bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perioperative Transgender Hormone Management: Avoiding Venous Thromboembolism and Other Complications
Authors: Hontscharuk Rayisa , Alba Brandon , Manno Catherine , Pine Elyse , Deutsch Madeline B. , Coon Devin , Schechter Loren 
Published: March 2021

Summary:
This review discusses the current evidence regarding perioperative hormone therapy for transgender individuals, with an emphasis on strategies to reduce the risk of perioperative venous thromboembolism. Historically, surgeons routinely discontinued estrogen therapy in the perioperative period with the goal of reducing the risk of venous thromboembolism. However, abrupt estrogen cessation may also lead to adverse emotional and physiologic effects, including an exacerbation of one’s gender dysphoria. The data on the relationship of feminizing hormones and venous thromboembolism in the perioperative setting are largely based on extrapolation of hormone regimens that are no longer in use and may not accurately reflect the actual risk of venous thromboembolism. Future studies will allow surgeons to engage in evidence-based, patient-centered, informed consent while also minimizing the risk of complications, such as venous thromboembolism.
When we can trust computers (and when we can't)
Authors: Coveney Peter V. , Highfield Roger R. 
Published: March 2021

With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering which are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer-generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine-learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation.This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantificationin silico’.
The importance of uncertainty quantification in model reproducibility
Authors: Volodina Victoria , Challenor Peter 
Published: March 2021

Many computer models possess high-dimensional input spaces and substantial computational time to produce a single model evaluation. Although such models are often ‘deterministic’, these models suffer from a wide range of uncertainties. We argue that uncertainty quantification is crucial for computer model validation and reproducibility. We present a statistical framework, termed history matching, for performing global parameter search by comparing model output to the observed data. We employ Gaussian process (GP) emulators to produce fast predictions about model behaviour at the arbitrary input parameter settings allowing output uncertainty distributions to be calculated. History matching identifies sets of input parameters that give rise to acceptable matches between observed data and model output given our representation of uncertainties. Modellers could proceed by simulating computer models’ outputs of interest at these identified parameter settings and producing a range of predictions. The variability in model results is crucial for inter-model comparison as well as model development. We illustrate the performance of emulation and history matching on a simple one-dimensional toy model and in application to a climate model.This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantificationin silico’.
Uncertainty quantification patterns for multiscale models
Authors: Ye D. , Veen L. , Nikishova A. , Lakhlili J. , Edeling W. , Luk O. O. , Krzhizhanovskaya V. V. , Hoekstra A. G. 
Published: March 2021

Uncertainty quantification (UQ) is a key component when using computational models that involve uncertainties, e.g. in decision-making scenarios. In this work, we present uncertainty quantification patterns (UQPs) that are designed to support the analysis of uncertainty in coupled multi-scale and multi-domain applications. UQPs provide the basic building blocks to create tailored UQ for multiscale models. The UQPs are implemented as generic templates, which can then be customized and aggregated to create a dedicated UQ procedure for multiscale applications. We present the implementation of the UQPs with multiscale coupling toolkit Multiscale Coupling Library and Environment 3. Potential speed-up for UQPs has been derived as well. As a proof of concept, two examples of multiscale applications using UQPs are presented.This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantificationin silico’.
Assessing uncertainties from physical parameters and modelling choices in an atmospheric large eddy simulation model
Authors: Jansson Fredrik , Edeling Wouter , Attema Jisk , Crommelin Daan 
Published: March 2021

In this study, we investigate uncertainties in a large eddy simulation of the atmosphere, employing modern uncertainty quantification methods that have hardly been used yet in this context. When analysing the uncertainty of model results, one can distinguish between uncertainty related to physical parameters whose values are not exactly known, and uncertainty related to modelling choices such as the selection of numerical discretization methods, of the spatial domain size and resolution, and the use of different model formulations. While the former kind is commonly studied e.g. with forward uncertainty propagation, we explore the use of such techniques to also assess the latter kind. From a climate modelling perspective, uncertainties in the convective response and cloud formation are of particular interest, since these affect the cloud-climate feedback, one of the dominant sources of uncertainty in current climate models. Therefore we analyse the DALES model in the RICO case, a well-studied convection benchmark. We use the VECMA toolkit for uncertainty propagation, assessing uncertainties stemming from physical parameters as well as from modelling choices. We find substantial uncertainties due to small random initial state perturbations, and that the choice of advection scheme is the most influential of the modelling choices we assessed.This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantificationin silico’.
Contemporary syphilis is characterised by rapid global spread of pandemicTreponema pallidumlineages
Authors: Beale Mathew A. , Marks Michael , Cole Michelle J. , Lee Min-Kuang , Pitt Rachel , Ruis Christopher , Balla Eszter , Crucitti Tania , Ewens Michael , Fernández-Naval Candela , Grankvist Anna , Guiver Malcolm , Kenyon Chris R. , Khairulin Rafil , Kularatne Ranmini , Arando Maider , Molini Barbara J. , Obukhov Andrey , Page Emma E. , Petrovay Fruzsina , Rietmeijer Cornelis , Rowley Dominic , Shokoples Sandy , Smit Erasmus , Sweeney Emma L. , Taiaroa George , Vera Jaime H. , Wennerås Christine , Whiley David M. , Williamson Deborah A. , Hughes Gwenda , Naidu Prenilla , Unemo Magnus , Krajden Mel , Lukehart Sheila A. , Morshed Muhammad G. , Fifer Helen , Thomson Nicholas R. 
Published: March 2021

