Macroplastics — or plastic litter more than 5mm in size — travel much slower through river systems than previously believed, at an average speed of less than 0.01 km per hour, and can remain in place for significant periods of time.

If not removed, not only may this pollution eventually emerge in the ocean, but it is also likely to negatively impact marine wildlife and human uses of river systems. Polyethylene terephthalate (PET), a common material for single-use plastic bottles, requires UV light to break down over more than 450 years¹.

A proof of concept study, believed to be one of the first studies of its kind, was led by Robert Newbould, a PhD researcher within the School of Geography, Geology and the Environment at Leicester, alongside Dr Mark Powell and Professor Mick Whelan.

The study involved tracking 90 PET sample plastic bottle ‘tracers’ released into a tributary of the River Soar near Wistow, Leicester. The average travel distance for each tracer was 231m in 24 hours, with the furthest distance recorded at just under 1.1km.

Robert said: “We were surprised at how easily the plastic bottles were trapped and their relatively low travel distances.

“Our work supports other research that existing estimates of riverine plastic flux to the ocean may have been overestimated, but more research is needed to confirm this.”

Researchers recovered 96% of plastic tracers from the river system at the conclusion of the experiment, and also retrieved other litter to ensure a net reduction in macroplastic pollution.

The study was completed while Robert completed an MPhil in Physical Geography. His PhD is supported by the Central England NERC Training Alliance (CENTA), a consortium of six research-intensive Universities and research institutes working within the remit of the Natural Environment Research Council (NERC).

The University of Leicester is also home to the Centre for Landscape and Climate Research, which carries out discovery-led and applied research to address pressing global challenges, often in collaboration with industrial partners.

Research themes include Earth Observation methods for better ecosystem monitoring, understanding human interactions with the environment, plus water and carbon cycle connections.

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Materials provided by University of Leicester. Note: Content may be edited for style and length.

Researchers at Rush University Medical Center have found that older adults may benefit from a specific diet called the MIND diet even when they develop these protein deposits, known as amyloid plaques and tangles. Plaques and tangles are a pathology found in the brain that build up in between nerve cells and typically interfere with thinking and problem-solving skills.

Developed by the late Martha Clare Morris, ScD, who was a Rush nutritional epidemiologist, and her colleagues, the MIND diet is a hybrid of the Mediterranean and DASH (Dietary Approaches to Stop Hypertension) diets. Previous research studies have found that the MIND diet may reduce a person’s risk of developing Alzheimer’s disease dementia.

Now a study has shown that participants in the study who followed the MIND diet moderately later in life did not have cognition problems, according to a paper published on Sept. 14 in the Journal of Alzheimer’s Disease.

“Some people have enough plaques and tangles in their brains to have a postmortem diagnosis of Alzheimer’s disease, but they do not develop clinical dementia in their lifetime,” said Klodian Dhana, MD, PhD, lead author of the paper and an assistant professor in the Division of Geriatrics and Palliative Medicine in the Department of Internal Medicine at Rush Medical College .

“Some have the ability to maintain cognitive function despite the accumulation of these pathologies in the brain, and our study suggests that the MIND diet is associated with better cognitive functions independently of brain pathologies related to Alzheimer’s disease.

In this study, the researchers examined the associations of diet — from the start of the study until death — brain pathologies and cognitive functioning in older adults who participated in the Rush Alzheimer’s Disease Center’s ongoing Memory and Aging Project, which began in 1997 and includes people living in greater Chicago. The participants were mostly white without known dementia, and all of them agreed to undergo annual clinical evaluations while alive and brain autopsy after their death.

The researchers followed 569 participants, who were asked to complete annual evaluations and cognitive tests to see if they had developed memory and thinking problems. Beginning in 2004, participants were given an annual food frequency questionnaire about how often they ate 144 food items in previous year.

Using the questionnaire answers, the researchers gave each participant a MIND diet score based on how often the participants ate specific foods. The MIND diet has 15 dietary components, including 10 “brain-healthy food groups” and five unhealthy groups — red meat, butter and stick margarine, cheese, pastries and sweets, and fried or fast food.

