Cytoskeletal control of early mammalian development.

The cytoskeleton - comprising actin filaments, microtubules and intermediate filaments - serves instructive roles in regulating cell function and behaviour during development. However, a key challenge in cell and developmental biology is to dissect how these different structures function and interact in vivo to build complex tissues, with the ultimate aim to understand these processes in a mammalian organism. The preimplantation mouse embryo has emerged as a primary model system for tackling this challenge. Not only does the mouse embryo share many morphological similarities with the human embryo during its initial stages of life, it also permits the combination of genetic manipulations with live-imaging approaches to study cytoskeletal dynamics directly within an intact embryonic system. These advantages have led to the discovery of novel cytoskeletal structures and mechanisms controlling lineage specification, cell-cell communication and the establishment of the first forms of tissue architecture during development. Here we highlight the diverse organization and functions of each of the three cytoskeletal filaments during the key events that shape the early mammalian embryo, and discuss how they work together to perform key developmental tasks, including cell fate specification and morphogenesis of the blastocyst. Collectively, these findings are unveiling a new picture of how cells in the early embryo dynamically remodel their cytoskeleton with unique spatial and temporal precision to drive developmental processes in the rapidly changing in vivo environment.

Expanding Actin Rings Zipper the Mouse Embryo for Blastocyst Formation.

Transformation from morula to blastocyst is a defining event of preimplantation embryo development. During this transition, the embryo must establish a paracellular permeability barrier to enable expansion of the blastocyst cavity. Here, using live imaging of mouse embryos, we reveal an actin-zippering mechanism driving this embryo sealing. Preceding blastocyst stage, a cortical F-actin ring assembles at the apical pole of the embryo's outer cells. The ring structure forms when cortical actin flows encounter a network of polar microtubules that exclude F-actin. Unlike stereotypical actin rings, the actin rings of the mouse embryo are not contractile, but instead, they expand to the cell-cell junctions. Here, they couple to the junctions by recruiting and stabilizing adherens and tight junction components. Coupling of the actin rings triggers localized myosin II accumulation, and it initiates a tension-dependent zippering mechanism along the junctions that is required to seal the embryo for blastocyst formation.

Gastric epithelial stem cells in development, homeostasis and regeneration.

The stem/progenitor cell pool is indispensable for the development, homeostasis and regeneration of the gastric epithelium, owing to its defining ability to self-renew whilst supplying the various functional epithelial lineages needed to digest food efficiently. A detailed understanding of the intricacies and complexities surrounding the behaviours and roles of these stem cells offers insights, not only into the physiology of gastric epithelial development and maintenance, but also into the pathological consequences following aberrations in stem cell regulation. Here, we provide an insightful synthesis of the existing knowledge on gastric epithelial stem cell biology, including the in vitro and in vivo experimental techniques that have advanced such studies. We highlight the contributions of stem/progenitor cells towards patterning the developing stomach, specification of the differentiated cell lineages and maintenance of the mature epithelium during homeostasis and following injury. Finally, we discuss gaps in our understanding and identify key research areas for future work.

Keratins are asymmetrically inherited fate determinants in the mammalian embryo.

To implant in the uterus, the mammalian embryo first specifies two cell lineages: the pluripotent inner cell mass that forms the fetus, and the outer trophectoderm layer that forms the placenta1. In many organisms, asymmetrically inherited fate determinants drive lineage specification2, but this is not thought to be the case during early mammalian development. Here we show that intermediate filaments assembled by keratins function as asymmetrically inherited fate determinants in the mammalian embryo. Unlike F-actin or microtubules, keratins are the first major components of the cytoskeleton that display prominent cell-to-cell variability, triggered by heterogeneities in the BAF chromatin-remodelling complex. Live-embryo imaging shows that keratins become asymmetrically inherited by outer daughter cells during cell division, where they stabilize the cortex to promote apical polarization and YAP-dependent expression of CDX2, thereby specifying the first trophectoderm cells of the embryo. Together, our data reveal a mechanism by which cell-to-cell heterogeneities that appear before the segregation of the trophectoderm and the inner cell mass influence lineage fate, via differential keratin regulation, and identify an early function for intermediate filaments in development.

