Chocolate is a £61billion-per-year global industry that has seen the volatile price of cocoa lead to a surge in traders seeking to buy cheaper beans from deforested regions, with lower quality plants, and human rights abuses. This has affected the prices and practices of legitimate farmers, reducing sustainability gains.

In the findings, published in the journal Supply Chain Management, the multi-university research team reveal that biomarkers can create “meta-barcodes,” which are like biochemical fingerprints, an unchanging barcode extracted from the plant’s DNA, providing a unique identifier of a plant that is also observed in its beans and subsequent chocolate products. The biomarker of cocoa beans used in chocolate manufacturing could accurately identify the farm, production facility or cooperative where a cocoa product came from.

To make this new process a reality, a controlled data set of biomarkers of registered locations is required for audit. The study goes on to explain that this missing piece — a biomarker database that identifies the origin of cocoa products, can be built by companies at an estimated cost of £5 per sample — around the cost of a box of chocolates.

Glenn Parry, Professor of Digital Transformation at University of Surrey, said: “The chocolate market has become turbulent, and we have evidence of over 100 years of slavery in the supply chain. Governments and chocolate producers are faced with an ethical challenge and drastically need to improve a trade that is rife with environmental destruction and human misery.

“We have an effective approach for them to make progress. We demonstrate that biomarkers can provide supply chain visibility from the individual farm to the retail chocolate bar. This solution could now be within reach, where the journey of the chocolate in your fridge could be traced back to the cocoa trees where it began.”

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

“It is vital to be able to explain how a technology works to someone before you attempt to adopt it. Our new paper defines how light interacts with microscale concave interfaces,” says University at Buffalo engineering researcher Qiaoqiang Gan, noting that future applications of these effects could include aiding autonomous vehicles in recognizing traffic signs.

The research was published online on Aug. 15 in Applied Materials Today, and is featured in the journal’s September issue.

Gan, PhD, professor of electrical engineering in the UB School of Engineering and Applied Sciences, led the collaborative study, which was conducted by a team from UB, the University of Shanghai for Science and Technology, Fudan University, Texas Tech University and Hubei University. The first authors are Jacob Rada, UB PhD student in electrical engineering, and Haifeng Hu, PhD, professor of optical-electrical and computer engineering at the University of Shanghai for Science and Technology.

Reflections that form concentric rings of light

The study focuses on a retroreflective material — a thin film that consists of polymer microspheres laid down on the sticky side of a transparent tape. The microspheres are partially embedded in tape, and the parts that protrude form MCIs.?

White light shining on this film is reflected in a way that causes the light to create concentric rainbow rings, the new paper reports. Alternately, hitting the material with a single-colored laser (red, green or blue, in the case of this study) generates a pattern of bright and dark rings. Reflections from infrared lasers also produced distinctive signals consisting of concentric rings.

The research describes these effects in detail, and reports on experiments that used the thin film in a stop sign. The patterns formed by the material showed up clearly on both a visual camera that detects visible light, and a LIDAR (laser imaging, detection and ranging) camera that detects infrared signals, says Rada, the co-first author from UB.

“Currently, autopilot systems face many challenges in recognizing traffic signs, especially in real-world conditions,” Gan says. “Smart traffic signs made from our material could provide more signals for future systems that use LIDAR and visible pattern recognition together to identify important traffic signs. This may be helpful to improve the traffic safety for autonomous cars.”

“We demonstrated a new combined strategy to enhance the LIDAR signal and visible pattern recognition that are currently performed by both visible and infrared cameras,” Rada says. “Our work showed that the MCI is an ideal target for LIDAR cameras, due to the constantly strong signals that are produced.”

A U.S. patent for the retroreflective material has been issued, as well as a counterpart in China, with Fudan University and UB as the patent-holders. The technology is available for licensing.

Gan says future plans include testing the film using different wavelengths of light, and different materials for the microspheres, with the goal of enhancing performance for possible applications such as traffic signs designed for future autonomous systems.

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Materials provided by University at Buffalo. Original written by Charlotte Hsu. Note: Content may be edited for style and length.

“Behavior drives everything we do,” Yttri said.

As a behavioral neuroscientist, Yttri studies what happens in the brain when animals walk, eat, sniff or do any action. This kind of research could help answer questions about neurological diseases or disorders like Parkinson’s disease or stroke. But identifying and predicting animal behavior is extremely difficult.

