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Victory and Memory: WW2 Narratives in Modern Day Russia and Ukraine

Invitation Only Research Event

11 May 2020 - 4:00pm to 5:30pm
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Nina Tumarkin, Kathryn Wasserman Davis Professor of Slavic Studies; Professor of History; Director, Russian Area Studies Program, Wellesley College
Georgiy Kasianov, Head, Department of Contemporary History and Politics, Institute of History of Ukraine, National Academy of Sciences of Ukraine
Chair: Robert Brinkley, Chairman, Steering Committee, Ukraine Forum, Chatham House
In 2020 the world commemorates the 75th anniversary of the end of World War II. The Russian government has organized a wide range of activities to mark the USSR’s victory, aiming to raise the already prominent role of the USSR to a new level. Moscow also uses its narrative about the war as a propaganda tool. Ukraine, which suffered disproportionally huge human losses and material destruction during WWII, is departing from its Soviet legacy by focusing commemorative efforts on honouring the victims of WWII rather than on glorifying victory. 
 
This event will analyze the evolution of the WWII narratives in Russia and Ukraine in recent years. The panellists will discuss the role of those narratives in shaping national discourses and their implications for the countries' respective futures.
 
This event will be held on the record.

Anna Morgan

Administrator, Ukraine Forum
+44 (0)20 7389 3274

Department/project




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Can Old Economics Help Solve Modern Economic Problems?




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Stopping the Use of Chemical Weapons in Modern Conflicts




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The Belt and Road Initiative: Modernity, Geopolitics and the Global Order




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euromicron involved in modernizing the campus of Kiel University

ssm euromicron GmbH, a system house subsidiary of euromicron AG, is involved in a project to provide the technical equipment for the new building for the Institute of Geosciences at Kiel’s Christian Albrechts University. The seven-story new building is part of a campus-wide modernization initiative that is one of the largest public high-rise projects in the federal state of Schleswig-Holstein.




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Some of the latest climate models provide unrealistically high projections of future warming

A new study from University of Michigan climate researchers concludes that some of the latest-generation climate models may be overly sensitive to carbon dioxide increases and therefore project future warming that is unrealistically high.




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How modelling articulates the science of climate change

To imagine earth without greenhouse gases in its atmosphere is to turn the familiar blue marble into a barren lump of rock and ice on which the average surface temperature hovers around -18ºC. Such a planet would not receive less of the sunlight which is the ultimate source of all Earth's warmth. But when the energy it absorbed from the sunlight was re-emitted as infrared radiation, as the laws of physics require, it would head unimpeded back out into space.




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New Publication: Brochure on the Cartagena Protocol on Biosafety: Reducing the environmental risks of modern technology




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CBD News: Statement by Ahmed Djoghlaf, Executive Secretary of the Convention on Biological Diversity, to the International Model Forest Network Global Forum - 16-21 June 2008 - Alberta, Canada.




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CBD News: Message from Mr Ahmed Djoghlaf, Executive Secretary of the Convention on Biological Diversity, to the International Seminar "The Andalusí Experience: A Model for the Conservation of Biodiversity", 7 October 2009, Granada, Spain.




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CBD Press Release: A United Nations Conference on the Safe Use of Modern Biotechnology Opens Tomorrow




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CBD Communiqué: City of Edmonton a model of local government support for the CBD




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CBD News: An informal meeting for government experts and relevant stakeholders to discuss model contractual clauses, voluntary codes of conduct, guidelines and best practices and/or standards, as set out in Articles 19 and 20 of the Nagoya Protocol on Acc




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CBD News: The GBIF Secretariat has launched the inaugural GBIF Ebbe Nielsen Challenge, hoping to inspire innovative applications of open-access biodiversity data by scientists, informaticians, data modelers, cartographers and other experts competing for a




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CBD News: Fifteen years ago, the Cartagena Protocol on Biosafety to the Convention on Biological Diversity entered into force aiming to ensure the safe handling, transfer and use of living modified organisms (or LMOs) resulting from modern biotechnology.




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Developer models COVID-19 sneezing simulation on Gran Turismo software

A lone developer from the company that provided aerodynamic analysis for the cars in the PlayStation game Gran Turismo has applied the tooling to demonstrate the spread of germs with and without wearing a mask




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Calculation of the convexity adjustment to the forward rate in the Vasicek model for the forward in-arrears contracts on LIBOR rate

N. O. Malykh and I. S. Postevoy
Theor. Probability and Math. Statist. 99 (2020), 189-198.
Abstract, references and article information




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Asymptotic distribution of the maximum likelihood estimator in the fractional Vašíček model

S. S. Lohvinenko and K. V. Ralchenko
Theor. Probability and Math. Statist. 99 (2020), 149-168.
Abstract, references and article information




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Goodness-of-fit test in the Cox proportional hazards model with measurement errors

