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.

The paper also shows how Teotihuacan’s engineers re-routed two rivers to align with points of astronomical significance, identified hundreds of previously unknown architectural features, and documented over 200 archaeological features that have been destroyed by mining and urbanization since the 1960s.

“We don’t live in the past, but we live with the legacies of past actions. In a monumental city like Teotihuacan, the consequences of those actions are still fresh on the landscape,” said first author Nawa Sugiyama, a professor of anthropology at UC Riverside.

Teotihuacan, about 25 miles northeast of modern Mexico City, was the largest city in the Americas and one of the largest anywhere in the ancient world. It existed from about 100 BCE-550 CE — about 1,000-2,000 years ago — and covered 8 square miles. At its height, it consisted of numerous pyramids, plazas, and well-designed residential and commercial neighborhoods housing a population of around 100,000. Some of the pyramids and other structures are visible above ground today, but most of the city’s remains lie buried beneath modern fields, buildings, and other activity areas.

To map the below-ground parts of Teotihuacan, Nawa Sugiyama and co-authors Saburo Sugiyama at Arizona State University; Tanya Catignani at George Mason University; Adrian S. Z. Chase at Claremont University; and Juan C. Fernandez-Diaz at Houston University used lidar, a mapping technology that measures the amount of time it takes light from a laser to bounce back from an object. Archaeologists often use lidar to discover buried features covered by dense forests or open fields but rarely deploy the technology where archaeological remains lie beneath urban areas.

“Lidar is often perceived as revolutionary tool to find ancient features hidden in plain sight, but we found the lidar map to be extremely messy and hard to interpret. Many of the features we identified were modern with ancient roots. But then we realized there is a far more interesting story behind this trend,” said Nawa Sugiyama.

Because the sheer scale of construction at Teotihuacan suggested massive modification of the ancient landscape, Sugiyama’s group thought that lidar could help elucidate the relationship between the layout of Teotihuacan and modern activities that overlay it. The researchers confirmed the lidar findings with surveys by foot and comparisons to previous mapping efforts.

They found that the builders of Teotihuacan leveled the ground down to the bedrock and, in some cases, quarried the bedrock itself to use as construction and fill material. In just one portion of the city, called the Plaza of the Columns Complex, the authors calculated that roughly 372,056 square meters of artificial ground accumulated over the course of roughly 300 years of construction that had been quarried elsewhere in the Teotihuacan Valley. In three of the main pyramid complexes, the authors estimate that 2,423,411 square meters of rock, dirt, and adobe had been used.

This major reshaping of the landscape affects the arrangement of modern construction and activities. The authors found that 65% of urban areas contained property or modern features that aligned orthogonally within 3 degrees of 15 degrees east of astronomical north — the same alignment as Teotihuacan. Rock fences were built along areas that lidar and excavation revealed to have underground ancient walls that made modern-day plowing difficult.

Teotihuacan engineers also rerouted the Rio San Juan and the San Lorenzo River, which cross the city. Rio San Juan follows the Teotihuacan orientation for 3 km as it traverses the city center while the San Lorenzo River has a very distinct orientation, 8 degrees south of astronomical east for 4.9 km. Previous research has interpreted them as major canals of symbolic and calendric significance.

The lidar map also showed that other sections of canals and rivers, many still actively used today, were altered at various points along its course, frequently coinciding with the Teotihuacan directionalities. A total of 16.9 km of the hydrological systems visible on the modern terrain had origins in the Early Classic Teotihuacan landscape.

On the lidar map, the group identified 298 features and 5,795 human-made terraces that had not been previously recorded. However, they also identified over 200 known features that have been destroyed by mining since 2015.

“We can’t fight modern urbanization. The lidar map provides a snapshot of these ancient features that are being abolished at an alarming rate that would otherwise go unnoticed. It’s one of many ways we can preserve our heritage landscape,” said Nawa Sugiyama.

The authors plan to use their lidar map to create a three-dimensional geospatial database that allows them to visualize stratigraphic and surface data, natural and artificial features, and to document the true extent of humans as geomorphic agents over long periods of time in the Teotihuacan Valley.

In a new paper, “Consequential Life Cycle Assessment and Optimization of High-Density Polyethylene Plastic Waste Chemical Recycling,” published in the Sept. 13 issue of the journal ACS Sustainable Chemistry & Engineering, Fengqi You, the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering and doctoral student Xiang Zhao detail a framework incorporating several mathematical models and methodologies that factor everything from chemical recycling equipment, processes and energy sources, to environmental effects and the market for end products.

The framework is the first comprehensive analysis of its kind that quantifies the life-cycle environmental impacts of plastic waste chemical recycling, such as climate change and human toxicity.

