Enduring questions over which part of the brain helps produce that feeling of being'awake' have been answered, thanks to stunningly detailed new brain imagery. Researchers' new high-resolution brain scans allowed them to see brain connections at a granular'submillimeter' level -- meaning down to a tiny 3/100ths of an inch. The images were then used to map a neural network of previously unseen pathways in the brain, called the'default ascending arousal network' or dAAN, which they now theorize is the core region that helps humans sustain wakeful consciousness. In recent years, the neuroscientists studying consciousness have divided this curious mystery of how the human brain is self-aware into two sub-categories: 'arousal' (wakefulness) and'awareness' (the subjective experience of being alive). The researchers hope their work exploring this dAAN pathway will help develop new treatments for patients with comas, or other conditions that hinge on wakefulness.

The adoption of deep learning techniques in genomics has been hindered by the difficulty of mechanistically interpreting the models that these techniques produce. In recent years, a variety of post-hoc attribution methods have been proposed for addressing this neural network interpretability problem in the context of gene regulation. Here we describe a complementary way of approaching this problem. Our strategy is based on the observation that two large classes of biophysical models of cis-regulatory mechanisms can be expressed as deep neural networks in which nodes and weights have explicit physiochemical interpretations. We also demonstrate how such biophysical networks can be rapidly inferred, using modern deep learning frameworks, from the data produced by certain types of massively parallel reporter assays (MPRAs). These results suggest a scalable strategy for using MPRAs to systematically characterize the biophysical basis of gene regulation in a wide range of biological contexts. They also highlight gene regulation as a promising venue for the development of scientifically interpretable approaches to deep learning.

In this age of "big data," artificial intelligence (AI) has become a valuable ally for scientists. Machine learning algorithms, for instance, are helping biologists make sense of the dizzying number of molecular signals that control how genes function. But as new algorithms are developed to analyze even more data, they also become more complex and more difficult to interpret. Quantitative biologists Justin B. Kinney and Ammar Tareen have a strategy to design advanced machine learning algorithms that are easier for biologists to understand. The algorithms are a type of artificial neural network (ANN).

In this age of "big data," artificial intelligence (AI) has become a valuable ally for scientists. Machine learning algorithms, for instance, are helping biologists make sense of the dizzying number of molecular signals that control how genes function. But as new algorithms are developed to analyze even more data, they also become more complex and more difficult to interpret. Quantitative biologists Justin B. Kinney and Ammar Tareen have a strategy to design advanced machine learning algorithms that are easier for biologists to understand. The algorithms are a type of artificial neural network (ANN).

IMAGE: A mathematical thermodynamic model for gene regulation (top, left) is formulated as an artificial neural network (ANN) (bottom, left). Large DNA datasets are fed through the new ANN (right). In this age of "big data," artificial intelligence (AI) has become a valuable ally for scientists. Machine learning algorithms, for instance, are helping biologists make sense of the dizzying number of molecular signals that control how genes function. But as new algorithms are developed to analyze even more data, they also become more complex and more difficult to interpret.

"Dealing with small datasets is a fundamental part of doing science," CSHL Assistant Professor Justin Kinney explained. The challenge is that, with very little data, it's not only hard to come to a conclusion; it's also hard to determine how certain your conclusions are. "It's important to not only produce the best guess for what's going on, but also to say, 'This guess is probably correct,'" said Kinney. A good example is clinical drug trials. "When each data point is a patient, you will always be dealing with small datasets, and for very good reasons," he said.

Big Data is all the rage today, but Small Data matters too! Drawing reliable conclusions from small datasets, like those from clinical trials for rare diseases or in studies of endangered species, remains one of the trickiest obstacles in statistics. Now, Cold Spring Harbor Laboratory (CSHL) researchers have developed a new way to analyze small data, one inspired by advanced methods in theoretical physics, but available as easy-to-use software. "Dealing with small datasets is a fundamental part of doing science," CSHL Assistant Professor Justin Kinney explained. The challenge is that, with very little data, it's not only hard to come to a conclusion; it's also hard to determine how certain your conclusions are.

Big Data is all the rage today, but Small Data matters too! Drawing reliable conclusions from small datasets, like those from clinical trials for rare diseases or in studies of endangered species, remains one of the trickiest obstacles in statistics. Now, Cold Spring Harbor Laboratory (CSHL) researchers have developed a new way to analyze small data, one inspired by advanced methods in theoretical physics, but available as easy-to-use software. "Dealing with small datasets is a fundamental part of doing science," CSHL Assistant Professor Justin Kinney explained. The challenge is that, with very little data, it's not only hard to come to a conclusion; it's also hard to determine how certain your conclusions are.

Last summer, Adobe researchers surveyed over 2,000 office employees and nine thought leaders on the evolving definition of work. Through this exercise, researches uncovered several interesting insights about the future of work, including the most effective "carrots" used to motivate employees. "Employers may be focusing too much on ping pong tables and free dry cleaning instead of technology that helps their employees feel valued and encouraged," says Jeff Vijungco, vice president of global talent at Adobe. To help them find fulfillment, "employers need to pay attention to productivity more than perks," he adds. Workspaces that encourage subconscious thought, smart coffee machines and conference rooms that remember what you like, virtual assistants that delight, flexible attendance policies, and performance-based rewards were at the top of the list, according to the study.

Auto worker Imani Long assembles parts that will be welded by a robot at a Fiat Chrysler plant in Warren, Michigan. This story is the third in a four part look at'Robots and Michigan.' Check back later this month, when we'll bring you the series finale. They cut and weld car parts, they make pumps and fans, they mill paper and pulp. Factory workers on Michigan's remote Upper Peninsula have been producing manufactured goods for generations.