Abstract Seroprevalence surveys reveal the extent of humoral immunity against pathogens such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and under some circumstances represent cumulative incidence of prior infection. However, antibody waning–or seroreversion– biases these estimates by reducing assay sensitivity in a time-varying manner. Because assay sensitivity decays over time, naively using serosurveys can substantially bias estimates of SARS-CoV-2 cumulative incidence and fatality rates. The Bayesian assay-specific, time-varying sensitivity adjustment developed in this paper can reliably correct for this bias and account for the delay between infection and serosurvey. In seroprevalence studies conducted in the United States in 2020, adjusting for time-varying sensitivity increased cumulative incidence by up to 1.4-fold, with an adjustment of 1.08 for a national study. Our estimates contrast with a previously published 2-fold adjustment that did not account for assay design. This suggests that previous analyses overestimated cumulative incidence by applying seroreversion corrections that did not account for assay-specific effects, or underestimated cumulative incidence by not applying seroreversion corrections. These biases imply fatality rate underestimation and overestimation, respectively. Our model provides a framework for design-specific time-varying sensitivity corrections in seroprevalence surveys for other pathogens. Topic diagnostic sensitivity, seroprevalence, SARS-CoV-2, COVID-19

Nonparametric tests for functional data are a challenging class of tests to work with because of the potentially high dimensional nature of the data. One of the main challenges for considering rank-based tests, like the Mann-Whitney or Wilcoxon Rank Sum tests (MWW), is that the unit of observation is typically a curve. Thus any rank-based test must consider ways of ranking curves. While several procedures, including depth-based methods, have recently been used to create scores for rank-based tests, these scores are not constructed under the null and often introduce additional, uncontrolled for variability. We therefore reconsider the problem of rank-based tests for functional data and develop an alternative approach that incorporates the null hypothesis throughout. Our approach first ranks realizations from the curves at each measurement occurrence, then calculates a summary statistic for the ranks of each subject, and finally re-ranks the summary statistic in a procedure we refer to as a doubly ranked test. We propose two summaries for the middle step: a sufficient statistic and the average rank. As we demonstrate, doubly rank tests are more powerful while maintaining ideal type I error in the two sample, MWW setting. We also extend our framework to more than two samples, developing a Kruskal-Wallis test for functional data which exhibits good test characteristics as well. Finally, we illustrate the use of doubly ranked tests in functional data contexts from material science, climatology, and public health policy.

Line work is a core element for the stylization of computer animations used by recent shows. However, existing stylization techniques are limited to edge treatments based on brush strokes or textures applied solely on top of curves. In this work, we propose new stylization effects by offering artists direct control over the inside and outside of surface contours. To this end, we introduce a method that creates ribbons, geometry strips of possibly varying width, that extrude from each side of the surface contour with temporally coherent orientations. Our contributions include the generation of spatially and temporally consistent normal orientations along visible contours and a trimming routine that converts arrangements of offset curves into ribbons free of intersections. We demonstrate the expressiveness and versatility of stylized ribbons by applying various effects on both character and shadow edges from animation sequences.

Historical functional linear models or HFLMs are a class of function-on-function regression models that seek to restrict the relationship between two or more time-dependent functions, or curves, of data where one function is a natural outcome and the others are natural predictors. A natural predictor is a predictor that occurs in time before, or at most concurrently with, an outcome. The primary challenge in developing methods for HFLMs is ensuring that the time-dependent relationship between the outcome and predictors is enforced and that no "unnatural" relationships are allowed, for example, where estimation, inference, or prediction are conducted on or using coefficients such that the outcome occurs before the predictor. A number of authors consider a variety of modeling frameworks for HFLMs. This work seeks to introduce the basic HFLM, explore the various approaches for its estimation, and discuss recent advances.This article is categorized under:Statistical Models > Linear ModelsData: Types and Structure > Time Series, Stochastic Processes, and Functional DataStatistical Models > Model Selection

Linework on 3D animated characters is an important aspect of stylized looks for films. We present CurveCrafter, a system allowing animators to create new lines on 3D models and to edit the shape and opacity of silhouette curves. Our tools allow users to draw, redraw, erase, edit and retime user created curves. Silhouette curves can have their shape edited or reverted, and their opacity erased or revealed. Our algorithm for propagating edits over tracked silhouette curves ensures temporal consistency even as curves expand and merge. Five professional animators used our system to animate lines on three shots with different characters. Additionally, the effects lead from the short film Pete used our system to more easily recreate edits on a film shot. CurveCrafter was able to successfully enhance the resulting animations with additional linework.

