Acknowledges Funding Research Articles

Third-Degree Price Discrimination in Two-Sided Markets

We investigate the welfare effects of third-degree price discrimination by a two-sided platform that enables interaction between buyers and sellers. Sellers are heterogeneous with respect to their per-interaction benefit, and, under price discrimination, the platform can condition its fee on sellers’ type. In a model with linear demand on each side, we show that price discrimination (i) increases participation on both sides, (ii) enhances total welfare, and (iii) may result in a strict Pareto improvement, with both seller types being better off than under uniform pricing. These results, which are in stark contrast to the traditional analysis of price discrimination, are driven by the existence of cross-group network effects. By improving the ability to monetize seller participation, price discrimination induces the platform to attract more buyers, which then increases seller participation. The Pareto improvement result means that even those sellers who pay a higher price under discrimination can be better off, because of the increased buyer participation. This paper was accepted by Joshua Gans, business strategy. Funding: A. de Cornière acknowledges funding from ANR [Grant ANR-17-EURE-0010] (Investissements d’Avenir program). Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.02788 .

Strong isolation by distance and evidence of population microstructure reflect ongoing Plasmodium falciparum transmission in Zanzibar.

The Zanzibar archipelago of Tanzania has become a low-transmission area for Plasmodium falciparum. Despite being considered an area of pre-elimination for years, achieving elimination has been difficult, likely due to a combination of imported infections from mainland Tanzania and continued local transmission. To shed light on these sources of transmission, we applied highly multiplexed genotyping utilizing molecular inversion probes to characterize the genetic relatedness of 282 P. falciparum isolates collected across Zanzibar and in Bagamoyo district on the coastal mainland from 2016 to 2018. Overall, parasite populations on the coastal mainland and Zanzibar archipelago remain highly related. However, parasite isolates from Zanzibar exhibit population microstructure due to the rapid decay of parasite relatedness over very short distances. This, along with highly related pairs within shehias, suggests ongoing low-level local transmission. We also identified highly related parasites across shehias that reflect human mobility on the main island of Unguja and identified a cluster of highly related parasites, suggestive of an outbreak, in the Micheweni district on Pemba island. Parasites in asymptomatic infections demonstrated higher complexity of infection than those in symptomatic infections, but have similar core genomes. Our data support importation as a main source of genetic diversity and contribution to the parasite population in Zanzibar, but they also show local outbreak clusters where targeted interventions are essential to block local transmission. These results highlight the need for preventive measures against imported malaria and enhanced control measures in areas that remain receptive to malaria reemergence due to susceptible hosts and competent vectors. This research was funded by the National Institutes of Health, grants R01AI121558, R01AI137395, R01AI155730, F30AI143172, and K24AI134990. Funding was also contributed from the Swedish Research Council, Erling-Persson Family Foundation, and the Yang Fund. RV acknowledges funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 program supported by the European Union. RV also acknowledges funding by Community Jameel.

Dynamic ESG Equilibrium

This paper proposes a conditional asset pricing model that integrates environmental, social, and governance (ESG) demand and supply dynamics. Shocks in the demand for sustainable investing represent a novel risk source, characterized by diminishing marginal utility and positive premium. Green assets exhibit positive exposure to ESG demand shocks, hence commanding higher premia. Conversely, time-varying convenience yield leads to lower expected returns for green assets. Moreover, ESG demand shocks have positive contemporaneous effects on unexpected returns, contributing to large positive payoffs in the green-minus-brown portfolio over extended horizons. The model predictions align closely with evidence on return spreads between green and brown assets, further reinforcing the apparent gap between realized and expected spreads. This paper was accepted by Lin William Cong, finance. Funding: Y. Liu acknowledges funding by the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant T35/710/20R]. A. Tarelli acknowledges funding by the European Union - Next Generation EU [Project Code 2022FWZ2CR]. The views and opinions expressed are only those of the authors. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.03491 .

