Tuesday, September 06, 2022

Canadian Researchers develop novel way to prevent waterborne infectious diseases at refugee settlements

Peer-Reviewed Publication

YORK UNIVERSITY

Michael De Santi 

IMAGE: YORK UNIVERSITY PHD STUDENT MICHAEL DE SANTI view more 

CREDIT: YORK UNIVERSITY

TORONTO, Sept. 6, 2022 - Waterborne illness is one of the leading causes of infectious disease outbreaks in refugee and internally displaced persons (IDP) settlements, but a team led by York University has developed a new technique to keep drinking water safe using machine learning, and it could be a game changer.

As drinking water is not piped into homes in most settlements, residents instead collect it from public tap stands using storage containers.

“When water is stored in a container in a dwelling it is at high risk of being exposed to contaminants, so it’s imperative there is enough free residual chlorine to kill any pathogens,” says Lassonde School of Engineering PhD student Michael De Santi, part of York’s Dahdaleh Institute for Global Health Research, who led the research.

Recontamination of previously safe drinking water during its collection, transport and storage has been a major factor in outbreaks of cholera, hepatitis E, and shigellosis in refugee and IDP settlements in Kenya, Malawi, Sudan, South Sudan, and Uganda.

“A variety of factors can affect chlorine decay in stored water. You can have safe water at that collection point, but once you bring it home and store it, sometimes up to 24 hours, you can lose that residual chlorine, pathogens can thrive and illness can spread,” says Lassonde Adjunct Professor Syed Imran Ali, a Research Fellow at York's Dahdaleh Institute for Global Health Research, who has first-hand experience working in a settlement in South Sudan.

Using machine learning, the research team, including Associate Professor Usman Khan also of Lassonde, has developed a new way to predict the probability that enough chlorine will remain until the last glass is consumed. They used an artificial neural network (ANN) along with ensemble forecasting systems (EFS), something that is not typically done. EFS is a probabilistic model commonly used to predict the probability of precipitation in weather forecasts.

“ANN-EFS can generate forecasts at the time of consumption that take a variety of factors into consideration that affect the level of residual chlorine, unlike the typically used models. This new probabilistic modelling is replacing the currently used universal guideline for chlorine use, which has been shown to be ineffective,” says Ali.

Factors such as local temperature, how the water is stored and handled from home to home, the type and quality of the water pipes, water quality or did a child dipped their hand in the water container, can all play a role in how safe the water is to drink.

“However, it’s really important that these probabilistic models be trained on data at a specific settlement as each one is as unique as a snowflake,” says De Santi. “Two people could collect the same water on the same day, both store it for six hours, and one could still have all the chlorine remaining in the water and the other could have almost none of it left. Another 10 people could have varying ranges of chlorine.”

The researchers used routine water quality monitoring data from two refugee settlements in Bangladesh and Tanzania collected through the Safe Water Optimization Tool Project. In Bangladesh, the data was collected from 2,130 samples by Médecins Sans Frontières from Camp 1 of the Kutupalong-Balukhali Extension Site, Cox’s Bazaar between June and December 2019 when it hosted 83,000 Rohingya refugees from neighbouring Myanmar.

Determining how to teach the ANN-EFS to come up with realistic probability forecasts with the smallest possible error required out-of-the-box thinking.

“How that error is measured is key as it determines how the model behaves in the context of probabilistic modelling,” says De Santi. “Using cost-sensitive learning, a tool that morphs the cost function towards a targeted behaviour when using machine learning, we found it could improve probabilistic forecasts and reliability. We are not aware of this being done before in this context.”

For example, this model can say that under certain conditions at the tap with a particular amount of free residual chlorine in the water, there is a 90 per cent chance that the remaining chlorine in the stored water after 15 hours will be below the safety level for drinking.

“That’s the kind of probabilistic determination this modelling can give us,” says De Santi. “Like with weather forecasts, if there is a 90 per cent chance of rain, you should bring an umbrella. Instead of an umbrella, we can ask water operators to increase the chlorine concentration so there will be a greater percentage of people with safe drinking water.”

“Our Safe Water Optimization Tool takes this machine learning work and makes it available to aid workers in the field. The only difference for the water operators is we ask them to sample water in the container at the tap and in that same container at the home after several hours,” says Ali.

“This work Michael is doing is advancing the state of practice of machine learning models. Not only can this be used to ensure safe drinking water in refugee and IDP settlements, it can also be used in other applications.”

