Mess is best: disordered structure of battery-like devices improves performance
Researchers led by the University of Cambridge used experimental and computer modelling techniques to study the porous carbon electrodes used in supercapacitors. They found that electrodes with a more disordered chemical structure stored far more energy than electrodes with a highly ordered structure.
Supercapacitors are a key technology for the energy transition and could be useful for certain forms of public transport, as well as for managing intermittent solar and wind energy generation, but their adoption has been limited by poor energy density.
The researchers say their results, reported in the journal Science, represent a breakthrough in the field and could reinvigorate the development of this important net-zero technology.
Like batteries, supercapacitors store energy, but supercapacitors can charge in seconds or a few minutes, while batteries take much longer. Supercapacitors are far more durable than batteries, and can last for millions of charge cycles. However, the low energy density of supercapacitors makes them unsuitable for delivering long-term energy storage or continuous power.
“Supercapacitors are a complementary technology to batteries, rather than a replacement,” said Dr Alex Forse from Cambridge’s Yusuf Hamied Department of Chemistry, who led the research. “Their durability and extremely fast charging capabilities make them useful for a wide range of applications.”
A bus, train or metro powered by supercapacitors, for example, could fully charge in the time it takes to let passengers off and on, providing it with enough power to reach the next stop. This would eliminate the need to install any charging infrastructure along the line. However, before supercapacitors are put into widespread use, their energy storage capacity needs to be improved.
While a battery uses chemical reactions to store and release charge, a supercapacitor relies on the movement of charged molecules between porous carbon electrodes, which have a highly disordered structure. “Think of a sheet of graphene, which has a highly ordered chemical structure,” said Forse. “If you scrunch up that sheet of graphene into a ball, you have a disordered mess, which is sort of like the electrode in a supercapacitor.”
Because of the inherent messiness of the electrodes, it’s been difficult for scientists to study them and determine which parameters are the most important when attempting to improve performance. This lack of clear consensus has led to the field getting a bit stuck.
Many scientists have thought that the size of the tiny holes, or nanopores, in the carbon electrodes was the key to improved energy capacity. However, the Cambridge team analysed a series of commercially available nanoporous carbon electrodes and found there was no link between pore size and storage capacity.
Forse and his colleagues took a new approach and used nuclear magnetic resonance (NMR) spectroscopy – a sort of ‘MRI’ for batteries – to study the electrode materials. They found that the messiness of the materials – long thought to be a hindrance – was the key to their success.
“Using NMR spectroscopy, we found that energy storage capacity correlates with how disordered the materials are – the more disordered materials can store more energy,” said first author Xinyu Liu, a PhD candidate co-supervised by Forse and Professor Dame Clare Grey. “Messiness is hard to measure – it’s only possible thanks to new NMR and simulation techniques, which is why messiness is a characteristic that’s been overlooked in this field.”
When analysing the electrode materials with NMR spectroscopy, a spectrum with different peaks and valleys is produced. The position of the peak indicates how ordered or disordered the carbon is. “It wasn’t our plan to look for this, it was a big surprise,” said Forse. “When we plotted the position of the peak against energy capacity, a striking correlation came through – the most disordered materials had a capacity almost double that of the most ordered materials.”
So why is mess good? Forse says that’s the next thing the team is working on. More disordered carbons store ions more efficiently in their nanopores, and the team hope to use these results to design better supercapacitors. The messiness of the materials is determined at the point they are synthesised.
“We want to look at new ways of making these materials, to see how far messiness can take you in terms of improving energy storage,” said Forse. “It could be a turning point for a field that’s been stuck for a little while. Clare and I started working on this topic over a decade ago, and it’s exciting to see a lot of our previous fundamental work now having a clear application.”
The research was supported in part by the Cambridge Trusts, the European Research Council, and UK Research and Innovation (UKRI).
Reference:
Xinyu Liu et al. ‘Structural disorder determines capacitance in nanoporous carbons.’ Science (2024). DOI: 10.1126/science.adn6242
For more information on energy-related research in Cambridge, please visit the Energy IRC, which brings together Cambridge’s research knowledge and expertise, in collaboration with global partners, to create solutions for a sustainable and resilient energy landscape for generations to come.
The energy density of supercapacitors – battery-like devices that can charge in seconds or a few minutes – can be improved by increasing the ‘messiness’ of their internal structure.
This could be a turning point for a field that’s been stuck for a little while. Alex ForseNathan PittLeft to right: Clare Grey, Xinyu Liu, Alex Forse
The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.
Tue 04 Jun 12:00: DNA damage response control by RNA and its therapeutic potential
Abstract not available
- Speaker: Fabrizio d'Adda di Fagagna
- Tuesday 04 June 2024, 12:00-13:00
- Venue: Jean Thomas Lecture theatre, Sanger Building, Tennis Court Road.
- Series: Department of Biochemistry - Tea Club Seminars; organiser: reception.
Tue 04 Jun 12:00: DNA damage response control by RNA and its therapeutic potential
Abstract not available
- Speaker: Fabrizio d'Adda di Fagagna
- Tuesday 04 June 2024, 12:00-13:00
- Venue: Jean Thomas Lecture theatre, Sanger Building, Tennis Court Road.
- Series: Department of Biochemistry - Tea Club Seminars; organiser: reception.
Sea stack plots: Replacing bar charts with histograms
Ecol Evol. 2024 Apr 16;14(4):e11237. doi: 10.1002/ece3.11237. eCollection 2024 Apr.
ABSTRACT
Graphs in research articles can increase the comprehension of statistical data but may mislead readers if poorly designed. We propose a new plot type, the sea stack plot, which combines vertical histograms and summary statistics to represent large univariate datasets accurately, usefully, and efficiently. We compare five commonly used plot types (dot and whisker plots, boxplots, density plots, univariate scatter plots, and dot plots) to assess their relative strengths and weaknesses when representing distributions of data commonly observed in biological studies. We find the assessed plot types are either difficult to read at large sample sizes or have the potential to misrepresent certain distributions of data, showing the need for an improved method of data visualisation. We present an analysis of the plot types used in four ecology and conservation journals covering multiple areas of these research fields, finding widespread use of uninformative bar charts and dot and whisker plots (60% of all panels showing univariate data from multiple groups for the purpose of comparison). Some articles presented more informative figures by combining plot types (16% of panels), generally boxplots and a second layer such as a flat density plot, to better display the data. This shows an appetite for more effective plot types within conservation and ecology, which may further increase if accurate and user-friendly plot types were made available. Finally, we describe sea stack plots and explain how they overcome the weaknesses associated with other alternatives to uninformative plots when used for large and/or unevenly distributed data. We provide a tool to create sea stack plots with our R package 'seastackplot', available through GitHub.
PMID:38633526 | PMC:PMC11021675 | DOI:10.1002/ece3.11237
No evidence that a transmissible cancer has shifted from emergence to endemism in Tasmanian devils
R Soc Open Sci. 2024 Apr 17;11(4):231875. doi: 10.1098/rsos.231875. eCollection 2024 Apr.
ABSTRACT
Tasmanian devils are endangered by a transmissible cancer known as Tasmanian devil facial tumour 1 (DFT1). A 2020 study by Patton et al. (Science 370, eabb9772 (doi:10.1126/science.abb9772)) used genome data from DFT1 tumours to produce a dated phylogenetic tree for this transmissible cancer lineage, and thence, using phylodynamics models, to estimate its epidemiological parameters and predict its future trajectory. It concluded that the effective reproduction number for DFT1 had declined to a value of one, and that the disease had shifted from emergence to endemism. We show that the study is based on erroneous mutation calls and flawed methodology, and that its conclusions cannot be substantiated.
PMID:38633353 | PMC:PMC11022658 | DOI:10.1098/rsos.231875
Steven Barrett appointed Regius Professor of Engineering
Professor Steven Barrett has been appointed Regius Professor of Engineering at the University of Cambridge, effective 1 June. He joins the University from the Massachusetts Institute of Technology (MIT), where he is head of the Department of Aeronautics and Astronautics (AeroAstro).
Barrett’s appointment marks his return to Cambridge, where he was an undergraduate at Pembroke College, and received his PhD. He was a Lecturer in the Department of Engineering from 2008 until 2010, when he joined the faculty at MIT.
The Regius Professorships are royal academic titles created by the monarch. The Regius Professorship in Engineering was announced in 2011, in honour of HRH Prince Philip, The Duke of Edinburgh’s 35 years as Chancellor of the University.
“It’s a pleasure to welcome Steven back to Cambridge to take up one of the University’s most prestigious roles,” said Vice-Chancellor Professor Deborah Prentice. “His work on sustainable aviation will build on Cambridge’s existing strengths, and will help us develop the solutions we need to address the threat posed by climate change.”
Barrett’s research focuses on the impact aviation has on the environment. He has developed a number of solutions to mitigate the impact aviation has on air quality, climate, and noise pollution. The overall goal of his research is to help develop technologies that eliminate the environmental impact of aviation. His work on the first-ever plane with no moving propulsion parts was named one of the 10 Breakthroughs of 2018 by Physics World.
“This is an exciting time to work on sustainable aviation, and Cambridge, as well as the UK more generally, is a wonderful platform to advance that,” said Barrett. “Cambridge’s multidisciplinary Department of Engineering, as well as the platform that the Regius Professorship provides, makes this a great opportunity. I’ve learned a lot at MIT, but I’d always hoped to come back to Cambridge at some point.”
Much of Barrett’s research focuses on the elimination of contrails, line-shaped clouds produced by aircraft engine exhaust in cold and humid conditions. Contrails cause half of all aviation-related global warming – more than the entirety of the UK economy. Barrett uses a combination of satellite observation and machine learning techniques to help determine whether avoiding certain regions of airspace could reduce or eliminate contrail formation.
“It will take several years to make this work, but if it does, it could drastically reduce emissions at a very low cost to the consumer,” said Barrett. “We could make the UK the first ‘Blue Skies’ country in the world – the first without any contrails in the sky.”
“Steven’s pioneering work on contrail formation and avoidance is a key element in reducing the environmental impact of aviation, and will strengthen the UK’s position as a world leader in this area,” said Professor Colm Durkan, Head of Cambridge’s Department of Engineering. “Together with Steven’s work on alternative aviation propulsion systems, this will strengthen Cambridge’s vision of helping us all achieve net zero at an accelerated rate.”
In addition to the Professorship in Engineering, there are seven other Regius Professorships at Cambridge: Divinity, Hebrew, Greek, Civil Law and Physic (all founded by Henry VIII in 1540), History (founded by George I in 1724) and Botany (founded in 2009, to mark the University’s 800th anniversary).
An expert on the environmental impacts of aviation, Barrett joins the University of Cambridge from MIT.
MITSteven Barrett
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Artificial Intelligence beats doctors in accurately assessing eye problems
The clinical knowledge and reasoning skills of GPT-4 are approaching the level of specialist eye doctors, a study led by the University of Cambridge has found.
GPT-4 - a ‘large language model’ - was tested against doctors at different stages in their careers, including unspecialised junior doctors, and trainee and expert eye doctors. Each was presented with a series of 87 patient scenarios involving a specific eye problem, and asked to give a diagnosis or advise on treatment by selecting from four options.
GPT-4 scored significantly better in the test than unspecialised junior doctors, who are comparable to general practitioners in their level of specialist eye knowledge.
GPT-4 gained similar scores to trainee and expert eye doctors - although the top performing doctors scored higher.
The researchers say that large language models aren’t likely to replace healthcare professionals, but have the potential to improve healthcare as part of the clinical workflow.
They say state-of-the-art large language models like GPT-4 could be useful for providing eye-related advice, diagnosis, and management suggestions in well-controlled contexts, like triaging patients, or where access to specialist healthcare professionals is limited.
“We could realistically deploy AI in triaging patients with eye issues to decide which cases are emergencies that need to be seen by a specialist immediately, which can be seen by a GP, and which don’t need treatment,” said Dr Arun Thirunavukarasu, lead author of the study, which he carried out while a student at the University of Cambridge’s School of Clinical Medicine.
He added: “The models could follow clear algorithms already in use, and we’ve found that GPT-4 is as good as expert clinicians at processing eye symptoms and signs to answer more complicated questions.
“With further development, large language models could also advise GPs who are struggling to get prompt advice from eye doctors. People in the UK are waiting longer than ever for eye care.
Large volumes of clinical text are needed to help fine-tune and develop these models, and work is ongoing around the world to facilitate this.
The researchers say that their study is superior to similar, previous studies because they compared the abilities of AI to practicing doctors, rather than to sets of examination results.
“Doctors aren't revising for exams for their whole career. We wanted to see how AI fared when pitted against to the on-the-spot knowledge and abilities of practicing doctors, to provide a fair comparison,” said Thirunavukarasu, who is now an Academic Foundation Doctor at Oxford University Hospitals NHS Foundation Trust.
He added: “We also need to characterise the capabilities and limitations of commercially available models, as patients may already be using them - rather than the internet - for advice.”
The test included questions about a huge range of eye problems, including extreme light sensitivity, decreased vision, lesions, itchy and painful eyes, taken from a textbook used to test trainee eye doctors. This textbook is not freely available on the internet, making it unlikely that its content was included in GPT-4’s training datasets.
The results are published today in the journal PLOS Digital Health.
“Even taking the future use of AI into account, I think doctors will continue to be in charge of patient care. The most important thing is to empower patients to decide whether they want computer systems to be involved or not. That will be an individual decision for each patient to make,” said Thirunavukarasu.
GPT-4 and GPT-3.5 – or ‘Generative Pre-trained Transformers’ - are trained on datasets containing hundreds of billions of words from articles, books, and other internet sources. These are two examples of large language models; others in wide use include Pathways Language Model 2 (PaLM 2) and Large Language Model Meta AI 2 (LLaMA 2).
The study also tested GPT-3.5, PaLM2, and LLaMA with the same set of questions. GPT-4 gave more accurate responses than all of them.
GPT-4 powers the online chatbot ChatGPT to provide bespoke responses to human queries. In recent months, ChatGPT has attracted significant attention in medicine for attaining passing level performance in medical school examinations, and providing more accurate and empathetic messages than human doctors in response to patient queries.
The field of artificially intelligent large language models is moving very rapidly. Since the study was conducted, more advanced models have been released - which may be even closer to the level of expert eye doctors.
Reference: Thirunavukarasu, A.J. et al: ‘Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study.’ PLOS Digital Health, April 2024. DOI: 10.1371/journal.pdig.0000341
A study has found that the AI model GPT-4 significantly exceeds the ability of non-specialist doctors to assess eye problems and provide advice.
We could realistically deploy AI in triaging patients with eye issues to decide which cases are emergencies.Arun ThirunavukarasuMavocado on Getty
The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.
Lipoarabinomannan modification as a source of phenotypic heterogeneity in host-adapted <em>Mycobacterium abscessus</em> isolates
Proc Natl Acad Sci U S A. 2024 Apr 23;121(17):e2403206121. doi: 10.1073/pnas.2403206121. Epub 2024 Apr 17.
ABSTRACT
Mycobacterium abscessus is increasingly recognized as the causative agent of chronic pulmonary infections in humans. One of the genes found to be under strong evolutionary pressure during adaptation of M. abscessus to the human lung is embC which encodes an arabinosyltransferase required for the biosynthesis of the cell envelope lipoglycan, lipoarabinomannan (LAM). To assess the impact of patient-derived embC mutations on the physiology and virulence of M. abscessus, mutations were introduced in the isogenic background of M. abscessus ATCC 19977 and the resulting strains probed for phenotypic changes in a variety of in vitro and host cell-based assays relevant to infection. We show that patient-derived mutational variations in EmbC result in an unexpectedly large number of changes in the physiology of M. abscessus, and its interactions with innate immune cells. Not only did the mutants produce previously unknown forms of LAM with a truncated arabinan domain and 3-linked oligomannoside chains, they also displayed significantly altered cording, sliding motility, and biofilm-forming capacities. The mutants further differed from wild-type M. abscessus in their ability to replicate and induce inflammatory responses in human monocyte-derived macrophages and epithelial cells. The fact that different embC mutations were associated with distinct physiologic and pathogenic outcomes indicates that structural alterations in LAM caused by nonsynonymous nucleotide polymorphisms in embC may be a rapid, one-step, way for M. abscessus to generate broad-spectrum diversity beneficial to survival within the heterogeneous and constantly evolving environment of the infected human airway.
PMID:38630725 | DOI:10.1073/pnas.2403206121
Evaluating the impact of genomic epidemiology of methicillin-resistant <em>Staphylococcus aureus</em> (MRSA) on hospital infection prevention and control decisions
Microb Genom. 2024 Apr;10(4). doi: 10.1099/mgen.0.001235.
ABSTRACT
Genomic epidemiology enhances the ability to detect and refute methicillin-resistant Staphylococcus aureus (MRSA) outbreaks in healthcare settings, but its routine introduction requires further evidence of benefits for patients and resource utilization. We performed a 12 month prospective study at Cambridge University Hospitals NHS Foundation Trust in the UK to capture its impact on hospital infection prevention and control (IPC) decisions. MRSA-positive samples were identified via the hospital microbiology laboratory between November 2018 and November 2019. We included samples from in-patients, clinic out-patients, people reviewed in the Emergency Department and healthcare workers screened by Occupational Health. We sequenced the first MRSA isolate from 823 consecutive individuals, defined their pairwise genetic relatedness, and sought epidemiological links in the hospital and community. Genomic analysis of 823 MRSA isolates identified 72 genetic clusters of two or more isolates containing 339/823 (41 %) of the cases. Epidemiological links were identified between two or more cases for 190 (23 %) individuals in 34/72 clusters. Weekly genomic epidemiology updates were shared with the IPC team, culminating in 49 face-to-face meetings and 21 written communications. Seventeen clusters were identified that were consistent with hospital MRSA transmission, discussion of which led to additional IPC actions in 14 of these. Two outbreaks were also identified where transmission had occurred in the community prior to hospital presentation; these were escalated to relevant IPC teams. We identified 38 instances where two or more in-patients shared a ward location on overlapping dates but carried unrelated MRSA isolates (pseudo-outbreaks); research data led to de-escalation of investigations in six of these. Our findings provide further support for the routine use of genomic epidemiology to enhance and target IPC resources.
PMID:38630616 | DOI:10.1099/mgen.0.001235
AI speeds up drug design for Parkinson’s ten-fold
The researchers, from the University of Cambridge, designed and used an AI-based strategy to identify compounds that block the clumping, or aggregation, of alpha-synuclein, the protein that characterises Parkinson’s.
The team used machine learning techniques to quickly screen a chemical library containing millions of entries, and identified five highly potent compounds for further investigation.
Parkinson’s affects more than six million people worldwide, with that number projected to triple by 2040. No disease-modifying treatments for the condition are currently available. The process of screening large chemical libraries for drug candidates – which needs to happen well before potential treatments can be tested on patients – is enormously time-consuming and expensive, and often unsuccessful.
Using machine learning, the researchers were able to speed up the initial screening process ten-fold, and reduce the cost by a thousand-fold, which could mean that potential treatments for Parkinson’s reach patients much faster. The results are reported in the journal Nature Chemical Biology.
Parkinson’s is the fastest-growing neurological condition worldwide. In the UK, one in 37 people alive today will be diagnosed with Parkinson’s in their lifetime. In addition to motor symptoms, Parkinson’s can also affect the gastrointestinal system, nervous system, sleeping patterns, mood and cognition, and can contribute to a reduced quality of life and significant disability.
Proteins are responsible for important cell processes, but when people have Parkinson’s, these proteins go rogue and cause the death of nerve cells. When proteins misfold, they can form abnormal clusters called Lewy bodies, which build up within brain cells stopping them from functioning properly.
“One route to search for potential treatments for Parkinson’s requires the identification of small molecules that can inhibit the aggregation of alpha-synuclein, which is a protein closely associated with the disease,” said Professor Michele Vendruscolo from the Yusuf Hamied Department of Chemistry, who led the research. “But this is an extremely time-consuming process – just identifying a lead candidate for further testing can take months or even years.”
While there are currently clinical trials for Parkinson’s currently underway, no disease-modifying drug has been approved, reflecting the inability to directly target the molecular species that cause the disease.
This has been a major obstacle in Parkinson’s research, because of the lack of methods to identify the correct molecular targets and engage with them. This technological gap has severely hampered the development of effective treatments.
The Cambridge team developed a machine learning method in which chemical libraries containing millions of compounds are screened to identify small molecules that bind to the amyloid aggregates and block their proliferation.
A small number of top-ranking compounds were then tested experimentally to select the most potent inhibitors of aggregation. The information gained from these experimental assays was fed back into the machine learning model in an iterative manner, so that after a few iterations, highly potent compounds were identified.
“Instead of screening experimentally, we screen computationally,” said Vendruscolo, who is co-Director of the Centre for Misfolding Diseases. “By using the knowledge we gained from the initial screening with our machine learning model, we were able to train the model to identify the specific regions on these small molecules responsible for binding, then we can re-screen and find more potent molecules.”
Using this method, the Cambridge team developed compounds to target pockets on the surfaces of the aggregates, which are responsible for the exponential proliferation of the aggregates themselves. These compounds are hundreds of times more potent, and far cheaper to develop, than previously reported ones.
“Machine learning is having a real impact on drug discovery – it’s speeding up the whole process of identifying the most promising candidates,” said Vendruscolo. “For us, this means we can start work on multiple drug discovery programmes – instead of just one. So much is possible due to the massive reduction in both time and cost – it’s an exciting time.”
The research was conducted in the Chemistry of Health Laboratory in Cambridge, which was established with the support of the UK Research Partnership Investment Fund (UKRPIF) to promote the translation of academic research into clinical programmes.
Reference:
Robert I. Horne et al. ‘Discovery of Potent Inhibitors of α-Synuclein Aggregation Using Structure-Based Iterative Learning.’ Nature Chemical Biology (2024). DOI: 10.1038/s41589-024-01580-x
Researchers have used artificial intelligence techniques to massively accelerate the search for Parkinson’s disease treatments.
Machine learning is having a real impact on drug discovery – it’s speeding up the whole process of identifying the most promising candidatesMichele Vendruscolo Nathan PittMichele Vendruscolo
The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.
Interspecies competition led to even more forms of ancient human – defying evolutionary trends in vertebrates
Climate has long been held responsible for the emergence and extinction of hominin species. In most vertebrates, however, interspecies competition is known to play an important role.
Now, research shows for the first time that competition was fundamental to “speciation” – the rate at which new species emerge – across five million years of hominin evolution.
The study, published today in Nature Ecology & Evolution, also suggests that the species formation pattern of our own lineage was closer to island-dwelling beetles than other mammals.
“We have been ignoring the way competition between species has shaped our own evolutionary tree,” said lead author Dr Laura van Holstein, a University of Cambridge biological anthropologist at Clare College. “The effect of climate on hominin species is only part of the story.”
In other vertebrates, species form to fill ecological “niches” says van Holstein. Take Darwin’s finches: some evolved large beaks for nut-cracking, while others evolved small beaks for feeding on certain insects. When each resource niche gets filled, competition kicks in, so no new finches emerge and extinctions take over.
Van Holstein used Bayesian modelling and phylogenetic analyses to show that, like other vertebrates, most hominin species formed when competition for resources or space were low.
“The pattern we see across many early hominins is similar to all other mammals. Speciation rates increase and then flatline, at which point extinction rates start to increase. This suggests that interspecies competition was a major evolutionary factor.”
However, when van Holstein analysed our own group, Homo, the findings were “bizarre”.
For the Homo lineage that led to modern humans, evolutionary patterns suggest that competition between species actually resulted in the appearance of even more new species – a complete reversal of the trend seen in almost all other vertebrates.
“The more species of Homo there were, the higher the rate of speciation. So when those niches got filled, something drove even more species to emerge. This is almost unparalleled in evolutionary science.”
The closest comparison she could find was in beetle species that live on islands, where contained ecosystems can produce unusual evolutionary trends.
“The patterns of evolution we see across species of Homo that led directly to modern humans is closer to those of island-dwelling beetles than other primates, or even any other mammal.”
Recent decades have seen the discovery of several new hominin species, from Australopithecus sediba to Homo floresiensis. Van Holstein created a new database of “occurrences” in the hominin fossil record: each time an example of a species was found and dated, around 385 in total.
Fossils can be an unreliable measure of species’ lifetimes. “The earliest fossil we find will not be the earliest members of a species,” said van Holstein.
“How well an organism fossilises depends on geology, and on climatic conditions: whether it is hot or dry or damp. With research efforts concentrated in certain parts of the world, and we might well have missed younger or older fossils of a species as a result.”
Van Holstein used data modelling to address this problem, and factor in likely numbers of each species at the beginning and end of their existence, as well as environmental factors on fossilisation, to generate new start and end dates for most known hominin species (17 in total).
She found that some species thought to have evolved through “anagenesis” – when one slowly turns into another, but lineage doesn’t split – may have actually “budded”: when a new species branches off from an existing one.*
This meant that several more hominin species than previously assumed were co-existing, and so possibly competing.
While early species of hominins, such as Paranthropus, probably evolved physiologically to expand their niche – adapting teeth to exploit new types of food, for example – the driver of the very different pattern in our own genus Homo may well have been technology.
“Adoption of stone tools or fire, or intensive hunting techniques, are extremely flexible behaviours. A species that can harness them can quickly carve out new niches, and doesn’t have to survive vast tracts of time while evolving new body plans,” said van Holstein
She argues that an ability to use technology to generalise, and rapidly go beyond ecological niches that force other species to compete for habitat and resources, may be behind the exponential increase in the number of Homo species detected by the latest study.
But it also led to Homo sapiens – the ultimate generalisers. And competition with an extremely flexible generalist in almost every ecological niche may be what contributed to the extinction of all other Homo species.
Added van Holstein: “These results show that, although it has been conventionally ignored, competition played an important role in human evolution overall. Perhaps most interestingly, in our own genus it played a role unlike that across any other vertebrate lineage known so far.”
Competition between species played a major role in the rise and fall of hominins, and produced a “bizarre” evolutionary pattern for the Homo lineage.
This is almost unparalleled in evolutionary scienceLaura van HolsteinThe Duckworth LaboratoryA cast of the skull of Homo Heidelbergensis, one of the hominin species analysed in the latest study.
The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.
Wed 24 Apr 17:00: Molecular Mechanism of T-cell Exhaustion
This Cambridge Immunology and Medicine Seminar will take place on Wednesday 24th April 2024, starting at 5:00 pm.
Location: Max Perutz Lecture Theatre, Francis Crick Avenue, MRC Laboratory of Molecular Biology (LMB), Cambridge
Speaker: Prof John Wherry; Chair, Department of Systems Pharmacology & Translational Therapeutics Richard and Barbara Schiffrin President’s Distinguished Professor Director, Institute for Immunology Co-Program Leader, Immunobiology Program, Abramson Cancer Center Co-Director, Parker Institute for Cancer Immunotherapy. University of Pennsylvania
Title: Molecular Mechanism of T-cell Exhaustion
Host: Professor Menna Clatworthy, Director of CITIID , NIHR Research Professor and Professor of Translational Immunology, University of Cambridge
Refreshments will be available following the Seminar.
- Speaker: Professor John Wherry, Director, Institute for Immunology, University of Pennsylvania
- Wednesday 24 April 2024, 17:00-18:00
- Venue: Max Perutz Lecture Theatre, MRC LMB, Francis Crick Avenue, Cambridge CB2 0QH .
- Series: Immunology and Medicine Seminars; organiser: Ruth Paton.
Wed 24 Apr 17:00: Molecular Mechanism of T-cell Exhaustion
This Cambridge Immunology and Medicine Seminar will take place on Wednesday 24th April 2024, starting at 5:00 pm.
Location: Max Perutz Lecture Theatre, Francis Crick Avenue, MRC Laboratory of Molecular Biology (LMB), Cambridge
Speaker: Prof John Wherry; Chair, Department of Systems Pharmacology & Translational Therapeutics Richard and Barbara Schiffrin President’s Distinguished Professor Director, Institute for Immunology Co-Program Leader, Immunobiology Program, Abramson Cancer Center Co-Director, Parker Institute for Cancer Immunotherapy. University of Pennsylvania
Title: Molecular Mechanism of T-cell Exhaustion
Host: Professor Menna Clatworthy, Director of CITIID , NIHR Research Professor and Professor of Translational Immunology, University of Cambridge
Refreshments will be available following the Seminar.
- Speaker: Professor John Wherry, Director, Institute for Immunology, University of Pennsylvania
- Wednesday 24 April 2024, 17:00-18:00
- Venue: Max Perutz Lecture Theatre, MRC LMB, Francis Crick Avenue, Cambridge CB2 0QH .
- Series: Immunology and Medicine Seminars; organiser: Ruth Paton.
Wed 15 May 16:00: How use of SSRI impacts placenta and mammary gland development Contact Fiona Roby for zoom link
Selective serotonin reuptake inhibitors (SSRI) are the most commonly prescribed drug class in the US. Untreated depression during pregnancy creates a risk for maternal wellbeing and is coupled with adverse pregnancy outcomes with causes that are poorly understood and use of SSRI among pregnant women is increasing. Fluoxetine (Prozac) and sertraline (Zoloft) are the most prescribed medications for pregnant women in their first trimester. With nearly one in five women experiencing depression, SSRI use during pregnancy continues to increase in the US. Antenatal SSRI use has been demonstrated to also result in increased neonatal mortality and morbidity. Therefore, presenting a conundrum for medical care providers when making decisions as to how to treat pregnant women with depression and also protect the pregnancy, and health outcomes for the infant. Importantly, and often under looked, is that SSRI not only impact the neuronal serotonin transporter (SERT), but they also impact the effects of SERT throughout the body. Our work has recently demonstrated that treatment with fluoxetine prepartum results increased death of offspring and increased morbidity for the offspring that survive, which we have recapitulated in a sheep model. Further, our work demonstrates the SSRI also impact both mammary gland function and development, as well as maternal outcomes, recently demonstrating that use of fluoxetine increases adiposity in offspring up to 12 weeks of age, and that male offspring appear to be disproportionally affected. We continue to investigate the impact of SSRI use on the placenta, mammary gland development, and long-term effects on both dam and offspring. Our goal is to develop novel interventions that will allow the dam to continue SSRI treatment if needed that will not impact her long-term health, as well as the long-term health of the offspring.
Contact Fiona Roby for zoom link
- Speaker: Laura L. Hernandez, Department of Animal and Dairy Sciences University of Wisconsin-Madison
- Wednesday 15 May 2024, 16:00-17:00
- Venue: by zoom.
- Series: Departmental Seminar Programme, Department of Veterinary Medicine; organiser: Fiona Roby.
Wed 15 May 16:00: How use of SSRI impacts placenta and mammary gland development Contact Fiona Roby for zoom link
Selective serotonin reuptake inhibitors (SSRI) are the most commonly prescribed drug class in the US. Untreated depression during pregnancy creates a risk for maternal wellbeing and is coupled with adverse pregnancy outcomes with causes that are poorly understood and use of SSRI among pregnant women is increasing. Fluoxetine (Prozac) and sertraline (Zoloft) are the most prescribed medications for pregnant women in their first trimester. With nearly one in five women experiencing depression, SSRI use during pregnancy continues to increase in the US. Antenatal SSRI use has been demonstrated to also result in increased neonatal mortality and morbidity. Therefore, presenting a conundrum for medical care providers when making decisions as to how to treat pregnant women with depression and also protect the pregnancy, and health outcomes for the infant. Importantly, and often under looked, is that SSRI not only impact the neuronal serotonin transporter (SERT), but they also impact the effects of SERT throughout the body. Our work has recently demonstrated that treatment with fluoxetine prepartum results increased death of offspring and increased morbidity for the offspring that survive, which we have recapitulated in a sheep model. Further, our work demonstrates the SSRI also impact both mammary gland function and development, as well as maternal outcomes, recently demonstrating that use of fluoxetine increases adiposity in offspring up to 12 weeks of age, and that male offspring appear to be disproportionally affected. We continue to investigate the impact of SSRI use on the placenta, mammary gland development, and long-term effects on both dam and offspring. Our goal is to develop novel interventions that will allow the dam to continue SSRI treatment if needed that will not impact her long-term health, as well as the long-term health of the offspring.
Contact Fiona Roby for zoom link
- Speaker: Laura L. Hernandez, Department of Animal and Dairy Sciences University of Wisconsin-Madison
- Wednesday 15 May 2024, 16:00-17:00
- Venue: by zoom.
- Series: Departmental Seminar Programme, Department of Veterinary Medicine; organiser: Fiona Roby.
Is Democracy Dying?
2024 is the year of elections. A record number of elections will take place, with half the adult population of the world, some two billion people, having the chance to vote. Is this a milestone to be celebrated in our democratic history or are we at a crossroads where the fate of liberal democracy hangs in the balance?
Against a backdrop of polarising populist movements, the erosion of trust in traditional institutions and a decline of democratic norms, we ask: is democracy dying? Is the election of populists an expression of democracy or a breakdown of democracy? How resilient are our democratic institutions in the face of unprecedented challenges? Is the tension between liberal and democracy ultimately too great to resolve?
Join us on 24 April to grapple with these questions in our second Vice-Chancellor’s Dialogues, hosted by Vice-Chancellor Professor Deborah Prentice.
Our speakers- David Goodhart, founding editor of Prospect magazine and Head of the Demography, Immigration and Integration unit at the think tank Policy Exchange. He is the author of The Road to Somewhere: The Populist Revolt and the Future of Politics.
- Nabila Ramdani, award-winning journalist, broadcaster and academic. She is the author of Fixing France: How to Repair a Broken Republic.
- Helen Thompson, Professor of Political Economy at the University of Cambridge. She is a regular panellist on Talking Politics and a columnist for the New Statesman.
The discussion will be chaired by Roger Mosey, Master of Selwyn College and former Editorial Director of the BBC. The event is public and open to all, but attendees must register on Eventbrite.
If you're not able to attend, we'll publish a recording of the event in the coming weeks.
The second Vice-Chancellor’s Dialogues event grapples with the question: 'is liberal democracy dying?' The evening will be hosted by Vice-Chancellor Professor Deborah Prentice and chaired by the Master of Selwyn College.
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Wed 15 May 16:00: How use of SSRI impacts placenta and mammary gland development
Selective serotonin reuptake inhibitors (SSRI) are the most commonly prescribed drug class in the US. Untreated depression during pregnancy creates a risk for maternal wellbeing and is coupled with adverse pregnancy outcomes with causes that are poorly understood and use of SSRI among pregnant women is increasing. Fluoxetine (Prozac) and sertraline (Zoloft) are the most prescribed medications for pregnant women in their first trimester. With nearly one in five women experiencing depression, SSRI use during pregnancy continues to increase in the US. Antenatal SSRI use has been demonstrated to also result in increased neonatal mortality and morbidity. Therefore, presenting a conundrum for medical care providers when making decisions as to how to treat pregnant women with depression and also protect the pregnancy, and health outcomes for the infant. Importantly, and often under looked, is that SSRI not only impact the neuronal serotonin transporter (SERT), but they also impact the effects of SERT throughout the body. Our work has recently demonstrated that treatment with fluoxetine prepartum results increased death of offspring and increased morbidity for the offspring that survive, which we have recapitulated in a sheep model. Further, our work demonstrates the SSRI also impact both mammary gland function and development, as well as maternal outcomes, recently demonstrating that use of fluoxetine increases adiposity in offspring up to 12 weeks of age, and that male offspring appear to be disproportionally affected. We continue to investigate the impact of SSRI use on the placenta, mammary gland development, and long-term effects on both dam and offspring. Our goal is to develop novel interventions that will allow the dam to continue SSRI treatment if needed that will not impact her long-term health, as well as the long-term health of the offspring.
