Larry, the Downing Street cat, has had to adapt to seven different prime ministers moving into No 10 over the past decade. Yet while the nation’s attention is fixed on the departing and incoming political leaders, who is thinking about the effect these upheavals might have on one of the property’s most enduring residents?

Cats thrive on predictability and the consistency of their environment is important for their welfare. On the whole cats prefer an unchanging environment. Some cats cope with change better than others. Many cats are distressed by disturbances such as moving house, changes in familiar routines, people moving into or leaving the household, or even changes to the scent of their home environment.

Unlike dogs, cats aren’t obligate social species, therefore may not have such a strong proclivity to form social attachments. However, in certain situations domestic cats can be found living in social groups, particularly in environments with a plentiful food supply.

Some cats enjoy the company of people, while others prefer a more solitary life. This is determined partly by their early experiences. A young kitten that learns in the early weeks of their life that contact with people is associated with nice experiences is more likely to seek social interactions with people as an adult. Kittens raised by mothers who show friendly behaviour towards people can learn to value human company through the process of social learning.

Genetics also influence a cats friendliness towards people. Researchers in a 1995 study found that the kittens of friendlier fathers were more likely to show friendly behaviour towards unfamiliar people even if they weren’t socialised as a new kitten.

Some cats appear to suffer when separated from their caregivers, although some researchers question whether this can be described as a secure attachment (one that is necessary to maintain feelings of safety and security in threatening situations).

Researchers have identified five different forms of cat-owner relationship, based on the level of emotional investment of the owner, the way the cat relates to others, their aloofness and their need to be in close proximity to their owner. These types range from open relationships (characterised by a lightly emotionally invested owner and an avoidant cat) to casual relationships, co-dependence and friendship.

Although the Starmer family has moved out and the Burnhams have taken up residence, Larry’s main caretakers are civil servants, so his day-to-day care won’t change. Larry’s behaviour in recent days (strutting along Downing Street, nonplussed by the comings and goings and embarking on a successful hunting expedition) suggests that he takes such changes in cohabitants in his stride.

Other animals

The new prime minister has a dog called Axel, and although cats and dogs can live together harmoniously and even form close bonds, integrating Axel into Larry’s home might cause tensions if not handled well. However, not even the prime minster would dare to disturb Larry’s position as chief mouser. Andy Burnham has told the BBC he’s not bringing Axel to London with him because he doesn’t think the two animals will get on.

If he changes his mind there are a few things he could do to help Axel and Larry get along. Introducing a dog into a household with a cat should be done gradually, with the aim to prevent any chasing or aggression between the pets.

Before the dog arrives, settle the cat into a safe space, which the dog won’t have access to. This might be a bedroom, bathroom or any part of the house that the cat likes to relax in, ensuring the cat has access to everything they need such as places to hide, litter trays, food and water.

Scent is incredibly important to both cats and dogs, so getting them accustomed to each other’s aroma in the home helps to smooth the introduction. This can be done by introducing bedding or toys used by the other pet, or gently wiping a damp cloth across the body of the pet to pick up their scent.

It’s vital that the first meeting goes well and there are a few things that will help to ensure they get off on the right foot. Make sure the dog isn’t full of energy by exercising them before the meeting. Start with a barrier such as a baby gate between them.

When they first meet give both pets plenty of their favourite food treats, to reward calm behaviour and so that they associate the presence of other with something nice happening.

Signs of stress

Cats can be difficult to read but there are known warning signs of stress in cats. More obvious stress behaviour includes hissing, growling, swiping or puffed up fur. But indicators also include the cat running away or hiding and refusing treats they normally avoid. Flattened ears, dilated pupils, tense body posture and a flicking tail are all signals a cat is feeling tense.

Larry seems like quite an independent confident cat if reports “he has the run of the house” are anything to go by, so it’s likely that he will soon teach the new inhabitants of No 10 how he likes things done.

Emily Blackwell does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

People often take their muscles for granted. They pick up a bag, tighten a grip, climb a hill, hold a cup of coffee and the body quietly adjusts force, speed and position without the person ever thinking about it.

Muscle turns chemical energy into movement. It is able to produce force smoothly and reliably, even though it is built from huge numbers of tiny molecular components. When the load changes, muscles don’t simply wobble or fail. They adapt.

But the way that muscle works on a molecular level isn’t a settled issue. With colleagues, our team at the University of Bristol built a physical model using ordinary electric motors to try and simulate how muscles may perform its tasks.

The results, published in the Royal Society journal Interface, show that the coordination we see in real muscle may simply emerge as a property of their setup. It’s not something you have to programme and police continuously. The findings could even help improve the way that robots function.