The paper, Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?, will be published in the journal PLOS Water.

De Santi will deliver a seminar on this paper as part of the Dahdaleh Institute Seminar Series on Sept. 7, from 1 to 2 p.m. EST. It is open to the public and registration is free.

Photos: Headshot of Michael De SantiSyed Imran Ali and Usman Khan

CAPTION

Lassonde School of Enginneering Adjunct Professor Syed Imran Ali, a Research Fellow at York University's Dahdaleh Institute for Global Health Research

CREDIT

York University

CAPTION

Associate Professor Usman Khan of York University's Lassonde School of Engineering

CREDIT

York University


York University is a modern, multi-campus, urban university located in Toronto, Ontario. Backed by a diverse group of students, faculty, staff, alumni and partners, we bring a uniquely global perspective to help solve societal challenges, drive positive change and prepare our students for success. York's fully bilingual Glendon Campus is home to Southern Ontario's Centre of Excellence for French Language and Bilingual Postsecondary Education. York’s campuses in Costa Rica and India offer students exceptional transnational learning opportunities and innovative programs. Together, we can make things right for our communities, our planet, and our future. 

Media Contact:

Sandra McLean, York University Media Relations, 416-272-6317, sandramc@yorku.ca

Researchers develop new technique to keep drinking water safe using machine learning

refugee camp
Credit: CC0 Public Domain

Waterborne illness is one of the leading causes of infectious disease outbreaks in refugee and internally displaced persons (IDP) settlements, but a team led by York University has developed a new technique to keep drinking water safe using machine learning, and it could be a game changer. The research is published in the journal PLOS Water.

As drinking water is not piped into homes in most settlements, residents instead collect it from public tap stands using storage containers.

"When water is stored in a container in a dwelling it is at high risk of being exposed to contaminants, so it's imperative there is enough free residual chlorine to kill any pathogens," says Lassonde School of Engineering Ph.D. student Michael De Santi, who is part of York's Dahdaleh Institute for Global Health Research, and who led the research.

Recontamination of previously  during its collection, transport and storage has been a major factor in outbreaks of cholera, hepatitis E, and shigellosis in refugee and IDP settlements in Kenya, Malawi, Sudan, South Sudan, and Uganda.

"A variety of factors can affect chlorine decay in stored water. You can have  at that collection point, but once you bring it home and store it, sometimes up to 24 hours, you can lose that residual chlorine, pathogens can thrive and illness can spread," says Lassonde Adjunct Professor Syed Imran Ali, a Research Fellow at York's Dahdaleh Institute for Global Health Research, who has firsthand experience working in a settlement in South Sudan.

Using machine learning, the research team—including Associate Professor Usman Khan, also of Lassonde—has developed a new way to predict the probability that enough chlorine will remain until the last glass is consumed. They used an artificial neural network (ANN) along with ensemble forecasting systems (EFS), something that is not typically done. EFS is a probabilistic model commonly used to predict the probability of precipitation in weather forecasts.

"ANN-EFS can generate forecasts at the time of consumption that take a variety of factors into consideration that affect the level of residual chlorine, unlike the typically used models. This new probabilistic modeling is replacing the currently used universal guideline for chlorine use, which has been shown to be ineffective," says Ali.

Factors such as local temperature, how the water is stored and handled from home to home, the type and quality of the water pipes,  and whether a child dipped their hand in the water container can all play a role in how safe the water is to drink.

"However, it's really important that these probabilistic models be trained on data at a specific settlement as each one is as unique as a snowflake," says De Santi. "Two people could collect the same water on the same day, both store it for six hours, and one could still have all the chlorine remaining in the water and the other could have almost none of it left. Another 10 people could have varying ranges of chlorine."

The researchers used routine water quality monitoring data from two refugee settlements in Bangladesh and Tanzania collected through the Safe Water Optimization Tool Project. In Bangladesh, the data was collected from 2,130 samples by Médecins Sans Frontières from Camp 1 of the Kutupalong-Balukhali Extension Site, Cox's Bazaar between June and December 2019 when it hosted 83,000 Rohingya refugees from neighboring Myanmar.

Determining how to teach the ANN-EFS to come up with realistic probability forecasts with the smallest possible error required out-of-the-box thinking.

"How that error is measured is key as it determines how the model behaves in the context of probabilistic modeling," says De Santi. "Using cost-sensitive learning, a tool that morphs the cost function towards a targeted behavior when using machine learning, we found it could improve probabilistic forecasts and reliability. We are not aware of this being done before in this context."

