探花直播 of Cambridge - Big data

拢10 million programme to use AI and state-of-the-art analytics to fight cancer

cjb250 — Wed, 22 Jan 2025 00:05:20 +0000

探花直播Cancer Data-Driven Detection programme will be led by Antonis Antoniou, Professor of Cancer Risk Prediction at the 探花直播 of Cambridge. It is funded by Cancer Research UK, the National Institute for Health and Care Research, and the Engineering and Physical Sciences Research Council.

探花直播programme aims to access and link data from different sources - including health records, genomics, family history, demographics, and behavioural data - to develop statistical models that help scientists accurately predict who is most likely to get cancer. Alongside this, the programme will develop powerful new tools that use AI to analyse the data and calculate an individual鈥檚 risk of cancer throughout their lifetime.

Professor Antoniou said: 鈥淔inding people at the highest risk of developing cancer, including those with vague symptoms, is a major challenge. 探花直播UK鈥檚 strengths in population-scale data resources, combined with advanced analytical tools like AI, offer tremendous opportunities to link disparate datasets and uncover clues that could lead to earlier detection, diagnosis, and prevention of more cancers.鈥�

Over the next five years, the funding will build the infrastructure required to access and link these datasets, train new data scientists, create the algorithms behind the risk models and evaluate the algorithms and AI tools to ensure that they are giving accurate and clinically useful information about cancer risk. 探花直播scientific programme will be guided by partnerships with cancer patients, the public, clinical experts and industry, while addressing ethical and legal considerations to ensure that the models and tools work well in practice.

Professor Antoniou added: 鈥淯ltimately, [the Cancer Data Driven Detection programme] could inform public health policy and empower individuals and their healthcare providers to make shared decisions. By understanding individual cancer risks, people can take proactive steps to stop cancer before it gets worse or even begins in the first place.鈥�

探花直播models generated from this research could be used to help people at higher risk of cancer in different ways. For example, the NHS could offer more frequent cancer screening sessions or screening at a younger age to those at higher risk, whilst those at lower risk could be spared unnecessary tests. People identified as higher risk could also be sent for cancer testing faster when they go to their GP with possible cancer signs or symptoms. Individuals at higher risk could also access different ways to prevent cancer.

Earlier diagnosis of cancer saves lives, yet according to analysis of NHS figures by Cancer Research UK, only 54% of cancers in England are diagnosed at stages one and two, where treatment is more likely to be successful. NHS England has set a target to diagnose 75% of cancers at stages one and two by 2028, and this will only be achieved with research and embracing new technologies to catch cancer earlier. 聽

Last week, the Prime Minister announced backing for the power of big data and AI, which has the potential to help even more patients, including those with cancer.

Science Minister Lord Vallance said: 鈥淭here are huge opportunities in AI to improve UK healthcare, from scans detecting illnesses earlier to bringing NHS waiting lists down by planning appointments more efficiently, and these will continue to develop.

鈥淭his investment in harnessing the potential of data to spot those at risk of cancer represents the sort of innovation the Government鈥檚 new AI Opportunities Action Plan sets out to realise, so this technology improves lives, while transforming public services and boosting growth.鈥�

Minister for Public Health and Prevention, Andrew Gwynne said:聽鈥淯sing the latest technology could revolutionise how the NHS diagnoses and treats patients. As part of this government鈥檚 Plan for Change, we will transform our health service from analogue to digital, and innovative projects like this show exactly how we will achieve it.鈥澛�

探花直播Cancer Data Driven Detection programme is jointly supported by Cancer Research UK, the National Institute for Health & Care Research, the Engineering & Physical Sciences Research Council, Health Data Research UK, and Administrative Data Research UK.

