A wildlife research biologist with the Oregon Department of Fish and Wildlife (ODFW), Brent Wolf couldn't be happier with the career path he's found. “Coming to Oregon was a great choice, I love living out here,” he said. Wolf is also a graduate student in the College of Science’s two-year online Graduate Program in Data Analytics, a flexible program that allows him to continue working the job he loves while getting his degree. He was drawn to the program as a way to get a leg up in his current role, and so far it has paid off. “Going for the M.S. in data analytics has opened some doors for me that were previously closed,” he said.

Finding fulfillment with wildlife research

Wolf mapped out his career path early. “I grew up watching Steve Irwin and BBC nature shows, plus I enjoy being able to be outside and solve problems. Wildlife is just a good fit for all of that,” Wolf said. As a research biologist, he collects and analyzes data on a variety of Oregon species, including black-tailed deer, Columbian white-tailed deer and the American marten.

After graduating from the Florida Institute of Technology with a B.S. in ecology and marine biology, Wolf encountered some of the drawbacks of working with wildlife: low pay and frequent moves between seasonal jobs. “ODFW is the first spot where I actually have a permanent job with upward mobility,” he said.

To make a bigger impact at ODFW, Wolf decided to go back to school for data analytics. Oregon State’s award-winning Ecampus program “jumped out because it was an M.S. and offered some courses that I really wanted to take,” he said. Unlike most online learning opportunities, Ecampus courses are taught by the same faculty who teach in-person, giving students access to experienced, committed professionals in every field.

“I really enjoyed Professor Lan Xue, especially her survival statistics course,” said Wolf. “It’s really hard to get good at something like that without some instruction.”

“If you say some vaccine is x percent effective, what does that really mean?” asked Audrey Dickinson (M.S. Data Analytics ’21). The Oregon State alumna went back to school to learn how to answer just such important questions.

Dickinson was working as an engineer at HP in Corvallis when she realized that a better understanding of data would make her work even more impactful. The value of data for industry really stood out to her, she said. Oregon State’s two-year online Graduate Program in Data Analytics gave her the flexibility to earn her credentials while still working full-time at HP.

“There are a lot of great professors and the coursework was very valuable. Right away, I applied things that I was learning to my real-life job,” she said. Professor Charlotte Wickham in the statistics department was particularly engaging: “she has a data visualization course which has helped me immensely in communicating data analytics,” said Dickinson.

Ensuring ethical use of data

Since graduating from the Data Analytics program, Dickinson traded her engineering role for that of a Driver Analyst at HP, looking at key data points driving business metrics. In today’s unpredictable world, the value of professionals who can accurately interpret data and forecast results is greater than ever.

Data analytics is “very powerful,” Dickinson said, but understanding its limitations is also important. Equally vital are the ethical use of data and integrity in how we convey supposedly cut-and-dry scientific figures to a public not initiated into the scientific process.

“We talk a bit about that in our data science courses,” Dickinson said. “If you report something in a very scientific manner, how is that interpreted by the general public?” Although science is often thought of as objective, how we communicate it and present data has a big effect on how it is perceived.

“If someone comes to you and they say, ‘I can predict this with a certain amount of accuracy,’ what does that truly mean? And how much confidence can you really stake in their results? I think that’s powerful to know and understand,” she said.

Finding identity in STEM

Before delving into data analytics for her master’s, Dickinson earned her B.S. in chemical engineering at Oregon State. Although a minority as a woman in many of her classes, she “always had a lot of support” in her program, finding community in Oregon State’s engineering sorority Phi Sigma Rho and inspiration in the excellent mentoring and involvement of professors like Willie E. 'Skip' Rochefort. “He’s a very unique person,” she said, always participating in events like Discovery Days to get students more involved.

Dickinson knows the value of getting hands-on with math and science in early education, because her mom was a middle-school math teacher growing up. STEM was accessible and inviting to Dickinson from an early age, but she acknowledges that is not the case for students who may struggle with math and how to apply it practically.