A. G. Kukush and O. O. Chernova
Theor. Probability and Math. Statist. 99 (2020), 125-135.
Abstract, references and article information




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Construction of the Karhunen–Loève model for an input Gaussian process in a linear system by using the output process

Yu. V. Kozachenko and I. V. Rozora
Theor. Probability and Math. Statist. 99 (2020), 113-124.
Abstract, references and article information





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Persistence and extinction in a stochastic nonautonomous logistic model of population dynamics

O. D. Borysenko and D. O. Borysenko
Theor. Probability and Math. Statist. 99 (2020), 67-75.
Abstract, references and article information




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Classical solution of a PDE system stemming from auxin transport model for leaf venation

Bin Li and Jieqiong Shen
Proc. Amer. Math. Soc. 148 (2020), 2565-2578.
Abstract, references and article information




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Modeling COVID-19: A new video describing the types of models used

Below, Mac Hyman, Tulane University, talks about types of mathematical models--their strengths and weaknesses--the data that we currently have and what we really need, and what models can tell us about a possible second wave.

At the beginning of the video, he thanks the mathematics community for its work, and near the end says, "Our mathematical community is really playing a central role in helping to predict the spread, and help mitigate this epidemic, and prioritize our efforts. …Do not underestimate the power that mathematics can have in helping to mitigate this epidemic—-we have a role to play."

See the full set of videos on modeling COVID-19 and see media coverage of mathematics' role in modeling the pandemic.




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Determination of globotriaosylceramide analogs in the organs of a mouse model of Fabry disease [Lipids]

Fabry disease is a heritable lipid disorder caused by the low activity of α-galactosidase A and characterized by the systemic accumulation of globotriaosylceramide (Gb3). Recent studies have reported a structural heterogeneity of Gb3 in Fabry disease, including Gb3 isoforms with different fatty acids and Gb3 analogs with modifications on the sphingosine moiety. However, Gb3 assays are often performed only on the selected Gb3 isoforms. To precisely determine the total Gb3 concentration, here we established two methods for determining both Gb3 isoforms and analogs. One was the deacylation method, involving Gb3 treatment with sphingolipid ceramide N-deacylase, followed by an assay of the deacylated products, globotriaosylsphingosine (lyso-Gb3) and its analogs, by ultra-performance LC coupled to tandem MS (UPLC-MS/MS). The other method was a direct assay established in the present study for 37 Gb3 isoforms and analogs/isoforms by UPLC-MS/MS. Gb3s from the organs of symptomatic animals of a Fabry disease mouse model were mainly Gb3 isoforms and two Gb3 analogs, such as Gb3(+18) containing the lyso-Gb3(+18) moiety and Gb3(−2) containing the lyso-Gb3(−2) moiety. The total concentrations and Gb3 analog distributions determined by the two methods were comparable. Gb3(+18) levels were high in the kidneys (24% of total Gb3) and the liver (13%), and we observed Gb3(−2) in the heart (10%) and the kidneys (5%). These results indicate organ-specific expression of Gb3 analogs, insights that may lead to a deeper understanding of the pathophysiology of Fabry disease.




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A hydrological model leads to advances in the creation of a world water map

(University of Córdoba) The University of Cordoba participated in the first shaping of a hydrological model on a basin scale as a global model to advance in world hydrological predictions.




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All disease models are 'wrong,' but scientists are working to fix that

(University of Colorado at Boulder) What can researchers do when their mathematical models of the spread of infectious diseases don't match real-world data? One research team is working on a solution.




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Infectious disease modeling study casts doubt on impact of Justinianic plague

(University of Maryland) Many historians have claimed the Justinianic Plague (c. 541-750 CE) killed half of the population of Byzantine (Eastern Roman) Empire. New historical research and mathematical modeling challenge the death rate and severity of this first plague pandemic, named for Emperor Justinian I.




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Modeling gas diffusion in aggregated soils

(American Society of Agronomy) Researchers develop soil-gas diffusivity model based on two agricultural soils.




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Which COVID-19 models should we use to make policy decisions?

(Penn State) A new process to harness multiple disease models for outbreak management has been developed by an international team of researchers. The team will immediately implement the process to help inform policy decisions for the COVID-19 outbreak.




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A Model of Community-Based Behavioral Intervention for Depression in Diabetes: Program ACTIVE

Mary de Groot
Jan 1, 2010; 23:18-25
From Research to Practice




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A Novel Approach to Adolescents With Type 1 Diabetes: The Team Clinic Model

Jennifer K. Raymond
Feb 1, 2015; 28:68-71
Care Innovations




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The Breakthrough Series: IHI's Collaborative Model for Achieving Breakthrough Improvement


Apr 1, 2004; 17:97-101
Articles




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Overview of Peer Support Models to Improve Diabetes Self-Management and Clinical Outcomes

Michele Heisler
Oct 1, 2007; 20:214-221
Articles




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The State of the Modern Political Economy

Professor Tano Santos, Professor Ray Horton, and Dean Emeritus Glenn Hubbard discuss the impact of the pandemic on American and international political economies.




