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U of I Study: Some Vaccine Doubters Swayed by Outbreaks

This news article was written by Kathy Foss, Marketing and Communications Manager for the College of Letters, Arts and Social Sciences. Drs. Florian Justwan and Bert Baumgaertner are active CMCI faculty participants and part of the Social-Epi working group.

MOSCOW, Idaho — Aug. 28, 2019 — People skeptical of the medical establishment who live close to a measles outbreak have a greater chance of changing their mind, according to a University of Idaho study.

The study, led by Assistant Professor of Political Science Florian Justwan, found people who are skeptical of the Centers for Disease Control and Prevention (CDC) — as well as similar institutions — and live farther away from a disease outbreak harbor less favorable vaccination views than those who are skeptical but live in close proximity to an outbreak. People who have high levels of trust are not affected by disease proximity.

Bert Baumgaertner, an associate professor of philosophy at U of I, Juliet Carlisle, an associate professor of political science at the University of Utah, and former student researchers from U of I’s College of Letters, Arts and Social Sciences, contributed to the study, published today, Aug. 28, in the journal PLOS One.

“The implication of our study is that some people base their vaccine decision-making to a considerable degree on whether or not a given disease occurs in close vicinity to their community,” Justwan said. “If someone has high confidence in institutions such as the CDC, this person is likely to vaccinate regardless of whether he or she lives close to a recent measles outbreak. Fostering public trust in institutions such as the CDC is an important objective from a public health perspective.”

The researchers found an individual’s proximity to a measles outbreak independently had no effect on measles vaccination attitudes. Research suggests, however, that people who are skeptical of the CDC and similar institutions may consider whether or not a given disease occurs nearby when making decisions about vaccination. About 61 percent of low-trust individuals had a more favorable opinion of vaccines if they lived within 100 miles of an outbreak, That increase in favorability dropped to about 39 percent if a person lived within 500 miles of an outbreak and to 17 percent within 1,000 miles of an outbreak.

Researchers surveyed 1,006 online respondents across the U.S. about their political beliefs, vaccination attitudes and demographics as part of the study. The survey was carried out in January 2017, a year after two highly publicized outbreaks of measles in the U.S. The pool was generated by a market research firm to be a nationally representative sample of the U.S. voting age population and the final sample matched known population factors for gender, age, income race and census region.

A growing vaccine hesitancy in the U.S. and globally can manifest itself in increased non-medical exemption rates, decreased vaccination rates and increased outbreaks of vaccine-preventable diseases, according to the study. The formation of attitudes about vaccination is complex and linked to many factors including media and peer group influence, distrust of science, information access and socio-economic barriers.

The research team, housed in U of I’s Center for Modeling Complex Interactions, is continuing its study into other factors that may influence a person’s decision to vaccinate.

Which comes first, the chicken or the egg?

Which comes first, the chicken or the egg?

World-wide experiments have been conducted to understand the distinct relationships among various genes. However, it remains a challenge to identify the genomic causes and effects directly from the data, especially within a network. It’s the classic chicken and egg question: Which comes first, the chicken or the egg? In other words, how do you know which genes regulate which other genes?

Correlation between the expression of two genes is symmetrical. Therefore, scientists cannot infer which of the two genes is the regulator and which is the target. Similar levels of correlation can arise from different causal mechanisms. For example, between two genes with correlated expression levels, it is plausible that one gene regulates the other gene; it is also plausible that they do not regulate each other directly, but are regulated by a common genetic variant.

Audrey Fu, Assistant Professor in the Department of Statistical Science, and Postdoctoral Researcher Md. Bahadur Badsha, recently published a paper introducing a novel machine learning algorithm. “Our new method, namely the MRPC algorithm, can tease apart which correlation may suggest causality and which correlation is just indirect association through many other genes,” said Fu.

Figure 2. The MRPC algorithm. The MRPC algorithm consists of two steps. In Step I, it starts with a fully connected graph shown in (1), and learns a graph skeleton shown in (2), whose edges are present in the final graph but are undirected. In Step II, it orients the edges in the skeleton in the following order: edges involving at least one genetic variant (3), edges in a v-structure (if v-structures exist) (4), and remaining edges, for which MRPC iteratively forms a triplet and checks which of the five basic models under the PMR is consistent with the triplet (5). If none of the basic models matches the triplet, the edge is left unoriented (shown as bidirected). (A) An example illustrating the algorithm. (B)The pseudocode of the algorithm. 

Reproducibility Does Not Equal Truth

Reproducibility Does Not Equal Truth

The CMCI Reproducibility in Sciences working group, or SciRep for short, has been meeting since the fall of 2015. Today, their most recent publication, “Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity,” was published in PLOS ONE. Congratulations!

Fig 2. A transition of our process of scientific discovery for an epistemically diverse population with replicator. A scientist (Bo) is chosen uniformly randomly from the population (1). Given the global model, the set of proposal models and their probabilities (given in percentage points inside models) are determined. In this population with no replicator, Bo proposes only models formed by adding an interaction (2). The proposed model selected (3) and the data generated from the true model (4) are used with the model comparison statistic (SC or AIC) to update the global model (5).

The American Council on Science and Health also picked up the story with their article, “Reconsidering The ‘Replication Crisis’ In Science.”

The following article was written by Leigh Cooper, U of I Science and Content Writer.

