Gemma E. Moran, Veronika Ročková, Edward I. George.

Source: Bayesian Analysis, Volume 14, Number 4, 1091--1119.

Abstract:
Consider the problem of high dimensional variable selection for the Gaussian linear model when the unknown error variance is also of interest. In this paper, we show that the use of conjugate shrinkage priors for Bayesian variable selection can have detrimental consequences for such variance estimation. Such priors are often motivated by the invariance argument of Jeffreys (1961). Revisiting this work, however, we highlight a caveat that Jeffreys himself noticed; namely that biased estimators can result from inducing dependence between parameters a priori . In a similar way, we show that conjugate priors for linear regression, which induce prior dependence, can lead to such underestimation in the Bayesian high-dimensional regression setting. Following Jeffreys, we recommend as a remedy to treat regression coefficients and the error variance as independent a priori . Using such an independence prior framework, we extend the Spike-and-Slab Lasso of Ročková and George (2018) to the unknown variance case. This extended procedure outperforms both the fixed variance approach and alternative penalized likelihood methods on simulated data. On the protein activity dataset of Clyde and Parmigiani (1998), the Spike-and-Slab Lasso with unknown variance achieves lower cross-validation error than alternative penalized likelihood methods, demonstrating the gains in predictive accuracy afforded by simultaneous error variance estimation. The unknown variance implementation of the Spike-and-Slab Lasso is provided in the publicly available R package SSLASSO (Ročková and Moran, 2017).

Claudia Kirch, Matthew C. Edwards, Alexander Meier, Renate Meyer.

Source: Bayesian Analysis, Volume 14, Number 4, 1037--1073.

Abstract:
Nonparametric Bayesian inference has seen a rapid growth over the last decade but only few nonparametric Bayesian approaches to time series analysis have been developed. Most existing approaches use Whittle’s likelihood for Bayesian modelling of the spectral density as the main nonparametric characteristic of stationary time series. It is known that the loss of efficiency using Whittle’s likelihood can be substantial. On the other hand, parametric methods are more powerful than nonparametric methods if the observed time series is close to the considered model class but fail if the model is misspecified. Therefore, we suggest a nonparametric correction of a parametric likelihood that takes advantage of the efficiency of parametric models while mitigating sensitivities through a nonparametric amendment. We use a nonparametric Bernstein polynomial prior on the spectral density with weights induced by a Dirichlet process and prove posterior consistency for Gaussian stationary time series. Bayesian posterior computations are implemented via an MH-within-Gibbs sampler and the performance of the nonparametrically corrected likelihood for Gaussian time series is illustrated in a simulation study and in three astronomy applications, including estimating the spectral density of gravitational wave data from the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO).

Miguel de Carvalho, Garritt L. Page, Bradley J. Barney.

Source: Bayesian Analysis, Volume 14, Number 4, 1013--1036.

Abstract:
We provide a geometric interpretation to Bayesian inference that allows us to introduce a natural measure of the level of agreement between priors, likelihoods, and posteriors. The starting point for the construction of our geometry is the observation that the marginal likelihood can be regarded as an inner product between the prior and the likelihood. A key concept in our geometry is that of compatibility, a measure which is based on the same construction principles as Pearson correlation, but which can be used to assess how much the prior agrees with the likelihood, to gauge the sensitivity of the posterior to the prior, and to quantify the coherency of the opinions of two experts. Estimators for all the quantities involved in our geometric setup are discussed, which can be directly computed from the posterior simulation output. Some examples are used to illustrate our methods, including data related to on-the-job drug usage, midge wing length, and prostate cancer.

Jon Cockayne, Chris J. Oates, Ilse C.F. Ipsen, Mark Girolami.

Source: Bayesian Analysis, Volume 14, Number 3, 937--1012.