Now, a new unsupervised machine learning algorithm developed by Yttri and Alex Hsu, a biological sciences Ph.D. candidate in his lab, makes studying behavior much easier and more accurate. The researchers published a paper on the new tool, B-SOiD (Behavioral segmentation of open field in DeepLabCut), in Nature Communications.

Previously, the standard method to capture animal behavior was to track very simple actions, like whether a trained mouse pressed a lever or whether an animal was eating food or not. Alternatively, the experimenter could spend hours and hours manually identifying behavior, usually frame by frame on a video, a process prone to human error and bias.

Hsu realized he could let an unsupervised learning algorithm do the time-consuming work. B-SOiD discovers behaviors by identifying patterns in the position of an animal’s body. The algorithm works with computer vision software and can tell researchers what behavior is happening at every frame in a video.

“It uses an equation to consistently determine when a behavior starts,” Hsu explained. “Once you reach that threshold, the behavior is identified, every time. A human experimenter might toggle between two frames or several categories, try to decide where behavior begins and become fatigued over time.”

Yttri said B-SOiD provides a huge improvement and opens up several avenues for new research.

“It removes user bias and, more importantly, removes the time cost and arduous work,” he said. “We can accurately process hours of data in a matter of minutes.”

Additionally, B-SOiD is very user friendly and openly available to any researcher. Yttri’s lab and their collaborators have used the new algorithm in research on many important areas, including research to better understand chronic pain, obsessive compulsive disorder and more.

Collaborators have even begun to use B-SOiD to study human movement in Parkinson’s disease.

“We are beginning to see if this can be used as part of an objective test by a doctor to show how far a patient’s disease has progressed. The hope is that a patient anywhere in the world would be diagnosed with one standardized metric,” Yttri said.

This is a breakthrough in how scientists can study natural behavior and how it changes rather than the overly simplistic or subjective measures that predominate neuroscience and ethology.

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Materials provided by Carnegie Mellon University. Original written by Caroline Sheedy. Note: Content may be edited for style and length.

The results, published in the journal Regenerative Medicine, showed that a single injection of leukocyte-rich/PRP in the knee joint significantly improved functional mobility, pain and quality of life after six weeks. The study supports using this combined approach to further evaluate this and other emerging biological therapies for musculoskeletal disorders in larger clinical trials.

“OA is a leading cause of disabilities, affecting nearly 52 million Americans,” said first and corresponding author Dr. Prathap Jayaram, director of regenerative sports medicine and assistant professor in the Department of Physical Medicine and Rehabilitation and Orthopedic Surgery at Baylor. “It has been estimated that more than 80% of individuals older than 55 years have some X-ray-based evidence of the disease.”

OA develops when the smooth cushion between bones, the cartilage, breaks down. Progressively, joints become painful, swollen and hard to move, Jayaram explained. Currently, there are no validated therapies that delay disease progression. The current standard of care is limited to the alleviation of symptoms with corticosteroids.

“However, although steroids seem to be helpful in the short term for pain, emerging evidence has associated steroid long-term use in OA with loss of cartilage,” Jayaram said. “As OA is a whole joint disease, there is a need for developing novel therapeutic strategies that ultimately prevent and/or delay disease progression while improving functional outcomes. PRP is emerging as one of the promising candidates to treat OA that are currently being used in clinical practice.”

Combining wearable technology and patient assessment to evaluate the treatment

One challenge of previous studies assessing PRP therapies in OA is that treatment evaluation is based on patient-reported outcomes that subjectively assess pain or aspects of joint function, such as the time up-and-go (TUG), how quick a person gets up from a chair.

In this study, Jayaram and colleagues incorporated wearable technology to objectively assess functional outcomes such as TUG, in addition to patient-reported outcomes to comprehensively evaluate the efficacy of PRP in knee OA (KOA).

The prospective pilot study included 12 patients diagnosed with KOA. Each patient received one ultrasound-guided injection of PRP and function and pain were evaluate six weeks later.

PRP is prepared from the patient’s own blood by removing the red blood cells and enriching the concentration of platelets. PRP also contains white blood cells, or leukocytes. PRPs are formulated either leucocyte-rich (LR) or leukocyte-poor. A preclinical study by Jayaram and his colleagues had previously shown that LR-PRP had potential disease-modifying effects that correlated with functional outcomes.

