Research Event

Event participants

Professor Stig Jarle Hansen, Professor, Norwegian University of Life Sciences; Author, Horn, Sahel and Rift: Fault-lines of the African Jihad
Bulama Bukarti, PhD Candidate, SOAS, University of London; Analyst, Tony Blair Institute for Global Change
Chair: Aoife McCullough, PhD Candidate, LSE

HNF4α is a nuclear receptor produced as 12 isoforms from two promoters by alternative splicing. To characterize the transcriptional capacities of all 12 HNF4α isoforms, stable lines expressing each isoform were generated. The entire transcriptome associated with each isoform was analyzed as well as their respective interacting proteome. Major differences were noted in the transcriptional function of these isoforms. The α1 and α2 isoforms were the strongest regulators of gene expression whereas the α3 isoform exhibited significantly reduced activity. The α4, α5, and α6 isoforms, which use an alternative first exon, were characterized for the first time, and showed a greatly reduced transcriptional potential with an inability to recognize the consensus response element of HNF4α. Several transcription factors and coregulators were identified as potential specific partners for certain HNF4α isoforms. An analysis integrating the vast amount of omics data enabled the identification of transcriptional regulatory mechanisms specific to certain HNF4α isoforms, hence demonstrating the importance of considering all isoforms given their seemingly diverse functions.

The peroxisome is a subcellular organelle that functions in essential metabolic pathways, including biosynthesis of plasmalogens, fatty acid β-oxidation of very-long-chain fatty acids, and degradation of hydrogen peroxide. Peroxisome biogenesis disorders (PBDs) manifest as severe dysfunction in multiple organs, including the central nervous system (CNS), but the pathogenic mechanisms in PBDs are largely unknown. Because CNS integrity is coordinately established and maintained by neural cell interactions, we here investigated whether cell-cell communication is impaired and responsible for the neurological defects associated with PBDs. Results from a noncontact co-culture system consisting of primary hippocampal neurons with glial cells revealed that a peroxisome-deficient astrocytic cell line secretes increased levels of brain-derived neurotrophic factor (BDNF), resulting in axonal branching of the neurons. Of note, the BDNF expression in astrocytes was not affected by defects in plasmalogen biosynthesis and peroxisomal fatty acid β-oxidation in the astrocytes. Instead, we found that cytosolic reductive states caused by a mislocalized catalase in the peroxisome-deficient cells induce the elevation in BDNF secretion. Our results suggest that peroxisome deficiency dysregulates neuronal axogenesis by causing a cytosolic reductive state in astrocytes. We conclude that astrocytic peroxisomes regulate BDNF expression and thereby support neuronal integrity and function.

Heat shock factor 1 (HSF1) regulates cellular adaptation to challenges such as heat shock and oxidative and proteotoxic stresses. We have recently reported a previously unappreciated role for HSF1 in the regulation of energy metabolism in fat tissues; however, whether HSF1 is differentially expressed in adipose depots and how its levels are regulated in fat tissues remain unclear. Here, we show that HSF1 levels are higher in brown and subcutaneous fat tissues than in those in the visceral depot and that HSF1 is more abundant in differentiated, thermogenic adipocytes. Gene expression experiments indicated that HSF1 is transcriptionally regulated in fat by agents that modulate cAMP levels, by cold exposure, and by pharmacological stimulation of β-adrenergic signaling. An in silico promoter analysis helped identify a putative response element for activating transcription factor 3 (ATF3) at −258 to −250 base pairs from the HSF1 transcriptional start site, and electrophoretic mobility shift and ChIP assays confirmed ATF3 binding to this sequence. Furthermore, functional assays disclosed that ATF3 is necessary and sufficient for HSF1 regulation. Detailed gene expression analysis revealed that ATF3 is one of the most highly induced ATFs in thermogenic tissues of mice exposed to cold temperatures or treated with the β-adrenergic receptor agonist CL316,243 and that its expression is induced by modulators of cAMP levels in isolated adipocytes. To the best of our knowledge, our results show for the first time that HSF1 is transcriptionally controlled by ATF3 in response to classic stimuli that promote heat generation in thermogenic tissues.