Abstract:
A fundamental task in numerical computation is the solution of large linear systems. The conjugate gradient method is an iterative method which offers rapid convergence to the solution, particularly when an effective preconditioner is employed. However, for more challenging systems a substantial error can be present even after many iterations have been performed. The estimates obtained in this case are of little value unless further information can be provided about, for example, the magnitude of the error. In this paper we propose a novel statistical model for this error, set in a Bayesian framework. Our approach is a strict generalisation of the conjugate gradient method, which is recovered as the posterior mean for a particular choice of prior. The estimates obtained are analysed with Krylov subspace methods and a contraction result for the posterior is presented. The method is then analysed in a simulation study as well as being applied to a challenging problem in medical imaging.

Lizhen Lin, Niu Mu, Pokman Cheung, David Dunson.

Source: Bayesian Analysis, Volume 14, Number 3, 907--926.

Abstract:
Gaussian processes (GPs) are very widely used for modeling of unknown functions or surfaces in applications ranging from regression to classification to spatial processes. Although there is an increasingly vast literature on applications, methods, theory and algorithms related to GPs, the overwhelming majority of this literature focuses on the case in which the input domain corresponds to a Euclidean space. However, particularly in recent years with the increasing collection of complex data, it is commonly the case that the input domain does not have such a simple form. For example, it is common for the inputs to be restricted to a non-Euclidean manifold, a case which forms the motivation for this article. In particular, we propose a general extrinsic framework for GP modeling on manifolds, which relies on embedding of the manifold into a Euclidean space and then constructing extrinsic kernels for GPs on their images. These extrinsic Gaussian processes (eGPs) are used as prior distributions for unknown functions in Bayesian inferences. Our approach is simple and general, and we show that the eGPs inherit fine theoretical properties from GP models in Euclidean spaces. We consider applications of our models to regression and classification problems with predictors lying in a large class of manifolds, including spheres, planar shape spaces, a space of positive definite matrices, and Grassmannians. Our models can be readily used by practitioners in biological sciences for various regression and classification problems, such as disease diagnosis or detection. Our work is also likely to have impact in spatial statistics when spatial locations are on the sphere or other geometric spaces.

Mengyang Gu.

Source: Bayesian Analysis, Volume 14, Number 3, 877--905.

Abstract:
Gaussian stochastic process (GaSP) has been widely used in two fundamental problems in uncertainty quantification, namely the emulation and calibration of mathematical models. Some objective priors, such as the reference prior, are studied in the context of emulating (approximating) computationally expensive mathematical models. In this work, we introduce a new class of priors, called the jointly robust prior, for both the emulation and calibration. This prior is designed to maintain various advantages from the reference prior. In emulation, the jointly robust prior has an appropriate tail decay rate as the reference prior, and is computationally simpler than the reference prior in parameter estimation. Moreover, the marginal posterior mode estimation with the jointly robust prior can separate the influential and inert inputs in mathematical models, while the reference prior does not have this property. We establish the posterior propriety for a large class of priors in calibration, including the reference prior and jointly robust prior in general scenarios, but the jointly robust prior is preferred because the calibrated mathematical model typically predicts the reality well. The jointly robust prior is used as the default prior in two new R packages, called “RobustGaSP” and “RobustCalibration”, available on CRAN for emulation and calibration, respectively.

Brian Neelon.

Source: Bayesian Analysis, Volume 14, Number 3, 849--875.

Abstract:
Motivated by a study examining spatiotemporal patterns in inpatient hospitalizations, we propose an efficient Bayesian approach for fitting zero-inflated negative binomial models. To facilitate posterior sampling, we introduce a set of latent variables that are represented as scale mixtures of normals, where the precision terms follow independent Pólya-Gamma distributions. Conditional on the latent variables, inference proceeds via straightforward Gibbs sampling. For fixed-effects models, our approach is comparable to existing methods. However, our model can accommodate more complex data structures, including multivariate and spatiotemporal data, settings in which current approaches often fail due to computational challenges. Using simulation studies, we highlight key features of the method and compare its performance to other estimation procedures. We apply the approach to a spatiotemporal analysis examining the number of annual inpatient admissions among United States veterans with type 2 diabetes.