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Determination of globotriaosylceramide analogs in the organs of a mouse model of Fabry disease [Lipids]

Fabry disease is a heritable lipid disorder caused by the low activity of α-galactosidase A and characterized by the systemic accumulation of globotriaosylceramide (Gb3). Recent studies have reported a structural heterogeneity of Gb3 in Fabry disease, including Gb3 isoforms with different fatty acids and Gb3 analogs with modifications on the sphingosine moiety. However, Gb3 assays are often performed only on the selected Gb3 isoforms. To precisely determine the total Gb3 concentration, here we established two methods for determining both Gb3 isoforms and analogs. One was the deacylation method, involving Gb3 treatment with sphingolipid ceramide N-deacylase, followed by an assay of the deacylated products, globotriaosylsphingosine (lyso-Gb3) and its analogs, by ultra-performance LC coupled to tandem MS (UPLC-MS/MS). The other method was a direct assay established in the present study for 37 Gb3 isoforms and analogs/isoforms by UPLC-MS/MS. Gb3s from the organs of symptomatic animals of a Fabry disease mouse model were mainly Gb3 isoforms and two Gb3 analogs, such as Gb3(+18) containing the lyso-Gb3(+18) moiety and Gb3(−2) containing the lyso-Gb3(−2) moiety. The total concentrations and Gb3 analog distributions determined by the two methods were comparable. Gb3(+18) levels were high in the kidneys (24% of total Gb3) and the liver (13%), and we observed Gb3(−2) in the heart (10%) and the kidneys (5%). These results indicate organ-specific expression of Gb3 analogs, insights that may lead to a deeper understanding of the pathophysiology of Fabry disease.




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Quantitative proteomics of human heart samples collected in vivo reveal the remodeled protein landscape of dilated left atrium without atrial fibrillation

Nora Linscheid
Apr 14, 2020; 0:RA119.001878v1-mcp.RA119.001878
Research




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Arginine in C9ORF72 Dipolypeptides Mediates Promiscuous Proteome Binding and Multiple Modes of Toxicity

Mona Radwan
Apr 1, 2020; 19:640-654
Research




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Predictions and Policymaking: Complex Modelling Beyond COVID-19

1 April 2020

Yasmin Afina

Research Assistant, International Security Programme

Calum Inverarity

Research Analyst and Coordinator, International Security Programme
The COVID-19 pandemic has highlighted the potential of complex systems modelling for policymaking but it is crucial to also understand its limitations.

GettyImages-1208425931.jpg

A member of the media wearing a protective face mask works in Downing Street where Britain's Prime Minister Boris Johnson is self-isolating in central London, 27 March 2020. Photo by TOLGA AKMEN/AFP via Getty Images.

Complex systems models have played a significant role in informing and shaping the public health measures adopted by governments in the context of the COVID-19 pandemic. For instance, modelling carried out by a team at Imperial College London is widely reported to have driven the approach in the UK from a strategy of mitigation to one of suppression.

Complex systems modelling will increasingly feed into policymaking by predicting a range of potential correlations, results and outcomes based on a set of parameters, assumptions, data and pre-defined interactions. It is already instrumental in developing risk mitigation and resilience measures to address and prepare for existential crises such as pandemics, prospects of a nuclear war, as well as climate change.

The human factor

In the end, model-driven approaches must stand up to the test of real-life data. Modelling for policymaking must take into account a number of caveats and limitations. Models are developed to help answer specific questions, and their predictions will depend on the hypotheses and definitions set by the modellers, which are subject to their individual and collective biases and assumptions. For instance, the models developed by Imperial College came with the caveated assumption that a policy of social distancing for people over 70 will have a 75 per cent compliance rate. This assumption is based on the modellers’ own perceptions of demographics and society, and may not reflect all societal factors that could impact this compliance rate in real life, such as gender, age, ethnicity, genetic diversity, economic stability, as well as access to food, supplies and healthcare. This is why modelling benefits from a cognitively diverse team who bring a wide range of knowledge and understanding to the early creation of a model.

The potential of artificial intelligence

Machine learning, or artificial intelligence (AI), has the potential to advance the capacity and accuracy of modelling techniques by identifying new patterns and interactions, and overcoming some of the limitations resulting from human assumptions and bias. Yet, increasing reliance on these techniques raises the issue of explainability. Policymakers need to be fully aware and understand the model, assumptions and input data behind any predictions and must be able to communicate this aspect of modelling in order to uphold democratic accountability and transparency in public decision-making.

