The UnconTrust Database for Studies of Unconscious Semantic Processing and Attentional Allocation.

The question of what processes can take place without conscious awareness has generated extensive research. Yet there is still no consensus regarding the extent and scope of unconscious processing, and past research abounds with conflicting results. A possible reason for this lack of consensus is the diversity of methods in the field, as the methodological choices might influence the results.

Feeding Expressed Breast Milk Alters the Microbial Network of Breast Milk and Increases Breast Milk Microbiome Diversity over Time.

Breastfeeding supplies nutrition, immunity, and hormonal cues to infants. Feeding expressed breast milk may result in de-phased milk production and feeding times, which distort the real-time circadian cues carried by breast milk. We hypothesized that providing expressed breast milk alters the microbiotas of both breast milk and the infant's gut.

Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors.

We introduce a quantum-classical generative model for small-molecule design, specifically targeting KRAS inhibitors for cancer therapy. We apply the method to design, select and synthesize 15 proposed molecules that could notably engage with KRAS for cancer therapy, with two holding promise for future development as inhibitors. This work showcases the potential of quantum computing to generate experimentally validated hits that compare favorably against classical models.

Memory engram synapse 3D molecular architecture visualized by cryoCLEM-guided cryoET.

Memory is incorporated into the brain as physicochemical changes to engram cells. These are neuronal populations that form complex neuroanatomical circuits, are modified by experiences to store information, and allow for memory recall. At the molecular level, learning modifies synaptic communication to rewire engram circuits, a mechanism known as synaptic plasticity.

Eukaryotic composition across seasons and social groups in the gut microbiota of wild baboons.

Background: Animals coexist with complex microbiota, including bacteria, viruses, and eukaryotes (e.g., fungi, protists, and helminths).

Early-life gut inflammation drives sex-dependent shifts in the microbiome-endocrine-brain axis.

Despite recent advances in understanding the connection between the gut microbiota and the adult brain, significant knowledge gaps remain regarding how gut inflammation affects brain development. We hypothesized that gut inflammation during early life would negatively affect neurodevelopment by disrupting microbiota communication to the brain. We therefore developed a novel pediatric chemical model of inflammatory bowel disease (IBD), an incurable condition affecting millions of people worldwide.

The CALERIE Genomic Data Resource.

Caloric restriction (CR) slows biological aging and prolongs healthy lifespan in model organisms. Findings from the CALERIE randomized, controlled trial of long-term CR in healthy, nonobese humans broadly supports a similar pattern of effects in humans. To expand our understanding of the molecular pathways and biological processes underpinning CR effects in humans, we generated a series of genomic datasets from stored biospecimens collected from n = 218 participants during the trial.

Reactive carbon capture using electrochemical reactors.

The electrolytic upgrading of CO presents a promising strategy to mitigate global CO emissions while generating valuable carbon-based products such as carbon monoxide, formate, and ethylene. However, the adoption of industrial-scale CO electrolyzers is hindered by the high energy and capital costs associated with the purification and pressurization of captured CO prior to electrolysis. One promising solution is "reactive carbon capture," which involves the electrolytic conversion of the eluent from CO capture units, or the "reactive carbon solution," directly into valuable products.

A sensitive period for the development of episodic-like memory in mice.

Episodic-like memory is a later-developing cognitive function supported by the hippocampus. In mice, the formation of extracellular perineuronal nets in subfield CA1 of the dorsal hippocampus controls the emergence of episodic-like memory during the fourth postnatal week (Ramsaran et al., 2023).

Introspective psychophysics for the study of subjective experience.

Studying subjective experience is hard. We believe that pain is not identical to nociception, nor pleasure a computational reward signal, nor fear the activation of "threat circuitry". Unfortunately, introspective self-reports offer our best bet for accessing subjective experience, but many still believe that introspection is "unreliable" and "unverifiable".

Beat-based dancing to music has evolutionary foundations in advanced vocal learning.

Dancing to music is ancient and widespread in human cultures. While dance shows great cultural diversity, it often involves nonvocal rhythmic movements synchronized to musical beats in a predictive and tempo-flexible manner. To date, the only nonhuman animals known to spontaneously move to music in this way are parrots.

Natural forgetting reversibly modulates engram expression.

Memories are stored as ensembles of engram neurons and their successful recall involves the reactivation of these cellular networks. However, significant gaps remain in connecting these cell ensembles with the process of forgetting. Here, we utilized a mouse model of object memory and investigated the conditions in which a memory could be preserved, retrieved, or forgotten.

Performance Prediction of High-Entropy Perovskites LaSrMnCoFeO with Automated High-Throughput Characterization of Combinatorial Libraries and Machine Learning.

Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition-structure-performance relationships of high-entropy LaSrMnCoFeO perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells.

Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.

Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development.

More than words: can free reports adequately measure the richness of perception?

The question of the richness (or sparseness) of conscious experience has evoked ongoing debate and discussion. Claims for both richness and sparseness are supported by empirical data, yet they are often indirect, and alternative explanations have been put forward. Recently, it has been suggested that current experimental methods limit participants' responses, thereby preventing researchers from assessing the actual richness of perception.

Markers of consciousness in infants: Towards a 'cluster-based' approach.

As recently as the 1980s, it was not uncommon for paediatric surgeons to operate on infants without anaesthesia. Today, the same omission would be considered criminal malpractice, and there is an increased concern with the possibility of consciousness in the earliest stage of human infancy. This concern reflects a more general trend that has characterised science since the early 1990s of taking consciousness seriously.

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science.

Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity.

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs).

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures.

Aligning consciousness science and U.S. funding agency priorities.

We recently completed the Fund Consciousness Science! Project: a workshop and subawards program aimed to align United States federal funding mechanisms and consciousness research. Here we describe the project’s motivation, execution, and outcomes to motivate similar efforts both locally and globally.