Head of Prevention and Early Detection Research at Cancer Research UK, Dr David Crosby, said: 鈥� 探花直播single most important thing we can do to beat cancer is to find it earlier, when treatment is more likely to be successful. With half a million cancer cases per year expected in the UK by 2040, we need a major shift towards more accurate diagnosis and detection of early cancer.鈥�

Find out how Cambridge is Changing the Story of Cancer

Adapted from a press release from Cancer Research UK

Cambridge researchers are to lead a 拢10 million project that could result in doctors being able to predict your individual chances of getting cancer and offer personalised detection and prevention.

探花直播UK鈥檚 strengths in population-scale data resources, combined with advanced analytical tools like AI, offer tremendous opportunities to link disparate datasets and uncover clues that could lead to earlier detection, diagnosis, and prevention of more cancers

Antonis Antoniou

BrianPenny (Pixabay)

Image representing AI and Big Data

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Scientists begin building AI for scientific discovery using tech behind ChatGPT

sc604 — Fri, 13 Oct 2023 10:55:15 +0000

While ChatGPT deals in words and sentences, the team鈥檚 AI will learn from numerical data and physics simulations from across scientific fields to aid scientists in modelling everything from supergiant stars to the Earth鈥檚 climate.

探花直播team launched the initiative, called Polymathic AI earlier this week, alongside the publication of a series of related scientific papers on the arXiv.org open access repository.

鈥淭his will completely change how people use AI and machine learning in science,鈥� said Polymathic AI principal investigator Shirley Ho, a group leader at the Flatiron Institute鈥檚 Center for Computational Astrophysics in New York City.

探花直播idea behind Polymathic AI 鈥渋s similar to how it鈥檚 easier to learn a new language when you already know five languages,鈥� said Ho.

Starting with a large, pre-trained model, known as a foundation model, can be both faster and more accurate than building a scientific model from scratch. That can be true even if the training data isn鈥檛 obviously relevant to the problem at hand.

鈥淚t鈥檚 been difficult to carry out academic research on full-scale foundation models due to the scale of computing power required,鈥� said co-investigator Miles Cranmer, from Cambridge鈥檚 Department of Applied Mathematics and Theoretical Physics and Institute of Astronomy. 鈥淥ur collaboration with Simons Foundation has provided us with unique resources to start prototyping these models for use in basic science, which researchers around the world will be able to build from 鈥� it鈥檚 exciting.鈥�

鈥淧olymathic AI can show us commonalities and connections between different fields that might have been missed,鈥� said co-investigator Siavash Golkar, a guest researcher at the Flatiron Institute鈥檚 Center for Computational Astrophysics. 鈥淚n previous centuries, some of the most influential scientists were polymaths with a wide-ranging grasp of different fields. This allowed them to see connections that helped them get inspiration for their work. With each scientific domain becoming more and more specialised, it is increasingly challenging to stay at the forefront of multiple fields. I think this is a place where AI can help us by aggregating information from many disciplines.鈥�

探花直播Polymathic AI team includes researchers from the Simons Foundation and its Flatiron Institute, New York 探花直播, the 探花直播 of Cambridge, Princeton 探花直播 and the Lawrence Berkeley National Laboratory. 探花直播team includes experts in physics, astrophysics, mathematics, artificial intelligence and neuroscience.

Scientists have used AI tools before, but they鈥檝e primarily been purpose-built and trained using relevant data. 鈥淒espite rapid progress of machine learning in recent years in various scientific fields, in almost all cases, machine learning solutions are developed for specific use cases and trained on some very specific data,鈥� said co-investigator Francois Lanusse, a cosmologist at the Centre national de la recherche scientifique (CNRS) in France. 鈥淭his creates boundaries both within and between disciplines, meaning that scientists using AI for their research do not benefit from information that may exist, but in a different format, or in a different field entirely.鈥�

Polymathic AI鈥檚 project will learn using data from diverse sources across physics and astrophysics (and eventually fields such as chemistry and genomics, its creators say) and apply that multidisciplinary savvy to a wide range of scientific problems. 探花直播project will 鈥渃onnect many seemingly disparate subfields into something greater than the sum of their parts,鈥� said project member Mariel Pettee, a postdoctoral researcher at Lawrence Berkeley National Laboratory.