AbstractSyphilis is an important sexually transmitted infection caused by the bacteriumTreponema pallidumsubspeciespallidum. The last two decades have seen syphilis incidence rise in many high-income countries, yet the evolutionary and epidemiological relationships that underpin this are poorly understood, as is the globalT. pallidumpopulation structure. We assembled a geographically and temporally diverse collection of clinical and laboratory samples comprising 726T. pallidumgenomes. We used detailed phylogenetic analysis and clustering to show that syphilis globally can be described by only two deeply branching lineages, Nichols and SS14. We show that both of these lineages can be found circulating concurrently in 12 of the 23 countries sampled. To provide further phylodynamic resolution we subdividedTreponema pallidumsubspeciespalliduminto 17 distinct sublineages. Importantly, like SS14, we provide evidence that two Nichols sublineages have expanded clonally across 9 countries contemporaneously with SS14. Moreover, pairwise genome analysis showed that recent isolates circulating in 14 different countries were genetically identical in their core genome to those from other countries, suggesting frequent exchange through international transmission pathways. This contrasts with the majority of samples collected prior to 1983, which are phylogenetically distinct from these more recently isolated sublineages. Bayesian temporal analysis provided evidence of a population bottleneck and decline occurring during the late 1990s, followed by a rapid population expansion a decade later. This was driven by the dominantT. pallidumsublineages circulating today, many of which are resistant to macrolides. Combined we show that the population of contemporary syphilis in high-income countries has undergone a recent and rapid global expansion.
IL-15 and sMAdCAM: Novel roles in COVID-19 pathogenesis
Authors: Singh Amit Kumar , Kasarpalkar Nandini , Bhowmick Shilpa , Paradkar Gaurav , Talreja Mayur , Shah Karan , Tiwari Abhishek , Palav Harsha , Kaginkar Snehal , Kulkarni Rajiv , Patil Ashwini , Kalsurkar Varsha , Agrawal Sachee , Shastri Jayanthi , Dere Rajesh , Bharmal Ramesh , Mahale Smita D. , Bhor Vikrant M. , Patel Vainav 
Published: March 2021

AbstractImmune cell dysregulation and lymphopenia characterize COVID-19 pathology in moderate to severe disease. While underlying inflammatory factors have been extensively studied, homeostatic and mucosal migratory signatures remain largely unexplored as causative factors. In this study we evaluated the association of circulating IL-6, soluble mucosal addressin cell adhesion molecule (sMAdCAM) and IL-15 with cellular dysfunction characterizing mild and hypoxemic stages of COVID-19. A cohort of SARS-CoV-2 infected individuals (n=125) at various stages of disease progression together with healthy controls (n=16) were recruited from COVID Care Centres (CCCs) across Mumbai, India. Multiparametric flow cytometry was used to perform in-depth immune subset characterization and to measure plasma IL-6 levels. sMAdCAM, IL-15 levels were quantified using ELISA. Distinct depletion profiles, with relative sparing of CD8 effector memory and CD4+ regulatory T cells was observed in hypoxemic disease within the lymphocyte compartment. An apparent increase in the frequency of intermediate monocytes characterized both mild as well as hypoxemic disease. IL-6 levels inversely correlated with those of sMAdCAM and both markers showed converse associations with observed lympho-depletion suggesting opposing roles in pathogenesis. Interestingly, IL-15, a key cytokine involved in lymphocyte activation and homeostasis, was detected in symptomatic individuals but not in healthy controls or asymptomatic cases. Further, negative association of plasma IL-15 with depleted T, B and NK subsets suggested a compensatory production of this cytokine in response to the profound lymphopenia. Finally, higher levels of plasma IL-15 and IL-6, but not sMAdCAM, were associated with longer duration of hospitalization.
A two-stage super learner for healthcare expenditures
Authors: Wu Ziyue , Berkowitz Seth , Heagerty Patrick , Benkeser David 
Published: March 2021

