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Category: food

A research team from the School of Engineering at the Hong Kong University of Science and Technology has developed a new computational model to study the movement of granular materials such as soils, sands and powders. By integrating the dynamic interactions among particles, air and water phases, this state-of-the-art system can accurately predict landslides, improve irrigation and oil extraction systems, and enhance food and drug production processes.

The flow of granular materials—such as soil, sand and powders used in pharmaceuticals and food production—is the underlying mechanism governing many natural settings and industrial operations. Understanding how these particles interact with surrounding fluids like water and air is crucial for predicting behaviors such as soil collapse or fluid leakage.

However, existing models face challenges in accurately capturing these interactions, especially in partially saturated conditions where forces like and viscosity come into play.

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When zebrafish are relocated to a new environment, they seek protection by diving and staying at the safety home, until they feel safe enough to explore the unfamiliar environment (26). The swimming trajectories showed that zebrafish in the control group can swiftly explore and adapt to the novel environment, but chronic exposure to acrylamide reduces the ability to adapt to the unfamiliar environment (Fig. 3 A). Visualized heatmaps showed significant changes in swimming trajectories of zebrafish in the acrylamide exposure groups compared with those in the control group (Fig. 3 B). Furthermore, we found the swimming time and distance ratios in Zone 1 exhibited a dose-dependent decreasing trend in acrylamide exposure groups. Chronic exposure to acrylamide (0.5 mM) significantly decreased both swimming time and distance in Zone 1 and increased those in Zone 2 (Fig. 3 C and D). We recorded the novel object exploration test to visualize the behavioral alteration between the control and each acrylamide-treated group (Movie S3). The movie displays that zebrafish in the control group could swiftly explore and adapt to the novel environment, but chronic exposure to acrylamide reduced the ability to adapt to the unfamiliar environment, which indicated that acrylamide induces anxiety-and depressive-like behaviors by reducing exploration ability of zebrafish.

Moreover, the social preference test was used to assess sociality of zebrafish. Since the zebrafish are a group preference species, they frequently swim by forming a school and swim closely to one another (27). In the current social preference test, representative radar maps and visualized heatmaps exhibited significant changes of preference in swimming trajectories of zebrafish in acrylamide exposure groups compared to those in the control group, indicating that chronic exposure to acrylamide remarkably impairs the sociality of zebrafish (Fig. 3 E–G). For detailed parameters of behavioral trajectories, chronic exposure to acrylamide (0.5 mM) significantly increased both swimming time and distance ratios in the left zone and decreased those in the right zone (Fig. 3 H and I). Notably, chronic exposure to acrylamide (0.5 mM) significantly elevated traversing times and number of crossing the middle line (Fig. 3 J and K).

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Sustainably produced, biodegradable materials are an important focus of modern materials science. However, when working with natural materials such as cellulose, lignin or chitin, researchers face a trade-off. Although these substances are biodegradable in their pure form, they are often not ideal when it comes to performance. Chemical processing steps can be used to make them stronger, more resistant or more supple—but in doing so, their sustainability is often compromised.

Empa researchers from the Cellulose and Wood Materials laboratory have now developed a bio-based material that cleverly avoids this compromise. Not only is it completely biodegradable, it is also tear-resistant and has versatile functional properties. All this takes place with minimal processing steps and without chemicals—you can even eat it. Its secret: It’s alive.

The study is published in the journal Advanced Materials.

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In Biology 101, we learn that RNA is a single, ribbon-like strand of base pairs that is copied from our DNA and then read like a recipe to build a protein. But there’s more to the story. Some RNA strands fold into complex shapes that allow them to drive cellular processes like gene regulation and protein synthesis, or catalyze biochemical reactions.

We know that these active molecules, called non-coding RNAs, are present in all life forms, yet we’re just starting to understand their many roles—and how they can be harnessed for applications in environmental science, agriculture, and medicine.

To study—and potentially modify—the functions of non-coding RNAs, we need to determine their structure. Scientists from Lawrence Berkeley National Laboratory (Berkeley Lab) and the Hebrew University of Jerusalem have developed a streamlined process that predicts the structure of an RNA molecule down to the atomic level.