In addition, models using machine learning techniques require extensive amounts of data, which must also be of high quality and as free from bias as possible to ensure accuracy and address the issues at stake. Although technology may be used in the process (i.e. automated extraction and processing of information with big data), data is ultimately created, collected, aggregated and analysed by and for human users. Datasets will reflect the individual and collective biases and assumptions of those creating, collecting, processing and analysing this data. Algorithmic bias is inevitable, and it is essential that policy- and decision-makers are fully aware of how reliable the systems are, as well as their potential social implications.

The age of distrust

Increasing use of emerging technologies for data- and evidence-based policymaking is taking place, paradoxically, in an era of growing mistrust towards expertise and experts, as infamously surmised by Michael Gove. Policymakers and subject-matter experts have faced increased public scrutiny of their findings and the resultant policies that they have been used to justify.

This distrust and scepticism within public discourse has only been fuelled by an ever-increasing availability of diffuse sources of information, not all of which are verifiable and robust. This has caused tension between experts, policymakers and public, which has led to conflicts and uncertainty over what data and predictions can be trusted, and to what degree. This dynamic is exacerbated when considering that certain individuals may purposefully misappropriate, or simply misinterpret, data to support their argument or policies. Politicians are presently considered the least trusted professionals by the UK public, highlighting the importance of better and more effective communication between the scientific community, policymakers and the populations affected by policy decisions.

Acknowledging limitations

While measures can and should be built in to improve the transparency and robustness of scientific models in order to counteract these common criticisms, it is important to acknowledge that there are limitations to the steps that can be taken. This is particularly the case when dealing with predictions of future events, which inherently involve degrees of uncertainty that cannot be fully accounted for by human or machine. As a result, if not carefully considered and communicated, the increased use of complex modelling in policymaking holds the potential to undermine and obfuscate the policymaking process, which may contribute towards significant mistakes being made, increased uncertainty, lack of trust in the models and in the political process and further disaffection of citizens.

The potential contribution of complexity modelling to the work of policymakers is undeniable. However, it is imperative to appreciate the inner workings and limitations of these models, such as the biases that underpin their functioning and the uncertainties that they will not be fully capable of accounting for, in spite of their immense power. They must be tested against the data, again and again, as new information becomes available or there is a risk of scientific models becoming embroiled in partisan politicization and potentially weaponized for political purposes. It is therefore important not to consider these models as oracles, but instead as one of many contributions to the process of policymaking.




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PARP-1-targeted Auger emitters display high-LET cytotoxic properties in vitro but show limited therapeutic utility in solid tumor models of human neuroblastoma

The currently available therapeutic radiopharmaceutical for high-risk neuroblastoma, 131I-MIBG, is ineffective at targeting micrometastases due to the low linear energy transfer (LET) properties of high-energy beta particles. In contrast, Auger radiation has high-LET properties with nanometer ranges in tissue, efficiently causing DNA damage when emitted in close proximity to DNA. The aim of this study was to evaluate the cytotoxicity of targeted Auger therapy in pre-clinical models of high-risk neuroblastoma. Methods: Using a radiolabeled poly(ADP-ribose) polymerase (PARP) inhibitor, 125I-KX1, we delivered an Auger emitter iodine-125 to PARP-1: a chromatin-binding enzyme overexpressed in neuroblastoma. In vitro cytotoxicity of 125I-KX1 was assessed in nineteen neuroblastoma cell lines, followed by in-depth pharmacological analysis in a sensitive and resistant pair of cell lines. Immunofluorescence microscopy was used to characterize 125I-KX1-induced DNA damage. Finally, in vitro/in vivo microdosimetry was modeled from experimentally derived pharmacological variables. Results: 125I-KX1 was highly cytotoxic in vitro across a panel of neuroblastoma cell lines, directly causing double strand DNA breaks. Based on subcellular dosimetry, 125I-KX1 was approximately twice as effective compared to 131I-KX1, whereas cytoplasmic 125I-MIBG demonstrated low biological effectiveness. Despite the ability to deliver focused radiation dose to the cell nuclei, 125I-KX1 remained less effective than its alpha-emitting analog 211At-MM4, and required significantly higher activity for equivalent in vivo efficacy based on tumor microdosimetry. Conclusion: Chromatin-targeted Auger therapy is lethal to high-risk neuroblastoma cells with potential use in micrometastatic disease. This study provides the first evidence for cellular lethality from a PARP-1 targeted Auger emitter, calling for further investigation into targeted Auger therapy.




