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Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions 1 – 3 , the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems 4 . Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks 5 , 6 . We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players. AlphaStar uses a multi-agent reinforcement learning algorithm and has reached Grandmaster level, ranking among the top 0.2% of human players for the real-time strategy game StarCraft II.

Solving the AC optimal power flow problem (AC-OPF) is critical to the efficient and safe planning and operation of power grids. Small efficiency improvements in this domain have the potential to lead to billions of dollars of cost savings, and significant reductions in emissions from fossil fuel generators. Recent work on data-driven solution methods for AC-OPF shows the potential for large speed improvements compared to traditional solvers; however, no large-scale open datasets for this problem exist. We present the largest readily-available collection of solved AC-OPF problems to date. This collection is orders of magnitude larger than existing readily-available datasets, allowing training of high-capacity data-driven models. Uniquely, it includes topological perturbations - a critical requirement for usage in realistic power grid operations. We hope this resource will spur the community to scale research to larger grid sizes with variable topology.

Optimal Power Flow (OPF) refers to a wide range of related optimization problems with the goal of operating power systems efficiently and securely. In the simplest setting, OPF determines how much power to generate in order to minimize costs while meeting demand for power and satisfying physical and operational constraints. In even the simplest case, power grid operators use approximations of the AC-OPF problem because solving the exact problem is prohibitively slow with state-of-the-art solvers. These approximations sacrifice accuracy and operational feasibility in favor of speed. This trade-off leads to costly \"uplift payments\" and increased carbon emissions, especially for large power grids. In the present work, we train a deep learning system (CANOS) to predict near-optimal solutions (within 1% of the true AC-OPF cost) without compromising speed (running in as little as 33--65 ms). Importantly, CANOS scales to realistic grid sizes with promising empirical results on grids containing as many as 10,000 buses. Finally, because CANOS is a Graph Neural Network, it is robust to changes in topology. We show that CANOS is accurate across N-1 topological perturbations of a base grid typically used in security-constrained analysis. This paves the way for more efficient optimization of more complex OPF problems which alter grid connectivity such as unit commitment, topology optimization and security-constrained OPF.

We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos. The model can be prompted to generate an endless variety of action-controllable virtual worlds described through text, synthetic images, photographs, and even sketches. At 11B parameters, Genie can be considered a foundation world model. It is comprised of a spatiotemporal video tokenizer, an autoregressive dynamics model, and a simple and scalable latent action model. Genie enables users to act in the generated environments on a frame-by-frame basis despite training without any ground-truth action labels or other domain-specific requirements typically found in the world model literature. Further the resulting learned latent action space facilitates training agents to imitate behaviors from unseen videos, opening the path for training generalist agents of the future.

We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language the most compelling means for such communication. To achieve this in a scalable fashion, agents must be able to relate language to the world and to actions; that is, their understanding of language must be grounded and embodied. However, learning grounded language is a notoriously challenging problem in artificial intelligence research. Here we present an agent that learns to interpret language in a simulated 3D environment where it is rewarded for the successful execution of written instructions. Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions. The agent's comprehension of language extends beyond its prior experience, enabling it to apply familiar language to unfamiliar situations and to interpret entirely novel instructions. Moreover, the speed with which this agent learns new words increases as its semantic knowledge grows. This facility for generalising and bootstrapping semantic knowledge indicates the potential of the present approach for reconciling ambiguous natural language with the complexity of the physical world.

For many decades, the predominant view in the cerebellar field has been that the olivocerebellar system’s primary function is to induce plasticity in the cerebellar cortex, specifically, at the parallel fiber-Purkinje cell synapse. However, it has also long been proposed that the olivocerebellar system participates directly in motor control by helping to shape ongoing motor commands being issued by the cerebellum. Evidence consistent with both hypotheses exists; however, they are often investigated as mutually exclusive alternatives. In contrast, here, we take the perspective that the olivocerebellar system can contribute to both the motor learning and motor control functions of the cerebellum and might also play a role in development. We then consider the potential problems and benefits of it having multiple functions. Moreover, we discuss how its distinctive characteristics (e.g., low firing rates, synchronization, and variable complex spike waveforms) make it more or less suitable for one or the other of these functions, and why having multiple functions makes sense from an evolutionary perspective. We did not attempt to reach a consensus on the specific role(s) the olivocerebellar system plays in different types of movements, as that will ultimately be determined experimentally; however, collectively, the various contributions highlight the flexibility of the olivocerebellar system, and thereby suggest that it has the potential to act in both the motor learning and motor control functions of the cerebellum.

