate for the smaller movements of table tennis. So Ferris and Studnicki doubled the 120 electrodes in a typical brain-scanning cap, each bonus electrode providing a control for the rapid head movements during a table tennis match.

With all these electrodes scanning the brain activity of players, Studnicki and Ferris were able to tune into the brain region that turns sensory information into movement. This area is known as the parieto-occipital cortex.

“It takes all your senses – visual, vestibular, auditory – and it gives information on creating your motor plan. It’s been studied a lot for simple tasks, like reaching and grasping, but all of them are stationary,” Studnicki said. “We wanted to understand how it worked for complex movements like tracking a ball in space and intercepting it, and table tennis was perfect for this.”

The researchers analyzed dozens of hours of play against both Studnicki and the ball machine. When playing against another human, players’ neurons worked in unison, like they were all speaking the same language. In contrast, when players faced a ball-serving machine, the neurons in their brains were not aligned with one another. In the neuroscience world, this lack of alignment is known as desynchronization.

“If we have 100,000 people in a football stadium and they’re all cheering together, that’s like synchronization in the brain, which is a sign the brain is relaxed" Ferris said. “If we have those same 100,000 people but they’re all talking to their friends, they’re busy but they’re not in sync. In a lot of cases, that desynchronization is an indication that the brain is doing a lot of calculations as opposed to sitting and idling.”

The team suspects that the players’ brains were so active while waiting for robotic serves because the machine provides no cues of what they are going to do next. What’s clear is that our brains process these two experiences very differently, which suggests that training with a machine might not offer the same experience as playing against a real opponent.

“I still see a lot of value in practicing with a machine,” Studnicki said. “But I think machines are going to evolve in the next 10 or 20 years, and we could see more naturalistic behaviors for players to practice against.”

More than 100 electrodes capture fine detail of the brain activity of participants while they play a fast-paced game of table tennis.

To keep participants mobile, the EEG recording took place in a backpack.

A research participant prepared to play table tennis while their brain is imaged with an EEG cap.

A research participant plays against a ball-serving machine while his brain is imaged with an EEG cap. The research revealed that our brains respond differently when playing against human or machine opponents in sports.

CREDIT

Frazier Springfield

Playing against a machine oppo [VIDEO] | 

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JOURNAL

eNeuro

METHOD OF RESEARCH

Experimental study

SUBJECT OF RESEARCH

People

ARTICLE TITLE

Parieto-Occipital Electrocortical Dynamics during Real-World Table Tennis

ARTICLE PUBLICATION DATE

10-Apr-2023

Brain-inspired intelligent robotics: Theoretical analysis and systematic application

Peer-Reviewed Publication

BEIJING ZHONGKE JOURNAL PUBLISING CO. LTD.

 

IMAGE: THE RESEARCHERS CONSTRUCTED A HARDWARE PLATFORM WITH THE SAME MUSCLE DISTRIBUTION AND STRUCTURE. view more 

CREDIT: BEIJING ZHONGKE JOURNAL PUBLISING CO. LTD.

Robots have become a crucial indicator for measuring the competitive strength of a country in science and technology. Robotic systems have made advancements in fields such as mechanical engineering, control and artificial intelligence technologies. However, the performance of current robotic systems still exists limitations and cannot satisfy the demands of an increasing number of applications. In order to deal with these problems, a brain-inspired intelligent robotic system is constructed.

A team of scientists led by Professor Qiao Hong from the State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, have conducted a review on the cutting-edge works along the research chain of brain-inspired robots. Firstly, they introduce the core neural mechanisms in vision, decision-making, control, and body structure and the corresponding brain-inspired algorithm. Secondly, they present the software and hardware system integration. The simulation platform for brain-inspired robots integrates brain-inspired algorithms in vision, decision making, and movement control, providing efficient tools for researchers from different fields. The hardware platform was designed to mimic the human musculoskeletal system, providing a physical system to validate the performance of the brain-inspired algorithm.

“Brain-inspired motion-learning algorithms can use sparse rewards to realize generalized control policy learning. With this method, robotics can accomplish a series of manipulations after simple training.” “System robustness comes from redundancy and anti-interference can improve system reliability.” “The special muscle actuator provides nonlinear dynamics and coupled feedback modulation, which can reduce the effects of disturbances from the control input and environment.” They describe the advantages of the brain-inspired intelligent robotics.

Furthermore, they make assumptions about the future development of next-generation robotics. “Next-generation robotics could be developed with numerous brain-inspired algorithms and novel musculoskeletal structures.” “Organic structural design and hardware construction should be reinforced and emphasized.” “We hope that this generation of robotics can provide inspiration and reference for brain-computer interface control.” More time and efforts are supposed to be devoted to the development of the brain-inspired intelligent robotics.