鈥淗ow far we can make these jumps between disciplines is unclear,鈥� said Ho. 鈥淭hat鈥檚 what we want to do 鈥� to try and make it happen.鈥�

ChatGPT has well-known limitations when it comes to accuracy (for instance, the chatbot says 2,023 times 1,234 is 2,497,582 rather than the correct answer of 2,496,382). Polymathic AI鈥檚 project will avoid many of those pitfalls, Ho said, by treating numbers as actual numbers, not just characters on the same level as letters and punctuation. 探花直播training data will also use real scientific datasets that capture the physics underlying the cosmos.

Transparency and openness are a big part of the project, Ho said. 鈥淲e want to make everything public. We want to democratise AI for science in such a way that, in a few years, we鈥檒l be able to serve a pre-trained model to the community that can help improve scientific analyses across a wide variety of problems and domains.鈥�

An international team of scientists, including from the 探花直播 of Cambridge, have launched a new research collaboration that will leverage the same technology behind ChatGPT to build an AI-powered tool for scientific discovery.

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Network and data connection on a dark blue background.

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Machine learning models can produce reliable results even with limited training data

sc604 — Tue, 19 Sep 2023 10:03:16 +0000

探花直播researchers, from the 探花直播 of Cambridge and Cornell 探花直播, found that for partial differential equations 鈥� a class of physics equations that describe how things in the natural world evolve in space and time 鈥� machine learning models can produce reliable results even when they are provided with limited data.

Their results, reported in the Proceedings of the National Academy of Sciences, could be useful for constructing more time- and cost-efficient machine learning models for applications such as engineering and climate modelling.

Most machine learning models require large amounts of training data before they can begin returning accurate results. Traditionally, a human will annotate a large volume of data 鈥� such as a set of images, for example 鈥� to train the model.

鈥淯sing humans to train machine learning models is effective, but it鈥檚 also time-consuming and expensive,鈥� said first author Dr Nicolas Boull茅, from the Isaac Newton Institute for Mathematical Sciences. 鈥淲e鈥檙e interested to know exactly how little data we actually need to train these models and still get reliable results.鈥�

Other researchers have been able to train machine learning models with a small amount of data and get excellent results, but how this was achieved has not been well-explained. For their study, Boull茅 and his co-authors, Diana Halikias and Alex Townsend from Cornell 探花直播, focused on partial differential equations (PDEs).

鈥淧DEs are like the building blocks of physics: they can help explain the physical laws of nature, such as how the steady state is held in a melting block of ice,鈥� said Boull茅, who is an INI-Simons Foundation Postdoctoral Fellow. 鈥淪ince they are relatively simple models, we might be able to use them to make some generalisations about why these AI techniques have been so successful in physics.鈥�

探花直播researchers found that PDEs that model diffusion have a structure that is useful for designing AI models. 鈥淯sing a simple model, you might be able to enforce some of the physics that you already know into the training data set to get better accuracy and performance,鈥� said Boull茅.

探花直播researchers constructed an efficient algorithm for predicting the solutions of PDEs under different conditions by exploiting the short and long-range interactions happening. This allowed them to build some mathematical guarantees into the model and determine exactly how much training data was required to end up with a robust model.

鈥淚t depends on the field, but for physics, we found that you can actually do a lot with a very limited amount of data,鈥� said Boull茅. 鈥淚t鈥檚 surprising how little data you need to end up with a reliable model. Thanks to the mathematics of these equations, we can exploit their structure to make the models more efficient.鈥�

探花直播researchers say that their techniques will allow data scientists to open the 鈥榖lack box鈥� of many machine learning models and design new ones that can be interpreted by humans, although future research is still needed.

鈥淲e need to make sure that models are learning the right things, but machine learning for physics is an exciting field 鈥� there are lots of interesting maths and physics questions that AI can help us answer,鈥� said Boull茅.