“I think math and science can be this ladder-esque study, where if you feel like you struggle with it at a certain point in your life, and then you progress, you may never feel like you’re confident or you’re good at it.” Students can have bias in how they perceive their own skills, she said, due to past experiences.

Katherine McLaughlin is a statistical detective of sorts, employing sampling and data analysis methods to identify and understand hard-to-reach or hidden populations. An assistant professor in the Department of Statistics at Oregon State University, McLaughlin’s work explores a large number and wide variety of at-risk populations around the globe and involves collaborations with epidemiologists, statisticians, and public health officials.

Last summer the onset of the pandemic drove her statistical research towards a different direction. McLaughlin was appointed co-principal investigator with Oregon State University’s nationally recognized TRACE-COVID-19 project. She is TRACE’s lead researcher on providing statistical analyses and guidance. In this role, McLaughlin develops robust and innovative sampling designs and data analyses for community testing, as well as dissemination of public facing results for TRACE-OSU.

The highly successful TRACE-COVID-19 project has set a national example in containing COVID-19 risk. Oregon State researchers, including McLaughlin, recently received a $2 million grant from the David and Lucile Packard Foundation to create a national TRACE Center that will expand OSU’s COVID-19 public health project to other states.

“I was drawn to respondent-driven sampling because it merges my interest in designing specialized sampling methods tailored to the needs of a population with the possibility to help groups that are typically underserved and face elevated risk of HIV and other diseases.”

Team-based Rapid Assessment of Community-Level Coronavirus Epidemics, or TRACE-COVID-19, was launched by OSU in April, 2020 with door-to-door sampling in Corvallis, home to Oregon State’s main campus, and expanded to other cities around the state while also adding a wastewater testing component.

In late September, at the start of the academic year, TRACE also started conducting prevalence testing among OSU students, faculty and staff in Corvallis, at OSU-Cascades in Bend and at the Hatfield Marine Science Center in Newport, Oregon.

Putting statistics to use in the cause of public health

McLaughlin’s research on sampling methods made her the ideal scientist for TRACE. “My work on TRACE aligns well with my interest in sampling methodology, where the challenge is to design a data collection strategy tailored to the unique needs of the population that will best allow the research question to be addressed,” said McLaughlin.

While spearheading sampling and modeling methods at TRACE, McLaughlin has responded to unique challenges in different communities across Oregon, with several variables at play. For instance, she and her colleagues had to figure how to best incorporate wastewater data collected in advance of TRACE sampling to inform allocation of field teams to households within the community.

In her own research on vulnerable populations, McLaughlin has similarly estimated prevalence of HIV or the proportion of a population that are victims of human trafficking, and typically elude standard sampling and estimation procedures. More broadly, McLaughlin is interested in social science applications of statistics, for example, understanding how human behaviors contribute to things like missing data on surveys of at-risk populations.

This dimension of her research also exists in her work at TRACE, where she analyzes “how differential participation rates may be impacting our estimates.”

Hidden populations constitute socially stigmatized groups that are reluctant to disclose their identities, remaining largely invisible to researchers. Due to this difficulty in locating members of a small target population, researchers have encountered a dearth of data on the characteristics and demographics of hidden populations.

In her statistical research, McLaughlin has developed new data sampling designs and computational models to estimate characteristics of hidden populations around the world. These comprise female sex workers (FSW), men who have sex with men (MSM), victims of sexual violence, people who inject drugs (PWID) and migrants — some of the groups most vulnerable to infectious diseases, substance misuse and behavioral health issues.

Her models to estimate the size of hidden populations attempt to address shortcomings in existing methods of population inference. Her research contributions have included important modifications and extensions to respondent-driven sampling — a type of chain-referral sample used by the CDC, WHO, UNAIDS and other organizations that utilizes the social/peer network of relationships and friendships of a population to recruit and enroll individuals who may be at high risk for HIV/AIDS and related infections.

McLaughlin is currently working on new models that account for measurement error and examining their effectiveness using different parameters and in a wide variety of real data applications. According to McLaughlin, these models have the potential to correct numerous biases that arise from self-reported social network size due to missing data and intentional and unintentional misreporting.