Gernot F. Grabner
Apr 29, 2020; 0:jlr.RA119000516v1-jlr.RA119000516
Research Articles

Bis(monoacylglycero)phosphate (BMP), also known as lysobisphosphatidic acid (LBPA), is a phospholipid that promotes lipid sorting in late endosomes/lysosomes by activating lipid hydrolases and lipid transfer proteins. Changes in the cellular BMP content therefore reflect an altered metabolic activity of the endo-lysosomal system. Surprisingly, little is known about the physiological regulation of BMP. In this study, we investigated the effects of nutritional and metabolic factors on BMP profiles of whole tissues and  parenchymal and non-parenchymal cells. Tissue samples were obtained from fed, fasted, two-hours refed, and insulin-treated mice, as well as from mice housed at  5°C, 22°C, or 30°C. These tissues exhibited distinct BMP profiles, which were regulated by the nutritional state in a tissue-specific manner. Insulin treatment was not sufficient to mimic refeeding-induced changes in tissue BMP levels indicating that BMP metabolism is regulated by other hormonal or nutritional factors. Tissue fractionation experiments revealed that fasting drastically elevates BMP levels in hepatocytes and pancreatic cells. Furthermore, we observed that the BMP content in brown adipose tissue strongly depends on housing temperatures. In conclusion, our observations suggest that BMP concentrations adapt to the metabolic state in a tissue-and cell type-specific manner in mice. Drastic changes observed in hepatocytes, pancreatic cells, and brown adipocytes suggest that BMP possesses a role in the functional adaption to nutrient starvation and ambient temperature.

Xanthophyllomyces dendrorhous is a basidiomycete yeast known as a natural producer of astaxanthin, a carotenoid of commercial interest because of its antioxidant properties. Recent studies indicated that X. dendrorhous has a functional SREBP pathway involved in the regulation of isoprenoid compound biosynthesis, which includes ergosterol and carotenoids. SREBP is a major regulator of sterol metabolism and homeostasis in mammals; characterization in fungi also provides information about its role in the hypoxia adaptation response and virulence. SREBP protease processing is required to activate SREBP pathway functions in fungi. Here, we identified and described the STP1 gene, which encodes a metallopeptidase of the M50 family involved in the proteolytic activation of the transcription factor Sre1 of the SREBP pathway, in X. dendrorhous. We assessed STP1 function in stp1 strains derived from the wild-type and a mutant of ergosterol biosynthesis that overproduces carotenoids and sterols. Bioinformatic analysis of the deduced protein predicted the presence of characteristic features identified in homologs from mammals and fungi. The stp1 mutation decreased yeast growth in the presence of azole drugs and reduced transcript levels of Sre1-dependent genes. This mutation also negatively affected the carotenoid- and sterol-overproducing phenotype. Western blot analysis demonstrated that Sre1 was activated in the yeast ergosterol biosynthesis mutant and that the stp1 mutation introduced in this strain prevented Sre1 proteolytic activation. Overall, our results demonstrate that STP1 encodes a metallopeptidase involved in proteolytic activation of Sre1 in X. dendrorhous, contributing to our understanding of fungal SREBP pathways.

Nonalcoholic steatohepatitis has emerged as a major cause of liver diseases with no effective therapies. Here, we evaluate the efficacies and pharmacokinetics of B1344, a long-acting PEGylated FGF21 analog, in a nongenetically modified nonhuman primate species that underwent liver biopsy, and demonstrate the potential for efficacies in humans. B1344 is sufficient to selectively activate signaling from the βKlotho/FGFR1c receptor complex. In cynomolgus monkeys with nonalcoholic fatty liver disease, administration of B1344 via subcutaneous injection for eleven weeks caused a profound reduction of hepatic steatosis, inflammation and fibrosis, and amelioration of liver injury and hepatocyte death as evidenced by liver biopsy and biochemical analysis. Moreover, improvement of metabolic parameters was observed in the monkey, including reduction of body weight and improvement of lipid profiles and glycemic control. To determine the role of B1344 in the progression of murine NAFLD independent of obesity, administration of B1344 were performed in mice fed with methionine and choline deficiency diet. Consistently, B1344 administration prevented the mice from lipotoxicity damage and nonalcoholic steatohepatitis at a dose-dependent manner. These results provide preclinical validation for an innovative therapeutics to NAFLD, and support further clinical testing of B1344 for treating nonalcoholic steatohepatitis and other metabolic diseases in humans.

