Sleep deficiency is well known to negatively impact human functioning. For example, it is associated with deficits in attention and memory, as well as increased risk of car accidents, heart problems, and other medical issues. However, while some research has addressed recovery after chronic sleep deprivation, it has been unclear how much time is needed to fully recover from prolonged periods of deficient sleep.

To shed more light on this topic, Ochab and colleagues conducted a small study with several healthy adults who underwent 10 days of purposeful sleep restriction followed by 7 recovery days of unrestricted sleep. Participants completed the study in their normal day-to-day environments and wore wrist sensors to monitor daily patterns of sleep and activity. They also underwent daily electroencephalography (EEG) to monitor brain activity, and they answered daily questions (Stroop tasks) to measure reaction times and accuracy.

After 7 days of recovery, the participants had not yet returned to pre-sleep deprivation performance on most measures of functioning. These included several EEG measures of brain activity, rest-versus-activity patterns captured by wrist sensors, and accuracy on Stroop tasks. Only their reaction times had recovered to baseline levels.

While the researchers note that it is difficult to compare these results with other studies that employed different methods, the findings contribute new insights into recovery from chronic sleep loss. Future research could expand to a greater number of participants, investigate longer recovery periods, and disentangle the order in which different functions return to normal.

The authors add: “The investigation of the recovery process following an extended period of sleep restriction reveal that the differences in behavioral, motor, and neurophysiological responses to both sleep loss and recovery.”

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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.