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.

Luis Gutiérrez, Andrés F. Barrientos, Jorge González, Daniel Taylor-Rodríguez.

Source: Bayesian Analysis, Volume 14, Number 2, 649--675.

Abstract:
We propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Most of the existing tests for this type of comparison are based on the differences between location parameters. In contrast, our approach identifies differences across the entire distribution, avoids strong modeling assumptions over the distributions for each treatment, and accounts for multiple testing through the prior distribution on the space of hypotheses. The proposal is compared to other commonly used hypothesis testing procedures under simulated scenarios. Two real applications are also analyzed with the proposed methodology.

Marko Järvenpää, Michael U. Gutmann, Arijus Pleska, Aki Vehtari, Pekka Marttinen.

Source: Bayesian Analysis, Volume 14, Number 2, 595--622.

Abstract:
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the computational cost, Bayesian optimisation (BO) and surrogate models such as Gaussian processes have been proposed. Bayesian optimisation enables one to intelligently decide where to evaluate the model next but common BO strategies are not designed for the goal of estimating the posterior distribution. Our paper addresses this gap in the literature. We propose to compute the uncertainty in the ABC posterior density, which is due to a lack of simulations to estimate this quantity accurately, and define a loss function that measures this uncertainty. We then propose to select the next evaluation location to minimise the expected loss. Experiments show that the proposed method often produces the most accurate approximations as compared to common BO strategies.

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.

Alberto Cassese, Weixuan Zhu, Michele Guindani, Marina Vannucci.

Source: Bayesian Analysis, Volume 14, Number 2, 553--572.

Abstract:
In many applications, investigators monitor processes that vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or “normal” behavior. In this manuscript, we consider the monitoring of pneumonia and influenza (P&I) mortality, to detect influenza outbreaks in the continental United States, and propose a Bayesian nonparametric model selection approach to take into account the spatio-temporal dependence of outbreaks. More specifically, we introduce a zero-inflated conditionally identically distributed species sampling prior which allows borrowing information across time and to assign data to clusters associated to either a null or an alternate process. Spatial dependences are accounted for by means of a Markov random field prior, which allows to inform the selection based on inferences conducted at nearby locations. We show how the proposed modeling framework performs in an application to the P&I mortality data and in a simulation study, and compare with common threshold methods for detecting outbreaks over time, with more recent Markov switching based models, and with spike-and-slab Bayesian nonparametric priors that do not take into account spatio-temporal dependence.

Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy.

Source: Bayesian Analysis, Volume 14, Number 2, 495--519.

Abstract:
Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems. Data in these tests may be difficult to collect and outcomes may have high variance, resulting in potentially large measurement error. Bayesian optimization is a promising technique for efficiently optimizing multiple continuous parameters, but existing approaches degrade in performance when the noise level is high, limiting its applicability to many randomized experiments. We derive an expression for expected improvement under greedy batch optimization with noisy observations and noisy constraints, and develop a quasi-Monte Carlo approximation that allows it to be efficiently optimized. Simulations with synthetic functions show that optimization performance on noisy, constrained problems outperforms existing methods. We further demonstrate the effectiveness of the method with two real-world experiments conducted at Facebook: optimizing a ranking system, and optimizing server compiler flags.

Łukasz Rajkowski.

Source: Bayesian Analysis, Volume 14, Number 2, 477--494.

Abstract:
Mixture models are a natural choice in many applications, but it can be difficult to place an a priori upper bound on the number of components. To circumvent this, investigators are turning increasingly to Dirichlet process mixture models (DPMMs). It is therefore important to develop an understanding of the strengths and weaknesses of this approach. This work considers the MAP (maximum a posteriori) clustering for the Gaussian DPMM (where the cluster means have Gaussian distribution and, for each cluster, the observations within the cluster have Gaussian distribution). Some desirable properties of the MAP partition are proved: ‘almost disjointness’ of the convex hulls of clusters (they may have at most one point in common) and (with natural assumptions) the comparability of sizes of those clusters that intersect any fixed ball with the number of observations (as the latter goes to infinity). Consequently, the number of such clusters remains bounded. Furthermore, if the data arises from independent identically distributed sampling from a given distribution with bounded support then the asymptotic MAP partition of the observation space maximises a function which has a straightforward expression, which depends only on the within-group covariance parameter. As the operator norm of this covariance parameter decreases, the number of clusters in the MAP partition becomes arbitrarily large, which may lead to the overestimation of the number of mixture components.