Accommodating misclassification effects on optimizing dynamic treatment regimes with Q-learning.

Research on dynamic treatment regimes has enticed extensive interest. Many methods have been proposed in the literature, which, however, are vulnerable to the presence of misclassification in covariates. In particular, although Q-learning has received considerable attention, its applicability to data with misclassified covariates is unclear. In this article, we investigate how ignoring misclassification in binary covariates can impact the determination of optimal decision rules in randomized treatment settings, and demonstrate its deleterious effects on Q-learning through empirical studies. We present two correction methods to address misclassification effects on Q-learning. Numerical studies reveal that misclassification in covariates induces non-negligible estimation bias and that the correction methods successfully ameliorate bias in parameter estimation.

Trivariate Joint Modeling for Family Data with Longitudinal Counts, Recurrent Events and a Terminal Event with Application to Lynch Syndrome.

Trivariate joint modeling for longitudinal count data, recurrent events, and a terminal event for family data has increased interest in medical studies. For example, families with Lynch syndrome (LS) are at high risk of developing colorectal cancer (CRC), where the number of polyps and the frequency of colonoscopy screening visits are highly associated with the risk of CRC among individuals and families. To assess how screening visits influence polyp detection, which in turn influences time to CRC, we propose a clustered trivariate joint model. The proposed model facilitates longitudinal count data that are zero-inflated and over-dispersed and invokes individual-specific and family-specific random effects to account for dependence among individuals and families. We formulate our proposed model as a latent Gaussian model to use the Bayesian estimation approach with the integrated nested Laplace approximation algorithm and evaluate its performance using simulation studies. Our trivariate joint model is applied to a series of 18 families from Newfoundland, with the occurrence of CRC taken as the terminal event, the colonoscopy screening visits as recurrent events, and the number of polyps detected at each visit as zero-inflated count data with overdispersion. We showed that our trivariate model fits better than alternative bivariate models and that the cluster effects should not be ignored when analyzing family data. Finally, the proposed model enables us to quantify heterogeneity across families and individuals in polyp detection and CRC risk, thus helping to identify individuals and families who would benefit from more intensive screening visits.

Predicting Health Misperceptions: The Role of eHealth Literacy and Situational Perceptions.

This study sought to understand how health misperceptions develop among individuals after exposure to misinformation messages, and how eHealth literacy and situational motivation in problem solving are associated with the negative effects of misinformation exposure. We also sought to understand the differentiated effects of misinformation exposure on the four misinformation-susceptible publics. Results from two studies revealed that situational motivation was positively associated with the formation of misperceptions after misinformation exposure as well as individuals' likelihood of amplifying the misinformation message. However, eHealth literacy does not reduce misperceptions, as had been hypothesized. In fact, eHealth literacy was not significantly associated with misperceptions or with misinformation amplification likelihood. Results also provide support for the typology of misinformation-susceptible publics as misinformation-amplifying publics were the most susceptible to misinformation messages.

The astrocytic Na+ -HCO3 - cotransporter, NBCe1, is dispensable for respiratory chemosensitivity.

