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Penn State agricultural engineers have developed, for the first time, a prototype “end-effector” capable of deftly removing unwanted apples from trees—the first step toward robotic, green-fruit thinning.

The development is important, according to Long He, assistant professor of agricultural and biological engineering, because manual thinning is a labor-intensive task, and the shrinking labor force in apple production makes manual thinning economically infeasible. His research group in the College of Agricultural Sciences conducted a new study that led to the end-effector.

The apple crop is a high-value agricultural commodity in the U.S., with an annual total production of nearly 10 billion pounds and valued at nearly $3 billion, according to He, who is a leader in agricultural robotics research, previously developing automated components for mushroom picking and apple tree pruning. Green-fruit thinning—the process of discarding excess fruitlets in early summer, mainly to increase the remaining fruit size and quality—is one of the most important aspects of apple production.

We’re proud to be a platinum sponsor of ICML, the annual conference on machine learning. Learn about Amazon’s presence at the conference, accepted publications,… See more.

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. The conference is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics.

This Review provides a comprehensive overview of presynaptic applications of optogenetic tools, including the associated challenges, current limitations and future directions for this approach.

Biological synapses are known to store multiple memories on top of each other at different time scales, much like representations of the early techniques of manuscript writing known as “palimpsest,” where annotations can be superimposed alongside traces of earlier writing.

Biological palimpsest consolidation occurs via hidden biochemical processes that govern synaptic efficacy at varying lifetimes. The arrangement can facilitate idle memories to be overwritten without forgetting them, while using previously unseen memories short-term. Embedded artificial intelligence can significantly benefit from such functionality; however, the hardware has yet to be demonstrated in practice.

In a new report, now published in Science Advances, Christos Giotis and a team of scientists in Electronics and Computer Science at the University of Southampton and the University of Edinburgh, U.K., showed how the intrinsic properties of metal-oxide volatile memristors mimicked the process of biological palimpsest consolidation.

In the deep subsurface that plunges into the Earth for miles, microscopic organisms inhabit vast bedrock pores and veins. Belowground microorganisms, or microbes, comprise up to half of all living material on the planet and support the existence of all life forms up the food chain. They are essential for realizing an environmentally sustainable future and can change the chemical makeup of minerals, break down pollutants, and alter the composition of groundwater.

While the significance of bacteria and archaea is undeniable, the only evidence of their existence in the deep subsurface comes from traces of biological material that seep through mine walls, cave streams, and drill holes that tap into aquifers.

Many scientists have assumed that the composition of microbial communities in the deep subsurface is primarily shaped by local environmental pressures on microbial survival such as temperature, acidity, and oxygen concentration. This process, environmental selection, can take years to millennia to cause significant community-level changes in slow-growing communities like the subsurface.

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Sustainable cell cultured mollusk seafood products — nikita michelsen, founder & CEO, pearlita foods.

Nikita Michelsen, is Founder & CEO of Pearlita Foods (https://www.pearlitafoods.com/), the world’s first cell-based mollusk company, which is developing sustainably & ethically grown products, like oysters and abalone, that are contaminant free without compromising flavor or nutrition.

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For the function of many biomolecules, their three-dimensional structure is crucial. Researchers are therefore not only interested in the sequence of the individual building blocks of biomolecules, but also in their spatial structure. With the help of artificial intelligence (AI), bioinformaticians can already reliably predict the three-dimensional structure of a protein from its amino acid sequence. For RNA molecules, however, this technology is still in its infancy. Researchers at Ruhr-Universität Bochum (RUB) describe a way to use AI to reliably predict the structure of certain RNA molecules from their nucleotide sequence in the journal PLOS Computational Biology on July 7, 2022.

For the work, the teams led by Vivian Brandenburg and Professor Franz Narberhaus from the RUB Chair of Biology of Microorganisms cooperated with Professor Axel Mosig from the Bioinformatics Competence Area of the Bochum Center for Protein Diagnostics.

As you’re reading this sentence, the cells in your brain, called neurons, are sending rapid-fire electrical signals between each other, transmitting information. They’re doing so via tiny, specialized junctions between them called synapses.

There are many different types of synapses that form between neurons, including “excitatory” or “inhibitory,” and the exact mechanisms by which these structures are generated remain unclear to scientists. A Colorado State University biochemistry lab has uncovered a major insight into this question by showing that the types of chemicals released from synapses ultimately guide which kinds of synapses form between neurons.

Soham Chanda, assistant professor in the Department of Biochemistry and Molecular Biology, led the study published in Nature Communications that demonstrates the possibility of changing the identity of synapses between neurons, both in vitro and in vivo, through enzymatic means. The other senior scientists who contributed to the project were Thomas Südhof of Stanford University and Matthew Xu-Friedman of the University at Buffalo.