Black-legged ticks can carry the bacterium that causes Lyme disease, an infection that can affect the nervous system, heart and joints. The new model will help local authorities prepare for the onset of Lyme disease in their counties before the first cases appear, researchers say. They report their findings in the Proceedings of the Royal Society B.

Black-legged ticks were first found in the Midwest in the 1960s in a few counties in Wisconsin and Minnesota. The first known case of what was later named Lyme disease occurred in the Midwest in 1969. Since then, black-legged ticks have expanded into numerous counties across those states and into Illinois, Indiana, Iowa, Michigan, Ohio, Nebraska, North Dakota and South Dakota. The first Lyme disease cases in those counties track closely with the first reports of ticks.

Understanding that local health departments report new Lyme disease cases in their counties to federal officials and that the National Land Cover Database includes information about landscape features of each county, the researchers chose to use county-level data in their model. Their goal was to identify factors associated with the spread of ticks and Lyme disease to new counties.

“We used historical information to build a model that forecasts the future spread of Lyme disease in the Midwest,” said Brian Allan, an entomology professor at the University of Illinois Urbana-Champaign who led the research with former doctoral student Allison Gardner, a professor of biology and ecology at the University of Maine.

“Our model was based on a few landscape factors that were highly predictive of the spread of ticks and Lyme disease and could be used as an early warning system to forecast areas likely to undergo invasion next,” Gardner said.

The researchers observed “a wavelike pattern of spread, where counties that get invaded with black-legged ticks tend to be adjacent to a county that has already been invaded,” Allan said. “And in some Midwestern states, we see that areas adjacent to major rivers are invaded in sequence. In Illinois, for example, the ticks first arrived along the Illinois River and then spread up and down the river quite quickly.”

The percentage of forest cover in a county also was important in predicting whether black-legged ticks would occur there. These three factors — proximity to a county where ticks had been detected, the presence of a river and the percentage of forest cover — together can predict the future occurrence of ticks in counties where none had been previously reported, the researchers found.

To test their model, Gardner used data gathered before 2012 to determine how ticks would spread into new areas in the Midwest from 2012 to 2016. The model predicted the appearance of ticks in new counties with greater than 90% accuracy.

“It was a little surprising to me that so few parameters could make these predictions with such high accuracy,” Gardner said.

Looking forward, the researchers identified 42 additional counties in the Midwest where black-legged ticks are likely to be detected by the end of 2021. The evidence suggests those ticks will carry the Lyme disease bacterium.

Understanding where ticks may be present before they have been reported may prompt public health officials and clinicians to include Lyme disease as a possible diagnosis for patients appearing with symptoms consistent with the infection, Allan said.

“If they don’t think the tick occurs in their area, doctors may be reluctant to diagnose a patient with Lyme disease,” he said.

Many animals use acoustic signals for communication. These signals have evolved to maximize the effectiveness of the transmission and reception of the sounds, because this helps finding a mate or avoiding predation. One of the fundamental characteristics of acoustic signals is the frequency of the sound. In forested habitats, acoustic signals become attenuated because of sound absorption and scattering from foliage, which is particularly problematic for high-frequency sounds. Hence, a theory from the 1970s predicts that animals living in habitats with dense vegetation emit lower-frequency sounds compared to those living in open areas.

A team of researchers led by Bart Kempenaers from the Max Planck Institute for Ornithology in Seewiesen and Tomáš Albrecht from the Charles University in Praha and the Czech Academy of Sciences analysed the variation in song frequency of more than 5,000 passerine bird species, encompassing 85% of all passerines and half of all avian taxa. PhD student Peter Mikula collected song recordings primarily from xeno-canto, a citizen science repository of bird vocalizations, and from the Macaulay Library of the Cornell Lab of Ornithology.

Relationship between song frequency and body size.

Contrary to the theory, the study reveals that the peak frequency of passerine song does not depend on habitat type. If anything, the data suggest that species living in densely vegetated habitats sing at lower frequencies, which is the opposite of what was predicted. As expected from basic physical principles, the researchers found a strong relationship between song frequency and body size and an effect of shared ancestry. “Both limit the range of sound frequencies an animal can produce,” says first author Peter Mikula. Heavier species sing at lower frequencies simply due to the larger vibratory structures of the vocal apparatus.

The study further reveals that species in which males are larger than females produce songs with lower frequencies than expected from their size. “This supports the hypothesis that the frequency of acoustic signals is affected by competition for access to mates,” says Bart Kempenaers. Song frequency may act as an indicator of an individual’s size and therefore of its dominance or fighting abilities. Thus, song frequency could influence reproductive success through competition with other males or even because it influences male attractiveness to females.

“Our results suggest that the global variation in passerine song frequency is mostly driven by natural and sexual selection causing evolutionary shifts in body size rather than by habitat-related selection on sound propagation,” summarizes Tomáš Albrecht.

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The study, published today in Neuron, sheds light on how the brain represents causally-controlled objects. The researchers found that when mice were controlling the cursor, brain activity in the higher visual cortex was goal-directed and contained information about the animal’s intention. This research could one day help to improve BMI design.

“Brain machine interfaces are devices that allow a person or animal to control a computer with their mind. In humans, that could be controlling a robotic arm to pick up a cup of water, or moving a cursor on a computer to type a message using the mind. In animals, we are using these devices as models for understanding how to make BMIs better,” said the paper’s first author, Dr Kelly Clancy, who completed the study at the Sainsbury Wellcome Centre, University College London, following previous work at Biozentrum, University of Basel.

“Right now, BMIs tend to be difficult for humans to use and it takes a long time to learn how to control a robotic arm for example. Once we understand the neural circuits supporting how intentional control is learned, which this work is starting to elucidate, we will hopefully be able to make it easier for people to use BMIs,” said co-author of the paper, Professor Tom Mrsic-Flogel, Director of the Sainsbury Wellcome Centre, University College London.

Traditionally it has been difficult to study how causally-controlled objects are represented in the brain. Imagine trying to determine how the brain represents a cursor it is controlling versus a cursor it is passively watching. There are motor signals in the first case but not in the second, so it is difficult to compare the two. With BMIs, the subject doesn’t physically move, so a cleaner comparison can be made.

In this study, the researchers used a technique called widefield brain imaging, which allowed them to look at the whole dorsal surface of the cortex while the animal was using the BMI. This technique enabled an unbiased screen of the cortex to locate the areas that were involved in learning to intentionally control the cursor.

Visual cortical areas in mice were found to be involved during the task. These areas included the parietal cortex, an area of the brain implicated in intention in humans.

“Researchers have been studying the parietal cortex in humans for a long time. However, we weren’t necessarily expecting this area to pop out in our unbiased screen of the mouse brain. There seems to be something special about parietal cortex as it sits between sensory and motor areas in the brain and may act as a way station between them,” added Dr Kelly Clancy.

By delving deeper into how this way station works, the researchers hope to understand more about how control is exerted by the brain. In this study, mice learned to map their brain activity to sensory feedback. This is analogous to how we learn to interact with the world — for example, we adjust how we use a computer mouse depending on its gain setting. Our brains build representations of how objects typically behave, and execute actions accordingly. By understanding more about how such rules are generated and updated in the brain, the researchers hope to be able to improve BMIs.

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