Jami J. Mulgrave, Subhashis Ghosal.

Source: Bayesian Analysis, Volume 15, Number 2, 449--475.

Abstract:
Gaussian graphical models have been used to study intrinsic dependence among several variables, but the Gaussianity assumption may be restrictive in many applications. A nonparanormal graphical model is a semiparametric generalization for continuous variables where it is assumed that the variables follow a Gaussian graphical model only after some unknown smooth monotone transformations on each of them. We consider a Bayesian approach in the nonparanormal graphical model by putting priors on the unknown transformations through a random series based on B-splines where the coefficients are ordered to induce monotonicity. A truncated normal prior leads to partial conjugacy in the model and is useful for posterior simulation using Gibbs sampling. On the underlying precision matrix of the transformed variables, we consider a spike-and-slab prior and use an efficient posterior Gibbs sampling scheme. We use the Bayesian Information Criterion to choose the hyperparameters for the spike-and-slab prior. We present a posterior consistency result on the underlying transformation and the precision matrix. We study the numerical performance of the proposed method through an extensive simulation study and finally apply the proposed method on a real data set.

Andrés F. Barrientos, Víctor Peña.

Source: Bayesian Analysis, Volume 15, Number 2, 363--388.

Abstract:
In this article, we present data-subsetting algorithms that allow for the approximate and scalable implementation of the Bayesian bootstrap. They are analogous to two existing algorithms in the frequentist literature: the bag of little bootstraps (Kleiner et al., 2014) and the subsampled double bootstrap (Sengupta et al., 2016). Our algorithms have appealing theoretical and computational properties that are comparable to those of their frequentist counterparts. Additionally, we provide a strategy for performing lossless inference for a class of functionals of the Bayesian bootstrap and briefly introduce extensions to the Dirichlet Process.

Qingpo Cai, Jian Kang, Tianwei Yu.

Source: Bayesian Analysis, Volume 15, Number 1, 79--102.

Abstract:
Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).

Andrea Cremaschi, Raffaele Argiento, Katherine Shoemaker, Christine Peterson, Marina Vannucci.

Source: Bayesian Analysis, Volume 14, Number 4, 1271--1301.

Abstract:
Gaussian graphical models are useful tools for exploring network structures in multivariate normal data. In this paper we are interested in situations where data show departures from Gaussianity, therefore requiring alternative modeling distributions. The multivariate $t$ -distribution, obtained by dividing each component of the data vector by a gamma random variable, is a straightforward generalization to accommodate deviations from normality such as heavy tails. Since different groups of variables may be contaminated to a different extent, Finegold and Drton (2014) introduced the Dirichlet $t$ -distribution, where the divisors are clustered using a Dirichlet process. In this work, we consider a more general class of nonparametric distributions as the prior on the divisor terms, namely the class of normalized completely random measures (NormCRMs). To improve the effectiveness of the clustering, we propose modeling the dependence among the divisors through a nonparametric hierarchical structure, which allows for the sharing of parameters across the samples in the data set. This desirable feature enables us to cluster together different components of multivariate data in a parsimonious way. We demonstrate through simulations that this approach provides accurate graphical model inference, and apply it to a case study examining the dependence structure in radiomics data derived from The Cancer Imaging Atlas.

Abhirup Datta, Sudipto Banerjee, James S. Hodges, Leiwen Gao.

Source: Bayesian Analysis, Volume 14, Number 4, 1221--1244.

Abstract:
Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and networks. Theoretical results establish a link between the parameters in our model and the variance and covariances of the random effects. Simulation studies demonstrate that the improved interpretability of our model reaps benefits in terms of accurately recovering the latent spatial random effects as well as for inference on the spatial covariance parameters. Under modest spatial correlation, our model far outperforms the CAR models, while the performances are similar when the spatial correlation is strong. We also assess sensitivity to the choice of the ordering in the DAG construction using theoretical and empirical results which testify to the robustness of our model. We also present a large-scale public health application demonstrating the competitive performance of the model.

Amir Bashir, Carlos M. Carvalho, P. Richard Hahn, M. Beatrix Jones.

Source: Bayesian Analysis, Volume 14, Number 4, 1075--1090.

Abstract:
A variety of computationally efficient Bayesian models for the covariance matrix of a multivariate Gaussian distribution are available. However, all produce a relatively dense estimate of the precision matrix, and are therefore unsatisfactory when one wishes to use the precision matrix to consider the conditional independence structure of the data. This paper considers the posterior predictive distribution of model fit for these covariance models. We then undertake post-processing of the Bayes point estimate for the precision matrix to produce a sparse model whose expected fit lies within the upper 95% of the posterior predictive distribution of fit. The impact of the method for selecting the zero elements of the precision matrix is evaluated. Good results were obtained using models that encouraged a sparse posterior (G-Wishart, Bayesian adaptive graphical lasso) and selection using credible intervals. We also find that this approach is easily extended to the problem of finding a sparse set of elements that differ across a set of precision matrices, a natural summary when a common set of variables is observed under multiple conditions. We illustrate our findings with moderate dimensional data examples from finance and metabolomics.