For example, this model can say that under certain conditions at the tap with a particular amount of free residual chlorine in the water, there is a 90 percent chance that the remaining chlorine in the stored water after 15 hours will be below the safety level for drinking.

"That's the kind of probabilistic determination this modeling can give us," says De Santi. "Like with , if there is a 90 percent chance of rain, you should bring an umbrella. Instead of an umbrella, we can ask water operators to increase the  concentration so there will be a greater percentage of people with safe drinking water."

"Our Safe Water Optimization Tool takes this machine learning work and makes it available to aid workers in the field. The only difference for the water operators is we ask them to sample water in the container at the tap and in that same container at the home after several hours," says Ali.

"This work Michael is doing is advancing the state of practice of machine learning models. Not only can this be used to ensure safe drinking water in refugee and IDP settlements, it can also be used in other applications."

How to deliver drinking water chlorine-free
More information: Michael De Santi et al, Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?, PLOS Water (2022). DOI: 10.1371/journal.pwat.000004
Provided by York University 

Walking and slithering aren't as different as you think

At least, if you have enough legs

Peer-Reviewed Publication

UNIVERSITY OF MICHIGAN


Images/Video 

Abrahamic texts treat slithering as a special indignity visited on the wicked serpent, but evolution may draw a more continuous line through the motion of swimming microbes, wriggling worms, skittering spiders and walking horses. 

A new study found that all of these kinds of motion are well represented by a single mathematical model.

"This didn't come out of nowhere—this is from our real robot data," said Dan Zhao, first author of the study in the Proceedings of the National Academy of Sciences and a recent Ph.D. graduate in mechanical engineering at the University of Michigan.

"Even when the robot looks like it's sliding, like its feet are slipping, its velocity is still proportional to how quickly it's moving its body."

Unlike the dynamic motion of gliding birds and sharks and galloping horses—where speed is driven, at least in part, by momentum—every bit of speed for ants, centipedes, snakes and swimming microbes is driven by changing the shape of the body. This is known as kinematic motion.

The expanded understanding of kinematic motion could change the way roboticists think about programming many-limbed robots, opening new possibilities for walking planetary rovers, for instance. 

Shai Revzen, professor of electrical and computer engineering at U-M and senior author of the study, explained that two- and four-legged robots are popular because more legs are extremely complex to model using current tools. 

"This never sat well with me because my work was on cockroach locomotion," Revzen said. "I can tell you many things about cockroaches. One of them is that they're not brilliant mathematicians."

And if cockroaches can walk without solving extremely complex equations, there has to be an easier way to program walking robots. The new finding offers a place to start.

Slipping feet complicates typical motion models for robots, and the assumption was that it might add an element of momentum to the motion of many-legged robots. But in the model reported by the U-M team, it is not so different from lizards that "swim" in sand or microbes swimming in water. 

Because microbes are small, the water seems a lot thicker and stickier—as if a human was trying to swim in honey. In all of these cases, the limbs move through the surrounding medium, or slide over a surface, rather than being connected at a stationary point.

The team discovered the connection by taking a known model that describes swimming microbes and then reconfiguring it to use with their multi-legged robots. The model reliably reflected their data, which came from multipods—modular robots that can operate with 6 to 12 legs—and a six-legged robot called BigAnt. 

The team also collaborated with Glenna Clifton, assistant professor of biology at the University of Portland in Oregon, who provided data on ants walking on a flat surface. While the robot legs slip a lot—up to 100% of the time for the multipods—ant feet have much firmer connections with the ground, slipping only 4.7% of the time. 

Even so, the ants and robots followed the same equations, with their speeds proportional to how quickly they moved their legs. It turned out that this kind of slipping didn't alter the kinematic nature of the motion.

As for what this suggests about how walking evolved, the team points to the worm believed to be the last common ancestor for all creatures that have two sides that are mirror images of each other. This worm, wriggling through water, already had the foundations of the motion that enabled the first animals to walk on land, they propose. Even humans begin learning to propel ourselves kinematically, crawling on hands and knees with the three points of contact on the ground at any time.

The skills of managing momentum—running with four legs or fewer, walking or running on two legs, flying or gliding—ladder on top of that older knowledge about how to move, the researchers suggest.

The research was supported by the Army Research Office (grants W911NF-17-1-0243 and W911NF-17-1-0306), the National Science Foundation (grants 1825918 and 2048235) and the D. Dan and Betty Kahn Michigan-Israel Partnership for Research and Education Autonomous Systems Mega-Project.