If we were to zoom inside a muscle, we would find bundles of smaller and smaller structures. The sarcomere is the key force producing unit. It is made of protein filaments, called actin, and molecular motors called myosin. Together, they form the actomyosin system.

Tiny myosin motors repeatedly grab, pull and release actin cables, helping muscle fibres contract. When you hold a cup of tea, thousands of these motors have to work together to produce a steady force. But there are unanswered questions about this system.

We first devised a mathematical model of muscle. Real muscle motors (myosin) run on a complex chemical cycle, but we didn’t try to reproduce that chemistry. Instead, we represented each force producing unit as something much simpler: a cycling “rotor” (or motor) that can contribute only when it is mechanically engaged to a physical “backbone”. When it is disengaged, it effectively drops out, and becomes isolated from the rest.

To test whether we could translate these ideas to a real physical system, we built a tabletop device: a line of simple electric motors interacting through a shared mechanical backbone. They pulled against a thick fluid, creating resistance similar to the viscous load experienced by biological motors inside muscle.

Our physical model produces force when supplied with current. We wanted to see whether, when many driving units were embedded in the right kind of mechanical setting, could the setting organise into coordinated muscle-like behaviour?

In both the mathematical model and the device, the motors don’t stay connected all the time. Instead, each motor briefly makes contact with a shared moving backbone, pushes on it, then lets go. As the backbone moves, it carries the mechanical effects of one motor to the next, allowing them to coordinate without ever communicating directly.

When they are not connected, each motor acts on its own. That single design choice makes the whole network of motors reorganise itself over time – much like biological motors do inside muscle, where motors attach and detach as part of their biochemical cycles.

In practical terms, the scaffold where the rotors live is doing a lot of the coordinating work, but it is hidden. It mediates how units can “feel” one another strongly enough for collective patterns to form.

Even in this stripped-down system, several muscle-like forms of behaviour spontaneously appear. As resistance increases, the motors collectively develop a load-speed relationship. Load refers to the mechanical tension and physical stress placed on the system.

More units end up contributing effectively as the load rises. Their participation increases under increasing loads. We also found that under certain conditions, motion can become jerky. For example, periods of coordinated pulling were interrupted by sudden slips. This echoes patterns seen in real actomyosin experiments.

Instead of everything working in unison, the system can self-organise into travelling waves of coordination, where the pattern moves along the array.

In a loose way, this “scales up” the core idea behind a sarcomere: many force units embedded in a shared structure that transmits forces from one unit to the next, allowing coordination to emerge spontaneously.

Coordinating without a conductor

Muscle isn’t the only place where nature has solved the problem of coordinating many tiny motors. Microscopic flagella are tail-like appendages seen on the cells of some eukaryotes – the group of organisms that includes animals and plants.

Examples of these flagella include the tail on human sperm. Cilia are microscopic, hair-like structures on many eukaryotic cells. Both flagella and cilia are powered by large numbers of molecular motors distributed along a long, slender scaffold.

Their internal core – the axoneme – is a beautifully organised structure that transmits forces along its length and turns local motor activity into bending waves. Those waves are essential: in sperm they underpin motility and fertility, and in cilia they help move fluids in the airways and other tissues, with clear links to health and disease.

In that sense, flagella are “muscle-like” systems in disguise. You again have drivers (motors that generate force) embedded in a coupling structure (a mechanical scaffold that transmits force and feedback). And the geometry determines how local activity adds up to a coherent, travelling wave.

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Read more: The way a sperm tail moves can be explained by mathematics worked out by Alan Turing

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If the structure and layout are doing some of the organising work, it speaks to a question that has long intrigued scientists: how evolution builds systems made up of a large number of individual motors that nevertheless coordinate themselves, remain reliable and adapt to changing conditions without a central control system?

Uses in robotics

Our results matter beyond biology. In robotics, getting many actuators (the “muscles” of a robot, which convert an energy source like electricity into motion) to cooperate is challenging and expensive. You add sensors, feedback loops, control algorithms, sometimes even mechanical ratchets or complex gearing to keep everything stable under loads. It works, but it can be fragile and prone to errors.

Our physical model of muscle, where coordination emerges as a property of the setup, suggests that simpler actuators for robotics are possible. This is exactly the kind of fundamental design that researchers have been chasing for soft robots (which use flexible materials) and artificial muscles for robotics.

Muscle may be an example of coordination without a conductor. Thousands of molecular motors working together, not because each one is individually controlled, but because the soft structure around them helps guide their collective behaviour.

Our findings suggest that robotics could begin to harness the same principle. In future, this could help bring robotics closer to biology, opening the way to more organic machines, whose bodies help shape how they move and adapt.