The interoceptive homeostatic mechanism that controls breathing, blood gases and acid-base balance in response to changes in CO2 /H+ is exquisitely sensitive, with convergent roles proposed for chemosensory brainstem neurons in the retrotrapezoid nucleus (RTN) and their supporting glial cells. For astrocytes, a central role for NBCe1, a Na+ -HCO3 - cotransporter encoded by Slc4a4, has been envisaged in multiple mechanistic models (i.e. underlying enhanced CO2 -induced local extracellular acidification or purinergic signalling). We tested these NBCe1-centric models by using conditional knockout mice in which Slc4a4 was deleted from astrocytes. In GFAP-Cre;Slc4a4fl/fl mice we found diminished expression of Slc4a4 in RTN astrocytes by comparison to control littermates, and a concomitant reduction in NBCe1-mediated current. Despite disrupted NBCe1 function in RTN-adjacent astrocytes from these conditional knockout mice, CO2 -induced activation of RTN neurons or astrocytes in vitro and in vivo, and CO2 -stimulated breathing, were indistinguishable from NBCe1-intact littermates; hypoxia-stimulated breathing and sighs were likewise unaffected. We obtained a more widespread deletion of NBCe1 in brainstem astrocytes by using tamoxifen-treated Aldh1l1-Cre/ERT2;Slc4a4fl/fl mice. Again, there was no difference in effects of CO2 or hypoxia on breathing or on neuron/astrocyte activation in NBCe1-deleted mice. These data indicate that astrocytic NBCe1 is not required for the respiratory responses to these chemoreceptor stimuli in mice, and that any physiologically relevant astrocytic contributions must involve NBCe1-independent mechanisms. KEY POINTS: The electrogenic NBCe1 transporter is proposed to mediate local astrocytic CO2 /H+ sensing that enables excitatory modulation of nearby retrotrapezoid nucleus (RTN) neurons to support chemosensory control of breathing. We used two different Cre mouse lines for cell-specific and/or temporally regulated deletion of the NBCe1 gene (Slc4a4) in astrocytes to test this hypothesis. In both mouse lines, Slc4a4 was depleted from RTN-associated astrocytes but CO2 -induced Fos expression (i.e. cell activation) in RTN neurons and local astrocytes was intact. Likewise, respiratory chemoreflexes evoked by changes in CO2 or O2 were unaffected by loss of astrocytic Slc4a4. These data do not support the previously proposed role for NBCe1 in respiratory chemosensitivity mediated by astrocytes.

Unveiling the Influence of Tumor Microenvironment and Spatial Heterogeneity on Temozolomide Resistance in Glioblastoma Using an Advanced Human In Vitro Model of the Blood-Brain Barrier and Glioblastoma.

Glioblastoma (GBM) is the most common primary malignant brain cancer in adults with a dismal prognosis. Temozolomide (TMZ) is the first-in-line chemotherapeutic; however, resistance is frequent and multifactorial. While many molecular and genetic factors have been linked to TMZ resistance, the role of the solid tumor morphology and the tumor microenvironment, particularly the blood-brain barrier (BBB), is unknown. Here, the authors investigate these using a complex in vitro model for GBM and its surrounding BBB. The model recapitulates important clinical features such as a dense tumor core with tumor cells that invade along the perivascular space; and a perfusable BBB with a physiological permeability and morphology that is altered in the presence of a tumor spheroid. It is demonstrated that TMZ sensitivity decreases with increasing cancer cell spatial organization, and that the BBB can contribute to TMZ resistance. Proteomic analysis with next-generation low volume sample workflows of these cultured microtissues revealed potential clinically relevant proteins involved in tumor aggressiveness and TMZ resistance, demonstrating the utility of complex in vitro models for interrogating the tumor microenvironment and therapy validation.

Generalized network structured models with mixed responses subject to measurement error and misclassification.

Research of complex associations between a gene network and multiple responses has attracted increasing attention. A great challenge in analyzing genetic data is posited by the presence of the genetic network that is typically unknown. Moreover, mismeasurement of responses introduces additional complexity to distort usual inferential procedures. In this paper, we consider the problem with mixed binary and continuous responses that are subject to mismeasurement and associated with complex structured covariates. We first start with the case where data are precisely measured. We propose a generalized network structured model and develop a two-step inferential procedure. In the first step, we employ a Gaussian graphical model to facilitate the covariates network structure, and in the second step, we incorporate the estimated graphical structure of covariates and develop an estimating equation method. Furthermore, we extend the development to accommodating mismeasured responses. We consider two cases where the information on mismeasurement is either known or estimated from a validation sample. Theoretical results are established and numerical studies are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to analyze the outbred Carworth Farms White mice data arising from a genome-wide association study.

