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Category: food

Animals have an innate preference for certain scents and tastes. Attractive scents are linked to things like good food. Less attractive scents—that of spoiled food, for example—instinctively give the animal a signal which says: “There could be danger here!” When it comes to taste, all animals have similar preferences: Sugars and fats are perceived positively, whereas a bitter taste is perceived rather negatively.

In order to be able to make such evaluations, we need signals in the that tell us “This is good” or “This is bad.” The in the brain, better known as the reward system, plays an important role in these evaluations.

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Researchers from the Max Delbrück Center for Molecular Medicine have developed a new tool that makes it easier to maximize the power of deep learning for studying genomics. They describe the new approach, Janggu, in the journal Nature Communications.

Imagine that before you could make dinner, you first had to rebuild the kitchen, specifically designed for each recipe. You’d spend way more time on preparation, than actually cooking. For computational biologists, it’s been a similar time-consuming process for analyzing . Before they can even begin their analysis, they spend a lot of valuable time formatting and preparing huge data sets to feed into deep learning models.

To streamline this process, researchers from MDC developed a universal programming tool that converts a wide variety of genomics data into the required format for analysis by deep learning models. “Before, you ended up wasting a lot of time on the technical aspect, rather than focusing on the biological question you were trying to answer,” says Dr. Wolfgang Kopp, a scientist in the Bioinformatics and Omics Data Science research group at MDC’s Berlin Institute of Medical Systems Biology (BIMSB), and first author of the paper. “With Janggu, we are aiming to relieve some of that technical burden and make it accessible to as many people as possible.”

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Cardiff University scientists have devised a new way of making reactions up to 70 times faster by using state-of-the-art equipment to spin chemicals around.

They found that efficient mixing within a chemical reaction could be achieved by spinning chemicals and catalysts around in a small tube, causing the reactions to happen much quicker.

The new findings could have a profound influence on the way that chemicals are made in a wide variety of industries, from drug development to agriculture and fragrances.

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Synthetic self-fuelled motors, which can spontaneously convert chemical energy into mechanical activity to induce autonomous locomotion, are excellent candidates for making self-powered machines, detectors/sensors, and novel robots. The present lab (Zhang et al. in Adv Mater 27:2648–2655, 2004 [1]). discovered an extraordinary self-propulsion mechanism of synthetic motors based on liquid metal objects. Such motors could swim in a circular Petri dish or different structured channels containing aqueous solution with a pretty high velocity on the order of centimeters per second, and surprisingly long lifetime lasting for more than one hour without any assistance of external energy. The soft material liquid metal enables the motors to self-deform, which makes them highly adaptable for accomplishing tough missions in special environment. Interestingly, the motors work just like biomimetic mollusk since they closely resemble the nature by “eating” aluminum as “food”, and can change shape by closely conforming to the geometrical space it voyages in. From practical aspect, one can thus develop a self-powered pump based on the actuation of the liquid metal enabled motor. Further, such pump can also be conceived to work as a cooler. Apart from different geometrical channels, several dominating factors, including the volume of the motor, the amount of aluminum, the property of the solution and the material of the substrate etc., have been disclosed to influence the performance of the autonomous locomotion evidently. This artificial mollusk system suggests an exciting platform for molding the liquid metal science to fundamentally advance the field of self-driven soft machine design, microfluidic systems, and eventually lead to the envisioned dynamically reconfigurable intelligent soft robots in the near future. In this chapter, the typical behaviors and fundamental phenomena of the self fuelled transformable liquid metal machines were illustrated.

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There you are wrist deep into a quart of Ben & Jerry’s Chunky Monkey, digging ever deeper. You can’t deny it. Your necklace is recording the ice-cream binge, which it will later dispatch to a coach or dietician.

The aim is not to induce guilt but rather answer the question: “How did you get here?”

Meet NeckSense, the first technology to precisely and passively record multiple eating behaviors. It can detect in the real world when people.

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Charles Goulding and Ryan Donley of R&D Tax Savers discuss 3D printing as it impacts chocolateering.

Chocolate has been around for millennia now, dating as far back as early 1750 B.C., presumably in the area of the Gulf Coast of Vera Cruz where cocoa beverages or chocolate drinks were used in ceremonies by pre-Olmec peoples. Evidence suggests cacao pods may have even been used in alcoholic beverages as early as 1400 B.C. Today, the cacao bean has evolved to encompass a $50 billion chocolate industry worldwide that consists of edible chocolate confections being brought to mass markets.

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While so many of us are working at home during the coronavirus pandemic, we do worry that serendipitous hallway conversations aren’t happening.

Last year, before the pandemic, it was one of those conversations that led researchers at ETH Zurich to develop a way of making chocolates shimmer with color—without any coloring agents or other additives.

The project, announced in December, involves what the scientists call “structural color”. The team indicated that it creates colors in a way similar to what a chameleon does—that is, using the structure of its skin to scatter a particular wavelength of light. The researchers have yet to release details, but Alissa M. Fitzgerald, founder of MEMS product development firm AMFitzgerald, has a pretty good guess.

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Clint Brauer’s farm outside of Cheney, Kansas, could be described as Old MacDonald’s Farm plus robots. Along with 5,500 square feet of vegetable-growing greenhouses, classes teaching local families to grow their food, a herd of 105 sheep, and Warren G—a banana-eating llama named after the rapper—is a fleet of ten, 140-pound, battery-operated robots.

Brauer, the co-founder of Greenfield Robotics, grew up a farm kid. He left for the big city tech and digital world, but eventually made his way back to the family farm. Now, it’s the R&D headquarters for the Greenfield Robotics team, plus a working farm.

When Brauer returned to his agricultural roots, he did so with a purpose: to prove that food could be grown without harmful chemicals and by embracing soil- and planet-friendly practices. He did just that, becoming one of the premier farmers growing vegetables in Kansas without pesticides, selling to local markets, grocery store chains, and chefs.

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