Surface-enhanced Raman spectroscopy (SERS) is a potential fast and inexpensive method of analyte quantification, which can be combined with deep learning to discover biomarker-disease relationships. This study aims to address present challenges of SERS through a novel SERS bio-quantification framework, including spectral processing, analyte quantification, and model explainability. To this end,serotonin quantification in urine media was assessed as a model task with 682 SERS spectra measured in a micromolar range using cucurbit[8]uril chemical spacers. A denoising autoencoder was utilized for spectral enhancement, and convolutional neural networks (CNN) and vision transformers were utilized for biomarker quantification. Lastly, a novel context representative interpretable model explanations (CRIME) method was developed to suit the current needs of SERS mixture analysis explainability. Serotonin quantification was most efficient in denoised spectra analysed using a convolutional neural network with a three-parameter logistic output layer (mean absolute error = 0.15 {\mu}M, mean percentage error = 4.67%). Subsequently, the CRIME method revealed the CNN model to present six prediction contexts, of which three were associated with serotonin. The proposed framework could unlock a novel, untargeted hypothesis generating method of biomarker discovery considering the rapid and inexpensive nature of SERS measurements, and the potential to identify biomarkers from CRIME contexts.

Most diners expect to be given bread and drinks before their order is even taken, and a neuroscientist has revealed it is more of a strategy than convenience. Daniel Amen, an American doctor who runs his clinic, shared that pre-meal items affect the brain and make it harder for people to control their urges. Both bread and alcohol release serotonin into the brain, making you feel happier and calmer. It may sound counterintuitive, since these items fill you up. But the chemical eventually drops, leaving people searching for that high - and they do so by filling their stomachs.

The fundamental goal of small molecule discovery is to generate chemicals with target functionality. While this often proceeds through structure-based methods, we set out to investigate the practicality of orthogonal methods that leverage the extensive corpus of chemical literature. We hypothesize that a sufficiently large text-derived chemical function dataset would mirror the actual landscape of chemical functionality. Such a landscape would implicitly capture complex physical and biological interactions given that chemical function arises from both a molecule's structure and its interacting partners. To evaluate this hypothesis, we built a Chemical Function (CheF) dataset of patent-derived functional labels. This dataset, comprising 631K molecule-function pairs, was created using an LLM- and embedding-based method to obtain functional labels for approximately 100K molecules from their corresponding 188K unique patents. We carry out a series of analyses demonstrating that the CheF dataset contains a semantically coherent textual representation of the functional landscape congruent with chemical structural relationships, thus approximating the actual chemical function landscape. We then demonstrate that this text-based functional landscape can be leveraged to identify drugs with target functionality using a model able to predict functional profiles from structure alone. We believe that functional label-guided molecular discovery may serve as an orthogonal approach to traditional structure-based methods in the pursuit of designing novel functional molecules.

Patience is determined by the levels of the hormone serotonin, dictating whether you can calmly anticipate a reward or crave instant gratification, a study has found. Researchers from Japan found that artificially triggering the release of the hormone in mice made the rodents more patient when waiting for food in lab experiments. Furthermore, the team found that two different areas of the brain are responsible for separately evaluating the benefits of waiting patiently for a reward. The findings may help to refine the development of antidepressants that modulate serotonin levels in humans -- such as by targeting specific areas of the brain. In their study, neuroscientist Katsuhiko Miyazaki of the Okinawa Institute of Science and Technology Graduate University and colleagues worked with special mice with light-sensitive neurons that release serotonin when triggered.

Robots and self-driving vehicles face a number of challenges when navigating through real environments. Successful navigation in dynamic environments requires prioritizing subtasks and monitoring resources. Animals are under similar constraints. It has been shown that the neuromodulator serotonin regulates impulsiveness and patience in animals. In the present paper, we take inspiration from the serotonergic system and apply it to the task of robot navigation. In a set of outdoor experiments, we show how changing the level of patience can affect the amount of time the robot will spend searching for a desired location. To navigate GPS compromised environments, we introduce a deep reinforcement learning paradigm in which the robot learns to follow sidewalks. This may further regulate a tradeoff between a smooth long route and a rough shorter route. Using patience as a parameter may be beneficial for autonomous systems under time pressure.

Gül Dölen and Eric Edsinger are probably the only people in the world who've watched an octopus have a bad ecstasy trip. "The first couple of animals we tried, we gave them way, way, way too much," says Dölen, a neuroscientist at John Hopkins University, "because I thought, 'well, if it's going to work it's probably going to need monster doses to see anything.'" The masters of disguise sent waves of color rippling down their arms, blanched white, and changed their breathing patterns. Suspecting that the ecstasy--also known as MDMA--was overwhelming the animals, Dölen and Edsinger, a marine biologist at the University of Chicago, dialed down the dosage. Three test trials later, they settled on one one-thousandth of the original as a reasonable amount.

It's fair to say that our world has reached a point where technology is so advanced that robots are almost expected to be lifelike – but what about robots that develop mental illnesses, hallucinations and depression like human beings do? Is this just science fiction, or can we really expect artificial intelligence to grow even more similar to humans in the not-so-distant future? Back in March, New York University hosted a symposium in New York City called Canonical Computations in Brains and Machines, where a group of neuroscientists and experts in the field of artificial intelligence spoke about overlaps in the ways in which human beings and machines think and process information. According to one of these neuroscientists – Zachary Mainen of the Champalimaud Centre for the Unknown – we might expect advanced machines to soon be able to experience some of the same mental problems that people do. "I'm drawing on the field of computational psychiatry, which assumes we can learn about a patient who's depressed or hallucinating from studying AI algorithms like reinforcement learning. If you reverse the arrow, why wouldn't an AI be subject to the sort of things that go wrong with patients?"

Getting depressed might seem a strange affliction for a robot, but artificially intelligent brains may also suffer from similar mental health problems to people. That's the claim put forward by an American neuroscientist, who says that machines could even hallucinate, if not monitored correctly. Developers will need to create software equivalents to anti-depressants, which help to control levels of the neurotransmitter serotonin in the brain, he recommends. Artificial intelligence and robotic brains may suffer from some of the mental health issues traditionally associated with humans. Zachary Mainen works at the Champalimaud Centre for the Unknown in Lisbon and studies how the brain makes decisions.

Scientists believe the introduction of a hormone-like system, such as the one found in the human brain, could give AI the ability to reason and make decisions like people do. Recent research indicates human emotion, to a certain extent, is the byproduct of learning. And that means machines may have to risk depression or worse if they ever want to think or feel. Zachary Mainen, a neuroscientist at the Champalimaud Centre for the Unknown in Lisbon, speaking at the Canonical Computation in Brains and Machines symposium, discussed the implications of recent experiments to discover the effects serotonin has on decision making. According to Mainen and his team, serotonin may not be related to'mood' or emotional states such as happiness, but instead is a neuro-modulator designed to update and change learning parameters in the brain.

A hallucinating artificial intelligence might see something like this product of Google's Deep Dream algorithm. As artificial intelligence (AI) allows machines to become more like humans, will they experience similar psychological quirks such as hallucinations or depression? And might this be a good thing? Last month, New York University in New York City hosted a symposium called Canonical Computations in Brains and Machines, where neuroscientists and AI experts discussed overlaps in the way humans and machines think. Zachary Mainen, a neuroscientist at the Champalimaud Centre for the Unknown, a neuroscience and cancer research institute in Lisbon, speculated that we might expect an intelligent machine to suffer some of the same mental problems people do.