U of I Study Finds Scientific Reproducibility Does Not Equate to Scientific Truth

MOSCOW, Idaho — May 15, 2019 — Reproducible scientific results are not always true and true scientific results are not always reproducible, according to a mathematical model produced by University of Idaho researchers. Their study, which simulates the search for that scientific truth, was published today, May 15, in the journal PLOS ONE.

Independent confirmation of scientific results — known as reproducibility — lends credibility to a researcher’s conclusion. But researchers have found the results of many well-known science experiments cannot be reproduced, an issue referred to as a “replication crisis.”

“Over the last decade, people have focused on trying to find remedies for the ‘replication crisis,’” said Berna Devezer, lead author of the study and U of I associate professor of marketing in the College of Business and Economics . “But proposals for remedies are being accepted and implemented too fast without solid justifications to support them. We need a better theoretical understanding of how science operates before we can provide reliable remedies for the right problems. Our model is a framework for studying science.”

Devezer and her colleagues investigated the relationship between reproducibility and the discovery of scientific truths by building a mathematical model that represents a scientific community working toward finding a scientific truth. In each simulation, the scientists are asked to identify the shape of a specific polygon.

The modeled scientific community included multiple scientist types, each with a different research strategy, such as performing highly innovative experiments or simple replication experiments. Devezer and her colleagues studied whether factors like the makeup of the community, the complexity of the polygon and the rate of reproducibility influenced how fast the community settled on the true polygon shape as the scientific consensus and the persistence of the true polygon shape as the scientific consensus.

Within the model, the rate of reproducibility did not always correlate with the probability of identifying the truth, how fast the community identified the truth and whether the community stuck with the truth once they identified it. These findings indicate reproducible results are not synonymous with finding the truth, Devezer said.

Compared to other research strategies, highly innovative research tactics resulted in a quicker discovery of the truth. According to the study, a diversity of research strategies protected against ineffective research approaches and optimized desirable aspects of the scientific process.

Variables including the makeup of the community and complexity of the true polygon influenced the speed scientists discovered the truth and persistence of that truth, suggesting the validity of scientific results should not be automatically blamed on questionable research practices or problematic incentives, Devezer said. Both have been pointed to as drivers of the “replication crisis.”

“We found that, within the model, some research strategies that lead to reproducible results could actually slow down the scientific process, meaning reproducibility may not always be the best — or at least the only — indicator of good science,” said Erkan Buzbas , U of I assistant professor in the College of Science , Department of Statistical Science and a co-author on the paper. “Insisting on reproducibility as the only criterion might have undesirable consequences for scientific progress.”

Ebola Research Publication Featured

Congratulations to Jagdish Patel and Marty Ytreberg! Their article, “Expanding the watch list for potential Ebola virus antibody escape mutations” was recently selected to be featured on the

PLOS Ebola Channel.

The PLOS Ebola Channel was launched in response to the ongoing Ebola outbreak in the Democratic Republic of Congo. They work with authors and editorial boards to provide rapid review and facilitate the responsible dissemination of preprints. These responses are needed during serious and rapidly developing threats to public health. The PLOS Ebola Channel will make it easy for researchers to keep up with developments and important research related to the outbreak.

University of Idaho Research Hits Science Magazine

University of Idaho Research Hits Science Magazine
The tube-eye (Stylephorus chordatus) is one of several species of deep-sea fish to have expanded its repertoire of rhodopsin genes to maximize visual sensitivity and, possibly, color detection. In the deep sea, multicolored bioluminescence replaces surface illumination as the main source of light. Many fishes that reside at great depths have evolved a visual system for recognizing bioluminescent signals and perceiving color in the dark. See pages 520 and 588.
Photo: Danté Fenolio/DEEPEND/Gulf of Mexico Research Initiative

The cover of Science Magazine currently features an important discovery made by an international research team: deep-sea fish can see more than just one color.

When a Switzerland- and Australia-based research team recently needed to validate their findings regarding what colors of light a deep-sea fish species could see at up to 1500 meters below the surface, they turned to scientists at the University of Idaho. Biological Sciences Research Assistant Professor and CMCI Modeling Fellow Jagdish Patel and Biological Sciences Professors Deborah Stenkamp and Celeste Brown used Patel’s newly developed computational molecular simulation based approach to generate a mathematical model to predict color sensitivity.

Deep-sea fish rhodopisn within the lipid bilayer.
Image created by Jagdish Patel.

The team was able to successfully predict the maximum wavelength of light, or color, absorbed by light sensitive proteins, called opsins, in the eye. In the case of deep-sea fish, these opsins are present in rod photoreceptors. In other species rods are not used for color vision. Thus, the research team was able to conclude that certain species of deep-sea fish may have highly sensitive, rod-based color vision. The range of sensitivity matches the dim light conditions present in the deep sea due to bioluminescence.

Blue and green dancing proteins

Movies showing molecular simulations of
blue- and green-light sensitive opsin models created by Jagdish.

“This discovery is both exciting and unexpected,” said College of Science Dean Ginger E. Carney. “We are pleased that groundbreaking discoveries by U of I researchers continue to be featured in premier research journals such as Science.”

An excerpt from the Science article, “In the deep, dark, ocean fish have evolved superpowered vision.”
The illustration from “Some Deep-Sea Fish Can See Color in Near Total Darkness” published by Gizmodo.