Zero-inflated Poisson models with measurement error in the response.

Zero-inflated count data arise frequently from genomics studies. Analysis of such data is often based on a mixture model which facilitates excess zeros in combination with a Poisson distribution, and various inference methods have been proposed under such a model. Those analysis procedures, however, are challenged by the presence of measurement error in count responses. In this article, we propose a new measurement error model to describe error-contaminated count data. We show that ignoring the measurement error effects in the analysis may generally lead to invalid inference results, and meanwhile, we identify situations where ignoring measurement error can still yield consistent estimators. Furthermore, we propose a Bayesian method to address the effects of measurement error under the zero-inflated Poisson model and discuss the identifiability issues. We develop a data-augmentation algorithm that is easy to implement. Simulation studies are conducted to evaluate the performance of the proposed method. We apply our method to analyze the data arising from a prostate adenocarcinoma genomic study.

Estimation of the COVID-19 mean incubation time: Systematic review, meta-analysis, and sensitivity analysis.

Providing sensible estimates of the mean incubation time for COVID-19 is important yet complex. This study aims to provide synthetic estimates of the mean incubation time of COVID-19 by capitalizing on available estimates reported in the literature and exploring different ways to accommodate heterogeneity involved in the reported studies. Online databases between January 1, 2020 and May 20, 2021 are first searched to obtain estimates of the mean incubation time of COVID-19, and meta-analyses are then conducted to generate synthetic estimates. Heterogeneity of the studies is examined via the use of Cochran's Q $Q$ statistic and Higgin's & Thompson's I 2 ${I}^{2}$ statistic, and subgroup analyses are conducted using mixed effects models. The publication bias issue is assessed using the funnel plot and Egger's test. Using all those reported mean incubation estimates for COVID-19, the synthetic mean incubation time is estimated to be 6.43 days with a 95% confidence interval (CI) [5.90, 6.96], and using all those reported mean incubation estimates together with those transformed median incubation estimates, the estimated mean incubation time is 6.07 days with a 95% CI [5.70, 6.45]. The reported estimates of the mean incubation time of COVID-19 vary considerably due to multiple reasons, including heterogeneity and publication bias. To alleviate these issues, we take different angles to provide a sensible estimate of the mean incubation time of COVID-19. Our analyses show that the mean incubation time of COVID-19 between January 1, 2020 and May 20, 2021 ranges from 5.68 to 8.30 days.

Feature screening with large-scale and high-dimensional survival data.

Data with a huge size present great challenges in modeling, inferences, and computation. In handling big data, much attention has been directed to settings with "large p small n", and relatively less work has been done to address problems with p and n being both large, though data with such a feature have now become more accessible than before, where p represents the number of variables and n stands for the sample size. The big volume of data does not automatically ensure good quality of inferences because a large number of unimportant variables may be collected in the process of gathering informative variables. To carry out valid statistical analysis, it is imperative to screen out noisy variables that have no predictive value for explaining the outcome variable. In this paper, we develop a screening method for handling large-sized survival data, where the sample size n is large and the dimension p of covariates is of non-polynomial order of the sample size n, or the so-called NP-dimension. We rigorously establish theoretical results for the proposed method and conduct numerical studies to assess its performance. Our research offers multiple extensions of existing work and enlarges the scope of high-dimensional data analysis. The proposed method capitalizes on the connections among useful regression settings and offers a computationally efficient screening procedure. Our method can be applied to different situations with large-scale data including genomic data.