聽

Reference

Nicolas Boull茅, Diana Halikias, and Alex Townsend. 鈥�Elliptic PDE learning is provably data-efficient.鈥� PNAS (2023). DOI: 10.1073/pnas.2303904120

Researchers have determined how to build reliable machine learning models that can understand complex equations in real-world situations while using far less training data than is normally expected.

It鈥檚 surprising how little data you need to end up with a reliable model

Nicolas Boull茅

Andriy Onufriyenko via Getty Images

Digital generated image of multi coloured glowing data over landscape.

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Is Data Justice key to Climate Justice?

plc32 — Thu, 17 Aug 2023 11:00:00 +0000

Bias in the collection of data on which Artificial Intelligence (AI) computer programmes depend can limit the usefulness of this rapidly growing tool for climate scientists predicting future scenarios and guiding global action, according to a new paper by researchers at the 探花直播 of Cambridge published in Nature鈥檚 npj |Climate Action series.聽

AI computer programmes used for climate science are trained to trawl through complex datasets looking for patterns and insightful information. However, missing information from certain locations on the planet, time periods or societal dynamics create 鈥渉oles鈥� in the data that can lead to unreliable climate predictions and misleading conclusions.

Primary author and Cambridge Zero Fellow Dr Ramit Debnath said that individuals with access to technology, such as scientists, teachers, professionals and businesses in the Global North are more likely to see their climate priorities and perceptions reflected in the digital information widely available for AI use.

By contrast, those without the same access to technology, such as indigenous communities in the Global South, are more likely to find their experiences, perceptions and priorities missing from those same digital sources.

鈥淲hen the information on climate change is over-represented by the work of well-educated individuals at high-ranking institutions within the Global North, AI will only see climate change and climate solutions through their eyes,鈥� Debnath said.聽

鈥淏iased鈥� AI has the potential to misrepresent climate information.

For example, it could generate ineffective weather predictions or underestimate carbon emissions from certain industries, which could then misguide governments trying to create policy and regulations aimed at mitigating or adapting to climate change.聽

AI-supported climate solutions which spring from biased data are in danger of harming under-represented communities, particularly those in the Global South with scant resources. These are often the same communities who also find themselves most vulnerable to the extreme weather events caused by climate change such as floods, fires, heatwaves and drought.

That is a combination which could lead to 鈥渟ocietal tipping events鈥�, the paper warns.聽

However, these 鈥渄ata holes鈥� can be filled by human knowledge. 探花直播authors advocate for a human-in-the loop design to offer AI climate change programmes with a sense check on which data is used and the context in which it is used, in an effort to improve the accuracy of predictions and the usefulness of any conclusions.

探花直播authors mention popular AI chatbot model ChatGPT, which has recently taken the world by storm for its ability to communicate conversationally with human users. On ChatGPT, the AI can ask its human users follow-up questions, admit mistakes, challenge incorrect premises and reject inappropriate requests.

This 鈥榟uman-in-the-loop鈥� style AI allows bias to be noticed and corrected, the authors said. Users can input critical social information, such as existing infrastructure and market systems, to allow the AI to better anticipate any unintended socio-political and economic consequences of climate action.聽

鈥淣o data is clean or without prejudice, and this is particularly problematic for AI which relies entirely on digital information,鈥� co-author, Cambridge Zero Director and climate scientist Professor Emily Shuckburgh said.

In highlighting the importance of globally inclusive datasets, the paper also promotes broadband internet access as a public necessity, rather than a private commodity, to engage as many users as possible in the design of AI for contemporary conversations about climate action.

探花直播paper concludes that human-guided technology remains instrumental in the development of socially responsible AI.

Less-biased AI will be critical to our understanding of how the climate is changing, and consequently in guiding realistic solutions to mitigate and adapt to the on-going climate crisis, the authors said.