See the article:

Brained-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application

http://doi.org/10.1007/s11633-022-1390-8

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JOURNAL

Machine Intelligence Research

DOI

10.1007/s11633-022-1390-8 

ARTICLE TITLE

Brain-inspired Intelligent Robotics: Theoretical Analysis and Systematic Application

Scientists advocate for integration of biogeography and behavioral ecology to rapidly respond to biodiversity loss

Peer-Reviewed Publication

UNIVERSITY OF OKLAHOMA

 

IMAGE: UNIVERSITY OF OKLAHOMA RESEARCHERS (PICTURED FROM LEFT) ASHLEE ROWE, HAYLEY LANIER, KATHARINE MARSKE, LAURA STEIN AND CAMERON SILER AUTHORED A PERSPECTIVE ARTICLE ADVOCATING FOR CONVERGENT RESEARCH THAT INTEGRATES THE FIELDS OF BIOGEOGRAPHY AND BEHAVIORAL ECOLOGY TO MORE RAPIDLY RESPOND TO CHALLENGES ASSOCIATED WITH CLIMATE CHANGE AND BIODIVERSITY LOSS. view more 

CREDIT: IMAGE PROVIDED BY THE UNIVERSITY OF OKLAHOMA

An interdisciplinary team of researchers at the University of Oklahoma has published a perspective article in the journal Proceedings of the National Academy of Sciences advocating for convergent research that integrates the fields of biogeography and behavioral ecology to more rapidly respond to challenges associated with climate change and biodiversity loss.

While news about climate change fills headlines, the crisis of biodiversity loss has gotten less attention. In their article, the authors contend that “identifying solutions that prevent large-scale extinction requires addressing critical questions about biodiversity dynamics that – despite widespread interest – have been challenging to answer thus far.”

From microorganisms that support soil health, fish that we eat, forests that clean water, to pollination, lumber and medicine, protecting ecosystems and the variety of plants and animals within them is vital to the health of the planet and for humanity to thrive.

“The ways that we respond to climate change also have a big impact on outcomes for biodiversity – which is also a critical part of how the global climate system works,” said article co-author Katharine Marske, Ph.D., assistant professor in the Department of Biology, Dodge Family College of Arts and Sciences.

“Climate change is a major threat to biodiversity, but it’s not the only threat. We also have habitat loss and degradation, direct overharvest of some species and so forth, so it’s also its own unique crisis that needs to be considered on equal footing.”

“Historically in Oklahoma, we can point to cases where we have rapidly removed or changed natural habitats, such as the Dust Bowl,” said co-author Hayler Lanier, Ph.D., assistant curator of mammalogy at the Sam Noble Museum and an assistant professor of biology. “That was a case where we came through and stripped out a lot of the existing natural systems that do things to hold onto the soil and create nutrients, and that was sort of one small example. As we move into the future, we need to think about what sort of world we want to live in, and it is definitely one where we have these sorts of ecosystem services.”

By integrating the fields of biogeography, or the study of how and why biological diversity varies across the Earth, with behavioral ecology, or the study of the evolution of behavior in relation to ecological pressures, the authors argue that scientists will be better able to develop a more comprehensive understanding of how to leverage “existing biodiversity knowledge into predictive frameworks for how biodiversity will respond to environmental change, and where habitat conservation can be most effective.”

“This interdisciplinary connection between behavioral ecologists and scientists who study biogeography has not been linked well to date,” said Laura Stein, Ph.D., article co-author and an assistant professor of biology. “I think in many cases, biogeographers are not thinking about day-to-day activities of animals as much as behavioral ecologists are, and behavioral ecologists are not necessarily considering differences and overlaps in both current and historical ranges and how behaviors have been shaped by past geographic events that might help predict where they will be in the future. And so, by combining these two fields, we can get a much broader picture of what we can do now and what is important for protecting biodiversity into the future.”

The article’s authors have led a pilot of such integrative efforts at the University of Oklahoma, supported by funding from the National Science Foundation.

Co-author Cameron Siler, Ph.D., associate professor of biology and associate curator of herpetology at the Sam Noble Museum, said “We, in the Department of Biology, together with the Sam Noble Museum, carried out a series of cluster hires over the last five years aimed strategically at bringing together integrative researchers with the capacity to think beyond these typically isolated fields, and what’s exciting is this work is a culmination of the success of that early effort to bring scientists like this together at OU.”