COVID-19 impact on mental health.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has posed a significant influence on public mental health. Current efforts focus on alleviating the impacts of the disease on public health and the economy, with the psychological effects due to COVID-19 relatively ignored. In this research, we are interested in exploring the quantitative characterization of the pandemic impact on public mental health by studying an online survey dataset of the United States. METHODS: The analyses are conducted based on a large scale of online mental health-related survey study in the United States, conducted over 12 consecutive weeks from April 23, 2020 to July 21, 2020. We are interested in examining the risk factors that have a significant impact on mental health as well as in their estimated effects over time. We employ the multiple imputation by chained equations (MICE) method to deal with missing values and take logistic regression with the least absolute shrinkage and selection operator (Lasso) method to identify risk factors for mental health. RESULTS: Our analysis shows that risk predictors for an individual to experience mental health issues include the pandemic situation of the State where the individual resides, age, gender, race, marital status, health conditions, the number of household members, employment status, the level of confidence of the future food affordability, availability of health insurance, mortgage status, and the information of kids enrolling in school. The effects of most of the predictors seem to change over time though the degree varies for different risk factors. The effects of risk factors, such as States and gender show noticeable change over time, whereas the factor age exhibits seemingly unchanged effects over time. CONCLUSIONS: The analysis results unveil evidence-based findings to identify the groups who are psychologically vulnerable to the COVID-19 pandemic. This study provides helpful evidence for assisting healthcare providers and policymakers to take steps for mitigating the pandemic effects on public mental health, especially in boosting public health care, improving public confidence in future food conditions, and creating more job opportunities. TRIAL REGISTRATION: This article does not report the results of a health care intervention on human participants.

Characterizing the COVID-19 dynamics with a new epidemic model: Susceptible-exposed-asymptomatic-symptomatic-active-removed.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread stealthily and presented a tremendous threat to the public. It is important to investigate the transmission dynamics of COVID-19 to help understand the impact of the disease on public health and the economy. In this article, we develop a new epidemic model that utilizes a set of ordinary differential equations with unknown parameters to delineate the transmission process of COVID-19. The model accounts for asymptomatic infections as well as the lag between symptom onset and the confirmation date of infection. To reflect the transmission potential of an infected case, we derive the basic reproduction number from the proposed model. Using the daily reported number of confirmed cases, we describe an estimation procedure for the model parameters, which involves adapting the iterated filter-ensemble adjustment Kalman filter (IF-EAKF) algorithm. To illustrate the use of the proposed model, we examine the COVID-19 data from Quebec for the period from 2 April 2020 to 10 May 2020 and carry out sensitivity studies under a variety of assumptions. Simulation studies are used to evaluate the performance of the proposed model under a variety of settings.

La maladie à coronavirus 2019 (COVID­19), causée par le coronavirus 2 du syndrome respiratoire aigu sévère (SARS­CoV­2), s'est rapidement propagée et représente une grande menace pour le public. Pour mieux comprendre l'impact de cette maladie sur la santé publique et l'économie, il est important d'étudier la dynamique de sa transmission. A cette fin, les auteurs de cet article proposent un nouveau modèle épidémiologique basé sur un ensemble d'équations différentielles ordinaires avec des paramètres inconnus et qui tient compte des infections asymptomatiques ainsi que du décalage entre l'apparition des symptômes et la date de confirmation de l'infection. Ils en déduisent le taux de reproduction de base qui traduit le potentiel de transmission d'un cas infecté. En utilisant le nombre rapporté de cas confirmés, les auteurs décrivent une procédure d'estimation des paramètres du modèle qui repose sur une adaptation de l'algorithme filtre itéré ­ filtre de Kalman énsemble àjustement (IF­EAKF). Une mise en application du modèle proposé est illustrée à travers l'examen des données COVID­19 du Québec pour la période du 2 avril 2020 au 10 mai 2020. Une analyse de sensibilité du modèle construit est explorée sous diverses hypothèses. Enfin, les auteurs ont fait appel à des études de simulation pour évaluer la performance du modèle proposé et ce sous différents scénarios.

Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes.