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.

Luis Gutiérrez, Eduardo Gutiérrez-Peña, Ramsés H. Mena.

Source: Bayesian Analysis, Volume 14, Number 2, 427--447.

Abstract:
This work presents a Bayesian predictive approach to statistical shape analysis. A modeling strategy that starts with a Gaussian distribution on the configuration space, and then removes the effects of location, rotation and scale, is studied. This boils down to an application of the projected normal distribution to model the configurations in the shape space, which together with certain identifiability constraints, facilitates parameter interpretation. Having better control over the parameters allows us to generalize the model to a regression setting where the effect of predictors on shapes can be considered. The methodology is illustrated and tested using both simulated scenarios and a real data set concerning eight anatomical landmarks on a sagittal plane of the corpus callosum in patients with autism and in a group of controls.

Haolun Shi, Guosheng Yin.

Source: Bayesian Analysis, Volume 14, Number 2, 399--425.

Abstract:
Bayesian approaches to phase II clinical trial designs are usually based on the posterior distribution of the parameter of interest and calibration of certain threshold for decision making. If the posterior probability is computed and assessed in a sequential manner, the design may involve the problem of multiplicity, which, however, is often a neglected aspect in Bayesian trial designs. To effectively maintain the overall type I error rate, we propose solutions to the problem of multiplicity for Bayesian sequential designs and, in particular, the determination of the cutoff boundaries for the posterior probabilities. We present both theoretical and numerical methods for finding the optimal posterior probability boundaries with $alpha$ -spending functions that mimic those of the frequentist group sequential designs. The theoretical approach is based on the asymptotic properties of the posterior probability, which establishes a connection between the Bayesian trial design and the frequentist group sequential method. The numerical approach uses a sandwich-type searching algorithm, which immensely reduces the computational burden. We apply least-square fitting to find the $alpha$ -spending function closest to the target. We discuss the application of our method to single-arm and double-arm cases with binary and normal endpoints, respectively, and provide a real trial example for each case.

Daniela Pauger, Helga Wagner.

Source: Bayesian Analysis, Volume 14, Number 2, 341--369.

Abstract:
We propose a Bayesian approach to obtain a sparse representation of the effect of a categorical predictor in regression type models. As this effect is captured by a group of level effects, sparsity cannot only be achieved by excluding single irrelevant level effects or the whole group of effects associated to this predictor but also by fusing levels which have essentially the same effect on the response. To achieve this goal, we propose a prior which allows for almost perfect as well as almost zero dependence between level effects a priori. This prior can alternatively be obtained by specifying spike and slab prior distributions on all effect differences associated to this categorical predictor. We show how restricted fusion can be implemented and develop an efficient MCMC (Markov chain Monte Carlo) method for posterior computation. The performance of the proposed method is investigated on simulated data and we illustrate its application on real data from EU-SILC (European Union Statistics on Income and Living Conditions).

T. Tony Cai.

Source: Statistical Science, Volume 34, Number 4, 635--656.