Lanier described their work as hopeful. Biodiversity loss and climate change are large, complex and challenging problems to solve. “What we’re trying to do is to harness a lot of information that we already have as scientific and conservation communities and bring it together in new ways to very quickly answer some of these questions.”

Agreeing, Marske added, “The scope of the challenges that society faces require integration, so providing opportunities for this across biology, and amongst all disciplines, increases your chances to bring people together and talk about novel solutions. The more people you can have at that table, the better.”

 

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About the Project

“Integrating biogeography and behavioral ecology to rapidly address biodiversity loss,” published April 5, 2023, in the journal Proceedings of the National Academy of Sciences, DOI 10.1073/pnas.2110866120. Marske is the first author, with co-authors Lanier, Siler, Ashlee H. Rowe, and Stein.

About the University of Oklahoma Office of the Vice President for Research and Partnerships

The University of Oklahoma is a leading research university classified by the Carnegie Foundation in the highest tier of research universities in the nation. Faculty, staff, and students at OU are tackling global challenges and accelerating the delivery of practical solutions that impact society in direct and tangible ways through research and creative activities. OU researchers expand foundational knowledge while moving beyond traditional academic boundaries, collaborating across disciplines and globally with other research institutions as well as decision makers and practitioners from industry, government and civil society to create and apply solutions for a better world. Find out more at ou.edu/research.

About the Sam Noble Museum

Designated as the official Oklahoma Museum of Natural History in 1987 by the Oklahoma Legislature, the Sam Noble Museum today houses more than 10 million objects divided between 12 collections, and maintains laboratories, offices, libraries and exhibit space within a 198,000-square-foot facility. The Sam Noble Museum is located on the University of Oklahoma Norman campus at 2401 Chautauqua Ave.

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JOURNAL

Proceedings of the National Academy of Sciences

DOI

10.1073/pnas.2110866120 

ARTICLE TITLE

Integrating biogeography and behavioral ecology to rapidly address biodiversity loss

Trees in areas prone to hurricanes have strong ability to survive even after severe damage

Even though the vast majority of trees studied in Dominica — 89% — were damaged during Hurricane Maria, only 10 percent were immediately killed

Peer-Reviewed Publication

CLEMSON UNIVERSITY

 

IMAGE: NEARLY 90 PERCENT OF THE TREES IN NINE DESIGNATED PLOTS IN DOMINICA WERE DAMAGED BY HURRICANE MARIA. ONLY 10 PERCENT DIED IMMEDIATELY. view more 

CREDIT: BENTON TAYLOR

As their plane flew low on its approach to land at the airport on the island of Dominica, researchers from Clemson and Harvard universities looked out the window to see miles of forests with trees that looked like matchsticks.

It was nine months after the island in the West Indies had taken a direct hit from Category 5 Hurricane Maria.

But when the researchers actually got into the forests and examined the trees more closely, they discovered that while 89% of the trees sustained damage — 76% of which had major damage —only 10% were immediately killed. Many of the trees had resprouted.

“These hurricane-prone forests are, in many regards, incredibly resistant to even extremely powerful hurricanes. I don’t want to minimize the scale of damage that these forests received — it was immense — but the fact that 90% of the trees survived shows an impressive level of resistance,” said Benton Taylor, a former graduate student in the Clemson Department of Biological Sciences who is now an assistant professor in the Harvard University Department of Organismic and Evolutionary Biology.

With climate change, hurricanes are increasing in frequency and severity. Many regions of the world experiencing frequent hurricane disturbance also play particularly important roles in carbon, water and nutrient cycling and are global “hotspots” of biodiversity.

Hurricane Maria hit Dominica on September 18, 2017, with winds topping 160 mph — the strongest hurricane on record to make landfall there. Days later, Maria devastated the U.S. territory of Puerto Rico.

With funding from the Clemson Caribbean Initiative, Department of Biological Sciences Chair Saara DeWalt, Taylor and Dominican researcher Elvis Stedman remeasured and assessed damage of all the trees in nine forest stands across Dominica. The plots were established in 2006 by DeWalt and former Clemson researcher Kalan Ickes. They also measured wood density and carbon content for the 44 most common tree species to pair with the tree measurements to estimate biomass and determine how much carbon had been relocated from living to dead by the hurricane.

They found the most common damage types were stem snapping (40% of trees) and major branch damage (26% of trees), but the damage types with the highest rates of mortality were uprooting and being crushed by a neighboring tree. Thirty-three percent of uprooted trees and 47% of trees that were crushed died.

“Snapping wasn’t as lethal as you might think,” said DeWalt, a senior researcher on the study.

