Florida’s outbreak of listeria has so far led to at least one death, 22 hospitalizations, and an ice cream recall since January 2022. Humans get sick with listeria infections, called listeriosis, from eating soil-contaminated food, undercooked meat, or dairy products that are raw or unpasteurized. Listeria can cause convulsions, coma, miscarriage, and birth defects. It’s the third leading cause of food poisoning deaths in the United States.

Avoiding unseen food hazards is the reason people often check the dates on food packaging. Printed with the month and year is often one of a dizzying array of phrases: “best by,” “use by,” “best if used before,” “best if used by,” “guaranteed fresh until,” “freeze by,” and even a “born on” label applied to some beer.

Drugs don’t always behave exactly as expected. While researchers may develop a drug to perform one specific function that may be tailored to work for a specific genetic profile, sometimes the drug might perform several other functions outside of its intended purpose.

This concept of drugs having multiple functions, called polypharmacology, may lead to unintended consequences. This is a common occurrence for cancer drugs in clinical trials that can have harmful side effects and treatment toxicity.

But polypharmacology may, in fact, be the norm for most drugs, not the exception. So rather than seeing a drug’s ability to perform many functions as a flaw, biomedical data scientists like me and my lab colleagues believe that it can be used to our advantage in designing drugs that address the full complexity of biology.

Food processors have long sought a safer, more energy-efficient means to convey product with less spillage, breakage, or downtime due to necessary cleaning and maintenance. Although tubular drag conveyors have offered these desired attributes compared to belt, bucket, or pneumatic systems, many in the industry selected the traditional options to move higher volumes or larger-sized products.

Now, however, 8-in.-diameter tubular drag conveyors have become widely available and can almost double the volumes of smaller 6-in. units. This provides comparable volumes and pricing to conventional industrial systems and enables transport of much larger product sizes than previously possible.

Even if the pandemic abates enough for a return to normal, all evidence indicates that a substantial share of Americans will continue to work from home, relying on videoconferencing to team up.

Yet, while the ease of gathering virtually has made the shift to widespread remote work possible, a new study finds that on-screen meetings have a significant drawback: They hinder creative collaboration.

The study, co-authored by Jonathan Levav of Stanford Graduate School of Business, and Melanie Brucks of Columbia Business School, finds that in-person teams generated more ideas than remote teams working on the same problem.

One of the unexpected rewards of working at NIST has been the opportunity to see other disciplines through the NIST prism of measurement science and standards. By working with NASA scientists, astronomers, oceanographers and geologists, I’ve had the opportunity to witness the lives of scientists in a variety of fields.

Often, my way of interacting with these researchers is by calibrating the sensors on their instruments. These calibrations help ensure that the instruments accurately measure the light and other electromagnetic radiation from objects the scientists are studying, whether it is the Pacific Ocean, a forest fire, or a faraway galaxy. To calibrate these researchers’ sensors properly, we need reliable ways to measure light itself. My NIST colleagues and I are currently engaged in some cutting-edge efforts to make these measurements better than they’ve ever been. But before I tell you about the high-altitude NASA aircraft we use, and the lunar observatory we’re building, let’s talk about the earliest standard for measuring light output: the humble candle.

One question led the founders of Nemo’s Garden, a subsea farming platform, to embark on its mission to take agriculture beneath the waves and bring better harvests to market: “Seventy percent of the planet is covered by water. Why don't we try to use part of the ocean to make more food, in a better way?”

The team has already successfully grown a variety of plants in this alien environment, and with help from Siemens, they’re looking to digitize the process so they can scale the project, validate the benefits their crops bring to the table, and expand their operations.

Project overview

The goal of Nemo’s Garden is to create an alternative agriculture system for areas where environmental, economic, or morphological reasons prevent traditional plant growth. The company has developed a prototype biosphere in which plants can be grown underwater. The biosphere can leverage the readily available, positive environmental factors in oceans or other bodies of water, such as temperature stability, water evaporation, CO² absorption, abundance of oxygen, and natural protection from pests.

Unlike a biological or identical twin, a digital twin does not have a universally accepted definition. In application, a digital twin will mean different things to different industries. On an assembly line, a digital twin of a robot may look identical to the physical robot, especially if it is photo-realistically rendered. The digital twin can mimic the physical robot’s movement, for example. The digital twin may not pass a close inspection for similarity, however. It cannot have the internal minutiae and complexity nature routinely provides. A robot’s digital twin may lack fastener threads, weld details, etc. found on the physical robot.

But unlike nature’s twins, digital twins need not replicate every bit, part, and function of their physical counterparts to be effective. If the digital twin can determine the reach of the assembly line robot and prevent interference with the production line or other robots, then the digital twin can consider itself complete enough for that particular mission.

Narrative frames are fundamental to unifying ideologies. They frame what is possible and impossible, which ideas can be accepted, and which must be rejected. In her book, Digital Democracy, Analogue Politics (Zed Books, 2018), storyteller and political analyst Nanjala Nyabola examines the framing of the Fourth Industrial Revolution narrative in this light.

She argues that it is being used by global elites to deflect from the drivers of inequality and enable ongoing processes of expropriation, exploitation, and exclusion. During a recent policy dialogue on the Future of Work(ers), she commented: “The real seduction of this idea is that it’s apolitical. We can talk about development and progress without having to grapple with power.”

Let’s consider how to build a data analytics community. Many organizations want to establish communities of practice or other structures with a similar aim, fostering best practice and collaboration, often with analysts working in different parts of a corporation.

A data analytics community can bring real value to people across your organization. But how do you set one up? What challenges will you face? And how can you make it as successful as possible?

I learned a lot from setting up a data analytics community in my last role. Here are the top three areas I recommend focusing on to make it a success.

1. Identify your content and your audience

This is the first step. What is your community for?

According to standard economic theory, an increase in the number of competitors decreases prices and profits. Typically, increased competition puts rival firms on the edge, doing what they can to win over customers—including reducing price. But a curious phenomenon emerged in a series of class exercises, challenging this general wisdom and suggesting that there is more than meets the eye.

Business students and executives played an extended version of a supply-chain simulation game known as the beer game, developed by one of the co-writers of this article. The setting was a serial supply chain where players assumed the roles of a retailer, a wholesaler, a distributor, and a manufacturer. Players had to overcome the challenges of managing inventory, logistics, and communication among independent decision-makers, and could adjust prices according to their strategy.