Trading Gamification and Investor Behavior

We study the effect of gamification on retail traders’ behavior using a randomized online experiment. Participants with lower financial literacy prefer platforms with hedonic gamification elements, such as confetti and achievement badges. On average, hedonic gamification increases trading volume by 5.17%. However, the difference in trading activity between gamified and nongamified platforms is driven primarily by self-selection (70%) rather than gamification (30%). Participants who prefer hedonic gamification exhibit noisy trading strategies, whereas those favoring nongamified platforms display stronger contrarian behavior. Further, price trend notifications enhance learning for investors with accurate beliefs, but they reinforce trading mistakes for those with incorrect beliefs. This paper has been This paper was accepted by Jean-Edouard Colliard for the special issue on the human-algorithm connection. Funding: P. Chapkovski acknowledges funding from the Deutsche Forschungsgemeinschaft [Germany’s Excellence Strategy—EXC 2126/1-390838866]. M. Khapko and M. Zoican acknowledge the Social Sciences and Humanities Research Council of Canada [Insight Development Grant 430-2018-00125] and the Canadian Securities Institute Research Foundation [research grant]. M. Zoican acknowledges financial support from the Quantitative Management Research Initiative (QMI) under the aegis of the Fondation du Risque, a joint initiative by Université Paris-Dauphine, l’École Nationale de la Statistique et de l’Administration ParisTech, and LFIS, France. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02650 .

Risks to Human Capital

We build a model with inalienable human capital, in which investors finance individuals who can potentially become skilled. Although investment in skill is always optimal, it does not take place in some states of the world, due to moral hazard. In intermediate states of the world, individuals acquire skill; however outside investors and individuals inefficiently share risk. We show that this simple moral hazard problem, combined with risk aversion of individuals and outside investors, amplifies the equity premium, lowers the risk-free rate, and leads to disaster states that fall especially heavily on some agents but not on others. We show that the possibility of disaster states distorts risk prices and affects wealth inequality, even under calibrations in which they never occur in equilibrium. This paper was accepted by Lukas Schmid, finance. Funding: M. Ebrahimian acknowledges funding from the Swedish House of Finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03068 .

Sharpen Your Sword for Litigation: Incumbent Strategic Reaction to the Threat of Entry

While patents have been shown to play a role as barriers to entry, they also reveal information about incumbent strategies and risk being invalidated. We examine the trade-off incumbents face between using their patents ex ante as entry deterrents or ex post once competitors have revealed their moves. Leveraging the unique characteristics of the pharmaceutical sector, in which it is possible to observe exactly when a competitor entry threat materializes, and exploiting exogenous variation in that timing, we show that incumbents intentionally fragment and delay the full disclosure of their intellectual property rights through continuation patents. They disproportionately reveal continuation patents after a competitor entry threat becomes concrete, tailoring their response to the threat they have received and successfully delaying competitor entry through litigation. The detected incumbents’ reaction is stronger when their attacked drugs are valuable and when the patents listed at the Food and Drug Administration approval of a drug are relatively narrow in scope. This paper was accepted by Alfonso Gambardella, business strategy accounting. Funding: A. Conti acknowledges funding from E4S. E. Sung acknowledges funding from the HEC Foundation [Grant 9B82F1905+] and the U.S. National Science Foundation [Grant 1759991]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00900 .

An Experiment on Gender Representation in Majoritarian Bargaining

Women are underrepresented in business, academic, and political decision-making bodies across the world. To investigate the causal effect of gender representation on multilateral negotiations, we experimentally manipulate the composition of triads in a majoritarian, divide-the-dollar game. We document a robust gender gap in earnings driven largely by the exclusion of women from alliances rather than differential shares within alliances. Experiments with different subject pools show that distinct bargaining dynamics can underlie the same inequitable outcomes; gender-biased outcomes can be caused by outright discrimination, but they can also be driven by more complex dynamics related to differences in bargaining strategies. Although replacing the male with a female majority all but eliminates the gap in one pool, it has minimal effect in the other. These findings show that there is no “one-size-fits-all” solution to the gender gap we uncovered and highlight the importance of studying bargaining dynamics in detail. This paper was accepted by Marie-Claire Villeval, behavioral economics and decision analysis. Funding: A. Baranski recognizes financial support from Tamkeen [under the New York University Abu Dhabi Research Institute; Grant CG005]. J. Tremewan acknowledges funding from the Agence Nationale de la Recherche [Grant ANR-21-CE03-0018 (Project ENDURA)]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01800 .