Ioannis Ntzoufras, Claudia Tarantola, Monia Lupparelli.

Source: Bayesian Analysis, Volume 14, Number 3, 797--823.

Abstract:
We introduce a novel Bayesian approach for quantitative learning for graphical log-linear marginal models. These models belong to curved exponential families that are difficult to handle from a Bayesian perspective. The likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities. Posterior distributions cannot be directly obtained, and Markov Chain Monte Carlo (MCMC) methods are needed. Finally, a well-defined model requires parameter values that lead to compatible marginal probabilities. Hence, any MCMC should account for this important restriction. We construct a fully automatic and efficient MCMC strategy for quantitative learning for such models that handles these problems. While the prior is expressed in terms of the marginal log-linear interactions, we build an MCMC algorithm that employs a proposal on the probability parameter space. The corresponding proposal on the marginal log-linear interactions is obtained via parameter transformation. We exploit a conditional conjugate setup to build an efficient proposal on probability parameters. The proposed methodology is illustrated by a simulation study and a real dataset.

Marcos Oliveira Prates, Renato Martins Assunção, Erica Castilho Rodrigues.

Source: Bayesian Analysis, Volume 14, Number 2, 623--647.

Abstract:
Spatial confounding between the spatial random effects and fixed effects covariates has been recently discovered and showed that it may bring misleading interpretation to the model results. Techniques to alleviate this problem are based on decomposing the spatial random effect and fitting a restricted spatial regression. In this paper, we propose a different approach: a transformation of the geographic space to ensure that the unobserved spatial random effect added to the regression is orthogonal to the fixed effects covariates. Our approach, named SPOCK, has the additional benefit of providing a fast and simple computational method to estimate the parameters. Also, it does not constrain the distribution class assumed for the spatial error term. A simulation study and real data analyses are presented to better understand the advantages of the new method in comparison with the existing ones.

Suprateek Kundu, Bani K. Mallick, Veera Baladandayuthapani.

Source: Bayesian Analysis, Volume 14, Number 2, 449--476.

Abstract:
There has been an intense development in the Bayesian graphical model literature over the past decade; however, most of the existing methods are restricted to moderate dimensions. We propose a novel graphical model selection approach for large dimensional settings where the dimension increases with the sample size, by decoupling model fitting and covariance selection. First, a full model based on a complete graph is fit under a novel class of mixtures of inverse–Wishart priors, which induce shrinkage on the precision matrix under an equivalence with Cholesky-based regularization, while enabling conjugate updates. Subsequently, a post-fitting model selection step uses penalized joint credible regions to perform model selection. This allows our methods to be computationally feasible for large dimensional settings using a combination of straightforward Gibbs samplers and efficient post-fitting inferences. Theoretical guarantees in terms of selection consistency are also established. Simulations show that the proposed approach compares favorably with competing methods, both in terms of accuracy metrics and computation times. We apply this approach to a cancer genomics data example.

1897.

[London], 2019.

Gregor Thut
Sep 13, 2006; 26:9494-9502
BehavioralSystemsCognitive

Jamie D. Roitman
Nov 1, 2002; 22:9475-9489
Behavioral

French translation of the BIS Quarterly Review, March 2018

French translation of the BIS press release about the BIS Quarterly Review, March 2018

French translation of the BIS Quarterly Review, June 2018

French translation of the Annual Economic Report 2018 of the BIS, June 2018 - Les responsables des politiques publiques peuvent prolonger la phase de croissance actuelle en engageant des réformes structurelles, en restaurant les marges de manœuvre monétaires et budgétaires pour faire face aux menaces futures et en encourageant une mise en œuvre rapide des réformes réglementaires, indique la Banque des Règlements Internationaux (BRI) dans son Rapport économique annuel.

French translation of the BIS press release about the BIS Quarterly Review, September 2018

French translation of the BIS Quarterly Review, September 2018

French translation of the BIS press release about the BIS Quarterly Review, December 2018

French translation of the BIS Quarterly Review, December 2018

French translation of the BIS press release about the BIS Quarterly Review, March 2019

French translation of the BIS Quarterly Review, March 2019

French translation of the BIS press release on the presentation of the Annual Economic Report 2019, 30 June 2019. La politique monétaire ne peut plus être le principal moteur de la croissance économique, et d'autres leviers de politique publique doivent être actionnés pour faire en sorte que l'économie mondiale connaisse une dynamique durable ...