聽Professor Shuckburgh, who also leads the UK national research funding body鈥檚 (UKRI) Centre for Doctoral Training on the Application of AI to the study of Environmental Risks (AI4ER), said that recognising the issue of data justice is the first step to better outcomes.聽聽

鈥淥nly with an active awareness of this data injustice can we begin to tackle it, and consequently, to build better and more trustworthy AI-led climate solutions,鈥� she said.

Biased artificial intelligence needs human help to avoid harmful climate action, Cambridge researchers say.

No data is clean or without prejudice, and this is particularly problematic for AI which relies entirely on digital information

Professor Emily Shuckburgh

Photo by NOAA on Unsplash

Vehicles with weather observation equipment track a storm

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Act now to prevent uncontrolled rise in carbon footprint of computational science

cjb250 — Mon, 26 Jun 2023 09:04:03 +0000

Writing in Nature Computational Science, researchers from the Department of Public Health and Primary Care at the 探花直播 of Cambridge argue that the scientific community needs to act now if it is to prevent a potentially uncontrolled rise in the carbon footprint of computational science as data science and algorithms increase in usage.

Dr Lo茂c Lannelongue, who is a research associate in biomedical data science and a postdoctoral associate at Jesus College, Cambridge, said: 鈥淪cience has transformed our understanding of the world around us and has led to great benefits to society. But this has come with a not-insignificant 鈥� and not always well understood 鈥� impact on the environment. As scientists 鈥� as with people working in every sector 鈥� it鈥檚 important that we do what we can to reduce the carbon footprint of our work to ensure that the benefits of our discoveries are not outweighed by their environmental costs.鈥�

Recent studies have begun to explore the environmental impacts of scientific research, with an initial focus on scientific conferences and experimental laboratories. For example, the 2019 Fall Meeting of the American Geophysical Union was estimated to emit 80,000 tons of CO2e* (tCO2e), equivalent to the average weekly emissions of the city of Edinburgh, UK. 探花直播annual carbon footprint of a typical life science laboratory has been estimated to be around 20 tCO2e.

But there is one aspect of research that often gets overlooked 鈥� and which can have a substantial environmental impact: high performance and cloud computing.

In 2020, the Information and Communication Technologies sector was estimated to have made up between 1.8% and 2.8% of global greenhouse gas emissions 鈥� more than aviation (1.9%). In addition to the environmental effects of electricity usage, manufacturing and disposal of hardware, there are also concerns around data centres鈥� water usage and land footprint.

Professor Michael Inouye said: 鈥淲hile the environmental impact of experimental 鈥榳et鈥� labs is more immediately obvious, the impact of algorithms is less clear and often underestimated. While new hardware, lower-energy data centres and more efficient high performance computing systems can help reduce their impact, the increasing ubiquity of artificial intelligence and data science more generally means their carbon footprint could grow exponentially in coming years if we don鈥檛 act now.鈥�

To help address this issue, the team has developed GREENER (Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research), a set of principles to allow the computational science community to lead the way in sustainable research practices, maximising computational science鈥檚 benefit to both humanity and the environment.

Governance and Responsibility

Everyone involved in computational science has a role to play in making the field more sustainable: individual and institutional responsibility is a necessary step to ensure transparency and reduction of greenhouse gas emission.

For example, institutions themselves can be key to managing and expanding centralised data infrastructures, and in ensuring that procurement decisions take into account both the manufacturing and operational footprint of hardware purchases. IT teams in high performance computing (HPC) centres can play a key role, both in terms of training and helping scientists monitor the carbon footprint of their work. Principal Investigators can encourage their teams to think about this issue and give access to suitable training. Funding bodies can influence researchers by requiring estimates of carbon footprints to be included in funding applications.

Estimate and report the energy consumption of algorithms

Estimating and monitoring the carbon footprint of computations identifies inefficiencies and opportunities for improvement.