Data-Driven Allocation of Preventive Care with Application to Diabetes Mellitus Type II

Problem definition: Increasing costs of healthcare highlight the importance of effective disease prevention. However, decision models for allocating preventive care are lacking. Methodology/results: In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk. Specifically, we combine counterfactual inference, machine learning, and optimization techniques to build a scalable decision model that can exploit high-dimensional medical data, such as the data found in modern electronic health records. Our decision model is evaluated based on electronic health records from 89,191 prediabetic patients. We compare the allocation of preventive treatments (metformin) prescribed by our data-driven decision model with that of current practice. We find that if our approach is applied to the U.S. population, it can yield annual savings of $1.1 billion. Finally, we analyze the cost-effectiveness under varying budget levels. Managerial implications: Our work supports decision making in health management, with the goal of achieving effective disease prevention at lower costs. Importantly, our decision model is generic and can thus be used for effective allocation of preventive care for other preventable diseases. Funding: M. Kraus acknowledges funding from Bundesministerium für Bildung und Forschung [Grant 01IS22080]. S. Feuerriegel acknowledges funding from Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grant 186932]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0251 .

The Editor and the Algorithm: Recommendation Technology in Online News

We run a field experiment to study the relative performance of human curation and automated personalized recommendation technology in the context of online news. We build a simple theoretical model that captures the relative efficacy of personalized algorithmic recommendations and curation based on human expertise. We highlight a critical tension between detailed, yet potentially narrow, information available to the algorithm versus broad (often private), but not scalable, information available to the human editor. Empirically, we show that, on average, algorithmic recommendations can outperform human curation with respect to clicks, but there is significant heterogeneity in this treatment effect. The human editor performs relatively better in the absence of sufficient personal data and when there is greater variation in preferences. These results suggest that reverting to human curation can mitigate the drawbacks of personalized algorithmic recommendations. Our computations show that the optimal combination of human curation and automated recommendation technology can lead to an increase of up to 13% in clicks. In absolute terms, we provide thresholds for when the estimated gains are larger than our estimate of implementation costs. This paper was accepted by Chris Forman, information systems. Funding: C. Peukert acknowledges funding from the Swiss National Science Foundation [Grant No. 100013_197807]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2023.4954 .

Ownership Concentration and Firm Value: New Evidence from Owner Stakes in IPOs

We study the relationship between ownership concentration and firm value using hand-collected data on the stakes of owner–managers before and after initial public offerings (IPOs). We instrument for the reduction in stake using market returns shortly before IPOs. Short-run market returns are plausible instruments because owners engage in market timing by selling more when prior returns are high, but high short-run returns are unlikely to directly affect firm value years after the IPO. As predicted by agency theory, a large reduction in ownership concentration at the IPO is negatively related to valuation. Future asset growth is low when owners have low stakes. This paper was accepted by Victoria Ivashina, finance. Funding: B. Larrain acknowledges funding from ANID/CONICYT Proyecto FONDECYT Regular [Grant 1180593]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.01039 .

From Corridor to Network Macroscopic Fundamental Diagrams: A Semi-Analytical Approximation Approach

The design of network-wide traffic management schemes or transport policies for urban areas requires computationally efficient traffic models. The macroscopic fundamental diagram (MFD) is a promising tool for such applications. Unfortunately, empirical MFDs are not always available, and semi-analytical estimation methods require a reduction of the network to a corridor that introduces substantial inaccuracies. We propose a semi-analytical methodology to estimate the MFD for realistic urban networks without the information loss induced by the reduction of networks to corridors. The methodology is based on the method of cuts but applies to networks with irregular topologies, accounts for different spatial demand patterns, and determines the upper bound of network flow. Therefore, we consider both flow conservation and the effects of spillbacks at the network level. Our framework decomposes a given network into a set of corridors, creates a hypernetwork, including the impacts of source terms, and then treats the dependencies across corridors (e.g., because of turning flows and spillbacks). Based on this hypernetwork, we derive the free-flow and capacity branch of the MFD. The congested branch is estimated by considering gridlock characteristics and utilizing recent advancements in MFD research. We showcase the applicability of the proposed methodology in a case study with a realistic setting based on the Sioux Falls network. We then compare the results to the original method of cuts and a ground truth derived from the cell transmission model. This comparison reveals that our method is more than five times more accurate than the state of the art in estimating the network-wide capacity and jam density. Moreover, the results clearly indicate the MFD’s dependency on spatial demand patterns. Compared with simulation-based MFD estimation approaches, the potential of the proposed framework lies in the modeling flexibility, explanatory value, and reduced computational cost. Funding: G. Tilg acknowledges support from the German Federal Ministry for Digital and Transport (BMDV) for the funding of the project LSS (capacity increase of urban networks). S. F. A. Batista and M. Menéndez acknowledge support from the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award [CG001]. L. Ambühl acknowledges support from the ETH Research Grant [ETH-27 16-1] under the project name SPEED. L. Leclercq acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program [Grant 646592 - MAGnUMproject]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0402 .