She has worked with data from populations of FSW, MSM, migrants, and PWID in Morocco; women with sexual violence related pregnancies in the Democratic Republic of the Congo; FSW, MSM, and PWID in Armenia; MSM and PWID in Kosovo; MSM in Italy, Lithuania, Romania, and Slovakia; and PWID from the United States in collaboration with the Centers for Disease Control and Prevention (CDC).

Discovering statistics

McLaughlin took her first statistics course as an undergraduate student at UC Berkeley and, in her own words, “was hooked from the first class.” She loved learning how to gather information and solve complex real-world problems using quantitative methods.

A REU project (Research Experiences for Undergraduates) on election auditing introduced McLaughlin to research and inspired her to apply to graduate school. As a graduate student at UCLA, McLaughlin wanted to pursue a thesis topic that would have “real-world impact and would benefit people.” As she learned more about the statistical challenges of sampling hidden populations, she discovered there was room for much needed improvement of standard sampling and estimation techniques, and it became a fruitful area to work on during her Ph.D.

“I was drawn to respondent-driven sampling because it merges my interest in designing specialized sampling methods tailored to the needs of a population with the possibility to help groups that are typically underserved and face elevated risk of HIV and other diseases,” said McLaughlin.

Several undergraduate and graduate mentors helped McLaughlin connect with statistics and succeed in the field. She considers herself fortunate for having the support of amazing mentors at all stages of her education.

“In particular, my undergraduate research and thesis mentor, Phillip Stark, helped me develop research and scientific writing skills and encouraged me to apply to graduate school. My Ph.D. advisor, Mark Handcock, guided me through the complexities of academia and introduced me to a wide range of collaborators. Lisa Johnston, an epidemiologist I collaborate with frequently for respondent-driven sampling studies, has also been a great mentor for interdisciplinary work,” said McLaughlin.

She is currently involved with a wide range of research projects that revolve around making hidden population estimation more broadly applicable. These include using multiple years of data in a capture-recapture framework (a collaboration with Brian Kim from the University of Maryland) and extending the methodology for clustered hidden populations (work by Oregon State Ph.D. student Laura Gamble).

McLaughlin received the College of Science Research and Innovation Seed Program Award to support her research on estimating the number of people who inject drugs in metropolitan areas in the U.S. as a way to contain the HIV epidemic and slow the rate of transmission. In collaboration with CDC, she is developing innovative sampling analysis and statistical methodologies to obtain more precise estimates of the size of the hidden populations that are at high risk for contracting and transmitting HIV.

McLaughlin has also helped to broaden the use of respondent-driven sampling to other types of hidden populations, including trafficked populations.

From designing models to collect accurate information about social groups to estimating the spread of the COVID-19 pandemic in Oregon, McLaughlin’s research has had tangible impact and implications for public health. Her statistical models hold great promise for unbiased estimates of hidden populations and effective public health interventions.

American life has been irrevocably altered by the deadliest pandemic in a century. Scientists at Oregon State University acted swiftly to the greatest public health emergency of our time, leveraging the College of Science’s unique capabilities in biomedical research and the quantitative sciences to investigate and contain the coronavirus crisis.

In April, several OSU scientists hailing from different colleges and centers on campus leapt to action to tackle surging coronavirus infections in America. They were driven by widespread diagnostic test shortages in America as well as the lack of data on asymptomatic individuals. The result was a public health study started in Corvallis called Team-based Rapid Assessment of Community-Level Coronavirus Epidemics, or TRACE-COVID-19. It was among the first of its kind in the country to test the prevalence of the virus in an entire community through door-to-door sampling in representative sets of neighborhoods.

“The impetus for us was that equipment required to do the laboratory tests to detect the virus is present in a lot of research labs on campus. We started to problem solve and understand how a land grant university that has relationships with communities across the state could help during this crisis,” said Benjamin Dalziel, lead investigator of the TRACE project and an assistant professor of integrative biology and mathematics.