Zhao is now a senior controls engineer at XPENG Robotics.

Study: Walking is like slithering: a unifying, data-driven view of locomotion (DOI: 10.1073/pnas.202113222)

Cancers in adults under 50 on the rise globally

Researchers identify risks factors and trends behind an increasing incidence of early-onset cancers around the world

Peer-Reviewed Publication

BRIGHAM AND WOMEN'S HOSPITAL

Over recent decades, more and more adults under the age of 50 are developing cancer. A study conducted by researchers from Brigham and Women’s Hospital reveals that the incidence of early onset cancers (those diagnosed before age 50), including cancers of the breast, colon, esophagus, kidney, liver, and pancreas among others, has dramatically increased around the world, with this drastic rise beginning around 1990. In an effort to understand why many more younger individuals are being diagnosed with cancer, scientists conducted extensive analyses of available data in the literature and online, including information on early life exposures that might have contributed to this trend. Results are published in Nature Reviews Clinical Oncology.

“From our data, we observed something called the birth cohort effect. This effect shows that each successive group of people born at a later time (e.g., decade-later) have a higher risk of developing cancer later in life, likely due to risk factors they were exposed to at a young age,” explained Shuji Ogino, MD, PhD, a professor and physician-scientist in the Department of Pathology at the Brigham. “We found that this risk is increasing with each generation. For instance, people born in 1960 experienced higher cancer risk before they turn 50 than people born in 1950 and we predict that this risk level will continue to climb in successive generations.”

To conduct this study, Ogino and lead author Tomotaka Ugai, MD, PhD, also of the Department of Pathology, and their colleagues first analyzed global data describing the incidence of 14 different cancer types that showed increased incidence in adults before age 50 from 2000 to 2012. Then, the team searched for available studies that examined trends of possible risk factors including early life exposures in the general populations. Finally, the team examined the literature describing clinical and biological tumor characteristics of early-onset cancers compared to later-onset cancers diagnosed after age 50.

In an extensive review, the team found that the early life exposome, which encompasses one’s diet, lifestyle, weight, environmental exposures, and microbiome, has changed substantially in the last several decades. Thus, they hypothesized that factors like the westernized diet and lifestyle may be contributing to the early-onset cancer epidemic. The team acknowledged that this increased incidence of certain cancer types is, in part, due to early detection through cancer screening programs. They couldn’t precisely measure what proportion of this growing prevalence could solely be attributed to screening and early detection. However, they noted that increased incidence of many of the 14 cancer types is unlikely solely due to enhanced screening alone.

Possible risk factors for early-onset cancer included alcohol consumption, sleep deprivation, smoking, obesity, and eating highly processed foods. Surprisingly, researchers found that while adult sleep duration hasn’t drastically changed over the several decades, children are getting far less sleep today than they were decades ago. Risk factors such as highly-processedhighly processed foods, sugary beverages, obesity, type 2 diabetes, sedentary lifestyle, and alcohol consumption have all significantly increased since the 1950s, which researchers speculate has accompanied altered microbiome.

“Among the 14 cancer types on the rise that we studied, eight were related to the digestive system. The food we eat feeds the microorganisms in our gut,” said Ugai. “Diet directly affects microbiome composition and eventually these changes can influence disease risk and outcomes.”

One limitation of this study is that researchers did not have an adequate amount of data from low- and middle-income countries to identify trends in cancer incidence over the decades. Going forward, Ogino and Ugai hope to continue this research by collecting more data and collaborating with international research institutes to better monitor global trends. They also explained the importance of conducting longitudinal cohort studies with parental consent to include young children who may be followed up for several decades.

“Without such studies, it’s difficult to identify what someone having cancer now did decades ago or when one was a child,” explained Ugai, “Because of this challenge, we aim to run more longitudinal cohort studies in the future where we follow the same cohort of participants over the course of their lives, collecting health data, potentially from electronic health records, and biospecimen at set time points. This is not only more cost effective considering the many cancer types needed to be studied, but I believe it will yield us more accurate insights into cancer risk for generations to come.”


Funding: The work of S.O. is supported in part by the U.S. National Institutes of Health grants (R35 CA197735 and R01 CA248857) and the Cancer Research UK Cancer Grand Challenge Award [6340201/A27140]. The work of T.U. is supported by grants from the Prevent Cancer Foundation, Japan Society for the Promotion of Science, and Mishima Kaiun Memorial Foundation.

