DNA is a long molecule that can be folded and twisted in various ways. It has a backbone and bases that stick out from the backbone and pair up with counterparts in other DNA strands. When a series of these matching pairs comes together, they form a twisted, ladder-like double strand — the familiar DNA double helix. The flexibility of DNA, which makes it possible to produce bends, loops, and a wide variety of other shapes, has inspired researchers to build DNA switches. These switches change shape after receiving an input, and can then affect their surroundings.

Hao Pei from Shanghai Key Laboratory of Green Chemistry and Chemical Processes at the East China Normal University in Shanghai, China, and colleagues have now developed a configurable, multi-mode logic switching network that reacts differently with its surroundings depending on pH and DNA input. All the components of the switching circuit were produced from DNA.

The team developed a series of four DNA switches, each with slightly different lengths and combinations of bases. These variations meant they reacted differently with a single DNA strand depending on the pH of their surroundings. For example, at a slightly alkaline pH of 8, two of the switches formed triple-stranded DNA (triplexes), while the others remained loosely stretched out. These reactions and folds led to secondary reactions, which were utilized by the researchers as logic functions in the switching circuit. The result was, for example, a fluorescent signal that could be read as an output.

To demonstrate the use of the switching circuit in a real mechanical system, the team incorporated the DNA switches into polyacrylamide gels. The DNA acted as a crosslinker, joining the polymer molecules in the gel together. The shorter the crosslinker, or the more folded the DNA, the denser the gel became. Once a piece of DNA with matching bases was added as an input, a logic circuit was set in place, causing the DNA switches to unfold, form triplexes, or relax. The reaction circuit was also dependent on the pH. As a result, certain combinations of DNA input and pH range caused the DNA crosslinker to grow longer and the gel to swell up, in some cases nearly doubling in size.

As DNA switches have almost infinite possibilities for combinations of twists and folds, the researchers consider their switching circuits to be a vital step toward soft matter robotics, where controllable, miniaturized logic functional networks are important.

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Most strawberries commercially grown in the U.S. come from California and Florida. With the expansion of local farmer’s markets and people’s excitement about fresh berries, growers in other states are trying to increase production. In Texas, for example, current commercial operations grow a few of the “day-neutral” and “spring-bearing” varieties that have a potentially high fruit output. But there are hundreds of options, including some that are more heat tolerant, and many factors to consider when choosing cultivars to grow that will produce strawberries appealing to consumers. So, Xiaofen Du and colleagues wanted to determine which ones grow well in Texas’ semi-arid, hot environment and have the most desirable berry characteristics — information that could help growers in similar climates.

The researchers grew 10 common strawberry cultivars in northwest Texas, comparing seven spring-bearing and three day-neutral varieties. First, they monitored plant growth and yields and found eight of the cultivars had plant survival rates of more than 96% before the first harvest. Overall, the day-neutral varieties had the lowest total berry weight per plant. Then, the team measured ripe berries’ characteristics, including color, sugar content, acidity and aroma compounds. Their results showed red intensity was not linked to berry sweetness; in fact, the redder varieties had more citric acid, which made them taste more sour than sweet. Taste tests on berry purees showed that desirable flavors were related to the varieties’ sugar content and 20 aroma compounds. Perhaps surprisingly, tasters ranked the two varieties that grew the fewest and smallest fruits as having the most intense flavors. The researchers concluded that five cultivars — Albion, Sweet Charlie, Camarosa, Camino Real and Chandler — can grow well in Texas’ climate and have the best flavor and aroma.

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The ability of vitamin D to protect against severe COVID-19 illness is of great interest to public health experts, but has limited supporting evidence. To assess the relationship between vitamin D levels and COVID-19 susceptibility and severity, researchers conducted a Mendelian randomization study using genetic variants strongly associated with increased vitamin D levels. The authors analyzed genetic variants of 4,134 individuals with COVID-19, and 1,284,876 without COVID-19, from 11 countries to determine whether genetic predisposition for higher vitamin D levels were associated with less-severe disease outcomes in people with COVID-19.