It goes without saying that a massive project like this would typically take shape over the course of several months. However, in a stunning feat, the TRACE team developed the project from scratch in a matter of weeks, and it is now garnering attention nationally as a model for other universities. The public health study is a joint effort by OSU’s Colleges of Science, Public Health and Human Sciences, Agricultural Sciences, Engineering and the Carlson College of Veterinary Medicine. It is co-directed by Jeff Bethel, associate professor in the College of Public Health and Human Sciences.

"TRACE’s primary goal is to mobilize the capacities of the land grant university to help the communities we serve.”

As a project director, Dalziel takes a leading role in data analysis and the design of the study to enable inferences important to understanding the infection rate and transmission patterns. He is at ease working with a wide range of collaborators, something he has done frequently in his academic career.

“I really enjoy working on a team where the expertise is diverse — partly because everybody has a chance to be humble and wear our learner’s hats,” said Dalziel. “We have this wonderful team of 10 co-investigators, and each of us is a non-expert in most of the areas we are working on. I think it brings out the best in TRACE as we learn from each other.”

The study, conducted in partnership with Benton County health officials, was initially funded by OSU and a grant from the David and Lucile Packard Foundation and has been aided by work from the OSU Foundation and the OSU Alumni Association. Funding from PacificSource Health plans has allowed for the project to expand to Bend, Newport and Hermiston in joint efforts with Deschutes, Lincoln and Umatilla counties, as well as increase sampling in Corvallis. Dalziel received $800K from PacificSource Health Plans and two grants from the David and Lucile Packard Foundation for $750K and $400K to aid the expansion of the TRACE-COVID-19 project.

The TRACE-COVID-19 team, comprising 10 scientists and more than 300 volunteers was selected for the prestigious 2020 Beaver Champion Award, which will be presented at a virtual celebration honoring University Day Award Recipients on Monday, September 14. This Oregon State president’s award recognizes an individual or individuals who continually demonstrate outstanding effort and achievement of excellence, extra effort beyond that requested, and performance of the highest quality.

Discovering ecology and mathematics

Dalziel grew up in Ontario, Canada. He spent a good chunk of summer each year in the wilderness of Northern Ontario, which fueled his passion for nature and the environment and led him to study ecology. Dalziel immersed himself in ecology and mathematical sciences as an undergraduate student at the University of Guelph in Ontario. He also obtained a master’s degree in biology at the University of Guelph before earning a Ph.D. in ecology and evolutionary biology at Cornell University.

The College of Science is proud to congratulate Charlotte Wickham, an assistant professor in the statistics department, for receiving the 2020 Ecampus Excellence in Online Teaching and Student Engagement Award. In a non-remote world, Charlotte would have received the award in a ceremony in the Alumni Center. In lieu an awards ceremony, please join us in congratulating her for making a difference for students and their education.

The fourth annual Oregon State University Ecampus Awards recognize outstanding faculty partners who go the extra mile to develop meaningful and innovative online learning environments for OSU’s distance students. Each winner is an exceptional partner of OSU’s Ecampus, a change-maker in the lives of distance students and a leader in the field of online education.

Charlotte Wickham, an assistant professor in the in the Department of Statistics in the College of Science, is one of three OSU faculty who have received this award. Wickham has developed multiple Ecampus courses built around students and their learning, leveraging open source materials and engaging texts from the OSU Library. Student nominations focused on several key areas, including Charlotte’s engagement in student discussions and her encouragement of experimentation, even if that approach requires more individualized time and feedback in facilitation.

One student noted “I never felt like I needed to rein in an assignment and only do just what was required when I really wanted to run with an idea. … That’s great teaching.”

Another student pointed out her thorough organization and the passion that goes into the development of each of her Ecampus course. Charlotte approaches her students with enthusiasm, and students leave her classes excited about statistics and prepared to take on today’s data-driven environment.

The 2020 Ecampus Excellence in Online Teaching and Student Engagement Award recognizes three faculty members who exemplify excellence in online teaching and student engagement. Ecampus faculty and instructors are nominated for this award by current online students. This year, 284 students nominated more than 140 faculty and instructors for this award.