SUMOylation is a posttranslational modification (PTM) at a lysine residue and is crucial for the proper functions of many proteins, particularly of transcription factors, in various biological processes. Zinc finger homeobox 3 (ZFHX3), also known as AT motif-binding factor 1 (ATBF1), is a large transcription factor that is active in multiple pathological processes, including atrial fibrillation and carcinogenesis, and in circadian regulation and development. We have previously demonstrated that ZFHX3 is SUMOylated at three or more lysine residues. Here, we investigated which enzymes regulate ZFHX3 SUMOylation and whether SUMOylation modulates ZFHX3 stability and function. We found that SUMO1, SUMO2, and SUMO3 each are conjugated to ZFHX3. Multiple lysine residues in ZFHX3 were SUMOylated, but Lys-2806 was the major SUMOylation site, and we also found that it is highly conserved among ZFHX3 orthologs from different animal species. Using molecular analyses, we identified the enzymes that mediate ZFHX3 SUMOylation; these included SUMO1-activating enzyme subunit 1 (SAE1), an E1-activating enzyme; SUMO-conjugating enzyme UBC9 (UBC9), an E2-conjugating enzyme; and protein inhibitor of activated STAT2 (PIAS2), an E3 ligase. Multiple analyses established that both SUMO-specific peptidase 1 (SENP1) and SENP2 deSUMOylate ZFHX3. SUMOylation at Lys-2806 enhanced ZFHX3 stability by interfering with its ubiquitination and proteasomal degradation. Functionally, Lys-2806 SUMOylation enabled ZFHX3-mediated cell proliferation and xenograft tumor growth of the MDA-MB-231 breast cancer cell line. These findings reveal the enzymes involved in, and the functional consequences of, ZFHX3 SUMOylation, insights that may help shed light on ZFHX3's roles in various cellular and pathophysiological processes.

Barbara H. Braffett
May 1, 2020; 69:1000-1010
Complications

Cheng Hu
Jan 1, 2018; 67:3-11
Perspectives in Diabetes

Gökhan S Hotamisligil
Nov 1, 1994; 43:1271-1278
Perspectives in Diabetes

OBJECTIVE

Dysglycemia, in this survey defined as impaired glucose tolerance (IGT) or type 2 diabetes, is common in patients with coronary artery disease (CAD) and associated with an unfavorable prognosis. This European survey investigated dysglycemia screening and risk factor management of patients with CAD in relation to standards of European guidelines for cardiovascular subjects.

Apr 1, 2018; 36:202-202
Patient Education

W B Kannel
Mar 1, 1979; 2:120-126
Proceedings of the Kroc Foundation International Conference on Epidemiology of Diabetes and its Macrovascular Complications

Jordi Salas-Salvadó
May 1, 2019; 42:777-788
Continuing Evolution of Nutritional Therapy for Diabetes

Dror Dicker
May 1, 2011; 34:S276-S278
Diabetes Treatments

OBJECTIVE

To investigate the association of metabolic and lifestyle factors with possible diabetic polyneuropathy (DPN) and neuropathic pain in patients with early type 2 diabetes.

OBJECTIVE

To determine the risk of cardiovascular disease (CVD), microvascular complications, and mortality in patients with type 2 diabetes who subsequently develop obstructive sleep apnea (OSA) compared with patients with type 2 diabetes without a diagnosis of OSA.

If you were Barack Obama, you would be scratching your head, too.

OBJECTIVE

The Diabetes Control and Complications Trial (DCCT) and its observational follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) demonstrated the dominant role of glycemia, second only to age, as a risk factor for a first cardiovascular event in type 1 diabetes (T1D). We now investigate the association between established risk factors and the total cardiovascular disease (CVD) burden, including subsequent (i.e., recurrent) events.