Recently, there has been exciting progress in frame interpolation for rendered content. In this offline rendering setting, additional inputs, such as albedo and depth, can be extracted from a scene at a very low cost and, when integrated in a suitable fashion, can significantly improve the quality of the interpolated frames. Although existing approaches have been able to show good results, most high-quality interpolation methods use a synthesis network for direct color prediction. In complex scenarios, this can result in unpredictable behavior and lead to color artifacts. To mitigate this and to increase robustness, we propose to estimate the interpolated frame by predicting spatially varying kernels that operate on image splats. Kernel prediction ensures a linear mapping from the input images to the output and enables new opportunities, such as consistent and efficient interpolation of alpha values or many other additional channels and render passes that might exist. Additionally, we present an adaptive strategy that allows predicting full or partial keyframes that should be rendered with color samples solely based on the auxiliary features of a shot. This content-based spatio-temporal adaptivity allows rendering significantly fewer color pixels as compared to a fixed-step scheme when wanting to maintain a certain quality. Overall, these contributions lead to a more robust method and significant further reductions of the rendering costs.

Previous Bayesian evaluations of the Conway-Maxwell-Poisson (COM-Poisson) distribution have little discussion of non- and weakly-informative priors for the model. While only considering priors with such limited information restricts potential analyses, these priors serve an important first step in the modeling process and are useful when performing sensitivity analyses. We develop and derive several weakly- and non-informative priors using both the established conjugate prior and Jeffreys' prior. Our evaluation of each prior involves an empirical study under varying dispersion types and sample sizes. In general, we find the weakly informative priors tend to perform better than the non-informative priors. We also consider several data examples for illustration and provide code for implementation of each resulting posterior.

Denoising is an integral part of production rendering pipelines that use Monte-Carlo (MC) path tracing. Machine learning based denoisers have been proven to effectively remove the residual noise and produce a clean image. However, denoising volumetric rendering remains a problem due to the lack of useful features and large-scale volume datasets. We have seen issues such as over-blurring and temporal flickering in the denoised sequence. In this work, we modify the production renderer to generate potential features that might improve the denoising quality, and then run a state-of-the-art feature selection algorithm to detect the best combination. We collect thousands of unique volumetric scenes from our recent films to create a large dataset for training. Our evaluation shows a good amount of quality gain compared to the version currently in use.

Multivariate matched proportions (MMP) data appear in a variety of contexts including post-market surveillance of adverse events in pharmaceuticals, disease classification, and agreement between care providers. It consists of multiple sets of paired binary measurements taken on the same subject. While recent work proposes methods to address the complexities of MMP data, the issue of sparse response, where no or very few “yes” responses are recorded for one or more sets, is unaddressed. The presence of sparse response sets results in the underestimation of variance components, loss of coverage, and lowered power in existing methods. Bayesian methods, which have not previously been considered for MMP data, provide a useful framework when sparse responses are present. In particular, the Bayesian probit model in combination with mean model prior specifications provides an elegant solution to the problem of variance underestimation. We examine a multivariate probit-based approach using hierarchical horseshoe-like priors along with a Bayesian functional principal component analysis (FPCA) to model the latent covariance. We show that our approach performs well on MMP data with sparse responses and outperforms existing methods. In a re-examination of a study on the system of care (SOC) framework for children with mental and behavioral disorders, we are able to provide a more complete picture of the relationships in the data. Our analysis provides additional insights into the functioning on the SOC that a previous univariate analysis missed.