Exiting low inflation traps by "consensus": nominal wages and price stability - Speech by Luiz A Pereira da Silva and Benoît Mojon, based on the keynote speech at the Eighth High-level Policy Dialogue between the Eurosystem and Latin American Central Banks, Cartagena de Indias, Colombia, 28-29 November 2019.

During differentiation, oligodendrocyte precursor cells (OPCs) extend a network of processes that make contact with axons and initiate myelination. Recent studies revealed that actin polymerization is required for initiation of myelination whereas actin depolymerization promotes myelin wrapping. Here, we used primary OPCs in culture isolated from neonatal rat cortices of both sexes and young male and female mice with oligodendrocyte-specific deletion of mechanistic target of rapamycin (mTOR) to demonstrate that mTOR regulates expression of specific cytoskeletal targets and actin reorganization in oligodendrocytes during developmental myelination. Loss or inhibition of mTOR reduced expression of profilin2 and ARPC3, actin polymerizing factors, and elevated levels of active cofilin, which mediates actin depolymerization. The deficits in actin polymerization were revealed in reduced phalloidin and deficits in oligodendrocyte cellular branching complexity at the peak of morphologic differentiation and a delay in initiation of myelination. We further show a critical role for mTOR in expression and localization of myelin basic protein (Mbp) mRNA and MBP protein to the cellular processes where it is necessary at the myelin membrane for axon wrapping. Mbp mRNA transport deficits were confirmed by single molecule RNA FISH. Moreover, expression of the kinesin family member 1B, an Mbp mRNA transport protein, was reduced in CC1+ cells in the mTOR cKO and in mTOR inhibited oligodendrocytes undergoing differentiation in vitro. These data support the conclusion that mTOR regulates both initiation of myelination and axon wrapping by targeting cytoskeletal reorganization and MBP localization to oligodendrocyte processes.

SIGNIFICANCE STATEMENT Myelination is essential for normal CNS development and adult axon preservation and function. The mechanistic target of rapamycin (mTOR) signaling pathway has been implicated in promoting CNS myelination; however, there is a gap in our understanding of the mechanisms by which mTOR promotes developmental myelination through regulating specific downstream targets. Here, we present evidence that mTOR promotes the initiation of myelination through regulating specific cytoskeletal targets and cellular process expansion by oligodendrocyte precursor cells as well as expression and cellular localization of myelin basic protein.

Phototransduction in Drosophila is mediated by phospholipase C (PLC) and Ca²⁺-permeable TRP channels, but the function of endoplasmic reticulum (ER) Ca²⁺ stores in this important model for Ca²⁺ signaling remains obscure. We therefore expressed a low affinity Ca²⁺ indicator (ER-GCaMP6-150) in the ER, and measured its fluorescence both in dissociated ommatidia and in vivo from intact flies of both sexes. Blue excitation light induced a rapid (tau ~0.8 s), PLC-dependent decrease in fluorescence, representing depletion of ER Ca²⁺ stores, followed by a slower decay, typically reaching ~50% of initial dark-adapted levels, with significant depletion occurring under natural levels of illumination. The ER stores refilled in the dark within 100–200 s. Both rapid and slow store depletion were largely unaffected in InsP₃ receptor mutants, but were much reduced in trp mutants. Strikingly, rapid (but not slow) depletion of ER stores was blocked by removing external Na⁺ and in mutants of the Na⁺/Ca²⁺ exchanger, CalX, which we immuno-localized to ER membranes in addition to its established localization in the plasma membrane. Conversely, overexpression of calx greatly enhanced rapid depletion. These results indicate that rapid store depletion is mediated by Na⁺/Ca²⁺ exchange across the ER membrane induced by Na⁺ influx via the light-sensitive channels. Although too slow to be involved in channel activation, this Na⁺/Ca²⁺ exchange-dependent release explains the decades-old observation of a light-induced rise in cytosolic Ca²⁺ in photoreceptors exposed to Ca²⁺-free solutions.

SIGNIFICANCE STATEMENT Phototransduction in Drosophila is mediated by phospholipase C, which activates TRP cation channels by an unknown mechanism. Despite much speculation, it is unknown whether endoplasmic reticulum (ER) Ca²⁺ stores play any role. We therefore engineered flies expressing a genetically encoded Ca²⁺ indicator in the photoreceptor ER. Although NCX Na⁺/Ca²⁺ exchangers are classically believed to operate only at the plasma membrane, we demonstrate a rapid light-induced depletion of ER Ca²⁺ stores mediated by Na⁺/Ca²⁺ exchange across the ER membrane. This NCX-dependent release was too slow to be involved in channel activation, but explains the decades-old observation of a light-induced rise in cytosolic Ca²⁺ in photoreceptors bathed in Ca²⁺-free solutions.