Billions of tons of plastic have been produced since the 1950s, yet most of it — 91%, according to one often cited study — has not been recycled. While growing landfills and contaminated natural areas are among the concerns, the failure to reduce and reuse plastic is also seen by some as a missed economic opportunity.

That’s why the emerging industry of chemical recycling is capturing the attention of the waste industry and researchers like You, who is helping to identify optimal technologies for chemical recycling and providing a roadmap for the future of the industry.

Not only does chemical recycling create a ‘circular economy,’ in which a waste product can be turned back into a natural resource, but it opens the door for plastics such as high-density polyethylene — used to produce items such as rigid bottles, toys, underground pipes, and mail package envelopes — to be recycled more commonly.

You’s framework can quantify the environmental consequences of market dynamics that typical life-cycle sustainability assessments would overlook. It’s also the first to combine superstructure optimization — a computational technique for searching over a large combinatorial space of technology pathways for minimizing cost — with life-cycle analysis, market information and economic equilibrium.

The paper highlights the benefits of consequential life-cycle optimization when compared with more traditional analytical tools. In one scenario, to maximize economic outcomes while minimizing environmental impacts, life-cycle optimization produced a more than 14% decrease in greenhouse gas emissions and a more than 60% reduction of photochemical air pollution when compared with the attributional life-cycle assessment approach typically used in environmental assessment studies.

While the analysis gives industry experts and policy makers a general pathway for advancing chemical recycling and a circular economy for plastics, a myriad of choices and variables along the technological path must be considered. For instance, if the market demand for basic chemicals like ethylene and propylene is strong enough, the framework recommends a specific type of chemical separation technology, while if butane or isobutene are desired, another type technology is optimal.

“It’s a chemical process and there are so many possibilities,” You said. “If we want to invest in chemical recycling, what technology would we use? That really depends on the composition of our waste, the variants of polyethylene plastic, and it depends on current market prices for end products like fuels and hydrocarbons.”

Environmental consequences of chemical recycling depend on variables such as supplier process of chemical feedstocks and products. For instance, the framework found that producing butene onsite as opposed to having it supplied can reduce photochemical air pollution from recycling plants by nearly 20%, while onsite use of natural gas increases more than 37% of potentially harmful ionizing radiation.

“There’s always something we can twist and adjust in the technology and process, and that’s the tricky part,” said You, who added that as new chemical recycling techniques emerge and markets change, consequential life-cycle optimization will remain a powerful tool for guiding the emerging industry.

The research was supported in part by the National Science Foundation.

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Materials provided by Cornell University. Original written by Syl Kacapyr, courtesy of the Cornell Chronicle. Note: Content may be edited for style and length.

Coral reefs are some of the most diverse ecosystems on Earth, providing critical habitat for thousands of species that sustain economies and provide for the diets and livelihoods of millions of people. In the largest and most comprehensive study of its kind, researchers from France and The University of Texas at Austin concluded that coral reef food webs are more delicate than previously thought. Researchers made the conclusion after synthesizing the stomach contents of most fish species found on six coral reef systems around the world.

The team revealed that the vast majority of reef fish species are very picky about what they eat, rather than opportunistically feeding on anything they could find. Although reefs will probably withstand the loss of several prey species, the loss of a single larger fish species may jeopardize the ecosystem.

“It’s remarkable that we see similar patterns in coral reef systems all over the world. Extinctions, particularly of larger fish species, may have significant impacts for coral reef systems,” said Jordan Casey, an assistant professor of marine science at UT Austin’s Marine Science Institute (UTMSI) and an author on the paper.

Many conditions can cause the local loss of fish species, such as climate change, commercial fishing and pollution. Such fish declines may leave an increasing number of prey uneaten, creating imbalances in the food web.

“Reefs rely on the efficient utilization of all creatures,” said Simon Brandl, also an author and an assistant professor at UTMSI. “If a fish specializes on eating a certain type of snail and that fish disappears, the snail might not be eaten and live happily ever after. This creates a dead end for the food it could provide to other coral reef creatures. Good news for the snail. Bad news for everything else living there.”

The study examined the feeding habits of more than 600 species found on six different coral reefs near Okinawa, Hawaii, the West Indies, New Caledonia, Madagascar and the Marshall Islands. Despite regional differences, the food webs had a strikingly similar structure: 67% of species were specialized feeders with strong preferences for specific prey items, despite the tremendous diversity of available food on coral reefs.

“Coral reefs are important to humans for a number of reasons,” Casey said. “They are a critical source of food for many people. They are also popular tourist destinations and economic engines for a number of countries. However, coral reefs can only provide these key ecosystem services if food webs remain intact.”

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