Rockville, Maryland : National Institute on Drug Abuse, 1988.

In many applications, observed data are influenced by some combination of latent causes. For example, suppose sensors are placed inside a building to record responses such as temperature, humidity, power consumption and noise levels. These random, observed responses are typically affected by many unobserved, latent factors (or features) within the building such as the number of individuals, the turning on and off of electrical devices, power surges, etc. These latent factors are usually present for a contiguous period of time before disappearing; further, multiple factors could be present at a time. This paper develops new probabilistic methodology and inference methods for random object generation influenced by latent features exhibiting temporal persistence. Every datum is associated with subsets of a potentially infinite number of hidden, persistent features that account for temporal dynamics in an observation. The ensuing class of dynamic models constructed by adapting the Indian Buffet Process — a probability measure on the space of random, unbounded binary matrices — finds use in a variety of applications arising in operations, signal processing, biomedicine, marketing, image analysis, etc. Illustrations using synthetic and real data are provided.

Factor analysis is a flexible technique for assessment of multivariate dependence and codependence. Besides being an exploratory tool used to reduce the dimensionality of multivariate data, it allows estimation of common factors that often have an interesting theoretical interpretation in real problems. However, standard factor analysis is only applicable when the variables are scaled, which is often inappropriate, for example, in data obtained from questionnaires in the field of psychology,where the variables are often categorical. In this framework, we propose a factor model for the analysis of multivariate ordered and non-ordered polychotomous data. The inference procedure is done under the Bayesian approach via Markov chain Monte Carlo methods. Two Monte-Carlo simulation studies are presented to investigate the performance of this approach in terms of estimation bias, precision and assessment of the number of factors. We also illustrate the proposed method to analyze participants' responses to the Motivational State Questionnaire dataset, developed to study emotions in laboratory and field settings.

We analyze risk factors correlated with the initial transmission growth rate of the COVID-19 pandemic. The number of cases follows an early exponential expansion; we chose as a starting point in each country the first day with 30 cases and used 12 days. We looked for linear correlations of the exponents with other variables, using 126 countries. We find a positive correlation with high C.L. with the following variables, with respective $p$-value: low Temperature ($4cdot10^{-7}$), high ratio of old vs.~working-age people ($3cdot10^{-6}$), life expectancy ($8cdot10^{-6}$), number of international tourists ($1cdot10^{-5}$), earlier epidemic starting date ($2cdot10^{-5}$), high level of contact in greeting habits ($6 cdot 10^{-5}$), lung cancer ($6 cdot 10^{-5}$), obesity in males ($1 cdot 10^{-4}$), urbanization ($2cdot10^{-4}$), cancer prevalence ($3 cdot 10^{-4}$), alcohol consumption ($0.0019$), daily smoking prevalence ($0.0036$), UV index ($0.004$, smaller sample, 73 countries), low Vitamin D levels ($p$-value $0.002-0.006$, smaller sample, $sim 50$ countries). There is highly significant correlation also with blood type: positive correlation with RH- ($2cdot10^{-5}$) and A+ ($2cdot10^{-3}$), negative correlation with B+ ($2cdot10^{-4}$). We also find positive correlation with moderate C.L. ($p$-value of $0.02sim0.03$) with: CO$_2$ emissions, type-1 diabetes, low vaccination coverage for Tuberculosis (BCG). Several such variables are correlated with each other and so they likely have common interpretations. We also analyzed the possible existence of a bias: countries with low GDP-per capita, typically located in warm regions, might have less intense testing and we discuss correlation with the above variables.

Subhadeep Paul, Yuguo Chen.

Source: The Annals of Statistics, Volume 48, Number 1, 230--250.