The 5th Stan Conference will take place at Oregon State University on August 11-14, 2020. The four-day conference will include two days of tutorials followed by an exciting scientific program comprising talks, posters, open discussions and statistical modeling.

Registration for Stan 2020 is now open. Researchers, students and professionals are encouraged to register for the conference which includes all tutorials. The conference is also soliciting session proposals, contributed talks and posters. Deadlines and other information can be found here.

Stan is a freedom-respecting, open-source software that has had an extensive and far-reaching impact on Bayesian computations for a broad range of applied statistics and data science problems.

The conference typically draws 300 attendees from academia, industry and government agencies. The conference offers a great opportunity for students and other participants to learn about Bayesian computation. Previous Stan Conferences were held at Columbia University, New York, and Cambridge University, U.K., among other places.

Plenary speakers at Stan 2020 are Elizabeth Wolkovich from the University of British Columbia and Adrian Rafftery, a member of the National Academy of Sciences, from the University of Washington, Seattle.

Debashis Mondal, associate professor in the Department of Statistics at OSU, is a co-organizer of Stan 2020. The other organizers of Stan 2020 are Susana Marquez, The Rockefeller Foundation; Eric J. Ward, Northwest Fisheries Science Center (NOAA); Yi Zhang, Metrum Research Group; and Daniel Lee, Generable.

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Associate Professor of statistics Lisa Madsen and statisticians from the United States Geological Survey (USGS) have come together to develop methodology to estimate the total mortality of bats, birds and other small creatures on wind farms and solar facilities. The Endangered Species Act requires that wind farms pay particular attention to endangered or threatened species such as golden eagles, brown pelicans, whooping cranes, condors and Indiana bats, which are killed when they accidentally collide with turbine blades.

“We want to keep track of our natural resources. We don’t want to end up depleting them, because we can’t tell we are taking too much.”

Monitoring fatalities at wind energy facilities can help government agencies, such as the U.S. Fish & Wildlife Service and the Bureau of Land Management, make better decisions about species management. Developing statistically accurate fatality prediction and estimation tools and monitoring protocols can also help agencies ensure that renewable energy facilities developers design operations to minimize the impact to wildlife, thus reducing environmental damage. “Fundamentally, what people want to know is ‘how many?’. This idea of keeping count and our desire to know ‘how many’ are important for conservation,” Madsen said. “We want to keep track of our natural resources. We don’t want to end up depleting them, because we can’t tell we are taking too much.”

How many? The missing bats and birds

Madsen’s collaborators, Manuela Huso and Dan Dalthorp, from the USGS Forest and Rangeland Ecosystem Science Center in Corvallis are contributing new statistical models, estimators and software tools to improve bird and bat fatality estimates at solar and wind power facilities. Huso initiated the research 10 years ago to come up with improved models and methods of estimating the count of carcasses. Dalthorp joined her shortly thereafter; Madsen began collaborating with the USGS team in a more substantial capacity during her sabbatical two years ago.

Last year, the team along with collaborators from consulting firm, Western EcoSystems Technology, Inc, data science lab DAPPER Stats, the Swiss Ornithological Institute, and Duke University developed a software package called GenEst (a generalized estimator of mortality) — a suite of statistical models and software tools specifically designed for estimating the total number of creatures arriving in an area during a specific time period when their detection probability is unknown but estimable. The latter can also be used more generally to estimate the size of open populations with imperfect detection probabilities.

However, as Madsen’s research on fatalities at wind farms shows, estimating an accurate count is anything but a straightforward process. In the case of wildlife fatalities due to collision with wind turbines or solar panels, carcasses invariably go missing, carried away by scavengers or fall in areas inaccessible to searchers. Therefore, simple counts of carcasses found at wind farms do not reflect the actual number of fatalities.

Madsen and her colleagues have developed complex statistical tools that estimate the actual number of carcasses when they are undetectable for any reason by taking into account a host of predictor variables such as searcher efficiency, variations in plot sizes and location of inaccessible areas.