User-level metrics are crucial to understanding environmental impacts and promoting personal responsibility. 探花直播financial cost of running computations is often negligible, particularly in academia, and scientists may have the impression of unlimited and inconsequential computing capacity. Quantifying the carbon footprint of individual projects helps raise awareness of the true costs of research.

Tackling Energy and embodied impacts through New collaborations

Minimising carbon intensity 鈥� that is, the carbon footprint of producing electricity 鈥� is one of the most immediately impactful ways to reduce greenhouse gas emissions. This could involve relocating computations to low-carbon settings and countries, but this needs to be done with equity in mind. Carbon intensities can differ by as much as three orders of magnitude between the top and bottom performing high-income countries (from 0.10 gCO2e/kWh in Iceland to 770 gCO2e/kWh in Australia).

探花直播footprint of user devices is also a factor: one estimate found that almost three-quarters (72%) of the energy footprint of streaming a video to a laptop is from the laptop, with 23% used in transmission and a mere 5% at the data centre.

Another key consideration is data storage. 探花直播carbon footprint of storing data depends on numerous factors, but the life cycle footprint of storing one terabyte of data for a year is of the order of 10 kg CO2e. This issue is exacerbated by the duplication of such datasets in order for each institution, and sometimes each research group, to have a copy. Large (hyperscale) data centres are expected to be more energy efficient, but they may also encourage unnecessary increases in the scale of computing (the 鈥榬ebound effect鈥�).

Education and Research

Education is essential to raise awareness of the issues with different stakeholders. Integrating sustainability into computational training courses is a tangible first step toward reducing carbon footprints. Investing in research that will catalyse innovation in the field of environmentally sustainable computational science is a crucial role for funders and institutions to play.

Recent studies found that the most widely-used programming languages in research, such as R and Python, tend to be the least energy efficient ones, highlighting the importance of having trained Research Software Engineers within research groups to ensure that the algorithms used are efficiently implemented. There is also scope to use current tools more efficiently by better understanding and monitoring how coding choices impact carbon footprints.

Dr Lannelongue said: 鈥淐omputational scientists have a real opportunity to lead the way in sustainability, but this is going to involve a change in our culture and the ways we work. There will need to more transparency, more awareness, better training and resources, and improved policies.

鈥淐ooperation, open science, and equitable access to low-carbon computing facilities will also be crucial. We need to make sure that sustainable solutions work for everyone, as they frequently have the least benefit for populations, often in low- and middle-income countries, who suffer the most from climate change.鈥�

Professor Inouye added: 鈥淓veryone in the field 鈥� from funders to journals to institutions down to individuals 鈥� plays an important role and can, themselves, make a positive impact. We have an immense opportunity to make a change, but the clock is ticking.鈥�

探花直播research was a collaboration with major stakeholders including Health Data Research UK, EMBL-EBI, Wellcome and UK Research and Innovation (UKRI).

*CO2e, or CO2-equivalent, summarises the global warming impacts of a range of greenhouse gases and is the standard metric for carbon footprints, although its accuracy is sometimes debated.

Reference
Lannelongue, L et al. GREENER principles for environmentally sustainable computational science. Nat Comp Sci; 26 June; DOI: 10.1038/s43588-023-00461-y

For more information on聽energy-related research in Cambridge, please visit聽Energy聽IRC, which brings together Cambridge鈥檚 research knowledge and expertise, in collaboration with global partners, to create solutions for a sustainable and resilient energy landscape for generations to come.聽

Cambridge scientists have set out principles for how computational science 鈥� which powers discoveries from unveiling the mysteries of the universe to developing treatments to fight cancer to improving our understanding of the human genome, but can have a substantial carbon footprint 鈥� can be made more environmentally sustainable.

Science has transformed our understanding of the world around us and has led to great benefits to society. But this has come with a not-insignificant 鈥� and not always well understood 鈥� impact on the environment

Loic Lannelongue

geralt

Binary world

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Big data's hidden cost

cjb250 — Mon, 16 Jan 2023 11:42:56 +0000

As the climate emergency and cost-of-living crisis focus our minds on how to reduce energy, a group of scientists have highlighted the hidden environmental cost behind some of our major breakthroughs.

