Beneath gilded portraits and suits of armour in Windsor Castle, 160 guests wined and dined at a lavish banquet to fete US President Donald Trump's unprecedented second state visit to the UK on Wednesday evening. Along with the impeccable table settings, three-course meal and custom cocktail, who was there and, just as importantly, who was seated next to who is carefully planned, since the event is as much about diplomacy as it is about fine dining. This year's guest list was conspicuously missing screen stars or celebrity faces, with not even royal perennials like Sir David Beckham or Sir Elton John attending. Instead, the list was mostly royals, tech and finance executives, and politicos from both sides of the Atlantic. From Trump's seat of honour at the centre of the table, next to his host King Charles III, those up and down the table ranged from lesser-known but influential White House players to professional golfers.

The number of planning approvals for new homes in England is unacceptable, the new housing secretary has said, after official data showed permission for building homes fell to a record low during Labour's first year in office. Fewer than 29,000 projects were granted permission by councils in the year ending June 2025 - striking a blow to the government's promise to deliver 1.5 million homes by the next election. Steve Reed, who has taken over from Angela Rayner as housing secretary, said fixing the planning system won't happen overnight. Conservative shadow housing secretary Sir James Cleverly said that Labour had promised to'build, build, build' but their flagship planning reforms clearly aren't working. You can see the figures for your local area in BBC Verify's housing tracker.

US and UK sign major nuclear power deal: What does it include? British Prime Minister Keir Starmer and United States President Donald Trump have signed a multibillion-pound deal to expand nuclear power across both nations. Known as the Atlantic Partnership for Advanced Nuclear Energy, the agreement aims to speed up the construction of new reactors and provide reliable, low-carbon energy for high-demand sectors, including energy-intensive artificial intelligence data centres. Britain's largest energy supplier, Centrica, will pair up with the US firm X-energy to develop up to 12 advanced modular reactors in Hartlepool, a port town in northeast England, which could power 1.5 million homes and create up to 2,500 jobs. US nuclear technology company Holtec, France's state-backed energy giant EDF Energy, and United Kingdom real estate and investment firm Tritax will develop advanced data centres powered by small modular reactors (SMRs) in Nottinghamshire, East Midlands, valued at about 11 billion pounds ($15bn).

A former Reform UK MP is under investigation over alleged racial abuse against a Sky News journalist. James McMurdock, who represents South Basildon and East Thurrock in Essex, is accused of starting a chain of posts on X that spelled out a racial slur on 4 August. He appeared to deny making the post, saying his accuser, Huntingdon MP Ben Obese-Jecty, had nothing better to do. The Parliamentary standards commissioner is due to rule if he breached the House of Commons code of conduct. It was investigating a potential violation of rule 11, defined as actions causing significant damage to the reputation to the House of Commons or its MPs.

Anti-Trump Protesters Take Aim at'Naive' US-UK AI Deal Thousands marched in London to protest President Donald Trump's second state visit. Among them were many environmental activists unhappy with Britain's new AI deal with the US. They played extremely loud music. They let off foul-smelling smoke from a can. Thousands of people gathered on Wednesday in central London to protest against Trump's presence in the UK, accusing the UK government of kowtowing to him by hosting him for a state visit for the second time.

A wave of AI users presenting in states of psychological distress gave birth to an unofficial diagnostic label. Experts say it's neither accurate nor needed, but concede that it's likely to stay. A new trend is emerging in psychiatric hospitals. People in crisis are arriving with false, sometimes dangerous beliefs, grandiose delusions, and paranoid thoughts. A common thread connects them: marathon conversations with AI chatbots.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? A Ukrainian drone has struck a car in Russia's Belgorod border region, killing one person and injuring another, according to the region's governor. The Ukrainian army lost more than 1,500 troops during front-line fighting over the past day, reported Russia's state TASS news agency, citing the Ministry of Defence.

Misinformation in healthcare, from vaccine hesitancy to unproven treatments, poses risks to public health and trust in medical systems. While machine learning and natural language processing have advanced automated fact-checking, validating biomedical claims remains uniquely challenging due to complex terminology, the need for domain expertise, and the critical importance of grounding in scientific evidence. We introduce CER (Combining Evidence and Reasoning), a novel framework for biomedical fact-checking that integrates scientific evidence retrieval, reasoning via large language models, and supervised veracity prediction. By integrating the text-generation capabilities of large language models with advanced retrieval techniques for high-quality biomedical scientific evidence, CER effectively mitigates the risk of hallucinations, ensuring that generated outputs are grounded in verifiable, evidence-based sources. Evaluations on expert-annotated datasets (HealthFC, BioASQ-7b, SciFact) demonstrate state-of-the-art performance and promising cross-dataset generalization. Code and data are released for transparency and reproducibility: https://github.com/PRAISELab-PicusLab/CER

Anatomical trees play an important role in clinical diagnosis and treatment planning. Yet, accurately representing these structures poses significant challenges owing to their intricate and varied topology and geometry. Most existing methods to synthesize vasculature are rule based, and despite providing some degree of control and variation in the structures produced, they fail to capture the diversity and complexity of actual anatomical data. We developed a Recursive variational Neural Network (RvNN) that fully exploits the hierarchical organization of the vessel and learns a low-dimensional manifold encoding branch connectivity along with geometry features describing the target surface. After training, the RvNN latent space can be sampled to generate new vessel geometries. By leveraging the power of generative neural networks, we generate 3D models of blood vessels that are both accurate and diverse, which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes. These results closely resemble real data, achieving high similarity in vessel radii, length, and tortuosity across various datasets, including those with aneurysms. To the best of our knowledge, this work is the first to utilize this technique for synthesizing blood vessels.

Institute of Mathematics and Statistics, Federal University of Rio Grande do Sul (UFRGS), Av. Center for Coastal and Oceanic Geology Studies (CECO), Federal University of Rio Grande do Sul (UFRGS), Av. Abstract The forecast of wave variables are important for several applications that depend on a better description of the ocean state. Due to the chaotic behaviour of the differential equations which model this problem, a well know strategy to overcome the difficulties is basically to run several simulations, by for instance, varying the initial condition, and averaging the result of each of these, creating an ensemble. Moreover, in the last few years, considering the amount of available data and the computational power increase, machine learning algorithms have been applied as surrogate to traditional numerical models, yielding comparative or better results. In this work, we present a methodology to create an ensemble of different artificial neural networks architectures, namely, MLP, RNN, LSTM, CNN and a hybrid CNN-LSTM, which aims to predict significant wave height on six different locations in the Brazilian coast. The networks are trained using NOAA's numerical reforecast data and target the residual between observational data and the numerical model output. A new strategy to create the training and target datasets is demonstrated. Introduction Numerical simulations of both weather and ocean parameters rely on the evolution of nonlinear dynamical systems that have a high sensitivity on initial conditions. Considering that errors in the observations and analysis are present, and therefore in the initial conditions, the concept of a unique deterministic solution of the governing equations becomes fragile [1, 2].