Abstract:
We consider the problem of estimating a consensus community structure by combining information from multiple layers of a multi-layer network using methods based on the spectral clustering or a low-rank matrix factorization. As a general theme, these “intermediate fusion” methods involve obtaining a low column rank matrix by optimizing an objective function and then using the columns of the matrix for clustering. However, the theoretical properties of these methods remain largely unexplored. In the absence of statistical guarantees on the objective functions, it is difficult to determine if the algorithms optimizing the objectives will return good community structures. We investigate the consistency properties of the global optimizer of some of these objective functions under the multi-layer stochastic blockmodel. For this purpose, we derive several new asymptotic results showing consistency of the intermediate fusion techniques along with the spectral clustering of mean adjacency matrix under a high dimensional setup, where the number of nodes, the number of layers and the number of communities of the multi-layer graph grow. Our numerical study shows that the intermediate fusion techniques outperform late fusion methods, namely spectral clustering on aggregate spectral kernel and module allegiance matrix in sparse networks, while they outperform the spectral clustering of mean adjacency matrix in multi-layer networks that contain layers with both homophilic and heterophilic communities.

Xinran Li, Peng Ding, Donald B. Rubin.

Source: The Annals of Statistics, Volume 48, Number 1, 43--63.

Abstract:
With many pretreatment covariates and treatment factors, the classical factorial experiment often fails to balance covariates across multiple factorial effects simultaneously. Therefore, it is intuitive to restrict the randomization of the treatment factors to satisfy certain covariate balance criteria, possibly conforming to the tiers of factorial effects and covariates based on their relative importances. This is rerandomization in factorial experiments. We study the asymptotic properties of this experimental design under the randomization inference framework without imposing any distributional or modeling assumptions of the covariates and outcomes. We derive the joint asymptotic sampling distribution of the usual estimators of the factorial effects, and show that it is symmetric, unimodal and more “concentrated” at the true factorial effects under rerandomization than under the classical factorial experiment. We quantify this advantage of rerandomization using the notions of “central convex unimodality” and “peakedness” of the joint asymptotic sampling distribution. We also construct conservative large-sample confidence sets for the factorial effects.

Kyoungjae Lee, Jaeyong Lee, Lizhen Lin.

Source: The Annals of Statistics, Volume 47, Number 6, 3413--3437.

Abstract:
In this paper we study the high-dimensional sparse directed acyclic graph (DAG) models under the empirical sparse Cholesky prior. Among our results, strong model selection consistency or graph selection consistency is obtained under more general conditions than those in the existing literature. Compared to Cao, Khare and Ghosh [ Ann. Statist. (2019) 47 319–348], the required conditions are weakened in terms of the dimensionality, sparsity and lower bound of the nonzero elements in the Cholesky factor. Furthermore, our result does not require the irrepresentable condition, which is necessary for Lasso-type methods. We also derive the posterior convergence rates for precision matrices and Cholesky factors with respect to various matrix norms. The obtained posterior convergence rates are the fastest among those of the existing Bayesian approaches. In particular, we prove that our posterior convergence rates for Cholesky factors are the minimax or at least nearly minimax depending on the relative size of true sparseness for the entire dimension. The simulation study confirms that the proposed method outperforms the competing methods.

Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark, Tyler H. McCormick.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 241--256.

Abstract:
The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data

Elizabeth Lorenzi, Ricardo Henao, Katherine Heller.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2637--2661.

Abstract:
Nearly a third of all surgeries performed in the United States occur for patients over the age of 65; these older adults experience a higher rate of postoperative morbidity and mortality. To improve the care for these patients, we aim to identify and characterize high risk geriatric patients to send to a specialized perioperative clinic while leveraging the overall surgical population to improve learning. To this end, we develop a hierarchical infinite latent factor model (HIFM) to appropriately account for the covariance structure across subpopulations in data. We propose a novel Hierarchical Dirichlet Process shrinkage prior on the loadings matrix that flexibly captures the underlying structure of our data while sharing information across subpopulations to improve inference and prediction. The stick-breaking construction of the prior assumes an infinite number of factors and allows for each subpopulation to utilize different subsets of the factor space and select the number of factors needed to best explain the variation. We develop the model into a latent factor regression method that excels at prediction and inference of regression coefficients. Simulations validate this strong performance compared to baseline methods. We apply this work to the problem of predicting surgical complications using electronic health record data for geriatric patients and all surgical patients at Duke University Health System (DUHS). The motivating application demonstrates the improved predictive performance when using HIFM in both area under the ROC curve and area under the PR Curve while providing interpretable coefficients that may lead to actionable interventions.