Cambridge spin-out using data analytics to extend the life of our built environment

skbf2 — Tue, 15 Mar 2022 15:15:35 +0000

In many countries,聽ageing infrastructure - such as roads, bridges and tunnels - remains in use beyond its design life and intended capacity.聽

In order to get more out of these assets using聽minimal resources, the construction industry needs to better understand how they are performing. Assessing their condition聽can be difficult, however. There are logistical challenges鈥攙isually inspecting remote and hard to access assets can be expensive and hazardous. Even in new construction, it is important to ensure safe environments and understand the short- and long-term environmental impact of a site.

Recently COVID-19 has compounded these challenges by limiting the number of people allowed onsite. In the longer term, the industry is facing pressures associated with the climate crisis and extreme weather events, which add strain to the condition and structural health of assets, making assessing their condition increasingly important.

The聽BKwai聽software platform brings together data from multiple sources鈥攐nsite sensors, environmental data, and remote satellite data鈥攑roviding a comprehensive view of an asset as well as its surrounding environment. Bkwai applies advanced data techniques to help engineers sift through huge volumes of data to find patterns, identify early warning signs, and predict what could happen. 探花直播platform puts this insight into the site engineer鈥檚 hands quickly and in real time, helping construction companies and asset owners to make more timely, cost-effective, and safe decisions.

BKwai鈥檚 founder, Sakthy Selvakumaran, said: " 探花直播proliferation of new sensors and satellite technology has the potential to fuel a revolution in monitoring the built environment. Data can be used to make smarter, safer, more economical decisions - from planning to construction, and operation to maintenance. In an industry rooted in spreadsheets and manual interventions, however, it is difficult to find key insights and values. 探花直播industry is crying out for better tools to help us understand the complex challenges facing our built environment. That's where we step in. We can provide data-driven engineering insights, to find the right solutions and take action."

Selvakumaran has over a decade of international experience in design, construction, and maintenance of infrastructure assets. She combines this with a PhD and academic research in satellite monitoring and data analysis methods in the 探花直播 of Cambridge聽Department of Engineering.聽聽

Although only founded in 2019, BKwai is already used by some of the biggest names in UK construction and on existing major infrastructure assets. Current customers include Thames Tideway, Laing O鈥橰ourke, Kier, WSP, and Highways England. 探花直播company is also supported by Geovation, part of Ordnance Survey, UK.

聽

BKwai, a construction data company that helps engineers develop smarter, more sustainable infrastructure, has raised 拢2.2 million in seed funding from investors聽led by Octopus Ventures with participation from Cambridge Enterprise and Deeptech Labs.

Photo by Wes Hicks on Unsplash

Viaduct

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UK plants flowering a month earlier due to climate change

sc604 — Wed, 02 Feb 2022 00:08:32 +0000

Using a citizen science database with records going back to the mid-18th century, a research team led by the 探花直播 of Cambridge has found that the effects of climate change are causing plants in the UK to flower one month earlier under recent global warming.

探花直播researchers based their analysis on more than 400,000 observations of 406 plant species from Nature鈥檚 Calendar, maintained by the Woodland Trust, and collated the first flowering dates with instrumental temperature measurements.

They found that the average first flowering date from 1987 to 2019 is a full month earlier than the average first flowering date from 1753 to 1986. 探花直播same period coincides with accelerating global warming caused by human activities. 探花直播results are reported in Proceedings of the Royal Society B.

While the first spring flowers are always a welcome sight, this earlier flowering can have consequences for the UK鈥檚 ecosystems and agriculture. Other species that synchronise their migration or hibernation can be left without the flowers and plants they rely on 鈥� a phenomenon known as ecological mismatch 鈥� which can lead to biodiversity loss if populations cannot adapt quickly enough.