Optimal Electricity Imbalance Pricing for the Emerging Penetration of Renewable and Low-Cost Generators

Problem definition: With the rise of renewables and the decline of fossil fuels, electricity markets are shifting toward a capacity mix in which low-cost generators (LCGs) are dominant. Within this transition, policymakers have been considering whether current market designs are still fundamentally fit for purpose. This research analyses a key aspect: the design of real-time imbalance pricing mechanisms. Currently, markets mostly use either single pricing or dual pricing as their imbalance pricing mechanisms. Single-pricing mechanisms apply identical prices for buying and selling, whereas dual-pricing mechanisms use different prices. The recent harmonization initiative in Europe sets single pricing as the default and dual pricing as the exception. This leaves open the question of when dual pricing is advantageous. We compare the economic efficiency of two dual-pricing mechanisms in current practice with that of a single-pricing design and identify conditions under which dual pricing can be beneficial. We also prove the existence of an optimal pricing mechanism. Methodology/results: We first analytically compare the economic efficiency of single-pricing and dual-pricing mechanisms. Furthermore, we formulate an optimal pricing mechanism that can deter the potential exercise of market power by LCGs. Our analytical results characterize the conditions under which a dual pricing is advantageous over a single pricing. We further compare the economic efficiency of these mechanisms with respect to our proposed optimal mechanism through simulations. We show that the proposed pricing mechanism would be the most efficient in comparison with others and discuss its practicability. Managerial implications: Our analytical comparison reveals market conditions under which each pricing mechanism is a better fit and whether there is a need for a redesign. In particular, our results suggest that existing pricing mechanisms are adequate at low/moderate market shares of LCGs but not for the high levels currently envisaged by policymakers in the transition to decarbonization, where the optimal pricing mechanism will become more attractive.Funding: R. Belo acknowledges funding by Fundação para a Ciência e a Tecnologia [UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab – PINFRA/22209/2016], POR Lisboa, and POR Norte [Social Sciences DataLab, PINFRA/22209/2016].Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0555 .

A One-Factor Model of Corporate Bond Premia

A one-factor model based on long-run consumption growth explains the risk premiums on corporate bond portfolios sorted on credit rating, credit spreads, downside risk, idiosyncratic volatility, long-term reversals, maturity, and sensitivity to the financial intermediary capital factor. The estimated risk-aversion coefficient is lower when we use the consumption growth of wealthy households over a longer horizon as a risk factor, and a model with a 20-quarter horizon yields a risk-aversion coefficient of 15, a value similar to the one estimated from equity portfolios. This paper was accepted by Bruno Biais, finance. Funding: Y. Nozawa acknowledges funding from the Center for Investing at the Hong Kong University and Science and Technology. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4784 .