Madsen developed a model to use data from field trials to estimate searcher efficiency. This model is incorporated into the larger GenEst model framework. “My collaborators are working on other aspects of the problem: getting a count of missing carcasses by estimating the amount of time a carcass is likely to stay before getting carried away by a predator. It is a highly involved project, where we put all the pieces of the puzzle together along with the uncertainty associated with all of these aspects,” explained Madsen.

“I think that non-statisticians could benefit from learning some statistical principles such as the concept of uncertainty, collecting useful data, and applying appropriate data analysis tools in a given situation.”

The software package, created by the team, will be utilized by government agencies as well as Western EcoSystems Technology, Inc., which has already begun to implement the software to assist their clients. The project has also attracted attention from environmental and government agencies in Canada, South Africa, Portugal and Scotland among others. In addition, the USGS statisticians have conducted workshops demonstrating how to use the software to estimate animal mortality at wind and solar energy facilities. “The methodology is generally applicable to any situation where you want to count something where the detection is not perfect,” said Madsen.

The path to ecological statistics

After graduating from the University of Oregon with a master’s degree in mathematics, Madsen taught mathematics in a community college in New York. She wanted to get a doctorate in math education because she enjoyed teaching the subject. But she quickly discovered it wasn’t an ideal academic match for her. In the meantime, her husband suggested she try a statistics course. Madsen enjoyed the experience and switched to the Ph.D. program in statistics at Cornell University.

She also obtained a minor in natural resources at Cornell, which inspired her to apply statistics to ecological problems. In recent years, Madsen has also worked on numerical models of geological data to estimate the risk of environmental disasters such as leaking oil wells and other phenomena.

Madsen excels at teaching courses on statistical methods to non-statistics students at the graduate and undergraduate levels. She enjoys helping her students develop a statistical mindset as they learn about extending statistical methods to different disciplines.

“I think that non-statisticians could benefit from learning some statistical principles such as the concept of uncertainty, collecting useful data, and applying appropriate data analysis tools in a given situation,” Madsen remarked.

The mysteries of human health and disease are as numerous as they are elusive. They pose complex problems that demand complex solutions. As science becomes increasingly interdisciplinary, the edges blurring and blending faster than we can name those evolutions, the challenges of human health require that we examine them from multiple perspectives, from biohealth, bioinformatics and biochemistry to chemistry, mathematics and biology.

In the 21^st century, human health and disease require that we as scientists working in the life, physical and mathematical sciences collaborate. That we put our heads together, step outside the traditional academic boundaries to ignite new thinking and spur innovative solutions to address the most pressing problems in human health.

The proliferation of data is transforming the scientific landscape. Scientists are grappling with how to analyze and integrate data quickly across disciplines. With the mounting need for better, faster ways to harness vast amounts of information, mathematical and statistical researchers make for natural partners who are well trained to manage and interpret data to deepen understanding of the scale of health issues. This approach enables scientists to test more theories and manage more data to develop a greater, more sophisticated understanding of human health.

This fall the National Science Foundation’s Division of Mathematical Sciences and the National Institutes of Health’s National Library of Medicine launched a Joint Initiative on Generalizable Data Science Methods for Biomedical Research to support the development of innovative and transformative mathematical and statistical approaches to address data-driven biomedical and health challenges.

OSU researchers are harnessing the power of global collaborations to deepen understanding of and to address our most important concerns in human health.

The chemistry behind aging

Biophysicist Elisar Barbar and team discovered that the intrinsically disordered state of the protein ASCIZ, a key transcription factor in cells, plays a major role in regulating production of the protein LC8, a hub protein regulating over 100 other proteins critical to a wide range of life processes from viral infection to tumor suppression to cell death. Her work on intrinsically disorganized proteins, a hot frontier of research in biochemical and medical research today, has far-reaching implications due to their critical role in a vast array of cellular functions.

Colleagues Afua Nyarko and Viviana Perez are studying the chemistry behind the biological processes and the synthesis of biologically active molecules. Nyarko studies protein interactions and their role in the formation of tumors. She is one of a handful of scientists worldwide studying proteins from a structural biology perspective, where detailed information on the structure of specific amino acids can reveal how tumor suppressor proteins inhibit specific growth-promoting proteins.