The results showed no evidence for an association between genetically predicted vitamin D levels and COVID-19 susceptibility, hospitalization, or severe disease, suggesting that raising circulating vitamin D levels through supplementation may not improve COVID-19 outcomes in the general population. However, the study had several important limitations, including that the research did not include individuals with vitamin D deficiency, and it remains possible that truly deficient patients may benefit from supplementation for COVID-19 related protection and outcomes. Additionally, the genetic variants were obtained only from individuals of European ancestry, so future studies will be needed to determine the relationship with COVID-19 outcomes in other populations.

According to the authors, “Vitamin D supplementation as a public health measure to improve outcomes is not supported by this study. Most importantly, our results suggest that investment in other therapeutic or preventative avenues should be prioritized for COVID-19 randomized clinical trials.”

Dr. Butler-Laporte notes, “Most vitamin D studies are very difficult to interpret since they cannot adjust for the known risk factors for severe Covid-19 (e.g. older age, institutionalization, having chronic diseases) which are also predictors of low vitamin D. Therefore, the best way to answer the question of the effect of vitamin D would be through randomized trials, but these are complex and resource intensive, and take a long time during a pandemic. Mendelian randomization can provide more clear insights into the role of risk factors like vitamin D because they can decrease potential bias from associated risk factors like institutionalization and chronic disease. In the past Mendelian randomization has consistently predicted results of large, expensive, and timely vitamin D trials. Here, this method does not show clear evidence that vitamin D supplementation would have a large effect on Covid-19 outcomes.”

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The study titled, “An atlas of alternative polyadenylation quantitative trait loci (3′aQTLs) contributing to complex trait and disease heritability,” was recently published in Nature Genetics. Led by University of California, Irvine professor of bioinformatics Wei Li, PhD, the Grace B. Bell chair of the Department of Biological Chemistry at UCI’s School of Medicine, this new research unlocks how much differences in people’s genes account for differences in their traits and what can be attributed to the effects of the surrounding environment.

“Our research is of particularly importance as it offers interpretations that explain how natural variations can shape human phenotypic diversity and tissue-specific diseases,” said Li. “Our most exciting finding was that certain events in human genes can explain a substantial proportion of trait heritability.”

It was well known that 3′-UTR alternative polyadenylation (APA) occurs in approximately 70 percent of human genes and substantively impacts cellular processes such as proliferation, differentiation and tumorigenesis. But, until now, the association of APA events with disease risk and complex human traits was not well understood.

Genome-wide association studies have identified thousands of noncoding variants associated with human traits and diseases. However, the functional interpretation of these variants has proven to be a major challenge. In this study, researchers constructed a multi-tissue atlas of human 3′ UTR alternative polyadenylation (APA) quantitative trait loci (3′aQTLs), containing approximately 0.4 million common genetic variants associated with the APA of target genes, identified in 46 tissues isolated from 467 individuals (Genotype-Tissue Expression Project).

“From our findings we could show that specific molecular features associated with human phenotypic variations contribute substantially to the molecular mechanisms underlying human complex traits and diseases,” explained Li.

The research team is continuing to study these molecular mechanisms to test the novel 3′aQTLs genes for diabetes, prostate cancer, Alzheimer’s Disease and Amyotrophic lateral sclerosis (ALS).

The study was supported in part by the Cancer Prevention Research Institute of Texas and the National Institutes of Health.

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However, the technology isn’t quite there yet.

In fact, despite widespread theories about the far-reaching impact of quantum technologies, very few researchers have been able to demonstrate, using the technology available now, that quantum methods have an advantage over their classical counterparts.

In a paper published on June 1 in the journal Physical Review X, University of Arizona researchers experimentally show that quantum has an advantage over classical computing systems.