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In the domain of artificial intelligence, human ingenuity has birthed entities capable of feats once relegated to science fiction. Yet within this triumph of creation resides a profound paradox: we have designed systems whose inner workings often elude our understanding. Like medieval alchemists who could transform substances without grasping the underlying chemistry, we stand before our algorithmic progeny with a similar mixture of wonder and bewilderment. This is the essence of the “black box” problem in AI — a philosophical and technical conundrum that cuts to the heart of our relationship with the machines we’ve created.

The term “black box” originates from systems theory, where it describes a device or system analyzed solely in terms of its inputs and outputs, with no knowledge of its internal workings. When applied to artificial intelligence, particularly to modern deep learning systems, the metaphor becomes startlingly apt. We feed these systems data, they produce results, but the transformative processes occurring between remain largely opaque. As Pedro Domingos (2015) eloquently states in his seminal work The Master Algorithm: “Machine learning is like farming. The machine learning expert is like a farmer who plants the seeds (the algorithm and the data), harvests the crop (the classifier), and sells it to consumers, without necessarily understanding the biological mechanisms of growth” (p. 78).

This agricultural metaphor points to a radical reconceptualization in how we create computational systems. Traditionally, software engineering has followed a constructivist approach — architects design systems by explicitly coding rules and behaviors. Yet modern AI systems, particularly neural networks, operate differently. Rather than being built piece by piece with predetermined functions, they develop their capabilities through exposure to data and feedback mechanisms. This observation led AI researcher Andrej Karpathy (2017) to assert that “neural networks are not ‘programmed’ in the traditional sense, but grown, trained, and evolved.”

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A recent study conducted in southern Italy presented some surprising findings that linked the regular consumption of poultry to potential increases in gastrointestinal cancers and all-cause mortality. This has caused one question to arise — is eating chicken really as healthy as we think it is?

The study’s findings indicated that exceeding the weekly recommended amounts — that is, eating more than 300 grams (g) of poultry, such as chicken and turkey, per week — resulted in a 27% higher risk of all-cause mortality compared to eating moderate amounts.

Moreover, the research suggested that higher poultry intake was linked to a 2.3% increase in the risk of gastrointestinal cancers, with a higher observed risk among men at 2.6%. The findings were published in the journal Nutrients.

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Lately, there have been many headlines about scientific fraud and journal article retractions. If this trend continues, it represents a serious threat to public trust in science.

One way to tackle this problem—and ensure public trust in science remains high—may be to slow it down. We sometimes refer to this philosophy as “slow science.” Akin to the slow food movement, slow science prioritizes quality over speed and seeks to buck incentive structures that promote mass production.

Slow science may not represent an obvious way to improve science because we often equate science with progress, and slowing down progress does not sound very appealing. However, progress is not just about speed, but about basing important societal decisions on strong scientific foundations. And this takes time.

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Some 460 million metric tons of plastic are produced globally each year, out of which a staggering 91% of plastic waste is never recycled—with 12% incinerated and 79% left to end up in landfills and oceans and linger in our environment.

Exposure to various elements causes the plastics to break down into microplastics (5 mm) and nanoplastics (1,000 nm). There is a growing public health concern as these nanoplastics (NPs) make their way into the human body through air, water, food and contact with skin.

A recent study published in ACS ES&T Water has revealed that the already detrimental effects of NPs are further amplified by their ability to interact with various toxic environmental contaminants, such as heavy metal ions.

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Humans are the only species on Earth known to use language. They do this by combining sounds into words and words into sentences, creating infinite meanings.

This process is based on linguistic rules that define how the meaning of calls is understood in different sentence structures. For example, the word “ape” can be combined with other words to form compositional sentences that add meaning: “the ape eats” or append meaning: “big ape,” and non-compositional idiomatic sentences that create a completely new meaning: “go ape.”

A key component of language is syntax, which determines how the order of words affects meaning. For instance, how “go ape” and “ape goes” convey different meanings.

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