Larger individual trees and species with lower wood density were more susceptible to snapping, uprooting and mortality. Trees on steeper slopes were more prone to being crushed by neighboring trees.

More frequent storms will shape the structure and composition of forests in hurricane-prone regions, DeWalt said. She expects that they’ll shift toward smaller, high wood-density species.

“Forests are adapted to this kind of disturbance, but we may see a shift in the types of species that are most common in these forests with increasing frequency of strong hurricanes. You might get more of the ‘live fast, die young’ species because you’re constantly resetting the forest,” she said.

Fewer big, old trees could impact wildlife, Taylor said. Two parrots native to Dominica — the Sisserou and Jaco, both of which occur only on this small island nation — rely on cavities in large trees to nest.

“Larger trees tended to suffer more damage and mortality. These large trees store immense amounts of carbon, and in Dominica many of these large trees create unique habitats for animals, such as the parrots,” he said. “The data we obtained on how different species and sizes of trees experience damage from hurricanes can help us predict the future of these forests and the many services they provide.”

Understanding forest responses to hurricanes in general translates to the hurricane-prone southern United States, but Taylor urges caution.

“In a field where opportunities to study a phenomenon are rare — hurricanes themselves are rare events and it’s even rarer that one hits a forest plot that was measured before the hurricane hit — any additional data are useful,” he said. “That said, our study highlights that the effects of hurricanes can be very different based on the local topography and tree species that make up a forest. So comparing a small mountainous island populated by tropical rainforest trees to the forests of the coastal plains and piedmont regions of the southern United States should be approached with caution.”

The findings appeared in the March 2023 issue of the journal Forest Ecology and Management. The paper is titled “Widespread stem snapping but limited mortality caused by a category 5 hurricane on the Caribbean Island of Dominica.”

In addition to DeWalt, Taylor and Stedman, the authors of the study were Professor Skip Van Bloem of the Clemson Department of Forestry and Environment Conservation and Assistant Professor Stefanie Whitmire of the Clemson Department of Agricultural Sciences.

  

Clemson University researcher Saara DeWalt measures a tree in Dominica.

CREDIT

Clemson University College of Science

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JOURNAL

Forest Ecology and Management

DOI

10.1016/j.foreco.2023.120833 

METHOD OF RESEARCH

Observational study

SUBJECT OF RESEARCH

Not applicable

ARTICLE TITLE

Widespread stem snapping but limited mortality caused by a category 5 hurricane on the Caribbean Island of Dominica

Roundtable on community engagement in data decision-making

Peer-Reviewed Publication

MARY ANN LIEBERT, INC./GENETIC ENGINEERING NEWS

 

IMAGE: FACILITATES AND SUPPORTS THE EFFORTS OF RESEARCHERS, ANALYSTS, STATISTICIANS, BUSINESS LEADERS, AND POLICYMAKERS TO IMPROVE OPERATIONS, PROFITABILITY, AND COMMUNICATIONS WITHIN THEIR ORGANIZATIONS. SPANNING A BROAD ARRAY OF DISCIPLINES FOCUSING ON NOVEL BIG DATA TECHNOLOGIES, POLICIES, AND INNOVATIONS, THE PEER-REVIEWED JOURNAL BRINGS TOGETHER THE COMMUNITY TO ADDRESS THE CHALLENGES AND DISCOVER NEW BREAKTHROUGHS AND TRENDS LIVING WITHIN THIS INFORMATION. view more 

CREDIT: MARY ANN LIEBERT, INC., PUBLISHERS

A Roundtable Discussion was recently held to discuss the importance of community voice in developing 21st century public health systems. Expert panelists emphasized the need to redefine measures, foster new ideas, and work to ensure that historically excluded populations are represented in the data collection process. The Roundtable transcript is now published in the peer-reviewed journal Big Data. Click here to read the transcript.

The discussion was moderated by Michael Crawford, Associate Dean for Strategy, Outreach, and Innovation at Howard University College of Medicine, and Commissioner on the Robert Wood Johnson Foundation’s (RWJF) National Commission to Transform Public Health Data Systems.

The expert panelists include Francisca Flores, from the Gulf Research Program at the National Academies of Sciences, Engineering, and Medicine; Amy Hawn Nelson, from the University of Pennsylvania’s Actionable Intelligence for Social Policy; and Marynia Kolak, from US Covid Atlas

The participants highlight their efforts to build data capacity in under-resourced communities and enhance community involvement in shaping equitable data systems. They also reflect on the importance of authentic community engagement in data decision-making, targeting community-relevant interventions, and measuring progress, which is necessary to pave the way for a healthy and more equitable future for all.