Adjuvant skewing of host immune response to vaccination and subsequent infection with influenza virus

Abstract Despite being a vaccine-preventable disease, seasonal influenza is responsible for thousands of hospitalizations and deaths yearly. Currently licensed quadrivalent inactivated influenza virus vaccines (QIV) aim to induce neutralizing antibodies but are poor inducers of T-cell responses, which could be potentially overcome with adjuvants. We evaluated adjuvant skewing of host-immune responses after vaccination and its persistence after infection. Hereto, we compared two squalene-based adjuvants (AddaVax, and AddaS03) and IMDQ-PC, a lymph node-draining TLR7/8 agonist. All adjuvants resulted in enhanced antibody titers and protection against challenge with H1N1 influenza virus. Lung cytokine/chemokine profiling at 5 days post-infection showed that AddaVax results in higher levels of type 2 immunity-stimulating cytokines IL-4, -5, and -13 in the lungs, which is also reflected by a higher IgG1:IgG2a ratio. Meanwhile, IMDQ-PC showed higher serological IgG2a titers after vaccination and higher levels of IFNγ in the lungs after challenge. To investigate the role of the myeloid compartment in adjuvant skewing of host responses, we examined adjuvant-mediated skewing of bone-derived macrophages in-vitro using Arginase I and iNOS as M2 or M1 markers, respectively. We also pre-licensed macrophages with IL-4 and IFNγ to evaluate adjuvant-intrinsic skewing effects. Licensing was required to induce M1 or M2 phenotypes by squalene-based adjuvants, whereas IMDQ-PC skewed towards M1 phenotype irrespective of licensing. Correlating with the observed type I immune host response, IMDQ-PC induced iNOS more robustly with IFNγ than the other adjuvants. And, AddaS03 induced the highest levels of Arginase I in the IL-4 group. This study was also partly funded by CRIPT (Center for Research on Inuenza Pathogenesis and Transmission), a NIH NIAID funded Center of Excellence for Influenza Research and Response (CEIRR, contract number 75N93021C00014), by the NIAID funded SEM-CIVIC consortium to improve inuenza vaccines (contract number 75N93019C00051), to AG-S. By NIH/NIAID HHSN272201400008C (L.C.), and NIH/NIAID R21AI151229 and R01AI160706 (M.S.). B.G.D.G. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant N 817938).

Effector-mediated partial and nonhost disease resistance in wheat.

Effector-mediated partial and nonhost disease resistance in wheat.

Learning from Their Daughters: Family Exposure to Gender Disparity and Female Representation in Male-Led Ventures

We build on recent studies on daughter-to-father influence to explore how male founders’ fatherhood of daughters impacts female representation in their ventures. We find that, conditional on the total number of children, fathering an additional daughter versus a son is associated with a 4% (11%) increase in female director (employee) representation. This daughter-to-father effect gradually matures as daughters grow up and socialize in schools and workplaces, and it increases as daughters age, suggesting that male founders vicariously learn from their daughters about the constraints women face throughout the daughters’ life cycles. Heterogeneity analyses (regarding founder cohort, divorce status, and social class), combined with qualitative evidence, further substantiate the plausibility of vicarious learning as a potential yet understudied mechanism underlying daughter effects. In addition, daughter effects on employee recruitment are concentrated in microbusinesses (number of employees is ≤10) where the founder is close in decision authority to all employees. These findings add important nuances to our understanding of daughter effects in organizational contexts and extend theory of gender homophily in organizations. This paper was accepted by Olav Sorenson, organizations. Funding: K. Wennberg acknowledges funding from the Swedish Royal Academy of Letter. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4727 .

Unexpected Interruptions, Idle Time, and Creativity: Evidence from a Natural Experiment

Interruptions are common in organizational life and last from seconds and minutes to hours and days. We rely on a quantitative abductive strategy to determine how extended work interruptions shape employees’ creativity. We start by studying how surprising interruptions that cause idle time affect employees’ creative performance. We do so by exploiting a natural experiment—a supply chain shortage that caused unexpected stops in production plants—to show that individuals exposed to such an interruption produce 58% more ideas than uninterrupted employees in the three weeks after the interruption. We corroborate this effect in a replication and extend it to idea quality. Investigating the effect’s causes, we then show that we do not find the same effects for two other interruption types: for unexpected interruptions without idle time (i.e., intrusions), we find a negative effect on creative performance because employees forcefully disengage from their work and switch their attention to the interrupting task. For expected interruptions with idle time (i.e., planned breaks), we also find no positive effect on creative performance because employees discretionally disengage from work and focus on nonwork and leisure goals. We consider and evaluate three different theoretical explanations for our findings: attention residue, cognitive stimulation, and recovery. We end our abductive process by putting attention residue forward as the most likely explanation. Finally, we suggest three propositions based on our findings and discuss our contributions to the literature on interruptions and creativity in organizations. Funding: T. G. Schweisfurth acknowledges funding from the Tempowerk Technology Center Hamburg.

Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic

The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient’s health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient’s health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients’ dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer. This paper was accepted by Chung-Piaw Teo, optimization. Funding: G. Forchini acknowledges funding from Jan Wallanders and Tom Hedelius Foundation and the Tore Browaldh Foundation, funding from MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1], jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, part of the EDCTP2 program supported by the European Union; and acknowledges funding by Community Jameel. D. Rizmie acknowledges partial funding from the MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1]. J. C. D’Aeth acknowledges funding from the Wellcome Trust [Reference 102169/Z/13/Z]. S. Moret acknowledges partial support from the Swiss National Science Foundation (SNSF) under [Grant P2ELP2_188028]. S. Ghosal was funded by the Imperial College President’s PhD Scholarship. F. Grimm was funded by the Health Foundation as part of core staff member activity. This research was funded in whole, or in part, by the Wellcome Trust [Grant 102169/Z/13/Z]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4679 .

Age acquired skewed X chromosome inactivation is associated with adverse health outcomes in humans.

Ageing is a heterogenous process characterised by cellular and molecular hallmarks, including changes to haematopoietic stem cells and is a primary risk factor for chronic diseases. X chromosome inactivation (XCI) randomly transcriptionally silences either the maternal or paternal X in each cell of 46, XX females to balance the gene expression with 46, XY males. Age acquired XCI-skew describes the preferential selection of cells across a tissue resulting in an imbalance of XCI, which is particularly prevalent in blood tissues of ageing females, and yet its clinical consequences are unknown. We assayed XCI in 1575 females from the TwinsUK population cohort using DNA extracted from whole blood. We employed prospective, cross-sectional, and intra-twin study designs to characterise the relationship of XCI-skew with molecular and cellular measures of ageing, cardiovascular disease risk, and cancer diagnosis. We demonstrate that XCI-skew is independent of traditional markers of biological ageing and is associated with a haematopoietic bias towards the myeloid lineage. Using an atherosclerotic cardiovascular disease risk score, which captures traditional risk factors, XCI-skew is associated with an increased cardiovascular disease risk both cross-sectionally and within XCI-skew discordant twin pairs. In a prospective 10 year follow-up study, XCI-skew is predictive of future cancer incidence. Our study demonstrates that age acquired XCI-skew captures changes to the haematopoietic stem cell population and has clinical potential as a unique biomarker of chronic disease risk. KSS acknowledges funding from the Medical Research Council [MR/M004422/1 and MR/R023131/1]. JTB acknowledges funding from the ESRC [ES/N000404/1]. MM acknowledges funding from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, Chronic Disease Research Foundation (CDRF), Zoe Global Ltd and the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London.

Unsupervised knowledge-transfer for learned image reconstruction* *The work of RB is substantially supported by the i4health PhD studentship (UK EPSRC EP/S021930/1) and from The Alan Turing Institute (UK EPSRC EP/N510129/1), and that of ZK, SA and BJ by UK EPSRC EP/T000864/1, and that of SA and BJ also by UK EPSRC EP/V026259/1. AH acknowledges funding by Academy of Finland Projects

Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities. These approaches usually require a large amount of high-quality paired training data, which is often not available in medical imaging. To circumvent this issue we develop a novel unsupervised knowledge-transfer paradigm for learned reconstruction within a Bayesian framework. The proposed approach learns a reconstruction network in two phases. The first phase trains a reconstruction network with a set of ordered pairs comprising of ground truth images of ellipses and the corresponding simulated measurement data. The second phase fine-tunes the pretrained network to more realistic measurement data without supervision. By construction, the framework is capable of delivering predictive uncertainty information over the reconstructed image. We present extensive experimental results on low-dose and sparse-view computed tomography showing that the approach is competitive with several state-of-the-art supervised and unsupervised reconstruction techniques. Moreover, for test data distributed differently from the training data, the proposed framework can significantly improve reconstruction quality not only visually, but also quantitatively in terms of PSNR and SSIM, when compared with learned methods trained on the synthetic dataset only.