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Imaging P-glycoprotein Induction at the Blood-Brain Barrier of a Beta-Amyloidosis Mouse Model with 11C-Metoclopramide PET

P-glycoprotein (ABCB1) plays an important role at the blood-brain barrier (BBB) in promoting the clearance of neurotoxic beta-amyloid (Aß) peptides from the brain into the blood. ABCB1 expression and activity were found to be decreased in the brains of Alzheimer disease (AD) patients. Treatment with drugs which induce cerebral ABCB1 activity may be a promising approach to delay the build-up of Aß deposits in the brain by enhancing the clearance of Aß peptides from the brain. The aim of this study was to investigate whether PET with the weak ABCB1 substrate radiotracer 11C-metoclopramide can measure ABCB1 induction at the BBB in a beta-amyloidosis mouse model (APP/PS1-21 mice) and in wild-type mice. Methods: Groups of wild-type and APP/PS1-21 mice aged 50 or 170 days underwent 11C-metoclopramide baseline PET scans or scans after intraperitoneal treatment with the rodent pregnane X receptor (PXR) activator 5-pregnen-3β-ol-20-one-16α-carbonitrile (PCN, 25 mg/kg) or its vehicle over 7 days. At the end of the PET scans, brains were harvested for immunohistochemical analysis of ABCB1 and Aß levels. In separate groups of mice, radiolabeled metabolites of 11C-metoclopramide were determined in plasma and brain at 15 min after radiotracer injection. As an outcome parameter of cerebral ABCB1 activity, the elimination slope of radioactivity washout from the brain (kE,brain) was calculated. Results: PCN treatment resulted in an increased clearance of radioactivity from the brain as reflected by significant increases in kE,brain (from +26% to +54% relative to baseline). Immunohistochemical analysis confirmed ABCB1 induction in the brains of PCN-treated APP/PS1-21 mice with a concomitant decrease in Aß levels. There was a significant positive correlation between kE,brain values and ABCB1 levels in the brain. In wild-type mice, a significant age-related decrease in kE,brain values was found. Metabolite analysis showed that the majority of radioactivity in the brain was composed of unmetabolized 11C-metoclopramide in all animal groups. Conclusion: 11C-metoclopramide can measure ABCB1 induction in the mouse brain without the need to consider an arterial input function and may find potential application in AD patients to non-invasively evaluate strategies to enhance the clearance properties of the BBB.




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Early Detection in a Mouse Model of Pancreatic Cancer by Imaging DNA Damage Response Signalling

Rationale: Despite its widespread use in oncology, the PET radiotracer 18F-FDG is ineffective for improving early detection of pancreatic ductal adenocarcinoma (PDAC). An alternative strategy for early detection of pancreatic cancer involves visualisation of high-grade pancreatic intraepithelial neoplasias (PanIN-3), generally regarded as the non-invasive precursors of PDAC. The DNA damage response is known to be hyper-activated in late-stage PanINs. Therefore, we investigated whether the SPECT imaging agent, 111In-anti-H2AX-TAT, allows visualisation of the DNA damage repair marker H2AX in PanIN-3s in an engineered mouse model of PDAC, to facilitate early detection of PDAC. Methods: Genetically engineered KPC mice (KRasLSL.G12D/+; p53LSL.R172H/+; PdxCre) were imaged with 18F-FDG and 111In-anti-H2AX-TAT. PanIN/PDAC presence visualised by histology was compared with autoradiography and immunofluorescence. Separately, the survival of KPC mice imaged with 111In-anti-H2AX-TAT was evaluated. Results: In KPC mouse pancreata, H2AX expression was increased in high-grade PanINs, but not in PDAC, corroborating earlier results obtained from human pancreas sections. Uptake of 111In-anti-H2AX-TAT, but not 111In-IgG-TAT or 18F-FDG, within the pancreas was positively correlated with the age of KPC mice, which was correlated with the number of high-grade PanINs. 111In-anti-H2AX-TAT localises preferentially in high-grade PanIN lesions, but not in established PDAC. Younger, non-tumour-bearing KPC mice that show uptake of 111In-anti-H2AX-TAT in the pancreas survive significantly shorter than mice with physiological 111In-anti-H2AX-TAT uptake. Conclusion: 111In-anti-H2AX-TAT imaging allows non-invasive detection of DNA damage repair signalling upregulation in pre-invasive PanIN lesions and is a promising new tool to aid in the early detection and staging of pancreatic cancer.




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177Lu-NM600 targeted radionuclide therapy extends survival in syngeneic murine models of triple-negative breast cancer

Triple negative breast cancer (TNBC) remains the most aggressive subtype of breast cancer leading to the worst prognosis. Because current therapeutic approaches lack efficacy, there is a clinically unmet need for effective treatment alternatives. Herein, we demonstrate a promising strategy utilizing a tumor-targeting alkylphosphocholine (NM600) radiolabeled with 177Lu for targeted radionuclide therapy (TRT) of TNBC. In two murine syngeneic models of TNBC, we confirmed excellent tumor targeting and rapid normal tissue clearance of the PET imaging analog 86Y-NM600. Based on longitudinal PET/CT data acquired with 86Y-NM600, we estimated the dosimetry of therapeutic 177Lu-NM600, which showed larger absorbed doses in the tumor compared to normal tissues. Administration of 177Lu-NM600 resulted in significant tumor growth inhibition and prolonged overall survival in mice bearing syngeneic 4T07 and 4T1 tumors. Complete response was attained in 60% of 4T07 bearing mice, but animals carrying aggressive 4T1 tumor grafts succumbed to metastatic progression. The injected activities used for treatment (9.25 and 18.5 MBq) were well tolerated, and only mild transient cytopenia was noted. Overall, our results suggest that 177Lu-NM600 TRT has potential for treatment of TNBC and merits further exploration in a clinical setting.