Nitric oxide (NO) is an important signaling molecule that fulfills diverse functional roles as a neurotransmitter or diffusible second messenger in the developing and adult CNS. Although the impact of NO on different behaviors such as movement, sleep, learning, and memory has been well documented, the identity of its molecular and cellular targets is still an area of ongoing investigation. Here, we identify a novel role for NO in strengthening inhibitory GABA_A receptor-mediated transmission in molecular layer interneurons of the mouse cerebellum. NO levels are elevated by the activity of neuronal NO synthase (nNOS) following Ca²⁺ entry through extrasynaptic NMDA-type ionotropic glutamate receptors (NMDARs). NO activates protein kinase G with the subsequent production of cGMP, which prompts the stimulation of NADPH oxidase and protein kinase C (PKC). The activation of PKC promotes the selective strengthening of α3-containing GABA_ARs synapses through a GABA receptor-associated protein-dependent mechanism. Given the widespread but cell type-specific expression of the NMDAR/nNOS complex in the mammalian brain, our data suggest that NMDARs may uniquely strengthen inhibitory GABAergic transmission in these cells through a novel NO-mediated pathway.

SIGNIFICANCE STATEMENT Long-term changes in the efficacy of GABAergic transmission is mediated by multiple presynaptic and postsynaptic mechanisms. A prominent pathway involves crosstalk between excitatory and inhibitory synapses whereby Ca²⁺-entering through postsynaptic NMDARs promotes the recruitment and strengthening of GABA_A receptor synapses via Ca²⁺/calmodulin-dependent protein kinase II. Although Ca²⁺ transport by NMDARs is also tightly coupled to nNOS activity and NO production, it has yet to be determined whether this pathway affects inhibitory synapses. Here, we show that activation of NMDARs trigger a NO-dependent pathway that strengthens inhibitory GABAergic synapses of cerebellar molecular layer interneurons. Given the widespread expression of NMDARs and nNOS in the mammalian brain, we speculate that NO control of GABAergic synapse efficacy may be more widespread than has been appreciated.

Did you know that cash transfer (CT) programmes in countries of the sub-Saharan Africa actually have a significant impact? In Malawi, these programmes helped families invest in agricultural equipment and livestock to produce their own food and reduce levels of negative coping strategies, like begging and school drop-outs. In Kenya, secondary school attendance rose by 9 percent and access to [...]

In 2015 we celebrated the “International Year of Soils” and with good reason. Soil sustains all our agricultural and livestock food production, wood for fuel production, filters water so that we can drink it and fish can live in it. We also use it for construction - therefore it sustains our homes and infrastructure. As we approach the end of #IYS [...]

In 2016 we celebrated the International Year of Pulses and it is obvious why. Pulses are good for you, beneficial to farmers' livelihoods and have a positive impact on the environment. It is clear that even though dried beans, lentils and peas have been around for centuries, they will play a fundamental role in our sustainable future. Even though #IYP2016 has [...]

The positive outcomes won’t be immediately apparent, but will help reduce the strain on our healthcare system

Through their images, these artists combat the stereotypes perpetuated by American history and culture

The new book 'The Rainbow Atlas' invites readers on a vivid journey across the globe

The premier’s office confirmed the news in an email statement, blaming visibility issues under "very specific lighting conditions."

A physiotherapist in Thunder Bay, Ont., has had his licence suspended until September, as part of a disciplinary hearing.

Visitors to the 1900 Paris Exposition would have had the opportunity to view an extraordinary display of photographs, charts, publications and other items meant to demonstrate the progress and resilience of African Americans in the United States, only a few decades after the abolition of slavery. The materials were assembled by African American intellectuals Thomas J. […]

This rapid and efficient method to prepare highly purified recombinant adeno-associated viruses (rAAVs) is based on binding of negatively charged rAAV capsids to an anion-exchange resin that is pH dependent.

The liquidity trap is synonymous with ineffective monetary policy. The common wisdom is that, as the short-term interest rate nears its effective lower bound, monetary policy cannot do much to stimulate the economy. However, central banks have resorted to alternative instruments, such as QE, credit easing and forward guidance.

This paper studies the relationship between bank geographic complexity and risk. We use a unique dataset of 96 bank holding companies around the world to measure the geographic dispersion of their affiliates. We study how this dispersion interacts with economic and regulatory conditions to affect the riskiness of the bank.

Classified released a music video for his song, Good News, this week featuring a pack of kids hanging out with seniors at a continuing care home in Enfield, N.S. It was filmed before the COVID-19 outbreak in the province.

For all the sporting moments Mark Blinch has captured through his lens for nearly two decades, there is only one he has taken the trouble to hang in his home.

Toronto Raptors guard Fred VanVleet was enjoying the best season of his career, and was set to strike it rich with a lucrative new deal this summer before the coronavirus brought the NBA and global sports to its knees in mid-March.