Diffuse midline gliomas (DMGs) with histone H3K27M mutations represent a devastating paediatric brain cancer characterised by abysmal prognosis and limited treatment options. The only approved treatment is radiotherapy (RT), but most of the tumours relapse with fatal consequences. The effects of RT remain unknown because patients are not biopsied during treatment. Here, we sought to investigate whether irradiation leads to senescence induction in DMG and explore the efficacy of senolytics. We show that ionising radiation induces senescence in various H3K27M-altered DMG cell lines. Senescence induction is demonstrated by immunocytochemistry, RNA-sequencing and analysis of SASP factors by ELISA. Through testing several senolytic compounds, we identify that Bcl2 family inhibitors (e.g., Navitoclax) act as potent senolytics, driving senescent DMG cells into apoptosis, primarily via Bcl-xL inhibition. Reinforcing these findings, proteolysis-targeting chimeras (PROTACs) targeting Bcl-xL and galacto-conjugated Navitoclax (Nav-Gal) also exhibit strong senolytic activity against senescent DMG cancer cells. Finally, we show that a combination of irradiation with Navitoclax enhances cancer cell apoptosis in an orthotopic xenograft DMG model. Together, the data demonstrate that ionising irradiation leads to senescence induction in H3K27M-altered human DMG cell lines, making them particularly sensitive to apoptosis through Bcl-xL inhibition.

IntroductionPulmonary Rehabilitation (PR) is an evidence-based intervention that has been recommended in guidelines to be available to those who may benefit. However, not all patients with chronic obstructive pulmonary disease (COPD) have access to this service. Healthcare services have shown the need for the provision of PR in other forms to enable patient choice and service capacity. There is an increase in evidence for the use of the internet in the management of long-term conditions to provide education and promote self-management. The aim of this study is to see if an interactive web-based PR programme is a feasible alternative compared with conventional PR.Methods and analysisThis is a feasibility study designed to evaluate the efficacy of providing a web-based PR programme to improve patients exercise capacity, quality of life and promote self-management in patients with moderate to severe COPD compared with conventional PR programmes. Eligible patients will be randomly allocated to receive either the web-based programme or conventional rehabilitation programme for 7 weeks using an internet-based randomisation system. Participants will be recruited from PR assessments, primary care and community rehabilitation programmes. Those randomised to the web-based programme work through the website which contains all the information that the patients receive in the PR classes. They receive weekly phone calls by a professional to help progress through the course on line. The outcome measures will be recruitment rates and eligibility as well as that standard for a PR assessment including measures of exercise capacity, quality of life questionnaires and physical activity.Ethics and disseminationThe research ethics committee for Northampton has provided ethical approval for the conduct of the study. The results of the study will be disseminated through appropriate conference presentations and peer reviewed journals.Trial registration numberISRCTN03142263.

Background: Diffuse midline gliomas (DMGs) with histone H3K27M mutations represent a devastating paediatric brain cancer characterized by abysmal prognosis and limited treatment options. The only approved treatment is radiotherapy (RT), but most of the tumours relapse with fatal consequences. In this study, we sought to investigate whether irradiation leads to senescence induction and explore the efficacy of senolytics against DMG. Methods: We have characterised the senescent phenotype of five genetically heterogeneous H3K27M-altered human DMG cell lines, combining cellular and/or molecular approaches. The sensitivity of senescent cells to Bcl-xL inhibition has been demonstrated in dose/response curves in vitro and in a PDX model of DMG. Results: Here, we show that ionizing radiation induces senescence and SASP responses in both TP53 mutant and wild-type H3K27M-altered human DMG cell lines. We identify Navitoclax as a potent senolytic agent that selectively targets senescent DMG cells into apoptosis by inhibiting Bcl-xL. Related compounds, such as a proteolysis-targeting chimera (PROTAC)-mediated Bcl-xL degradation and a galacto-conjugated form of Navitoclax also show an effective senolytic activity in senescent cancer cells. Finally, we show that a combination therapy of irradiation and Navitoclax results in reduced tumor burden and increased mouse survival in an orthotopic xenograft DMG model. Conclusion: These results offer a rationale for further clinical development of senolytic therapies as part of multimodal treatment approaches for DMG patientsCompeting Interest StatementThe authors have declared no competing interest.

Doppler velocity measurements from the Anglo-Australian Planet Search reveal planetary mass companions to HD23127, HD159868, and a possible second planetary companion to HD154857. These stars are all G dwarfs. The companions are all in eccentric orbits with periods ranging from 1.2 to >9.3yr, minimum (M sin i) masses ranging from 1.5 to >4.5 Mjup, and semimajor axes between 1 and >4.5 AU. The orbital parameters are updated for the inner planet to HD154857, while continued monitoring of the outer companion is required to confirm its planet status.