“Demonstrating a quantum advantage is a long-sought-after goal in the community, and very few experiments have been able to show it,” said paper co-author Zheshen Zhang, assistant professor of materials science and engineering, principal investigator of the UArizona Quantum Information and Materials Group and one of the paper’s authors. “We are seeking to demonstrate how we can leverage the quantum technology that already exists to benefit real-world applications.”

How (and When) Quantum Works

Quantum computing and other quantum processes rely on tiny, powerful units of information called qubits. The classical computers we use today work with units of information called bits, which exist as either 0s or 1s, but qubits are capable of existing in both states at the same time. This duality makes them both powerful and fragile. The delicate qubits are prone to collapse without warning, making a process called error correction — which addresses such problems as they happen — very important.

The quantum field is now in an era that John Preskill, a physicist from the California Institute of Technology, termed “noisy intermediate scale quantum,” or NISQ. In the NISQ era, quantum computers can perform tasks that only require about 50 to a few hundred qubits, though with a significant amount of noise, or interference. Any more than that and the noisiness overpowers the usefulness, causing everything to collapse. It is widely believed that 10,000 to several million qubits would be needed to carry out practically useful quantum applications.

Imagine inventing a system that guarantees every meal you cook will turn out perfectly, and then giving that system to a group of children who don’t have the right ingredients. It will be great in a few years, once the kids become adults and can buy what they need. But until then, the usefulness of the system is limited. Similarly, until researchers advance the field of error correction, which can reduce noise levels, quantum computations are limited to a small scale.

Entanglement Advantages

The experiment described in the paper used a mix of both classical and quantum techniques. Specifically, it used three sensors to classify the average amplitude and angle of radio frequency signals.

The sensors were equipped with another quantum resource called entanglement, which allows them to share information with one another and provides two major benefits: First, it improves the sensitivity of the sensors and reduces errors. Second, because they are entangled, the sensors evaluate global properties rather than gathering data about specific parts of a system. This is useful for applications that only need a binary answer; for example, in medical imaging, researchers don’t need to know about every single cell in a tissue sample that isn’t cancerous — just whether there’s one cell that is cancerous. The same concept applies to detecting hazardous chemicals in drinking water.

The experiment demonstrated that equipping the sensors with quantum entanglement gave them an advantage over classical sensors, reducing the likelihood of errors by a small but critical margin.

“This idea of using entanglement to improve sensors is not limited to a specific type of sensor, so it could be used for a range of different applications, as long as you have the equipment to entangle the sensors,” said study co-author Quntao Zhuang, assistant professor of electrical and computer engineering and principal investigator of the Quantum Information Theory Group. “In theory, you could consider applications like lidar (Light Detection and Ranging) for self-driving cars, for example.”

Zhuang and Zhang developed the theory behind the experiment and described it in a 2019 Physical Review X paper. They co-authored the new paper with lead author Yi Xia, a doctoral student in the James C. Wyant College of Optical Sciences, and Wei Li, a postdoctoral researcher in materials science and engineering.

Qubit Classifiers

There are existing applications that use a mix of quantum and classical processing in the NISQ era, but they rely on preexisting classical datasets that must be converted and classified in the quantum realm. Imagine taking a series of photos of cats and dogs, then uploading the photos into a system that uses quantum methods to label the photos as either “cat” or “dog.”

The team is tackling the labeling process from a different angle, by using quantum sensors to gather their own data in the first place. It’s more like using a specialized quantum camera that labels the photos as either “dog” or “cat” as the photos are taken.

“A lot of algorithms consider data stored on a computer disk, and then convert that into a quantum system, which takes time and effort,” Zhuang said. “Our system works on a different problem by evaluating physical processes that are happening in real time.”

The team is excited for future applications of their work at the intersection of quantum sensing and quantum computing. They even envision one day integrating their entire experimental setup onto a chip that could be dipped into a biomaterial or water sample to identify disease or harmful chemicals.

“We think it’s a new paradigm for both quantum computing, quantum machine learning and quantum sensors, because it really creates a bridge to interconnect all these different domains,” Zhang said.