Perez studies the biological processes of aging, specifically the protein aggregation in neurodegenerative diseases and protein misfolding. She discovered a new function for the compound rapamycin that, with its unusual properties, may help address neurologic damage.

Barbar and Nyarko’s work uses nuclear magnetic resonance to describe molecular structures of proteins. They also focus on protein informatics, from the analysis of experimental mass-spectrometry evidence for proteins to the integration and curation of large-scale data warehouses of protein sequence and functional annotation.

Genetics and bioinformatics

Our bioinformatics researchers are working on groundbreaking developments at the nexus of data science and human health. David Hendrix developed a neural network program that illuminates connections between mutant genetic material and disease. His team used deep learning to decipher which ribonucleic acids (RNA) have the potential to encode proteins, an important step toward better understanding RNA, one of life’s fundamental, essential molecules. Unlocking the mysteries of RNA means knowing its connections to human health and disease.

Hendrix compares it to a tool similar to calculus or linear algebra, but one used to learn biological patterns. Deep learning is helping his team manage vast amounts of data and learn new biological rules that distinguish the function of these types of molecules. He recently teamed up with the Barbar group to develop an algorithm that will predict new proteins that interact with LC8. This validates the importance of LC8 in many systems and opens up new interactions to study, underscoring the power of big data to guide new experiments.

David Koslicki recently discovered that the blood of patients with schizophrenia features genetic material from more types of microorganisms than the blood of people without the debilitating mental illness. His team performed whole-blood transcriptome analyses on 192 people, including healthy people and people with schizophrenia, bipolar disorder and Lou Gehrig’s disease. The findings showed that microbiota in the blood are similar to ones in the mouth and gut. There appears to be some permeability there into the bloodstream.

Koslicki and his collaborators received an NIH grant to build a biomedical translator, a software system that connects various distributed databases of biomedical knowledge and that can “reason” over these data sources to answer relevant biomedical questions. This is one example of how mathematical and computational sciences are syncing with biomedical research to accelerate translation for the scientific community.

Fighting disease

Microbiologist Bruce Geller scored a monumental win against antibiotic resistance. He crafted a compound known as a PPMO that genetically neutralizes a pathogen’s ability to thwart antibiotics. His team designed and tested PPMOs against Klebsiella pneumonia, an opportunistic pathogen that’s difficult to kill and resistant to many antibiotics. A platform technology, PPMOs can be quickly designed or modified to kill nearly any bacterium. They are not found in nature so bacteria have not developed resistance to them. PPMOs may be highly effective therapeutics.

Geller expects that the wave of the future will be molecular medicine, a broad field that draws on physical, chemical, biological, bioinformatics and medical techniques to describe molecular structures and mechanisms, identify molecular and genetic errors of disease and develop interventions. OSU scientists are combining these experimental and mathematical tools to develop anti-viral drugs.

Microbiologist Thomas Sharpton made a key advance toward understanding which of the trillions of gut microbes may play important roles in how humans and other mammals evolve. His global team created a new algorithm and software to taxonomize and clarify key microbial clades, or groups of microbes that appear frequently across mammalian species. A Western lifestyle tends to reduce microbial diversity so knowing which clades have been evolutionarily conserved opens up potential health interventions.

The more people a city has and the more organized its residents’ movement patterns, the longer its flu season is apt to last, according to population biologist Benjamin Dalziel.

The findings, published today in Science, are an important step toward predicting outbreak trends for a viral infection that each year in the United States sickens millions of people, sends hundreds of thousands to the hospital and kills tens of thousands.

Dalziel, the corresponding author of the new study, worked with an international collaboration to analyze weekly flu incidence data from 603 cities of varying size and “structure” – that is, patterns people follow in where they live and work.

The other factor the researchers looked at was the role a key weather metric – specific humidity – played in flu epidemics.

Flu is transmitted by virus-bearing moisture droplets that people exhale, cough out or sneeze out, creating a “cloud of risk” that emanates from an infected person and is breathed in by those around him or her.