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PET/CT imaging with a 18F-labeled galactodendritic unit in a galectin-1 overexpressing orthotopic bladder cancer model

Galectins are carbohydrate-binding proteins overexpressed in bladder cancer (BCa) cells. Dendritic galactose moieties have a high affinity for galectin-expressing tumor cells. We radiolabeled a dendritic galactose carbohydrate with fluorine-18 – 18F-labeled galactodendritic unit 4 – and examined its potential in imaging urothelial malignancies. Methods: The 18F-labeled 1st generation galactodendritic unit 4 was obtained from its tosylate precursor. We conducted in vivo studies in galectin-expressing UMUC3 orthotopic BCa model to determine the ability of 18F-labeled galactodendritic unit 4 to image BCa. Results: Intravesical administration of 18F-labeled galactodendritic unit 4 allowed specific accumulation of the carbohydrate radiotracer in galectin-1 overexpressing UMUC3 orthotopic tumors when imaged with PET. The 18F-labeled galactodendritic unit 4 was not found to accumulate in non-tumor murine bladders. Conclusion: The 18F-labeled galactodendritic unit 4 and similar analogs may be clinically relevant and exploitable for PET imaging of galectin-1 overexpressing bladder tumors.




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Kinetic modeling and test-retest reproducibility of 11C-EKAP and 11C-FEKAP, novel agonist radiotracers for PET imaging of the kappa opioid receptor in humans

The kappa opioid receptor (KOR) is implicated in various neuropsychiatric disorders. We previously evaluated an agonist tracer, 11C-GR103545, for PET imaging of KOR in humans. Although 11C-GR103545 showed high brain uptake, good binding specificity, and selectivity to KOR, it displayed slow kinetics and relatively large test-retest variability (TRV) of distribution volume (VT) estimates (15%). Therefore we set out to develop two novel KOR agonist radiotracers, 11C-EKAP and 11C-FEKAP, and in nonhuman primates, both tracers exhibited faster kinetics and comparable binding parameters to 11C-GR103545. The aim of this study was to assess their kinetic and binding properties in humans. Methods: Six healthy subjects underwent 120-min test-retest PET scans with both 11C-EKAP and 11C-FEKAP. Metabolite-corrected arterial input functions were measured. Regional time-activity curves (TACs) were generated for 14 regions of interest. One- and two-tissue compartment models (1TC, 2TC) and the multilinear analysis-1 (MA1) method were applied to the regional TACs to calculate VT. Time-stability of VT values and test-retest reproducibility were evaluated. Levels of specific binding, as measured by the non-displaceable binding potential (BPND) for the three tracers (11C-EKAP, 11C-FEKAP and 11C-GR103545), were compared using a graphical method. Results: For both tracers, regional TACs were fitted well with the 2TC model and MA1 method (t*=20min), but not with the 1TC model. Given unreliably estimated parameters in several fits with the 2TC model and a good match between VT values from MA1 and 2TC, MA1 was chosen as the appropriate model for both tracers. Mean MA1 VT values were highest for 11C-GR103545, followed by 11C-EKAP, then 11C-FEKAP. Minimum scan time for stable VT measurement was 90 and 110min for 11C-EKAP and 11C-FEKAP, respectively, compared with 140min for 11C-GR103545. The mean absolute TRV in MA1 VT estimates was 7% and 18% for 11C-EKAP and 11C-FEKAP, respectively. BPND levels were similar for 11C-FEKAP and 11C-GR103545, but ~25% lower for 11C-EKAP. Conclusion: The two novel KOR agonist tracers showed faster tissue kinetics than 11C-GR103545. Even with slightly lower BPND, 11C-EKAP is judged to be a better tracer for imaging and quantification of KOR in humans, based on the shorter minimum scan time and excellent test-retest.