“In the current study, we found that a single injection of LR-PRP into the knee does significantly improve functional mobility, pain and quality of life at six weeks,” Jayaram said. “To our knowledge, our study is the first to report the efficacy of LR-PRP on objective functional outcomes using wearable sensor technology and validated patient-reported outcomes. Our findings provide the basis to conduct larger randomized clinical trials of PRP.”

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Materials provided by Baylor College of Medicine. Original written by Homa Shalchi. Note: Content may be edited for style and length.

These qualities allow hydrogels to simulate living tissue for tissue replacement or regeneration. One of the most useful of these applications is for healing wounds. Hydrogels are ideal for this purpose, with their ability to hydrate and form a moist and supportive environment. This facilitates processes beneficial for wound healing, such as blood vessel formation, the breakdown of dead tissue, activation of immune cells, the prevention of live cell and tissue death and even the alleviation of pain.

Natural hydrogels, particularly gelatin methacryloyl (GelMA) hydrogels, are favored for wound healing due to their biosafety and exceptional biocompatibility. But their usage is hampered by their inherently poor mechanical properties such as limited stretchiness, relative brittleness and inflexibility, and their inability to adhere to onto tissue surfaces. In order to improve upon these characteristics, variations on preparation methods and components have been attempted.

When a GelMA hydrogel is prepared, a solution of gelatin is made by mixing and dissolving gelatin in water. This results in a dispersion of gelatin polymer chains in the water. A chemical called a photo-initiator is then added to the solution, which makes the polymer chains sticky and allows them to stick to one another. Exposure to UV light activates the photo-initiators and the polymer chains cross-link to each other to form a network. Water molecules enter this network, stretching the chains and becoming locked within them; this illustrates the hydrogels’ absorptive powers and is the point where gelation, or solidification, occurs.

The properties of this gel can be modified by adding chemicals that bind to the polymer chains before UV exposure, or the UV parameters themselves can be varied to tune the gel’s properties. Some of these modifications have been experimented with in previous attempts to improve GelMA’s physical properties.

One approach was to introduce additional chemicals into the GelMA solution before crosslinking; the resultant chemically conjugated hydrogel showed a slight improvement in tissue adhesion. Other attempts have been made at strengthening GelMA by reinforcing flexible thin chemically conjugated GelMA films with additional chemicals. But challenges remain with improving the three mechanical properties of toughness, stretchiness, and adhesive strength simultaneously in GelMA hydrogels.

A collaborative team from the Terasaki Institute for Biomedical Innovation (TIBI) has developed methods for improving all three of these properties in GelMA hydrogels in a simple procedure with tunable fabrication parameters.

The researchers first turned to an example found in nature in their approach to improving adhesion in the hydrogels. Marine mussels secrete strong threads that are used as attachments and pulling ropes on rocks and other irregular surfaces. To form these threads, the mussels produce adhesion proteins in an acidic environment; upon exposure to the slightly alkaline ocean water, the proteins undergo a chemical change which spurs thread formation.

In a corresponding fashion, the TIBI team added large quantities of dopamine, a chemical analog to mussel adhesion protein, to GelMA to increase its strength, stretchiness, and adhesive properties. They also subjected the mixture to alkaline conditions to further increase the GelMA’s adhesive strength.

The results showed that the addition of large quantities of dopamine to the GelMA solution could increase the stretchiness of the resultant hydrogel by almost six-fold and its strength by more than three-fold. Other experiments showed that when the dopamine is subjected to alkaline conditions before the crosslinking step, the adhesive strength could be increased up to four times and its resistance to shear forces by almost seven-fold.

“The experiments we have conducted provide valuable insight into procedures for activating toughness and adhesion in GelMA-based hydrogels,” said Hossein Montazarian, Ph.D., first author of the project.

The researchers will continue to experiment with other chemicals to optimize their effects on GelMA’s mechanical properties. This can lead to improvements in additional applications such as skin-attachable wearable devices or healing and regenerative internal implants.

“The knowledge gained here on the fundamental mechanical properties of hydrogels can have far-reaching effects on biomedical applications,” said Ali Khademhosseini, Ph.D., TIBI’s Director and CEO. “It is one of many examples of impactful research from our biomaterials platform.”

This work was supported by funding from the National Institutes of Health (1R01EB023052-01A1, 1R01HL140618-01).