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Category: food – Page 28

What’s in Your Honey? A New Study Finds More Than Just Sweetness

Inside every jar of honey lies a taste of the local environment. Its sticky-sweet flavor is shaped by the flowers that nearby bees choose to sample. However, a new study from Tulane University has revealed that honey can also provide insights into local pollution.

The study, published in Environmental Pollution, analyzed 260 honey samples from 48 states for traces of six toxic metals: arsenic, lead, cadmium, nickel, chromium, and cobalt. None of the samples contained unsafe levels of these metals based on a typical serving size of one tablespoon per day, and the concentrations in the United States were generally lower than global averages. Still, researchers identified regional variations in toxic metal distribution: the highest arsenic levels were detected in honey from a cluster of Pacific Northwest states (Oregon, Idaho, Washington, and Nevada); the Southeast, including Louisiana and Mississippi, showed the highest cobalt levels; and two of the three highest lead levels were found in samples from the Carolinas.

Overall, the study highlights a potential dual role for honey as both a food source and a tool for monitoring environmental pollution.

MIT Unveils a Biodegradable Alternative to Microplastic Beads

MIT researchers have developed an environmentally friendly alternative to the harmful microbeads used in some health and beauty products.

These new polymers break down into harmless sugars and amino acids and could also encapsulate nutrients for food fortification, showing promise in both cosmetic and nutritional applications.

Biodegradable Solutions by MIT.

A matter of taste: Electronic tongue reveals AI inner thoughts

UNIVERSITY PARK, Pa. — A recently developed electronic tongue is capable of identifying differences in similar liquids, such as milk with varying water content; diverse products, including soda types and coffee blends; signs of spoilage in fruit juices; and instances of food safety concerns. The team, led by researchers at Penn State, also found that results were even more accurate when artificial intelligence (AI) used its own assessment parameters to interpret the data generated by the electronic tongue.

(Many people already posted this. This is the press release from Penn Sate who did the research)

The tongue comprises a graphene-based ion-sensitive field-effect transistor, or a conductive device that can detect chemical ions, linked to an artificial neural network, trained on various datasets. Critically, Das noted, the sensors are non-functionalized, meaning that one sensor can detect different types of chemicals, rather than having a specific sensor dedicated to each potential chemical. The researchers provided the neural network with 20 specific parameters to assess, all of which are related to how a sample liquid interacts with the sensor’s electrical properties. Based on these researcher-specified parameters, the AI could accurately detect samples — including watered-down milks, different types of sodas, blends of coffee and multiple fruit juices at several levels of freshness — and report on their content with greater than 80% accuracy in about a minute.

“After achieving a reasonable accuracy with human-selected parameters, we decided to let the neural network define its own figures of merit by providing it with the raw sensor data. We found that the neural network reached a near ideal inference accuracy of more than 95% when utilizing the machine-derived figures of merit rather than the ones provided by humans,” said co-author Andrew Pannone, a doctoral student in engineering science and mechanics advised by Das. “So, we used a method called Shapley additive explanations, which allows us to ask the neural network what it was thinking after it makes a decision.”

This approach uses game theory, a decision-making process that considers the choices of others to predict the outcome of a single participant, to assign values to the data under consideration. With these explanations, the researchers could reverse engineer an understanding of how the neural network weighed various components of the sample to make a final determination — giving the team a glimpse into the neural network’s decision-making process, which has remained largely opaque in the field of AI, according to the researchers. They found that, instead of simply assessing individual human-assigned parameters, the neural network considered the data it determined were most important together, with the Shapley additive explanations revealing how important the neural network considered each input data.

New look at dopamine signaling suggests neuroscientists’ model of reinforcement learning may need to be revised

Dopamine is a powerful signal in the brain, influencing our moods, motivations, movements, and more. The neurotransmitter is crucial for reward-based learning, a function that may be disrupted in a number of psychiatric conditions, from mood disorders to addiction.

Now, researchers led by MIT Institute Professor Ann Graybiel have found surprising patterns of dopamine signaling that suggest neuroscientists may need to refine their model of how occurs in the brain. The team’s findings were published recently in the journal Nature Communications.

Dopamine plays a critical role in teaching people and other animals about the cues and behaviors that portend both positive and negative outcomes; the classic example of this type of learning is the dog that Ivan Pavlov trained to anticipate food at the sound of bell.

Cas9-PE system achieves precise editing and site-specific random mutation in rice

Achieving the aggregation of different mutation types at multiple genomic loci and generating transgene-free plants in the T0 generation is an important goal in crop breeding. Although prime editing (PE), as the latest precise gene editing technology, can achieve any type of base substitution and small insertions or deletions, there are significant differences in efficiency between different editing sites, making it a major challenge to aggregate multiple mutation types within the same plant.

Recently, a collaborative research team led by Li Jiayang from the Institute of Genetics and Developmental Biology (IGDB) of the Chinese Academy of Science, developed a multiplex gene editing tool named the Cas9-PE system, capable of simultaneously achieving precise editing and site-specific random mutagenesis in rice.

By co-editing the ALSS627I gene to confer resistance to the herbicide bispyribac-sodium (BS) as a selection marker, and using Agrobacterium-mediated transient transformation, the researchers also achieved transgene-free gene editing in the T0 generation.

How Medical Device Cybersecurity Evolved From Idea To Industry Imperative

Mike has over 15 years of experience in healthcare, including extensive experience designing and developing medical devices. MedCrypt, Inc.

On October 1, 2024, the Food and Drug Administration (FDA) marked a major milestone in medical device cybersecurity enforcement. This marks one year since the retracted Refuse to Accept (RTA) policy and the full implementation of the Protecting and Transforming Cyber Healthcare (PATCH) Act amendment to the Food, Drug & Cosmetic Act (FD&C). The FDA’s new requirements represent a fundamental shift in the regulatory landscape for medical device manufacturers (MDMs), as cybersecurity is now a non-negotiable element of device development and compliance.

The timing is not coincidental. In 2023, the FDA issued its final guidance entitled “Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions.” This outlined the detailed cybersecurity requirements and considerations that MDMs must address in their submissions, highlighting the security measures in place to gain regulatory approval. With these requirements, the FDA is taking a hard stance: Cybersecurity is a core consideration, with compliance being systematically enforced.

New foam filter achieves high microplastic removal rates in initial testing

Wuhan University-led research is reporting the development of a revivable self-assembled supramolecular biomass fibrous framework (a novel foam filter) that efficiently removes microplastics from complex aquatic environments.

Plastic waste is a growing global concern due to significant levels of microplastic pollution circulating in soil and waterways and accumulating in the environment, food webs and human tissues. There are no conventional methods for removing microplastics, and developing strategies to handle diverse particle sizes and chemistries is an engineering challenge.

Researchers have been looking for affordable, capable of universal microplastic adsorption. Most existing approaches involve expensive or difficult-to-recover adsorbents, fail under certain environmental conditions, or only target a narrow range of microplastic types.

A new biodegradable material to replace certain microplastics

Microplastics are an environmental hazard found nearly everywhere on Earth, released by the breakdown of tires, clothing, and plastic packaging. Another significant source of microplastics is tiny beads that are added to some cleansers, cosmetics, and other beauty products.

In an effort to cut off some of these microplastics at their source, MIT researchers have developed a class of biodegradable materials that could replace the plastic beads now used in beauty products. These polymers break down into harmless sugars and amino acids.

MIT researchers developed biodegradable materials that could replace the plastic microbeads now used in beauty products. The materials could also be used to encapsulate nutrients for food fortification.