AbstractObjectiveTo improve the estimation of healthcare expenditures by introducing a novel estimation method that is well-suited to situations where data exhibit strong skewness and zero-inflation.Data SourcesSimulations, and two sources of real-world data: the 2016-2017 Medical Expenditure Panel Survey (MEPS) and the Back Pain Outcomes using Longitudinal Data (BOLD) datasets.Study DesignSuper learner is an ensemble machine learning approach that can combine several algorithms in order to improve estimation. We propose a two-stage super learner that is well suited for use with healthcare expenditure data by separately estimating the probability of any healthcare expenditure and the mean amount of healthcare expenditure conditional on having healthcare expenditures. These estimates can be combined to yield a single estimate of expenditures for each observation. The method can flexibly incorporate a range of individual estimation approaches for each stage of estimation, including both regression-based approaches and machine learning algorithms such as random forests. We compare the performance of the proposed two-stage super learner with a one-stage super learner, and with multiple individual algorithms for estimation of healthcare cost under a broad range of data settings in simulated and real data. The predictive performance of alternative strategies was compared using Mean Squared Error and R2.Principal FindingsOur results indicate that the two-stage super learner has better performance compared with a one-stage super learner and individual algorithms, for healthcare cost estimation under a wide variety of settings in both simulations and empirical analyses. The improvement of the two-stage super learner over the one-stage super learner was particularly evident in settings when zero-inflation is high.ConclusionsThe two-stage super learner provides researchers an effective approach for healthcare cost analyses in environments where they cannot know the best single algorithm a priori.
Forecasting the Spreading Trajectory of the COVID-19 Pandemic
Authors: Cheng Baolian , Wang Yi-Ming 
Published: March 2021

AbstractPredictively forecasting future developments for the spread of the COVID-19 pandemic is extremely challenging. A recently published logistic mathematic model has achieved good predictions for infections weeks ahead. In this short communication, we summarize the Logistic spread model, which describes the dynamics of the pandemic evolution and the impacts of people social behavior in fighting against the pandemic. The new pandemic model has two parameters (i.e., transmission rate γ and social distancing d) to be calibrated to the data from the pandemic regions in the early stage of the outbreak while the social distancing is put in place. The model is capable to make early predictions about the spreading trajectory in the communities of any size (countries, states, counties and cities) including the total infections, the date of peak daily infections and the date of the infections reaching a plateau if the testing is sufficient. The results are in good agreement with data and have important applications for ongoing outbreaks and similar infectious disease pandemics in the future.
A randomized controlled trial of a video intervention shows evidence of increasing COVID-19 vaccination intention
Authors: Witus Leah S. , Larson Erik 
Published: March 2021

AbstractIncreasing acceptance of COVID-19 vaccines is imperative for public health, as unvaccinated individuals may impede the ability to reach herd immunity. Previous research on educational interventions to overcome vaccine hesitancy have shown mixed effects in increasing vaccination intention, although much of this work has focused on parental attitudes toward childhood vaccination. In this study, we conducted a randomized controlled trial to investigate whether vaccination intention changes after viewing an animated YouTube video explaining how COVID-19 mRNA vaccines work. We exposed participants to one of four interventions – watching the video with a male narrator, watching the same video with a female narrator, reading the text of the transcript of the video, or receiving no information (control group). We found that participants who watched the version of the video with a male narrator expressed statistically significant increased vaccination intention compared to the control group. The video with a female narrator had more variation in results. As a whole, there was a non-significant increased vaccination intention when analyzing all participants who saw the video with a female narrator; however, for politically conservative participants there was decreased vaccination intention for this intervention, particularly at a threshold between being currently undecided and expressing probable interest. These results are encouraging for the ability of interventions as simple as YouTube videos to increase vaccination propensity, although the inconsistent response to the video with a female narrator demonstrates the potential for bias to affect how certain groups respond to different messengers.Significance StatementWidespread vaccination is important for ending the COVID-19 pandemic. This study investigates whether communicating the science behind new COVID-19 vaccines can increase people’s willingness to get vaccinated. We examined the effectiveness of an eight-minute animated video explaining how COVID-19 mRNA vaccines work, varying between a male narrator, a female narrator, and a control group. Participants who saw the video with a male narrator expressed a greater intent to get vaccinated than the control group. Participants who saw the video with a female narrator had more varied responses, including a decreased intent to get vaccinated among political conservatives. These findings indicate that science education may help increase vaccine uptake, but that beliefs about gender may influence how people receive such information.
Long non-coding RNAs (lncRNAs) NEAT1 and MALAT1 are differentially expressed in severe COVID-19 patients: An integrated single cell analysis
Authors: Huang Kai , Wang Catherine , Vagts Christen , Raguveer Vanitha , Finn Patricia W. , Perkins David L. 
Published: March 2021