Big Data Editor-in-Chief Zoran Obradovic, PhD, Carnell Professor of Data Analytics, Temple University, Philadelphia, PA, states: “This roundtable will discuss success stories in various domains and lessons learned when addressing some of challenges related to facilitating the community engagement in data decision-making. We invite the Big Data community to share their experience on this high impact objective from big data perspectives.”

About the Journal
Big Data, published bi-monthly online with open access options and in print, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and communications within their organizations. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the peer-reviewed journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information. Complete tables of content and a sample issue may be viewed on the Big Data website.

About the Publisher
Mary Ann Liebert, Inc., publishers is a global media company dedicated to creating, curating, and delivering impactful peer-reviewed research and authoritative content services to advance the fields of biotechnology and the life sciences, specialized clinical medicine, and public health and policy. For complete information, please visit the  Mary Ann Liebert, Inc., publishers website.

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JOURNAL

Big Data

DOI

10.1089/big.2023.29057.rtd 

METHOD OF RESEARCH

Case study

SUBJECT OF RESEARCH

People

ARTICLE TITLE

Importance of Community Engagement in Data Decision Making

ARTICLE PUBLICATION DATE

10-Apr-2023

Temperature is stronger than light and flow as driver of oxygen in US rivers

Penn State researchers used a deep learning model to understand predictive value of each factor

Peer-Reviewed Publication

PENN STATE

UNIVERSITY PARK, Pa. — The amount of dissolved oxygen in a river is a matter of life or death for the plants and animals living within it, but this oxygen concentration varies drastically from one river to another, depending on their unique temperature, light and flow. To better understand which factor has the greatest impact on the concentration of dissolved oxygen, researchers at Penn State used a deep learning model to analyze data from hundreds of rivers across the United States. 

Oxygen concentration is an important measure of water quality because fish and other aquatic organisms require dissolved oxygen to breathe, according to Wei Zhi, assistant research professor of civil and environmental engineering and first author of the study, recently published in Nature Water. 

“Studies have shown that three major factors — flow, temperature and sunlight — influence the amount of dissolved oxygen found in a river or stream,” Zhi said. “We wanted to know, at the U.S. continental scale, which of these competing drivers was dominant.” 

According to corresponding author Li Li, Barry and Shirley Isett Professor of Civil and Environmental Engineering at Penn State, the common perception is that all three factors matter: how quickly a stream flows impacts how fast oxygen in the air can dissolve in the water; temperature affects how much oxygen the water can pull from the air; and the level of sunlight shining into the water affects how much oxygen the plants in the water can make on their own.  

“It is challenging, however, to figure out which of these factors is the most important at a continental scale because of different amounts of monitoring data in different rivers at different times,” Zhi said. “There has been little consistency in the way dissolved oxygen concentrations have been measured in different rivers. For example, some rivers were measured only in the 1980s in the summers, and some rivers were measured only in the 2000s in the spring.” 

Using 40 years of data from 580 rivers across the contiguous U.S. — each with unique temperature, flow and sunlight conditions — the researchers trained a long short-term memory deep learning model to figure out the relationship between the weather conditions and dissolved oxygen.  

“Traditionally, it has been very difficult to predict the dissolved oxygen levels on such a large scale, simultaneously with one model,” Li said. “But with a deep learning and big data approach, we can do that. Deep learning models enable large-scale systematic analysis of patterns and drivers.” 

The model revealed that, at a continental scale, temperature outweighed light and stream flow in controlling the dissolved oxygen dynamic. Light was the second-important factor on dissolved oxygen levels, while stream flow had minimal influence, according to the findings. 

“Temperature is the predominant driver of daily dissolved oxygen dynamics in U.S. rivers,” Zhi said. “Fairly accurate predictions of oxygen concentration can be made by temperature alone. Dissolved oxygen is declining in warming rivers, which has important implications for water security and ecosystem health in the future warming climate.” 

This project was supported by the Barry and Shirley Isett professorship from the Department of Civil and Environmental Engineering at Penn State and by the Office of Biological and Environmental Research of the U.S. Department of Energy. Chaopeng Shen, associate professor of civil and environmental engineering at Penn State, and Wenyu Ouyang from Dalian University of Technology in China also contributed to this research.  

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JOURNAL

Nature Water

DOI

10.1038/s44221-023-00038-z 

METHOD OF RESEARCH

Computational simulation/modeling

SUBJECT OF RESEARCH

Animals

ARTICLE TITLE

Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers