BACKGROUND Over the last few decades,3 pathogenic pandemics have impacted the global population;severe acute respiratory syndrome coronavirus(SARS-CoV),Middle East respiratory syndrome coronavirus(MERS-CoV)and SARS-Co...BACKGROUND Over the last few decades,3 pathogenic pandemics have impacted the global population;severe acute respiratory syndrome coronavirus(SARS-CoV),Middle East respiratory syndrome coronavirus(MERS-CoV)and SARS-CoV-2.The global disease burden has attributed to millions of deaths and morbidities,with the majority being attributed to SARS-CoV-2.As such,the evaluation of the mental health(MH)impact across healthcare professionals(HCPs),patients and the general public would be an important facet to evaluate to better understand short,medium and long-term exposures.AIM To identify and report:(1)MH conditions commonly observed across all 3 pandemics;(2)Impact of MH outcomes across HCPs,patients and the general public associated with all 3 pandemics;and(3)The prevalence of the MH impact and clinical epidemiological significance.METHODS A systematic methodology was developed and published on PROSPERO(CRD42021228697).The databases PubMed,EMBASE,ScienceDirect and the Cochrane Central Register of Controlled Trials were used as part of the data extraction process,and publications from January 1,1990 to August 1,2021 were searched.MeSH terms and keywords used included Mood disorders,PTSD,Anxiety,Depression,Psychological stress,Psychosis,Bipolar,Mental Health,Unipolar,Self-harm,BAME,Psychiatry disorders and Psychological distress.The terms were expanded with a‘snowballing’method.Cox-regression and the Monte-Carlo simulation method was used in addition to I2 and Egger’s tests to determine heterogeneity and publication bias.RESULTS In comparison to MERS and SARS-CoV,it is evident SAR-CoV-2 has an ongoing MH impact,with emphasis on depression,anxiety and post-traumatic stress disorder.CONCLUSION It was evident MH studies during MERS and SARS-CoV was limited in comparison to SARS-CoV-2,with much emphasis on reporting symptoms of depression,anxiety,stress and sleep disturbances.The lack of comprehensive studies conducted during previous pandemics have introduced limitations to the“know-how”for clinicians and researchers to better support patients and deliver care with limited healthcare resources.展开更多
Synchronization is a phenomenon that is ubiquitous in engineering and natural ecosystems.The study of explosive synchronization on a single-layer network gives the critical transition coupling strength that causes exp...Synchronization is a phenomenon that is ubiquitous in engineering and natural ecosystems.The study of explosive synchronization on a single-layer network gives the critical transition coupling strength that causes explosive synchronization.However, no significant findings have been made on multi-layer complex networks.This paper proposes a frequency-weighted Kuramoto model on a two-layer network and the critical coupling strength of explosive synchronization is obtained by both theoretical analysis and numerical validation.It is found that the critical value is affected by the interaction strength between layers and the number of network oscillators.The explosive synchronization will be hindered by enhancing the interaction and promoted by increasing the number of network oscillators.Our results have importance across a range of engineering and biological research fields.展开更多
The reconstruction of images from their corresponding noisy Radon transform is a typical example of an ill-posed linear inverse problem as arising in the application of computerized tomography(CT).As the(naive)solutio...The reconstruction of images from their corresponding noisy Radon transform is a typical example of an ill-posed linear inverse problem as arising in the application of computerized tomography(CT).As the(naive)solution does not depend on the measured data continuously,regularization is needed to reestablish a continuous dependence.In this work,we investigate simple,but yet still provably convergent approaches to learning linear regularization methods from data.More specifically,we analyze two approaches:one generic linear regularization that learns how to manipulate the singular values of the linear operator in an extension of our previous work,and one tailored approach in the Fourier domain that is specific to CT-reconstruction.We prove that such approaches become convergent regularization methods as well as the fact that the reconstructions they provide are typically much smoother than the training data they were trained on.Finally,we compare the spectral as well as the Fourier-based approaches for CT-reconstruction numerically,discuss their advantages and disadvantages and investigate the effect of discretization errors at differentresolutions.展开更多
Considering the improved interpretable performance of Kolmogorov–Arnold Networks(KAN)algorithm compared to multi-layer perceptron(MLP)algorithm,a fundamental research question arises on how modifying the loss functio...Considering the improved interpretable performance of Kolmogorov–Arnold Networks(KAN)algorithm compared to multi-layer perceptron(MLP)algorithm,a fundamental research question arises on how modifying the loss function of KAN affects its modelling performance for energy systems,particularly industrial-scale thermal power plants.In this regard,first,we modify the loss function of both KAN and MLP algorithms and embed Pearson Correlation Coefficient(PCC).Second,the algorithmic configurations built on PCC,i.e.,KAN_PCC and MLP_PCC as well as original architecture of KAN and MLP are deployed for modelling and optimisation analyses for two case studies of energy systems:(i)energy efficiency cooling and energy efficiency heating of buildings,and(ii)power generation operation of 660 MW capacity thermal power plant.The analysis reveals superior modelling performance of KAN and KAN_PCC algorithms than those of MLP and MLP_PCC for the two case studies.KAN models are embedded in the optimisation framework of nonlinear programming and feasible optimal solutions are estimated,maximising thermal efficiency up to 42.17±0.88%and minimising turbine heat rate to 7487±129 kJ/kWh corresponding to power generation of 500±14 MW for the thermal power plant.It is anticipated that the scientific,research and industrial community may benefit from the fundamental insights presented in this paper for the ML algorithm selection and carrying out model-based optimisation analysis for the performance enhancement of energy systems.展开更多
Uncertainty quantification of building design loads is essential to efficient and reliable building energy planning in the design stage.Current data-driven methods struggle to generalize across buildings with diverse ...Uncertainty quantification of building design loads is essential to efficient and reliable building energy planning in the design stage.Current data-driven methods struggle to generalize across buildings with diverse shapes due to limitations in representing complex geometric structures.To tackle this issue,a graph convolutional networks(GCN)-based uncertainty quantification method is proposed.This graph-based approach is introduced to represent building shapes by dividing them into blocks and defining their spatial relationships through nodes and edges.The method effectively captures complex building characteristics,enhancing the generalization abilities.An approach leveraging GCN could estimate design loads by understanding the impact of diverse uncertain factors.Additionally,a class activation map is formulated to identify key uncertain factors,guiding the selection of important design parameters during the building design stage.The effectiveness of this method is evaluated through comparison with four widely-used data-driven techniques.Results indicate that the mean absolute percentage errors(MAPE)for statistical indicators of uncertainty quantification are under 6.0%and 4.0%for cooling loads and heating loads,respectively.The proposed method is demonstrated to quantify uncertainty in building design loads with outstanding generalization abilities.With regard to time costs,the computation time of the proposed method is reduced from 331 hours to 30 seconds for a twenty-floor building compared to a conventional physics-based method.展开更多
Light perception at dawn plays a key role in coordinating multiple molecular processes and in entraining the plant circadian clock.The Arabidopsis mutant lacking the main photoreceptors,however,still shows clock entra...Light perception at dawn plays a key role in coordinating multiple molecular processes and in entraining the plant circadian clock.The Arabidopsis mutant lacking the main photoreceptors,however,still shows clock entrainment,indicating that the integration of light into the morning transcriptome is not well understood.In this study,we performed a high-resolution RNA-sequencing time-series experiment,sampling every 2 min beginning at dawn.In parallel experiments,we perturbed temperature,the circadian clock,photoreceptor signaling,and chloroplast-derived light signaling.We used these data to infer a gene network that describes the gene expression dynamics after light stimulus in the morning,and then validated key edges.By sampling time points at high density,we are able to identify three light-and temperature-sensitive bursts of transcription factor activity,one of which lasts for only about 8 min.Phytochrome and cryptochrome mutants cause a delay in the transcriptional bursts at dawn,and completely remove a burst of expression in key photomorphogenesis genes(HY5 and BBX family).Our complete network is available online(http://www-users.york.ac.uk/∼de656/dawnBurst/dawnBurst.html).Taken together,our results show that phytochrome and cryptochrome signaling is required for fine-tuning the dawn transcriptional response to light,but separate pathways can robustly activate much of the program in their absence.展开更多
During an infectious disease outbreak,biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy.Motivated by the ongoing r...During an infectious disease outbreak,biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy.Motivated by the ongoing response to COVID-19,we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions.In particular,we focus on parameter estimation in the presence of known biases in the data,and the effect of non-pharmaceutical interventions in enclosed subpopulations,such as households and care homes.We illustrate these methods by applying them to the COVID-19 pandemic.展开更多
Acute myeloid leukaemia(AML)patients harbouring certain chromosome abnormalities have particularly adverse prognosis.For these patients,targeted therapies have not yet made a significant clinical impact.To understand ...Acute myeloid leukaemia(AML)patients harbouring certain chromosome abnormalities have particularly adverse prognosis.For these patients,targeted therapies have not yet made a significant clinical impact.To understand the molecular landscape of poor prognosis AML we profiled 74 patients from two different centres(in UK and Finland)at the proteomic,phosphoproteomic and drug response phenotypic levels.These data were complemented with transcriptomics analysis for 39 cases.Data integration highlighted a phosphoproteomics signature that define two biologically distinct groups of KMT2A rearranged leukaemia,which we term MLLGA and MLLGB.MLLGA presented increased DOT1L phosphorylation,HOXA gene expression,CDK1 activity and phosphorylation of proteins involved in RNA metabolism,replication and DNA damage when compared to MLLGB and no KMT2A rearranged samples.MLLGA was particularly sensitive to 15 compounds including genotoxic drugs and inhibitors of mitotic kinases and inosine-5-monosphosphate dehydrogenase(IMPDH)relative to other cases.Intermediate-risk KMT2A-MLLT3 cases were mainly represented in a third group closer to MLLGA than to MLLGB.The expression of IMPDH2 and multiple nucleolar proteins was higher in MLLGA and correlated with the response to IMPDH inhibition in KMT2A rearranged leukaemia,suggesting a role of the nucleolar activity in sensitivity to treatment.In summary,our multilayer molecular profiling of AML with poor prognosis and KMT2A-MLLT3 karyotypes identified a phosphoproteomics signature that defines two biologically and phenotypically distinct groups of KMT2A rearranged leukaemia.These data provide a rationale for the potential development of specific therapies for AML patients characterised by the MLLGA phosphoproteomics signature identified in this study.展开更多
The potential of acoustic signatures to be used for State-of-Charge(SoC)estimation is demonstrated using artificial neural network regression models.This approach represents a streamlined method of processing the enti...The potential of acoustic signatures to be used for State-of-Charge(SoC)estimation is demonstrated using artificial neural network regression models.This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual,and often arbitrary,waveform peak selection.For applications where computational economy is prioritised,simple metrics of statistical significance are used to formally identify the most informative waveform features.These alone can be exploited for SoC inference.It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value,challenging the more common peak selection methods which focus on the latter.Although later echoes represent greater through-thickness coverage,and are intuitively more information-rich,their presence is not guaranteed.Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states.It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly,while also improving the estimation accuracy.Most importantly,it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates,without any complementary voltage information.This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed.Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell.Mean estimation errors as low as 0.75%are achieved on a SoC scale of 0-100%.展开更多
Everincreasing ambient ozone(O3)pollution in China has been exacerbating cardiopulmonary premature deaths.However,the urban-rural exposure inequity has seldom been explored.Here,we assess populationcale 03 exposure an...Everincreasing ambient ozone(O3)pollution in China has been exacerbating cardiopulmonary premature deaths.However,the urban-rural exposure inequity has seldom been explored.Here,we assess populationcale 03 exposure and mortality burdens between 1990 and 2019 based on integrated pollution tracking and epidemiological evidence.We find Chinese population have been suffering from climbing 03 exposure by 4.3±2.8 ppb per decade as a result of rapid urbanization and growing prosperity of socioeconomic activities.Rural residents are broadly exposed to 9.8±4.1 ppb higher ambient O3 than the adjacent urban citizens,and thus urbaniza-tion-oriented migration compromises the exposure-associated mortality on total population.Cardiopulmonary excess premature deaths attributable to longterm 03 exposure,373,500(95%uncertainty interval[U]:240,600-510,900)in 2019,is underestimated in previous studies due to ignorance of cardiovascular causes.Future 03 pollution policy should focus more on rural population who are facing an aggravating threat of mortality risks to ameliorate environmental health injustice.展开更多
This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to fo...This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.展开更多
This paper presents a dynamic knowledge graph approach that offers a reusable,interoperable,and extensible framework for modelling power systems.Domain ontologies have been developed to support a linked data represent...This paper presents a dynamic knowledge graph approach that offers a reusable,interoperable,and extensible framework for modelling power systems.Domain ontologies have been developed to support a linked data representation of infrastructure data,socio-demographic data,areal attributes like demand,and models describing power systems.The knowledge graph links the data with a hierarchical representation of administrative regions,supporting geospatial queries to retrieve information about the population within the vicinity of a power plant,the number of power plants,total generation capacity,and demand within specific areas.Computational agents were developed to operate on the knowledge graph.The agents performed tasks including data uploading,updating,retrieval,processing,model construction and scenario analysis.A derived information framework was used to track the provenance of information calculated by agents involved in each scenario.The knowledge graph was populated with data describing the UK power system.Two alternative models of the transmission grid with different levels of structural resolution were instantiated,providing the foundation for the power system simulation and optimisation tasks performed by the agents.The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.展开更多
We study the problem of the unsupervised learning of graphical models in mixed discrete-continuous domains.The problem of unsupervised learning of such models in discrete domains alone is notoriously challenging,compo...We study the problem of the unsupervised learning of graphical models in mixed discrete-continuous domains.The problem of unsupervised learning of such models in discrete domains alone is notoriously challenging,compounded by the fact that inference is computationally demanding.The situation is generally believed to be significantly worse in discrete-continuous domains:estimating the unknown probability distribution of given samples is often limited in practice to a handful of parametric forms,and in addition to that,computing conditional queries need to carefully handle low-probability regions in safety-critical applications.In recent years,the regime of tractable learning has emerged,which attempts to learn a graphical model that permits efficient inference.Most of the results in this regime are based on arithmetic circuits,for which inference is linear in the size of the obtained circuit.In this work,we show how,with minimal modifications,such regimes can be generalized by leveraging efficient density estimation schemes based on piecewise polynomial approximations.Our framework is realized on a recent computational abstraction that permits efficient inference for a range of queries in the underlying language.Our empirical results show that our approach is effective,and allows a study of the trade-off between the granularity of the learned model and its predictive power.展开更多
基金Supported by Southern Health NHS Foundation Trust.
文摘BACKGROUND Over the last few decades,3 pathogenic pandemics have impacted the global population;severe acute respiratory syndrome coronavirus(SARS-CoV),Middle East respiratory syndrome coronavirus(MERS-CoV)and SARS-CoV-2.The global disease burden has attributed to millions of deaths and morbidities,with the majority being attributed to SARS-CoV-2.As such,the evaluation of the mental health(MH)impact across healthcare professionals(HCPs),patients and the general public would be an important facet to evaluate to better understand short,medium and long-term exposures.AIM To identify and report:(1)MH conditions commonly observed across all 3 pandemics;(2)Impact of MH outcomes across HCPs,patients and the general public associated with all 3 pandemics;and(3)The prevalence of the MH impact and clinical epidemiological significance.METHODS A systematic methodology was developed and published on PROSPERO(CRD42021228697).The databases PubMed,EMBASE,ScienceDirect and the Cochrane Central Register of Controlled Trials were used as part of the data extraction process,and publications from January 1,1990 to August 1,2021 were searched.MeSH terms and keywords used included Mood disorders,PTSD,Anxiety,Depression,Psychological stress,Psychosis,Bipolar,Mental Health,Unipolar,Self-harm,BAME,Psychiatry disorders and Psychological distress.The terms were expanded with a‘snowballing’method.Cox-regression and the Monte-Carlo simulation method was used in addition to I2 and Egger’s tests to determine heterogeneity and publication bias.RESULTS In comparison to MERS and SARS-CoV,it is evident SAR-CoV-2 has an ongoing MH impact,with emphasis on depression,anxiety and post-traumatic stress disorder.CONCLUSION It was evident MH studies during MERS and SARS-CoV was limited in comparison to SARS-CoV-2,with much emphasis on reporting symptoms of depression,anxiety,stress and sleep disturbances.The lack of comprehensive studies conducted during previous pandemics have introduced limitations to the“know-how”for clinicians and researchers to better support patients and deliver care with limited healthcare resources.
基金Project supported by the National Natural Science Foundation of China(Grant No.61771299)the Key Laboratory of Speciality Fiber Optics and Optical Access Networks,Shanghai University,China(Grant No.SKLSFO2012-14)+1 种基金Funding of the Key Laboratory of Wireless Sensor Network and Communication,Shanghai Institute of Microsystem and Information Technology,ChinaFunding of the Shanghai Education Committee,Chinese Academy of Sciences,and Shanghai Science Committee(Grant Nos.12511503303,14511105602,and 14511105902)
文摘Synchronization is a phenomenon that is ubiquitous in engineering and natural ecosystems.The study of explosive synchronization on a single-layer network gives the critical transition coupling strength that causes explosive synchronization.However, no significant findings have been made on multi-layer complex networks.This paper proposes a frequency-weighted Kuramoto model on a two-layer network and the critical coupling strength of explosive synchronization is obtained by both theoretical analysis and numerical validation.It is found that the critical value is affected by the interaction strength between layers and the number of network oscillators.The explosive synchronization will be hindered by enhancing the interaction and promoted by increasing the number of network oscillators.Our results have importance across a range of engineering and biological research fields.
基金the support of the German Research Foundation,projects BU 2327/19-1 and MO 2962/7-1support from the EPSRC grant EP/R513106/1support from the Alan Turing Institute.
文摘The reconstruction of images from their corresponding noisy Radon transform is a typical example of an ill-posed linear inverse problem as arising in the application of computerized tomography(CT).As the(naive)solution does not depend on the measured data continuously,regularization is needed to reestablish a continuous dependence.In this work,we investigate simple,but yet still provably convergent approaches to learning linear regularization methods from data.More specifically,we analyze two approaches:one generic linear regularization that learns how to manipulate the singular values of the linear operator in an extension of our previous work,and one tailored approach in the Fourier domain that is specific to CT-reconstruction.We prove that such approaches become convergent regularization methods as well as the fact that the reconstructions they provide are typically much smoother than the training data they were trained on.Finally,we compare the spectral as well as the Fourier-based approaches for CT-reconstruction numerically,discuss their advantages and disadvantages and investigate the effect of discretization errors at differentresolutions.
文摘Considering the improved interpretable performance of Kolmogorov–Arnold Networks(KAN)algorithm compared to multi-layer perceptron(MLP)algorithm,a fundamental research question arises on how modifying the loss function of KAN affects its modelling performance for energy systems,particularly industrial-scale thermal power plants.In this regard,first,we modify the loss function of both KAN and MLP algorithms and embed Pearson Correlation Coefficient(PCC).Second,the algorithmic configurations built on PCC,i.e.,KAN_PCC and MLP_PCC as well as original architecture of KAN and MLP are deployed for modelling and optimisation analyses for two case studies of energy systems:(i)energy efficiency cooling and energy efficiency heating of buildings,and(ii)power generation operation of 660 MW capacity thermal power plant.The analysis reveals superior modelling performance of KAN and KAN_PCC algorithms than those of MLP and MLP_PCC for the two case studies.KAN models are embedded in the optimisation framework of nonlinear programming and feasible optimal solutions are estimated,maximising thermal efficiency up to 42.17±0.88%and minimising turbine heat rate to 7487±129 kJ/kWh corresponding to power generation of 500±14 MW for the thermal power plant.It is anticipated that the scientific,research and industrial community may benefit from the fundamental insights presented in this paper for the ML algorithm selection and carrying out model-based optimisation analysis for the performance enhancement of energy systems.
基金supported by the National Natural Science Foundation of China(No.52161135202)Hangzhou Key Scientific Research Plan Project(No.2023SZD0028)+1 种基金the Basic Research Funds for the Central Government‘Innovative Team of Zhejiang University’(No.2022FZZX01-09)China Scholarship Fund.
文摘Uncertainty quantification of building design loads is essential to efficient and reliable building energy planning in the design stage.Current data-driven methods struggle to generalize across buildings with diverse shapes due to limitations in representing complex geometric structures.To tackle this issue,a graph convolutional networks(GCN)-based uncertainty quantification method is proposed.This graph-based approach is introduced to represent building shapes by dividing them into blocks and defining their spatial relationships through nodes and edges.The method effectively captures complex building characteristics,enhancing the generalization abilities.An approach leveraging GCN could estimate design loads by understanding the impact of diverse uncertain factors.Additionally,a class activation map is formulated to identify key uncertain factors,guiding the selection of important design parameters during the building design stage.The effectiveness of this method is evaluated through comparison with four widely-used data-driven techniques.Results indicate that the mean absolute percentage errors(MAPE)for statistical indicators of uncertainty quantification are under 6.0%and 4.0%for cooling loads and heating loads,respectively.The proposed method is demonstrated to quantify uncertainty in building design loads with outstanding generalization abilities.With regard to time costs,the computation time of the proposed method is reduced from 331 hours to 30 seconds for a twenty-floor building compared to a conventional physics-based method.
基金funded by an Alan Turing Institute Research fellowship under an EPSRC research grant(TU/A/000017)D.E.,EPSRC/BBSRC Innovation fellowships(EP/S001360/1 and EP/S001360/2)D.E.and S.C.,and an EMBO fellowship(ALTF 1418-2015)to M.B.
文摘Light perception at dawn plays a key role in coordinating multiple molecular processes and in entraining the plant circadian clock.The Arabidopsis mutant lacking the main photoreceptors,however,still shows clock entrainment,indicating that the integration of light into the morning transcriptome is not well understood.In this study,we performed a high-resolution RNA-sequencing time-series experiment,sampling every 2 min beginning at dawn.In parallel experiments,we perturbed temperature,the circadian clock,photoreceptor signaling,and chloroplast-derived light signaling.We used these data to infer a gene network that describes the gene expression dynamics after light stimulus in the morning,and then validated key edges.By sampling time points at high density,we are able to identify three light-and temperature-sensitive bursts of transcription factor activity,one of which lasts for only about 8 min.Phytochrome and cryptochrome mutants cause a delay in the transcriptional bursts at dawn,and completely remove a burst of expression in key photomorphogenesis genes(HY5 and BBX family).Our complete network is available online(http://www-users.york.ac.uk/∼de656/dawnBurst/dawnBurst.html).Taken together,our results show that phytochrome and cryptochrome signaling is required for fine-tuning the dawn transcriptional response to light,but separate pathways can robustly activate much of the program in their absence.
文摘During an infectious disease outbreak,biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy.Motivated by the ongoing response to COVID-19,we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions.In particular,we focus on parameter estimation in the presence of known biases in the data,and the effect of non-pharmaceutical interventions in enclosed subpopulations,such as households and care homes.We illustrate these methods by applying them to the COVID-19 pandemic.
基金We thank Adrian Kontor for technical help with the manipulation of AML primary samples,Sarah Mueller for managing the supply of AML primary samples,Janet Matthews for assisting with the processing of patient clinical data,Ruth Osuntola for technical assistance with the mass spectrometry experiments and the FIMM High Throughput Biomedicine Unit for their expert technical support.This work was mainly funded by Cancer Research UK(C15966/A24375)with additional contribution from Blood Cancer UK(20008).All authors have read and approved the article.
文摘Acute myeloid leukaemia(AML)patients harbouring certain chromosome abnormalities have particularly adverse prognosis.For these patients,targeted therapies have not yet made a significant clinical impact.To understand the molecular landscape of poor prognosis AML we profiled 74 patients from two different centres(in UK and Finland)at the proteomic,phosphoproteomic and drug response phenotypic levels.These data were complemented with transcriptomics analysis for 39 cases.Data integration highlighted a phosphoproteomics signature that define two biologically distinct groups of KMT2A rearranged leukaemia,which we term MLLGA and MLLGB.MLLGA presented increased DOT1L phosphorylation,HOXA gene expression,CDK1 activity and phosphorylation of proteins involved in RNA metabolism,replication and DNA damage when compared to MLLGB and no KMT2A rearranged samples.MLLGA was particularly sensitive to 15 compounds including genotoxic drugs and inhibitors of mitotic kinases and inosine-5-monosphosphate dehydrogenase(IMPDH)relative to other cases.Intermediate-risk KMT2A-MLLT3 cases were mainly represented in a third group closer to MLLGA than to MLLGB.The expression of IMPDH2 and multiple nucleolar proteins was higher in MLLGA and correlated with the response to IMPDH inhibition in KMT2A rearranged leukaemia,suggesting a role of the nucleolar activity in sensitivity to treatment.In summary,our multilayer molecular profiling of AML with poor prognosis and KMT2A-MLLT3 karyotypes identified a phosphoproteomics signature that defines two biologically and phenotypically distinct groups of KMT2A rearranged leukaemia.These data provide a rationale for the potential development of specific therapies for AML patients characterised by the MLLGA phosphoproteomics signature identified in this study.
基金funding and support from the Faraday Institution(EP/S003053/1)as part of the Multi-Scale Modelling(FIRG025)and LiSTAR(FIRG014)projectsThe Royal Academy of Engineering is acknowledged for the financial support of Shearing(CiET1718\59)Brett under the Research Chairs and Senior Research Fellowships scheme(RCSRF2021/13/53).
文摘The potential of acoustic signatures to be used for State-of-Charge(SoC)estimation is demonstrated using artificial neural network regression models.This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual,and often arbitrary,waveform peak selection.For applications where computational economy is prioritised,simple metrics of statistical significance are used to formally identify the most informative waveform features.These alone can be exploited for SoC inference.It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value,challenging the more common peak selection methods which focus on the latter.Although later echoes represent greater through-thickness coverage,and are intuitively more information-rich,their presence is not guaranteed.Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states.It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly,while also improving the estimation accuracy.Most importantly,it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates,without any complementary voltage information.This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed.Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell.Mean estimation errors as low as 0.75%are achieved on a SoC scale of 0-100%.
基金UK Natural Environment Research Council(NERC)UK Na tional Centre for Atmospheric Science(NCAS),Australian Research Council(DP210102076)+8 种基金Australian National Health and Medical Research Council(APP2000581)H.Z.S andM.W.receive funding from the Engineering and Physical Sciences Research Council(EPSRC)via the UK Research and Innovation(UKRI)Centre for Doctoral Training in Application of Artificial Itelligence to the study of Environmental Risks(AI4ER,EP/S022961/1)HZ.S.also gives thanks for generous support from the US Fulbright Pro-gram.P.Y.is supported by China Scholarship Council(no.201906210065)Z.S.acknow-edges support from the UKRI NERC Cambridge Climate,Life and Earth Doctoral Training Partnership(C-CL EAR DTP,NE/S007164/1)M.M.C.is sponsored by the Croucher Founda-tion and Cambridge Commonwealth,European and Intemational Trust funding through a Croucher Cambridge Intemational ScholarshipH.L.is supported by the National NaturalSci ence Foundation of China(no.42061130213)Royal Society of the United Kingdom through the Newton Advanced Fllowship(NAF/R1/201166)A.TA.acknowledges funding from NERC(NE/P016383/1)through the Met Office UKRI Clean Air Program.Y.G.is supported by a Career Development Fellowship of the Australian Natinal Health and Med-|cal Research Council(APP1163693)Special appreciation is extended to Prof.Xiao Lu(School of Atmospheric Sciences,Sun Yat sen University)for his insightful discussion on the quality control of TOAR and CNEMC observations,and Prof.Aiyu Liu(Department of Sociology,Peking University)for her trenchant research perspectives on China's urbanization,to improve this curent interdiscilinary research.
文摘Everincreasing ambient ozone(O3)pollution in China has been exacerbating cardiopulmonary premature deaths.However,the urban-rural exposure inequity has seldom been explored.Here,we assess populationcale 03 exposure and mortality burdens between 1990 and 2019 based on integrated pollution tracking and epidemiological evidence.We find Chinese population have been suffering from climbing 03 exposure by 4.3±2.8 ppb per decade as a result of rapid urbanization and growing prosperity of socioeconomic activities.Rural residents are broadly exposed to 9.8±4.1 ppb higher ambient O3 than the adjacent urban citizens,and thus urbaniza-tion-oriented migration compromises the exposure-associated mortality on total population.Cardiopulmonary excess premature deaths attributable to longterm 03 exposure,373,500(95%uncertainty interval[U]:240,600-510,900)in 2019,is underestimated in previous studies due to ignorance of cardiovascular causes.Future 03 pollution policy should focus more on rural population who are facing an aggravating threat of mortality risks to ameliorate environmental health injustice.
文摘This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.
基金supported by the National Research Foundation,Prime Minister’s Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)programme.Part of this work was also supported by Towards Turing 2.0 under the EPSRC Grant EP/W037211/1.
文摘This paper presents a dynamic knowledge graph approach that offers a reusable,interoperable,and extensible framework for modelling power systems.Domain ontologies have been developed to support a linked data representation of infrastructure data,socio-demographic data,areal attributes like demand,and models describing power systems.The knowledge graph links the data with a hierarchical representation of administrative regions,supporting geospatial queries to retrieve information about the population within the vicinity of a power plant,the number of power plants,total generation capacity,and demand within specific areas.Computational agents were developed to operate on the knowledge graph.The agents performed tasks including data uploading,updating,retrieval,processing,model construction and scenario analysis.A derived information framework was used to track the provenance of information calculated by agents involved in each scenario.The knowledge graph was populated with data describing the UK power system.Two alternative models of the transmission grid with different levels of structural resolution were instantiated,providing the foundation for the power system simulation and optimisation tasks performed by the agents.The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.
基金Andreas Bueff was partly supported by EPSRC Platform Grant EP/N014758/1.
文摘We study the problem of the unsupervised learning of graphical models in mixed discrete-continuous domains.The problem of unsupervised learning of such models in discrete domains alone is notoriously challenging,compounded by the fact that inference is computationally demanding.The situation is generally believed to be significantly worse in discrete-continuous domains:estimating the unknown probability distribution of given samples is often limited in practice to a handful of parametric forms,and in addition to that,computing conditional queries need to carefully handle low-probability regions in safety-critical applications.In recent years,the regime of tractable learning has emerged,which attempts to learn a graphical model that permits efficient inference.Most of the results in this regime are based on arithmetic circuits,for which inference is linear in the size of the obtained circuit.In this work,we show how,with minimal modifications,such regimes can be generalized by leveraging efficient density estimation schemes based on piecewise polynomial approximations.Our framework is realized on a recent computational abstraction that permits efficient inference for a range of queries in the underlying language.Our empirical results show that our approach is effective,and allows a study of the trade-off between the granularity of the learned model and its predictive power.