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Multi-omic Characterization of the Mode of Action of a Potent New Antimalarial Compound, JPC-3210, Against Plasmodium falciparum [Research]

The increasing incidence of antimalarial drug resistance to the first-line artemisinin combination therapies underpins an urgent need for new antimalarial drugs, ideally with a novel mode of action. The recently developed 2-aminomethylphenol, JPC-3210, (MMV 892646) is an erythrocytic schizonticide with potent in vitro antimalarial activity against multidrug-resistant Plasmodium falciparum lines, low cytotoxicity, potent in vivo efficacy against murine malaria, and favorable preclinical pharmacokinetics including a lengthy plasma elimination half-life. To investigate the impact of JPC-3210 on biochemical pathways within P. falciparum-infected red blood cells, we have applied a "multi-omics" workflow based on high resolution orbitrap mass spectrometry combined with biochemical approaches. Metabolomics, peptidomics and hemoglobin fractionation analyses revealed a perturbation in hemoglobin metabolism following JPC-3210 exposure. The metabolomics data demonstrated a specific depletion of short hemoglobin-derived peptides, peptidomics analysis revealed a depletion of longer hemoglobin-derived peptides, and the hemoglobin fractionation assay demonstrated decreases in hemoglobin, heme and hemozoin levels. To further elucidate the mechanism responsible for inhibition of hemoglobin metabolism, we used in vitro β-hematin polymerization assays and showed JPC-3210 to be an intermediate inhibitor of β-hematin polymerization, about 10-fold less potent then the quinoline antimalarials, such as chloroquine and mefloquine. Further, quantitative proteomics analysis showed that JPC-3210 treatment results in a distinct proteomic signature compared with other known antimalarials. While JPC-3210 clustered closely with mefloquine in the metabolomics and proteomics analyses, a key differentiating signature for JPC-3210 was the significant enrichment of parasite proteins involved in regulation of translation. These studies revealed that the mode of action for JPC-3210 involves inhibition of the hemoglobin digestion pathway and elevation of regulators of protein translation. Importantly, JPC-3210 demonstrated rapid parasite killing kinetics compared with other quinolones, suggesting that JPC-3210 warrants further investigation as a potentially long acting partner drug for malaria treatment.




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Arginine in C9ORF72 Dipolypeptides Mediates Promiscuous Proteome Binding and Multiple Modes of Toxicity [Research]

C9ORF72-associated Motor Neuron Disease patients feature abnormal expression of 5 dipeptide repeat (DPR) polymers. Here we used quantitative proteomics in a mouse neuronal-like cell line (Neuro2a) to demonstrate that the Arg residues in the most toxic DPRS, PR and GR, leads to a promiscuous binding to the proteome compared with a relative sparse binding of the more inert AP and GA. Notable targets included ribosomal proteins, translation initiation factors and translation elongation factors. PR and GR comprising more than 10 repeats appeared to robustly stall on ribosomes during translation suggesting Arg-rich peptide domains can electrostatically jam the ribosome exit tunnel during synthesis. Poly-GR also recruited arginine methylases, induced hypomethylation of endogenous proteins, and induced a profound destabilization of the actin cytoskeleton. Our findings point to arginine in GR and PR polymers as multivalent toxins to translation as well as arginine methylation that may explain the dysfunction of biological processes including ribosome biogenesis, mRNA splicing and cytoskeleton assembly.




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Dietary plant stanol ester supplementation reduces peripheral symptoms in a mouse model of Niemann-Pick type C1 disease.

Inês Magro dos Reis
Apr 14, 2020; 0:jlr.RA120000632v1-jlr.RA120000632
Research Articles




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Dietary plant stanol ester supplementation reduces peripheral symptoms in a mouse model of Niemann-Pick type C1 disease. [Research Articles]

Niemann–Pick type C1 (NPC1) disease is a rare genetic condition in which the function of the lysosomal cholesterol transporter NPC1 protein is impaired. Consequently, sphingolipids and cholesterol accumulate in lysosomes of all tissues, triggering a cascade of pathological events that culminate in severe systemic and neurological symptoms. Lysosomal cholesterol accumulation is also a key-factor in the development of atherosclerosis and non-alcoholic steatohepatitis (NASH). In these two metabolic diseases, the administration of plant stanol esters has been shown to ameliorate cellular cholesterol accumulation and inflammation. Given the overlap of pathological mechanisms among atherosclerosis, NASH and NPC1 disease, we sought to investigate whether dietary supplementation with plant stanol esters improves the peripheral features of NPC1 disease. To this end, we used an NPC1 murine model featuring an Npc1 null allele (Npc1nih), creating a dysfunctional NPC1 protein. Npc1nih mice were fed a two or six percent plant stanol esters–enriched diet over the course of 5 weeks. During this period, hepatic and blood lipid and inflammatory profiles were assessed. Npc1nih mice fed the plant stanol–enriched diet exhibited lower hepatic cholesterol accumulation, damage and inflammation than regular chow–fed Npc1nih mice. Moreover, plant stanol consumption shifted circulating T-cells and monocytes in particular towards an anti-inflammatory profile. Overall, these effects were stronger following dietary supplementation with 6% stanols, suggesting a dose-dependent effect. The findings of our study highlight the potential use of plant stanols as an affordable complementary means to ameliorate disorders in hepatic and blood lipid metabolism and reduce inflammation in NPC1 disease.




