Accurate structure prediction of biomolecular interactions with AlphaFold 3.

The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.

Natural Infection of and with ' Liberibacter asiaticus' in Java.

The phloem-limited bacterium ' Liberibacter asiaticus' (Las) is the putative causal pathogen of the severe Asiatic form of huanglongbing (citrus greening) and is most commonly transmitted by the Asiatic citrus psyllid . Las severely affects many species and hybrids and has been recorded in the relative, orange jasmine, (L.) Jack (syn.

Human-level performance in 3D multiplayer games with population-based reinforcement learning.

Reinforcement learning (RL) has shown great success in increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete with other agents. We used a tournament-style evaluation to demonstrate that an agent can achieve human-level performance in a three-dimensional multiplayer first-person video game, in Capture the Flag mode, using only pixels and game points scored as input.

Human-level control through deep reinforcement learning.

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms.

The economic benefit of organizational restructuring of the cardiothoracic intensive care unit.

Objectives: Compare cost/benefits of organizational restructuring of the cardiac intensive care unit (CICU).

Design: Prospective, with a retrospective control period.

Setting: Academic medical center.

The modified nasal trumpet maneuver.

Unlabelled: The last decade has witnessed a proliferation of devices or methods that facilitate intubation in difficult circumstances, maintain ventilation, or which do both. These all require properly functioning and specially designed apparatus, the use of which requires variable degrees of expertise. This technical communication describes the author's experience with a simple technique that uses virtually universally available materials--a nasal trumpet (airway) and an endotracheal tube (ETT) connector--to rescue patients in the cannot-ventilate/cannot-intubate scenario.