Paper cited: Ugai T et al. “Is early-onset cancer an emerging global epidemic? Current evidence and future implications.” Nature Reviews Clinical Oncology DOI: 10.1038/s41571-022-00672-8

Do masculine leadership titles undermine women’s leadership?

Peer-Reviewed Publication

UNIVERSITY OF HOUSTON

Allison Archer 

IMAGE: ALLISON ARCHER, ASSISTANT PROFESSOR IN THE UNIVERSITY OF HOUSTON DEPARTMENT OF POLITICAL SCIENCE AND JACK. J. VALENTI SCHOOL OF COMMUNICATION view more 

CREDIT: UNIVERSITY OF HOUSTON

Debates about using masculine or gender-neutral words to describe leadership positions, jobs and awards affect nearly all domains of society from business to politics and media. Recently, local politicians have considered changing titles such as "alderman" or "councilman" to their gender-neutral counterparts (e.g., "council member"). While some dismiss calls for gender-neutral titles as mere acts of political correctness, proponents argue that masculine language is not a neutral stand-in for "person" or "leader." Instead, masculine language may undermine women's leadership by reinforcing harmful stereotypes that positions of power are reserved for men.

Allison Archer, assistant professor in the Department of Political Science and Jack J. Valenti School of Communication at the University of Houston, sought to understand if masculine language has this effect. Working with Cindy Kam from Vanderbilt University, the researchers studied what happens when masculine versus gender-neutral language is used when describing leadership positions — specifically, the titles of "chairman" versus "chair." Little research had previously analyzed the role of gendered language in reinforcing gendered stereotypes, which might contribute to the persistent gender gap in leadership, according to the researchers.

Two experimental studies were conducted to understand the effect of masculine leadership titles. The work is published in The Leadership Quarterly. In the first study, participants read about a hypothetical "chair" or "chairman" of a paperclip company, a state legislative Ways and Means Committee, or a sociology department at a university. The researchers purposefully chose a gender-neutral name for the leader: Taylor or Pat Simmons. Respondents were told about Simmons' leadership position, age and time spent at their institution. They were also given some information about the company, committee or department. After reading this brief paragraph, individuals were asked to write, in five complete sentences, what a typical morning for Chair or Chairman Simmons might look like.

“The pronouns used in participants' sentences revealed their assumptions about Simmons' gender. Our results first reflect the stereotype that leadership positions belong to men: when reading about Chair Simmons, a little more than half of respondents assumed the leader was a man even though Simmons' gender was not specified,” said Archer.

When reading about Chairman Simmons, study participants became more likely to assume the leader was a man than in the chair condition. “The results suggest masculine language further accentuates stereotypes that men hold leadership positions,” she added.

In the real world, unlike in the first experiment, the gender of a leader who uses a masculine leadership title is typically known. The second study looked at what happens when people know the gender of a leader who goes by either "chairman" or "chair." Study participants read a brief paragraph discussing a new leader of a state legislature's Ways and Means Committee. The leader in the vignette was either referred to as a "chair" or "chairman" and was either named Joan or John Davenport. Here, the gender of the leader was perfectly clear from Davenport's first name and the pronouns used to refer to Davenport. After reading the paragraph, participants shared their opinions about the leader and then were asked to recall the name of the new leader. They could choose between John, Joan, Joseph, Josie and Don't Know.

“In yet another demonstration of the power of gendered language and unconscious stereotypes, we found masculine titles affect recollections of women and men leaders differently,” said Kam.

The title "chairman" increased the accuracy of recall for male leaders yet undermined the accuracy of recall for women leaders: a woman who goes by "chairman" is less likely to be correctly remembered compared to a man who does the same. A woman who goes by "chairman" is more likely to have her leadership wrongly ascribed to a man.

In both studies, the researchers tested for but did not uncover any evidence that the participants' own gender made a difference: women participants were no less susceptible to the effects of masculine titles than men participants. This could be because gender stereotypes are transmitted and learned at the societal level (through television, books, and other forms of socialization) and can be applied unconsciously and unintentionally.

“Overall, we found that masculine leadership titles really do matter—they affect assumptions about and recollections of leaders' gender. Titles like ‘chairman’ increase people's assumptions that men are in leadership positions and decrease recollections that women hold such positions of power,” said Archer. “This suggests gender-neutral and masculine leadership titles are not just synonyms for each other. Masculine leadership titles reinforce stereotypes that tie men to leadership and undermine the connection between women and leadership.”