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Predictions and Policymaking: Complex Modelling Beyond COVID-19

1 April 2020

Yasmin Afina

Research Assistant, International Security Programme

Calum Inverarity

Research Analyst and Coordinator, International Security Programme
The COVID-19 pandemic has highlighted the potential of complex systems modelling for policymaking but it is crucial to also understand its limitations.

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A member of the media wearing a protective face mask works in Downing Street where Britain's Prime Minister Boris Johnson is self-isolating in central London, 27 March 2020. Photo by TOLGA AKMEN/AFP via Getty Images.

Complex systems models have played a significant role in informing and shaping the public health measures adopted by governments in the context of the COVID-19 pandemic. For instance, modelling carried out by a team at Imperial College London is widely reported to have driven the approach in the UK from a strategy of mitigation to one of suppression.

Complex systems modelling will increasingly feed into policymaking by predicting a range of potential correlations, results and outcomes based on a set of parameters, assumptions, data and pre-defined interactions. It is already instrumental in developing risk mitigation and resilience measures to address and prepare for existential crises such as pandemics, prospects of a nuclear war, as well as climate change.

The human factor

In the end, model-driven approaches must stand up to the test of real-life data. Modelling for policymaking must take into account a number of caveats and limitations. Models are developed to help answer specific questions, and their predictions will depend on the hypotheses and definitions set by the modellers, which are subject to their individual and collective biases and assumptions. For instance, the models developed by Imperial College came with the caveated assumption that a policy of social distancing for people over 70 will have a 75 per cent compliance rate. This assumption is based on the modellers’ own perceptions of demographics and society, and may not reflect all societal factors that could impact this compliance rate in real life, such as gender, age, ethnicity, genetic diversity, economic stability, as well as access to food, supplies and healthcare. This is why modelling benefits from a cognitively diverse team who bring a wide range of knowledge and understanding to the early creation of a model.

The potential of artificial intelligence

Machine learning, or artificial intelligence (AI), has the potential to advance the capacity and accuracy of modelling techniques by identifying new patterns and interactions, and overcoming some of the limitations resulting from human assumptions and bias. Yet, increasing reliance on these techniques raises the issue of explainability. Policymakers need to be fully aware and understand the model, assumptions and input data behind any predictions and must be able to communicate this aspect of modelling in order to uphold democratic accountability and transparency in public decision-making.

In addition, models using machine learning techniques require extensive amounts of data, which must also be of high quality and as free from bias as possible to ensure accuracy and address the issues at stake. Although technology may be used in the process (i.e. automated extraction and processing of information with big data), data is ultimately created, collected, aggregated and analysed by and for human users. Datasets will reflect the individual and collective biases and assumptions of those creating, collecting, processing and analysing this data. Algorithmic bias is inevitable, and it is essential that policy- and decision-makers are fully aware of how reliable the systems are, as well as their potential social implications.

The age of distrust

Increasing use of emerging technologies for data- and evidence-based policymaking is taking place, paradoxically, in an era of growing mistrust towards expertise and experts, as infamously surmised by Michael Gove. Policymakers and subject-matter experts have faced increased public scrutiny of their findings and the resultant policies that they have been used to justify.

This distrust and scepticism within public discourse has only been fuelled by an ever-increasing availability of diffuse sources of information, not all of which are verifiable and robust. This has caused tension between experts, policymakers and public, which has led to conflicts and uncertainty over what data and predictions can be trusted, and to what degree. This dynamic is exacerbated when considering that certain individuals may purposefully misappropriate, or simply misinterpret, data to support their argument or policies. Politicians are presently considered the least trusted professionals by the UK public, highlighting the importance of better and more effective communication between the scientific community, policymakers and the populations affected by policy decisions.

Acknowledging limitations

While measures can and should be built in to improve the transparency and robustness of scientific models in order to counteract these common criticisms, it is important to acknowledge that there are limitations to the steps that can be taken. This is particularly the case when dealing with predictions of future events, which inherently involve degrees of uncertainty that cannot be fully accounted for by human or machine. As a result, if not carefully considered and communicated, the increased use of complex modelling in policymaking holds the potential to undermine and obfuscate the policymaking process, which may contribute towards significant mistakes being made, increased uncertainty, lack of trust in the models and in the political process and further disaffection of citizens.

The potential contribution of complexity modelling to the work of policymakers is undeniable. However, it is imperative to appreciate the inner workings and limitations of these models, such as the biases that underpin their functioning and the uncertainties that they will not be fully capable of accounting for, in spite of their immense power. They must be tested against the data, again and again, as new information becomes available or there is a risk of scientific models becoming embroiled in partisan politicization and potentially weaponized for political purposes. It is therefore important not to consider these models as oracles, but instead as one of many contributions to the process of policymaking.