Though billions of passengers and crew travel by air each year and are exposed to altitude equivalents of 7000–8000 feet, the health impact of cabin oxygenation levels has not been well studied. The hypoxic environment may produce ectopic heartbeats that may increase the risk of acute in-flight cardiac events. We enrolled forty older and at-risk participants under a block-randomized crossover design in a hypobaric chamber study to examine associations between flight oxygenation and both ventricular (VE) and supraventricular ectopy (SVE). We monitored participant VE and SVE every 5 min under both flight and control conditions to investigate the presence and rate of VE and SVE. While the presence of VE did not differ according to condition, the presence of SVE was higher during flight conditions (e.g. OR ratio = 1.77, 95% CI: 1.21, 2.59 for SVE couplets). Rates of VE and SVE were higher during flight conditions (e.g. RR ratio = 1.25, 95% CI: 1.03, 1.52 for VE couplets, RR ratio = 1.76, 95% CI: 1.39, 2.22 for SVE couplets). The observed higher presence and rate of ectopy tended to increase with duration of the flight condition. Further study of susceptible passengers and crew may elucidate the specific associations between intermittent or sustained ectopic heartbeats and hypoxic pathways.

National laboratories are a fundamental capacity for public health, contributing to disease surveillance and outbreak response. The establishment of regional laboratory networks has been posited as a means of improving health security across multiple countries. Our study objective was to assess whether membership in regional laboratory networks in Africa has an effect on national health security capacities and outbreak response. We conducted a literature review to select regional laboratory networks in the Eastern and Western African regions. We examined data from the World Health Organization Joint External Evaluation (JEE) mission reports, the 2018 WHO States Parties Annual Report (SPAR), and the 2019 Global Health Security Index (GHS). We compared the average scores of countries that are members of a regional laboratory network to those that are not. We also assessed country-level diagnostic and testing indicators during the COVID-19 pandemic. We found no significant differences in any of the selected health security metrics for member versus non-member countries of the either the East Africa Public Health Laboratory Networking Project (EAPHLNP) in the Eastern Africa region, nor for the West African Network of Clinical Laboratories (RESAOLAB) in the Western Africa region. No statistically significant differences were observed in COVID-19 testing rates in either region. Small sample sizes and the inherent heterogeneities in governance, health, and other factors between countries within and between regions limited all analyses. These results suggest potential benefit in setting baseline capacity for network inclusion and developing regional metrics for measuring network impact, but also beyond national health security capacities, other effects that may be required to justify continued support for regional laboratory networks.

Background:Interviewer effects can have consequential impacts on survey data, particularly for reporting sensitive attitudes and behaviours such as sexual activity and drug use, yet these effects remain understudied in low- and middle-income countries. The Demographic and Health Surveys (DHS) present a unique opportunity to study interviewer effects on the self-report of sensitive topics in low- and middle-income countries by including interviewer characteristics data. This paper aims to narrow the gap in research on interviewer effects by studying the effects that age difference between interviewer and respondent and interviewer survey experience have on the reporting of ever having sexual intercourse.Methods:We used DHS data from 91 066 women and 56 336 men in 21 countries where the standard DHS was implemented among all women of reproductive age, and interviewer characteristics were included in the data set. Using a Bayesian cross-classified model with random intercepts for interviewer and cluster, we assessed whether the effect of an age difference of 10 years or greater was associated with a difference in self-report of ever having sexual intercourse, adjusting for respondent demographics.Results:There was a meaningful association between an age difference of greater than ten years and reporting of ever having had sexual intercourse in most countries for both genders after adjusting for interviewer age and experience, rural or urban cluster, and individual-level characteristics. Among women, the marginal posterior probability of reporting ever having sexual intercourse if the interviewer was ten years or more years older was lower for 17 of 19 countries (countries ranged from -12.50 to 3.90 percentage points). Among men, the marginal posterior probability was lower for 16 of 20 countries, ranging from -18.30 to 17.10 percentage points.Conclusions:In most countries, women and men were less likely to report ever having sexual activity if the interviewer was ten or more years older than them, adjusting for potential confounders. These findings have important implications for interpreting numerous sexual health indicators, such as unmet family planning needs and human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) risk. Survey administrators may consider more careful interviewer-respondent characteristic matching or novel approaches like Audio Computer Assisted Self Interview to minimize interviewer-induced variance.

Research in functional regression has made great strides in expanding to non-Gaussian functional outcomes, but exploration of ordinal functional outcomes remains limited. Motivated by a study of computer-use behavior in rhesus macaques (Macaca mulatta), we introduce the Ordinal Probit Functional Outcome Regression model (OPFOR). OPFOR models can be fit using one of several basis functions including penalized B-splines, wavelets, and O’Sullivan splines— the last of which typically performs best. Simulation using a variety of underlying covariance patterns shows that the model performs reasonably well in estimation under multiple basis functions with near nominal coverage for joint credible intervals. Finally, in application, we use Bayesian model selection criteria adapted to functional outcome regression to best characterize the relation between several demographic factors of interest and the monkeys’ computer use over the course of a year. In comparison with a standard ordinal longitudinal analysis, OPFOR outperforms a cumulative-link mixed-effects model in simulation and provides additional and more nuanced information on the nature of the monkeys’ computer-use behavior.