Susheela P. Singh, Ana-Maria Staicu, Robert R. Dunn, Noah Fierer, Brian J. Reich.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2341--2362.

Abstract:
The advent of high-throughput sequencing technologies has made data from DNA material readily available, leading to a surge of microbiome-related research establishing links between markers of microbiome health and specific outcomes. However, to harness the power of microbial communities we must understand not only how they affect us, but also how they can be influenced to improve outcomes. This area has been dominated by methods that reduce community composition to summary metrics, which can fail to fully exploit the complexity of community data. Recently, methods have been developed to model the abundance of taxa in a community, but they can be computationally intensive and do not account for spatial effects underlying microbial settlement. These spatial effects are particularly relevant in the microbiome setting because we expect communities that are close together to be more similar than those that are far apart. In this paper, we propose a flexible Bayesian spike-and-slab variable selection model for presence-absence indicators that accounts for spatial dependence and cross-dependence between taxa while reducing dimensionality in both directions. We show by simulation that in the presence of spatial dependence, popular distance-based hypothesis testing methods fail to preserve their advertised size, and the proposed method improves variable selection. Finally, we present an application of our method to an indoor fungal community found within homes across the contiguous United States.

Muteb Alharthi, Theodore Kypraios, Philip D. O’Neill.

Source: Bayesian Analysis, Volume 14, Number 3, 927--956.

Abstract:
We consider the problem of model choice for stochastic epidemic models given partial observation of a disease outbreak through time. Our main focus is on the use of Bayes factors. Although Bayes factors have appeared in the epidemic modelling literature before, they can be hard to compute and little attention has been given to fundamental questions concerning their utility. In this paper we derive analytic expressions for Bayes factors given complete observation through time, which suggest practical guidelines for model choice problems. We adapt the power posterior method for computing Bayes factors so as to account for missing data and apply this approach to partially observed epidemics. For comparison, we also explore the use of a deviance information criterion for missing data scenarios. The methods are illustrated via examples involving both simulated and real data.

Quan Zhou, Yongtao Guan.

Source: Bayesian Analysis, Volume 14, Number 2, 573--594.

Abstract:
Bayesian variable selection regression (BVSR) is able to jointly analyze genome-wide genetic datasets, but the slow computation via Markov chain Monte Carlo (MCMC) hampered its wide-spread usage. Here we present a novel iterative method to solve a special class of linear systems, which can increase the speed of the BVSR model-fitting tenfold. The iterative method hinges on the complex factorization of the sum of two matrices and the solution path resides in the complex domain (instead of the real domain). Compared to the Gauss-Seidel method, the complex factorization converges almost instantaneously and its error is several magnitude smaller than that of the Gauss-Seidel method. More importantly, the error is always within the pre-specified precision while the Gauss-Seidel method is not. For large problems with thousands of covariates, the complex factorization is 10–100 times faster than either the Gauss-Seidel method or the direct method via the Cholesky decomposition. In BVSR, one needs to repetitively solve large penalized regression systems whose design matrices only change slightly between adjacent MCMC steps. This slight change in design matrix enables the adaptation of the iterative complex factorization method. The computational innovation will facilitate the wide-spread use of BVSR in reanalyzing genome-wide association datasets.

Joris Mulder, Jean-Paul Fox.

Source: Bayesian Analysis, Volume 14, Number 2, 521--552.

Abstract:
The intraclass correlation plays a central role in modeling hierarchically structured data, such as educational data, panel data, or group-randomized trial data. It represents relevant information concerning the between-group and within-group variation. Methods for Bayesian hypothesis tests concerning the intraclass correlation are proposed to improve decision making in hierarchical data analysis and to assess the grouping effect across different group categories. Estimation and testing methods for the intraclass correlation coefficient are proposed under a marginal modeling framework where the random effects are integrated out. A class of stretched beta priors is proposed on the intraclass correlations, which is equivalent to shifted $F$ priors for the between groups variances. Through a parameter expansion it is shown that this prior is conditionally conjugate under the marginal model yielding efficient posterior computation. A special improper case results in accurate coverage rates of the credible intervals even for minimal sample size and when the true intraclass correlation equals zero. Bayes factor tests are proposed for testing multiple precise and order hypotheses on intraclass correlations. These tests can be used when prior information about the intraclass correlations is available or absent. For the noninformative case, a generalized fractional Bayes approach is developed. The method enables testing the presence and strength of grouped data structures without introducing random effects. The methodology is applied to a large-scale survey study on international mathematics achievement at fourth grade to test the heterogeneity in the clustering of students in schools across countries and assessment cycles.