- Speaker: Laura L. Hernandez, Department of Animal and Dairy Sciences University of Wisconsin-Madison
- Wednesday 15 May 2024, 16:00-17:00
- Venue: LT2.
- Series: Departmental Seminar Programme, Department of Veterinary Medicine; organiser: Fiona Roby.
Wed 15 May 16:00: How use of SSRI impacts placenta and mammary gland development
Selective serotonin reuptake inhibitors (SSRI) are the most commonly prescribed drug class in the US. Untreated depression during pregnancy creates a risk for maternal wellbeing and is coupled with adverse pregnancy outcomes with causes that are poorly understood and use of SSRI among pregnant women is increasing. Fluoxetine (Prozac) and sertraline (Zoloft) are the most prescribed medications for pregnant women in their first trimester. With nearly one in five women experiencing depression, SSRI use during pregnancy continues to increase in the US. Antenatal SSRI use has been demonstrated to also result in increased neonatal mortality and morbidity. Therefore, presenting a conundrum for medical care providers when making decisions as to how to treat pregnant women with depression and also protect the pregnancy, and health outcomes for the infant. Importantly, and often under looked, is that SSRI not only impact the neuronal serotonin transporter (SERT), but they also impact the effects of SERT throughout the body. Our work has recently demonstrated that treatment with fluoxetine prepartum results increased death of offspring and increased morbidity for the offspring that survive, which we have recapitulated in a sheep model. Further, our work demonstrates the SSRI also impact both mammary gland function and development, as well as maternal outcomes, recently demonstrating that use of fluoxetine increases adiposity in offspring up to 12 weeks of age, and that male offspring appear to be disproportionally affected. We continue to investigate the impact of SSRI use on the placenta, mammary gland development, and long-term effects on both dam and offspring. Our goal is to develop novel interventions that will allow the dam to continue SSRI treatment if needed that will not impact her long-term health, as well as the long-term health of the offspring.
- Speaker: Laura L. Hernandez, Department of Animal and Dairy Sciences University of Wisconsin-Madison
- Wednesday 15 May 2024, 16:00-17:00
- Venue: LT2.
- Series: Departmental Seminar Programme, Department of Veterinary Medicine; organiser: Fiona Roby.
Low-Density TaqMan® Array Cards for the Detection of Pathogens
Methods Microbiol. 2015;42:199-218. doi: 10.1016/bs.mim.2015.06.002. Epub 2015 Aug 3.
ABSTRACT
Real-time PCR assays have revolutionised diagnostic microbiology over the past 15 years or more. Adaptations and improvements over that time frame have led to the development of multiplex assays. However, limitations in terms of available fluorophores has meant the number of assays which can be combined has remained in single figures. This latter limitation has led to the focus tending to be on individual pathogens and their detection. This chapter describes the development of TaqMan® Array Cards (TACs), technology which allows the detection of multiple pathogens (up to 48 targets) from a single nucleic acid extract, utilising small volumes and real-time PCR. This in turn lends itself to a syndromic approach to infectious disease diagnosis. Using the examples of TACs we have developed in our own laboratory, as well as others, we explain the design, optimisation and use of TACs for respiratory, gastrointestinal and liver infections. Refinement of individual assays is discussed as well as the incorporation of appropriate internal and process controls onto the array cards. Finally, specific examples are given of instances where the assays have had a direct, positive impact on patient care.
PMID:38620215 | PMC:PMC7172410 | DOI:10.1016/bs.mim.2015.06.002
A lineage-specific protein network at the trypanosome nuclear envelope
Nucleus. 2024 Dec;15(1):2310452. doi: 10.1080/19491034.2024.2310452. Epub 2024 Apr 11.
ABSTRACT
The nuclear envelope (NE) separates translation and transcription and is the location of multiple functions, including chromatin organization and nucleocytoplasmic transport. The molecular basis for many of these functions have diverged between eukaryotic lineages. Trypanosoma brucei, a member of the early branching eukaryotic lineage Discoba, highlights many of these, including a distinct lamina and kinetochore composition. Here, we describe a cohort of proteins interacting with both the lamina and NPC, which we term lamina-associated proteins (LAPs). LAPs represent a diverse group of proteins, including two candidate NPC-anchoring pore membrane proteins (POMs) with architecture conserved with S. cerevisiae and H. sapiens, and additional peripheral components of the NPC. While many of the LAPs are Kinetoplastid specific, we also identified broadly conserved proteins, indicating an amalgam of divergence and conservation within the trypanosome NE proteome, highlighting the diversity of nuclear biology across the eukaryotes, increasing our understanding of eukaryotic and NPC evolution.
PMID:38605598 | DOI:10.1080/19491034.2024.2310452