Variational inference is an alternative estimation technique for Bayesian models. Recent work shows that variational methods provide consistent estimation via efficient, deterministic algorithms. Other tools, such as model selection using variational AICs (VAIC) have been developed and studied for the linear regression case. While mixed effects models have enjoyed some study in the variational context, tools for model selection are lacking. One important feature of model selection in mixed effects models, particularly longitudinal models, is the selection of the random effects which in turn determine the covariance structure for the repeatedly sampled outcome. To address this, we derive a VAIC specifically for variational mixed effects (VME) models. We also implement a parameter-efficient VME as part of our study which reduces any general random effects structure down to a single subject-specific score. This model accommodates a wide range of random effect structures including random intercept and slope models as well as random functional effects. Our VAIC can model and perform selection on a variety of VME models including more classic longitudinal models as well as longitudinal scalar-on-function regression. As we demonstrate empirically, our VAIC performs well in discriminating between correctly and incorrectly specified random effects structures. Finally, we illustrate the use of VAICs for VMEs on two datasets: a study of lead levels in children and a study of diffusion tensor imaging.

In Africa, international donors have increasingly promoted democracy and election monitoring. Do Africans want them to do this or would they prefer some other purpose? We argue respondents will least prefer democracy compared to other purposes because (i) there are other possible uses, like healthcare, that are more in need; (ii) aid has a political salience of control that other purposes do not have; and (iii) democracy and monitoring in Africa often yield negative externalities, while other purposes produce positive externalities. To test this claim, we conducted two rounds of survey experiments in Côte d’Ivoire and Uganda, and then again in Côte d’Ivoire with an extension to Senegal. Our surveys employ a conjoint analysis in which respondents compare two possible development projects. Each survey includes several dimensions, including the project’s purpose, which is where we locate democracy and monitoring and alternative purposes such as healthcare or education. Results indicate that democracy and monitoring are the least preferred purposes compared to other purposes. This does not mean that they do not want democracy, nor that they do not want donors to promote democracy, but rather that compared to other possible purposes, democracy is the least preferred use of aid funds.

Background: The use of a vascularized pedicle flap of diaphragmatic muscle (DF) for reconstructive procedures in the chest has many advantages. Yet, despite the excellent reported results, the use of DF has not been widespread. Some factors for the less widespread use of DF have been, concern about diaphragmatic function, hesitation to use such a vital muscle for reconstructive purposes, and most importantly, the technical aspects for the preparation of the flap. Methods: Using a cadaveric model, the vascular anatomy of the diaphragm and the steps for the preparation of the DF was defined and illustrated for both the right and left hemidiaphragm. Results: No perioperative mortality with the use of DF has been recorded. Function of the native diaphragm has not been impaired. Bronchopleural fistulas and pericardial defects have healed in all instances. Excellent repair has been achieved in all patients with esophageal lesions. The disruption of the repaired native diaphragm and visceral herniation has been reported but it has been attributed to the learning curve and the technique of repair. Conclusion: With a better understanding of the vascular anatomy of the diaphragm and a formal methodical approach to harvesting the DF, more surgeons will be encouraged to use DF with excellent results.

Although Monte Carlo path tracing is a simple and effective algorithm to synthesize photo-realistic images, it is often very slow to converge to noise-free results when involving complex global illumination. One of the most successful variance-reduction techniques is path guiding, which can learn better distributions for importance sampling to reduce pixel noise. However, previous methods require a large number of path samples to achieve reliable path guiding. We present a novel neural path guiding approach that can reconstruct high-quality sampling distributions for path guiding from a sparse set of samples, using an offline trained neural network. We leverage photons traced from light sources as the primary input for sampling density reconstruction, which is effective for challenging scenes with strong global illumination. To fully make use of our deep neural network, we partition the scene space into an adaptive hierarchical grid, in which we apply our network to reconstruct high-quality sampling distributions for any local region in the scene. This allows for effective path guiding for arbitrary path bounce at any location in path tracing. We demonstrate that our photon-driven neural path guiding approach can generalize to diverse testing scenes, often achieving better rendering results than previous path guiding approaches and opening up interesting future directions.