Steven Poser
Jun 1, 2003; 23:4420-4427
Cellular

The digital revolution has changed the way we work, access information and connect with each other. It offers opportunities to those who can use the new technologies, but also presents new challenges for those who are left behind. Often referred to collectively as Information and Communications Technologies or ICTs, these technologies are any method of electronically sharing or storing data: telephones, [...]

The 18th-century Edinburgh factory once produced a million bottles a week

Central Bank of Chile Working Papers by Andrés Sagner

BACKGROUND AND OBJECTIVES:

Children with autism spectrum disorder (ASD) have a higher prevalence of epilepsy compared with general populations. In this pilot study, we prospectively identified baseline risk factors for the development of seizures in individuals with ASD and also identified characteristics sensitive to seizure onset up to 6 years after enrollment in the Autism Speaks Autism Treatment Network.

The recent Chicago Teachers Union strike drew attention from Democratic presidential candidates in Illinois, a state won by Democrats in the last White House contest. For 2020, it's possible we could see a twist on that story: big-city teacher strikes in states with less predictable outcomes.

The swirl of attention around Democratic presidential candidate Elizabeth Warren’s story of being forced out of a teaching job when she was pregnant intensifies the spotlight on her background and K-12 credentials.

The recent Chicago Teachers Union strike drew attention from Democratic presidential candidates in Illinois, a state won by Democrats in the last White House contest. For 2020, it's possible we could see a twist on that story: big-city teacher strikes in states with less predictable outcomes.

Cardiovascular risk factors predict the development of premature atherosclerosis. As the number of risk factors increases, so does the extent of these lesions. Assessment of cardiovascular risk factors is an accepted practice in adults but is not used in pediatrics.

In this study, the authors discuss how the presence of ≥2 cardiovascular risk factors is associated with vascular changes in adolescents. The findings were compared with the Patholobiological Determinants of Atherosclerosis in Youth risk score to demonstrate that a simple method of clustering is a reliable tool to use in clinical practice. (Read the full article)

Gallbladder disease in children is an evolving entity and studies suggest an increasing frequency of symptomatic pediatric gallbladder disease and resultant cholecystectomies.

Hispanic ethnicity and obesity are epidemiologically significant risk factors for symptomatic gallbladder disease in the pediatric population. (Read the full article)

Prone sleep, bed-sharing, maternal smoking during pregnancy, and prematurity increase the risk of sudden infant death syndrome. The sudden infant death syndrome rate initially declined dramatically after the initiation of the US Back-to-Sleep campaign in 1994, but subsequently plateaued.

The risk profile has changed since the Back-to-Sleep campaign; the prevalence of simultaneous risks has remained consistent. Intrinsic and extrinsic risks provide unification into 1 underlying triple-risk model and insights into potential underlying mechanisms. (Read the full article)

Rural Alaska children have high rates of hospitalization with lower respiratory tract infections from a variety of pathogens. Past studies of risk factors for respiratory syncytial virus infection associated medically high-risk status, household crowding, and infant feeding practices with hospitalization.

This study reveals the importance of medically high-risk status and infant feeding practices as important factors in respiratory hospitalization. In addition, we identified woodstove use and the absence of 2 or more sinks in household as risk factors for hospitalization. (Read the full article)

Valid parental permission requires that the decision be both informed and voluntary. Previous research has focused on the informational components of decision-making (eg, disclosure and understanding), with little empirical attention to the voluntariness of decisions.

We address this gap by examining the voluntariness of parents making research or treatment decisions in pediatric oncology. We identify demographic and contextual correlates of voluntariness and highlight the clinical implications of the findings for physicians and investigators. (Read the full article)