We consider accelerated failure time models with error-prone time-to-event outcomes. The proposed models extend the conventional accelerated failure time model by allowing time-to-event responses to be subject to measurement errors. We describe two measurement error models, a logarithm transformation regression measurement error model and an additive error model with a positive increment, to delineate possible scenarios of measurement error in time-to-event outcomes. We develop Bayesian approaches to conduct statistical inference. Efficient Markov chain Monte Carlo algorithms are developed to facilitate the posterior inference. Extensive simulation studies are conducted to assess the performance of the proposed method, and an application to a study of Alzheimer's disease is presented.

Analysis of noisy survival data with graphical proportional hazards measurement error models.

In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High-dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error-contaminated. To hand such kinds of survival data, we propose graphical PH measurement error models and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods.

Assessing trauma and related distress in refugee youth and their caregivers: should we be concerned about iatrogenic effects?

Assessment of potentially traumatic events and related psychological symptoms in refugee youth is common in epidemiological and intervention research. The objective of this study is to characterize reactions to assessments of trauma exposure and psychological symptoms, including traumatic stress, in refugee youth and their caregivers. Eighty-eight Somali youth and their caregivers participated in a screening and baseline interview for a psychological intervention in three refugee camps in Ethiopia. Participants were asked about their levels of distress prior to, immediately after, and approximately two weeks after completing the interview. Other quantitative and qualitative questions inquired about specific reactions to interview questions and procedures. Children and caregivers became increasingly relaxed over the course of the interview, on average. Few children (5.3%) or caregivers (6.5%) who reported being relaxed at the beginning of the interview became upset by the end of the interview. Some children and caregivers reported that certain assessment questions were upsetting and that feeling upset interfered with their activities. Despite some participants reporting persistent negative reactions, most reported liking and benefitting from the interview. While the majority of refugee youth and their caregivers reported positive experiences associated with completing trauma-related assessments, some reported negative reactions. Researchers and practitioners must consider the necessity, risks, and benefits of including questions about potentially traumatic events and related symptoms that are particularly upsetting in screening, survey research, and clinical assessment. When included, it is important that researchers and practitioners monitor negative reactions to these assessments and connect participants who become distressed with appropriate services.

Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID-19: Meta-analysis and sensitivity analysis.

The coronavirus disease-2019 (COVID-19) has been found to be caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensive knowledge of COVID-19 remains incomplete and many important features are still unknown. This manuscript conducts a meta-analysis and a sensitivity study to answer the questions: What is the basic reproduction number? How long is the incubation time of the disease on average? What portion of infections are asymptomatic? And ultimately, what is the case fatality rate? Our studies estimate the basic reproduction number to be 3.15 with the 95% CI (2.41-3.90), the average incubation time to be 5.08 days with the 95% CI (4.77-5.39) (in day), the asymptomatic infection rate to be 46% with the 95% CI (18.48%-73.60%), and the case fatality rate to be 2.72% with 95% CI (1.29%-4.16%) where asymptomatic infections are accounted for.

Causal inference with noisy data: Bias analysis and estimation approaches to simultaneously addressing missingness and misclassification in binary outcomes.

Causal inference has been widely conducted in various fields and many methods have been proposed for different settings. However, for noisy data with both mismeasurements and missing observations, those methods often break down. In this paper, we consider a problem that binary outcomes are subject to both missingness and misclassification, when the interest is in estimation of the average treatment effects (ATE). We examine the asymptotic biases caused by ignoring missingness and/or misclassification and establish the intrinsic connections between missingness effects and misclassification effects on the estimation of ATE. We develop valid weighted estimation methods to simultaneously correct for missingness and misclassification effects. To provide protection against model misspecification, we further propose a doubly robust correction method which yields consistent estimators when either the treatment model or the outcome model is misspecified. Simulation studies are conducted to assess the performance of the proposed methods. An application to smoking cessation data is reported to illustrate the use of the proposed methods.