AbstractObjective:This study explored social and behavioural factors associated with a home fortification of complementary foods program among families of undernourished children in 14 rural communities in Honduras.Design:We collected and analysed survey data from a convenience sample of 196 households participating in a nutritional program using home fortification of complementary foods in 2017. The program supplied families with a soy-based atole powder fortified with micronutrients. A research team completed a face-to-face survey exploring social and behavioural factors associated with nutritional supplement use. Anthropometric measurements for participating children were abstracted from health clinic records of previous quarterly appointments.Setting:The study took place in San Jose del Negrito, Honduras.Participants:Participants were parents or guardians of children enrolled in the nutrition program.Results:Nearly half of participant families shared the nutritional supplement with other family members besides the index child, while 10 % reported using the supplement as a meal replacement for the child. Low education level of mothers was associated with improper use of the supplement (P= 0·005). Poorer families were more likely to share the supplement (P= 0·013).Conclusions:These results highlight the challenges of programs using home fortification of complementary foods in the context of food scarcity. Findings highlight the importance of increasing rural children’s overall caloric intake, perhaps by increasing access to locally available protein sources. Results also suggest transitioning nutritional programs to family-based interventions to increase overall intended compliance to nutrition programming.

Background: Historically, the pathophysiology of Hiatal Hernias (HH) has not been fully understood. As a result, the surgical therapy of HH has focused primarily on gastrointestinal symptoms and Gastroesophageal Reflux (GERD). This treatment strategy has been associated with poor relief of symptoms and poor long-term outcomes. In fact, until recently, most patients with HH have been watched and referred for surgery as a last resort. Recent experience has shown that a large (giant) Hiatal Hernia (GHH) is a common problem known to impact adjacent organs such as the hearts and lungs. Those referred for surgical repair often complain of dyspnea, which is erroneously attributed to pulmonary compression or aspiration, but has been shown to be from tamponade caused from compression of the heart by herniated abdominal contents. This article reviews the present understanding of GHH, the cardiac complications which result from GHH, and the most advanced robotic minimally invasive surgical approach to the anatomic and physiologic repair of GHH. Methods: In a prospective cohort study, we evaluated patients undergoing RRHH with at least a 2-year follow-up. All patients undergoing elective (RRHH) were identified preoperatively and enrolled prospectively in this study. Preoperative characteristics, medical comorbidities, and clinical information were all recorded prospectively and recorded into a secure surgical outcomes database. All patients received the previously validated Gastroesophageal Reflux Disease-Health-Related Quality of Life (GERD-HRQL) questionnaire preoperatively and at postoperative time points of 1 month, 1 year, and 2 years. Patients routinely had a barium swallow postoperatively before discharge but did not undergo a barHow to cite this paper: Gharagozloo, F., Meyer, M. and Poston, R. (2022) Cardiovascular Complications of Large Hiatal Hernias: Expanding the Indications for Robotic Surgical Anatomic and Physiologic Repair: A Review. World Journal of Cardiovascular Surgery, 12, 39-69. https://doi.org/10.4236/wjcs.2022.123005 Received: February 12, 2022 Accepted: March 14, 2022 Published: March 17, 2022 Copyright © 2022 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/

Population-based seroprevalence surveys can provide useful estimates of the number of individuals previously infected with serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and still susceptible, as well as contribute to better estimates of the case-fatality rate and other measures of coronavirus disease 2019 (COVID-19) severity. No serological test is 100% accurate, however, and the standard correction that epidemiologists use to adjust estimates relies on estimates of the test sensitivity and specificity often based on small validation studies. We have developed a fully Bayesian approach to adjust observed prevalence estimates for sensitivity and specificity. Application to a seroprevalence survey conducted in New York State in 2020 demonstrates that this approach results in more realistic-and narrower-credible intervals than the standard sensitivity analysis using confidence interval endpoints. In addition, the model permits incorporating data on the geographical distribution of reported case counts to create informative priors on the cumulative incidence to produce estimates and credible intervals for smaller geographic areas than often can be precisely estimated with seroprevalence surveys.