AbstractHyperactive and damaging inflammation is a hallmark of severe rather than mild COVID-19 syndrome. To uncover key inflammatory differentiators between severe and mild COVID-19 disease, we applied an unbiased single-cell transcriptomic analysis. We integrated a bronchoalveolar lavage (BAL) dataset with a peripheral blood mononuclear cell dataset (PBMC) and analyzed the combined cell population, focusing on genes associated with disease severity. Distinct cell populations were detected in both BAL and PBMC where the immunomodulatory long non-coding RNAs (lncRNAs) NEAT1 and MALAT1 were highly differentially expressed between mild and severe patients. The detection of other severity associated genes involved in cellular stress response and apoptosis regulation suggests that the pro-inflammatory functions of these lncRNAs may foster cell stress and damage. The lncRNAs NEAT1 andMALAT1 are potential components of immune dysregulation in COVID-19 that may provide targets for severity related diagnostic measures or therapy.
Involvement of denervated midbrain-derived factors in the formation of ectopic cortico-mesencephalic projection after hemispherectomy
Authors: Chang Leechung , Masada Mayuko , Kojima Masami , Yamamoto Nobuhiko 
Published: March 2021

AbstractNeuronal remodeling after brain injury is essential for functional recovery. After unilateral cortical lesion, axons from the intact cortex ectopically project to the denervated midbrain to compensate for the lost function, but the underlying molecular mechanisms remain largely unknown. To address this issue, we examined gene expression profiles in denervated and intact mouse midbrains after hemispherectomy at P6, when ectopic contralateral projection occurs robustly. The analysis showed that various axon growth-related genes were upregulated in the denervated midbrain, and most of these genes are reportedly expressed by astrocytes or microglia. To identify the underlying molecules, the receptors for candidate upregulated molecules were knocked out in layer 5 projection neurons in the intact cortex, using the CRISPR/Cas9-mediated method, and axonal projection from the knocked-out cortical neurons was examined after hemispherectomy. We found that the ectopic projection was significantly reduced when integrin subunit beta 3 (Itgb3) or neurotrophic receptor tyrosine kinase 2 (Ntrk2, also known as TrkB) was knocked out. Overall, the present study suggests that midbrain-derived glial factors whose expression is upregulated after hemispherectomy are involved in lesion-induced remodeling of the cortico-mesencephalic projection.
Axon guidance modalities in CNS regeneration revealed by quantitative proteomic analysis
Authors: Vilallongue Noemie , Schaeffer Julia , Hesse Anne Marie , Delpech Céline , Paccard Antoine , Couté Yohan , Belin Stephane , Nawabi Homaira 
Published: March 2021

ABSTRACTLong-distance regeneration of the central nervous system (CNS) has been achieved from the eye to the brain through activation of neuronal molecular pathways or pharmacological approaches. Unexpectedly, most of the regenerative fibers display guidance defects, which prevents reinnervation and further functional recovery. Therefore, characterizing the mature neuronal environment is essential to understand the adult axonal guidance in order to complete the circuit reconstruction. To this end, we used mass spectrometry to characterize the proteomes of major nuclei of the adult visual system: suprachiasmatic nucleus (SCN), ventral and dorsal lateral geniculate nucleus (vLGN, dLGN) and superior colliculus (SCol)), as well as the optic chiasm. These analyses revealed the presence of guidance molecules and guidance-associated factors in the adult visual targets. Moreover, by performing bilateral optic nerve crush, we showed that the expression of some proteins was significantly modulated by the injury in the visual targets, even in the ones most distal to the lesion site. On another hand, we found that the expression of guidance molecules was not modified upon injury. This implies that these molecules may possibly interfere with the reinnervation of the brain targets. Together, our results provides an extensive characterization of the molecular environment in intact and injured conditions. These findings open new ways to correct regenerating axon guidance notably by manipulating the expression of the corresponding guidance receptors in the nervous system.