To adhere to and benefit from the MIND diet, a person would need to eat at least three servings of whole grains, a green leafy vegetable and one other vegetable every day — along with a glass of wine — snack most days on nuts, have beans every other day or so, eat poultry and berries at least twice a week and fish at least once a week. A person also must limit intake of the designated unhealthy foods, limiting butter to less than 1 1/2 teaspoons a day and eating less than a serving a week of sweets and pastries, whole fat cheese, and fried or fast food.

Based on the frequency of intake reported for the healthy and unhealthy food groups, the researchers calculated the MIND diet score for each participant across the study period. An average of the MIND diet score from the start of the study until the participant’s death was used in the analysis to limit measurement error. Seven sensitivity measures were calculated to confirm accuracy of the findings.

“We found that a higher MIND diet score was associated with better memory and thinking skills independently of Alzheimer’s disease pathology and other common age-related brain pathologies. The diet seemed to have a protective capacity and may contribute to cognitive resilience in the elderly.” Dhana said.

“Diet changes can impact cognitive functioning and risk of dementia, for better or worse,” he continued. “There are fairly simple diet and lifestyle changes a person could make that may help to slow cognitive decline with aging, and contribute to brain health.”

NMR spectrometers allow scientists to characterize the structure of molecules, such as proteins, but it can take highly skilled human experts a significant amount of time to analyze that data. This new machine learning method can analyze the data much more quickly and just as accurately.

In a study recently published in Nature Communications, the scientists described their process, which essentially teaches computers to untangle complex data about atomic-scale properties of proteins, parsing them into individual, readable images.

“To be able to use these data, we need to separate them into features from different parts of the molecule and quantify their specific properties,” said Rafael Brüschweiler, senior author of the study, Ohio Research Scholar and a professor of chemistry and biochemistry at The Ohio State University. “And before this, it was very difficult to use computers to identify these individual features when they overlapped.”

The process, developed by Dawei Li, lead author of the study and a research scientist at Ohio State’s Campus Chemical Instrument Center, teaches computers to scan images from NMR spectrometers. Those images, known as spectra, appear as hundreds and thousands of peaks and valleys, which, for example, can show changes to proteins or complex metabolite mixtures in a biological sample, such as blood or urine, at the atomic level. The NMR data give important information about a protein’s function and important clues about what is happening in a person’s body.

But deconstructing the spectra into readable peaks can be difficult because often, the peaks overlap. The effect is almost like a mountain range, where closer, larger peaks obscure smaller ones that may also carry important information.

“Think of the QR code readers on your phone: NMR spectra are like a QR code of a molecule — every protein has its own specific ‘QR code,'” Brüschweiler said. “However, the individual pixels of these ‘QR codes’ can overlap with each other to a significant degree. Your phone would not be able to decipher them. And that is the problem we have had with NMR spectroscopy and that we were able to solve by teaching a computer to accurately read these spectra.”

The process involves creating an artificial deep neural network, a multi-layered network of nodes that the computer uses to separate and analyze data.

The researchers created that network, then taught it to analyze NMR spectra by feeding spectra that had already been analyzed by a person into the computer and telling the computer the previously known correct result. The process of teaching a computer to analyze spectra is almost like teaching a child to read — the researchers started with very simple spectra. Once the computer understood that, the researchers moved on to more complex sets. Eventually, they fed highly complex spectra of different proteins and from a mouse urine sample into the computer.

The computer, using the deep neural network that had been taught to analyze spectra, was able to parse out the peaks in the highly complex sample with the same accuracy as a human expert, the researchers found. And more, the computer did it faster and highly reproducibly.

Using machine learning as a tool to analyze NMR spectra is just one key step in the lengthy scientific process of NMR data interpretation, Brüschweiler said. But this research enhances the capabilities of NMR spectroscopists, including the users of Ohio State’s new National Gateway Ultrahigh Field NMR Center, a $17.5 million center funded by the National Science Foundation. The center is expected be commissioned in 2022 and will have the first 1.2 gigahertz NMR spectrometer in North America.

This work was supported by the National Science Foundation and the National Institutes of Health.

Other research scientists involved in this study include Alexandar Hansen, Chunhua Yuan and Lei Bruschweiler-Li, all of Ohio State’s Campus Chemical Instrument Center.

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Materials provided by Ohio State University. Original written by Laura Arenschield. Note: Content may be edited for style and length.