“As specific humidity decreases, the virus remains viable in the air for longer, effectively expanding that cloud,” Dalziel said. “However, if an infected person is right beside you, it matters less what the specific humidity is.”

Which is where city size and structure come in – if there are lots of people, and transportation patterns frequently draw them together, it helps flu viruses find new hosts even when climatic conditions aren’t at their most favorable for transmission.

For the full story, click here.

The College of Science welcomes James Molyneux, who joined the Department of Statistics as an assistant professor in Fall 2018. Molyneux joined the department from UCLA, where he completed his dissertation on earthquake forecasting models based on statistical and computational methods.

In his new role, Molyneux teaches a wide variety of undergraduate and graduate courses, including online courses, to both statistics students and those from majors in engineering and biological sciences in the areas of data analytics, statistical methods and theory.

In addition to research on statistical seismology, Molyneux brings deep expertise in statistics pedagogy and education to OSU. As a doctoral student, he collaborated with his professors, high school educators, and other graduate students to create a project on statistics education funded by the National Science Foundation. The result is an innovative Introduction to Data Science (IDS) curriculum, which introduces high school students to data and statistics.

Part of a math-science partnership grant between UCLA and the Los Angeles Unified School District, IDS has been designated as a core math course and has been implemented in 14 southern California high school districts with further plans of scaling it to other school districts in the United States and even abroad.

A revolutionary approach to 21^st-century mathematical learning, the year-long course engages students with real data, introducing statistical, computational and graphical tools for reasoning about the world.

Molyneux is excited about exploring the possibility of introducing Oregon high school students to data, statistics and coding through IDS. He had an opportunity to introduce the IDS program to the Oregon Department of Education during a Math Pathways seminar in December.

“There has been a lot of interest in changing how mathematics is taught in high schools in Oregon,” observed Molyneux. “What if we didn’t make every student learn calculus, and introduced them to data science instead?”

In recent times, educators have begun to question the longstanding tradition of high school mathematics curriculum, whose mainstays have been the much-feared algebra II and calculus courses, arguing in favor of multiple math pathways towards graduation and college, which would include new courses in data science, statistics and programming.

“I think students find a lot of utility and value in being shown how to type instructions to a computer in a coding language, hit enter and have something happen based on what they are writing. It teaches them a lot of fundamental statistical ideas and how to be a good citizen by learning to evaluate data critically and detect misrepresented graphs and data,” said Molyneux.

Molyneux eagerly looks forward to utilizing his experiences and background in statistics pedagogy in the classroom. His teaching is also guided by his own experience of transformation.

An indifferent student of mathematics during his undergraduate years at California State University, Fullerton, Molyneux’s academic interests underwent a radical metamorphosis when he crossed paths with a brilliant teacher in a calculus II class.

“I had barely passed calculus I, but my teacher — Kathy Lewis — changed everything for me. That’s when I thought for the first time math is for me.” The realization prompted him to add a major in mathematics along with his major in economics, which eventually led to several classes in statistics and a Ph.D. in statistics.

“Having come from a place where I did not initially like math, I really want to expose people to this field I fell in love with and why they may like it too. You can do powerful things with statistics; it has real-life applications. Enabling students to find meaning in statistics has a lot of value for me,” said Molyneux.

He is excited to collaborate with statistics colleagues and others on campus on several new projects. Some of these include creating software for hydrologists and joining forces with OSU’s Center for Genomics Research and Biotechnology to fashion a data science program for students from rural communities in Oregon, which will impart skills in data analytics and statistical applications in natural resources and agriculture.

“I am delighted to be here. The department has a data analytics program which is growing fast and it’s very fulfilling to be a part of it. The statistics faculty are incredible teachers and researchers. It has definitely been a highlight to get to do statistics with so many talented people,” said Molyneux.

A native of La Habra, California, Molyneux enjoys hiking, cycling and discovering the restaurants in Salem, Ore., where he resides. He harbors a dream to reach the summits of all the mountains in Oregon.