探花直播change can also have consequences for farmers and gardeners. If fruit trees, for example, flower early following a mild winter, entire crops can be killed off if the blossoms are then hit by a late frost.

While we can see the effects of climate change through extreme weather events and increasing climate variability, the long-term effects of climate change on ecosystems are more subtle and are therefore difficult to recognise and quantify.

鈥淲e can use a wide range of environmental datasets to see how climate change is affecting different species, but most records we have only consider one or a handful of species in a relatively small area,鈥� said Professor Ulf B眉ntgen from Cambridge鈥檚 Department of Geography, the study鈥檚 lead author. 鈥淭o really understand what climate change is doing to our world, we need much larger datasets that look at whole ecosystems over a long period of time.鈥�

探花直播UK has such a dataset: since the 18th century, observations of seasonal change have been recorded by scientists, naturalists, amateur and professional gardeners, as well as organisations such as the Royal Meteorological Society. In 2000, the Woodland Trust joined forced with the Centre for Ecology & Hydrology and collated these records into Nature鈥檚 Calendar, which currently has around 3.5 million records going back to 1736.

鈥淎nyone in the UK can submit a record to Nature鈥檚 Calendar, by logging their observations of plants and wildlife,鈥� said B眉ntgen. 鈥淚t鈥檚 an incredibly rich and varied data source, and alongside temperature records, we can use it to quantify how climate change is affecting the functioning of various ecosystem components across the UK.鈥�

For the current study, the researchers used over 400,000 records from Nature鈥檚 Calendar to study changes in 406 flowering plant species in the UK, between 1753 and 2019. They used observations of the first flowering date of trees, shrubs, herbs and climbers, in locations from the Channel Islands to Shetland, and from Northern Ireland to Suffolk.

探花直播researchers classified the observations in various ways: by location, elevation, and whether they were from urban or rural areas. 探花直播first flowering dates were then compared with monthly climate records.

To better balance the number of observations, the researchers divided the full dataset into records until 1986, and from 1987 onwards. 探花直播average first flowering advanced by a full month, and is strongly correlated with rising global temperatures.

鈥� 探花直播results are truly alarming, because of the ecological risks associated with earlier flowering times,鈥� said B眉ntgen. 鈥淲hen plants flower too early, a late frost can kill them 鈥� a phenomenon that most gardeners will have experienced at some point. But the even bigger risk is ecological mismatch. Plants, insects, birds and other wildlife have co-evolved to a point that they鈥檙e synchronised in their development stages. A certain plant flowers, it attracts a particular type of insect, which attracts a particular type of bird, and so on. But if one component responds faster than the others, there鈥檚 a risk that they鈥檒l be out of synch, which can lead species to collapse if they can鈥檛 adapt quickly enough.鈥�

B眉ntgen says that if global temperatures continue to increase at their current rate, spring in the UK could eventually start in February. However, many of the species that our forests, gardens and farms rely on could experience serious problems given the rapid pace of change.

鈥淐ontinued monitoring is necessary to ensure that we better understand the consequences of a changing climate,鈥� said co-author Professor Tim Sparks from Cambridge鈥檚 Department of Zoology. 鈥淐ontributing records to Nature鈥檚 Calendar is an activity that everyone can engage in.鈥�

探花直播research was supported in part by the European Research Council, the Fritz and Elisabeth Schweingruber Foundation, and the Woodland Trust.

Reference:
Ulf Bu虉ntgen et al. 鈥�Plants in the UK flower a month earlier under recent warming.鈥� Proceedings of the Royal Society B (2022). DOI: 10.1098/rspb.2021.2456

Climate change is causing plants in the UK to flower a month earlier on average, which could have profound consequences for wildlife, agriculture and gardeners.

To really understand what climate change is doing to our world, we need much larger datasets that look at whole ecosystems over a long period of time

Ulf B眉ntgen

Crab apple tree in bloom

Yes