This research was carried out by Benjamin Warmington as part of his doctorate under the supervision of Prof Jonathan Rossiter and Dr Hermes Bloomfield-Gadelha at the Polymaths and Soft-Robotics laboratories at Bristol. This research was funded by the Engineering and Physical Sciences Research Council Doctoral Training Partnership (grant no. EP/T517872/1) and carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol—http://www.bristol.ac.uk/acrc/.

Deletion is a common part of modern life. We send files and folders to the recycle bin all the time, and often get rid of unwanted personal accounts. But do you know what really happens when you hit the “delete” button?

The process of deletion is often poorly explained by tech providers. Lack of transparency around how requests are processed, and the absence of clear confirmation that data has been removed, are frequently highlighted in research.

These issues are not limited to operating systems. On social media platforms and a wide range of subscription services, deletion mechanisms are often just as unclear. Such misconceptions can have serious implications for the wellbeing of users, exposing them to security and privacy vulnerabilities.

A further complication is that many users fear losing data, files, photos and videos through accidental deletion – a condition dubbed “diagraphephobia”. Though this is not a recognised pathology, studies of digital hoarding behaviour show that some people get very anxious at the thought of losing or accidentally deleting personal information such as photos and music.

Such concerns extend the amount of personal data that sits untended but undeleted, ready for potential misappropriation.

Deletion vs erasure

A common misconception is thinking deletion means erasure – the idea that once a deleted item is no longer visible, it has been completely erased with no means of retrieving it. This is largely wrong.

When users delete files – moving them to the trash bin, then deleting permanently – the data isn’t actually erased. Rather, the system marks storage space as reusable and updates metadata to unlink the file. But the underlying content often remains recoverable with the right tools, especially when data is duplicated across multiple systems or devices.

More robust forms of deletion include physical destruction of the storage medium, overwriting the memory blocks with new data to bury the original, or encrypting the data and then destroying the key.

However, such methods can be expensive and may render the storage device unusable (for example, when overwriting data on magnetic media). The characteristics of cloud infrastructure also pose challenges to secure data deletion.

Similarly, content on public social networks may not really be erased after deletion due to replies, comments and internet archives which can all store the posts in some fashion. The meaning of a deleted tweet can, for example, be recreated based on replies and mentions.

Zombie accounts

Another misconception is that deleting an app from a device automatically deletes the associated account. Users often leave mobile app accounts undeleted because they are unaware of their existence, having deleted the related app.

These are called zombie accounts – abandoned profiles for a wide array of services, from shopping and storage to dating, finance and streaming. Millions of users possess zombie accounts, with personal data that is vulnerable to cyber-attacks and data breaches.

The exponential growth of AI raises a further question: can data really be deleted once it has been used to train AI models? Machine learning modules easily memorise the data they have been trained on, but “unlearning” is difficult.

In theory, people in Europe and many other countries around the world have a leagal “right to be forgotten”. This means they can request the full erasure of any personal data a platform or a company may hold. AI developers are considered data controllers under the EU’s General Data Protection Regulation (GDPR) laws, for example, and are subject to this obligation.

However, there are no clear guidelines on how erasure should be enforced within AI systems. Regulators may request deletion, but AI companies can argue that compliance is infeasible on account of technical constraints.

Explainable deletion

To counter the risks posed by incomplete deletion, I believe there is a pressing need for provision of concise, accessible and clear information about how deletion really works.

One potential approach is “explainable deletion” – a protocol developed by Marvin Ramokapane at the UK’s National Research Centre on Privacy, Harm Reduction and Adversarial Influence Online (Rephrain), based at the University of Bristol.

The intention is to make deletion processes more transparent and understandable without overwhelming the user with too much information in one go.

Explainable deletion breaks down information into six categories: what, how, when, who, where and why (see diagram). Each offers a user bite-size information about that part of the process.

Explainable deletion’s six categories:

Explainable deletion is designed to give users control over their actions, the autonomy to choose the deletion type that is right for them, and the assurance that their desired actions have been fulfilled. It may also tackle the anxiety of accidentally losing data by giving options for data recovery.

While explainable deletion has not yet been adopted in practice, service providers – both platforms and developers – could heighten user trust by adopting such protocols. This framework can also be proof of compliance with GDPR regulations and the right to be forgotten.

Most of us use systems that collect and store our data on a daily basis. If these systems clearly explained what they store and what deletion really means, it could help us to spot the accounts and data that pose a real risk – and to take back control of our digital footprints.

Dana Lungu does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment. This article draws on research on 'explainable deletion' by the University of Bristol’s Marvin Ramokapane.