Abstract:
A wide range of nonparametric function estimation models have been studied individually in the literature. Among them the homoscedastic nonparametric Gaussian regression is arguably the best known and understood. Inspired by the asymptotic equivalence theory, Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046) and Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433) developed a unified approach to turn a collection of non-Gaussian function estimation models into a standard Gaussian regression and any good Gaussian nonparametric regression method can then be used. These Gaussianization Machines have two key components, binning and transformation. When combined with BlockJS, a wavelet thresholding procedure for Gaussian regression, the procedures are computationally efficient with strong theoretical guarantees. Technical analysis given in Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046) and Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433) shows that the estimators attain the optimal rate of convergence adaptively over a large set of Besov spaces and across a collection of non-Gaussian function estimation models, including robust nonparametric regression, density estimation, and nonparametric regression in exponential families. The estimators are also spatially adaptive. The Gaussianization Machines significantly extend the flexibility and scope of the theories and methodologies originally developed for the conventional nonparametric Gaussian regression. This article aims to provide a concise account of the Gaussianization Machines developed in Brown, Cai and Zhou ( Ann. Statist. 36 (2008) 2055–2084; Ann. Statist. 38 (2010) 2005–2046), Brown et al. ( Probab. Theory Related Fields 146 (2010) 401–433).

Georg Lindgren.

Source: Statistical Science, Volume 34, Number 1, 100--128.

Abstract:
We describe and compare how methods based on the classical Rice’s formula for the expected number, and higher moments, of level crossings by a Gaussian process stand up to contemporary numerical methods to accurately deal with crossing related characteristics of the sample paths. We illustrate the relative merits in accuracy and computing time of the Rice moment methods and the exact numerical method, developed since the late 1990s, on three groups of distribution problems, the maximum over a finite interval and the waiting time to first crossing, the length of excursions over a level, and the joint period/amplitude of oscillations. We also treat the notoriously difficult problem of dependence between successive zero crossing distances. The exact solution has been known since at least 2000, but it has remained largely unnoticed outside the ocean science community. Extensive simulation studies illustrate the accuracy of the numerical methods. As a historical introduction an attempt is made to illustrate the relation between Rice’s original formulation and arguments and the exact numerical methods.

02020-02-03: Pink Lakes in Tunisia

02020-02-06: Flooding in Louisiana and Mississippi

The transcription factor Nurr1 has been identified to be ectopically induced in the striatum of rodents expressing l-DOPA-induced dyskinesia (LID). In the present study, we sought to characterize Nurr1 as a causative factor in LID expression. We used rAAV2/5 to overexpress Nurr1 or GFP in the parkinsonian striatum of LID-resistant Lewis or LID-prone Fischer-344 (F344) male rats. In a second cohort, rats received the Nurr1 agonist amodiaquine (AQ) together with l-DOPA or ropinirole. All rats received a chronic DA agonist and were evaluated for LID severity. Finally, we performed single-unit recordings and dendritic spine analyses on striatal medium spiny neurons (MSNs) in drug-naïve rAAV-injected male parkinsonian rats. rAAV-GFP injected LID-resistant hemi-parkinsonian Lewis rats displayed mild LID and no induction of striatal Nurr1 despite receiving a high dose of l-DOPA. However, Lewis rats overexpressing Nurr1 developed severe LID. Nurr1 agonism with AQ exacerbated LID in F344 rats. We additionally determined that in l-DOPA-naïve rats striatal rAAV-Nurr1 overexpression (1) increased cortically-evoked firing in a subpopulation of identified striatonigral MSNs, and (2) altered spine density and thin-spine morphology on striatal MSNs; both phenomena mimicking changes seen in dyskinetic rats. Finally, we provide postmortem evidence of Nurr1 expression in striatal neurons of l-DOPA-treated PD patients. Our data demonstrate that ectopic induction of striatal Nurr1 is capable of inducing LID behavior and associated neuropathology, even in resistant subjects. These data support a direct role of Nurr1 in aberrant neuronal plasticity and LID induction, providing a potential novel target for therapeutic development.

SIGNIFICANCE STATEMENT The transcription factor Nurr1 is ectopically induced in striatal neurons of rats exhibiting levodopa-induced dyskinesia [LID; a side-effect to dopamine replacement strategies in Parkinson's disease (PD)]. Here we asked whether Nurr1 is causing LID. Indeed, rAAV-mediated expression of Nurr1 in striatal neurons was sufficient to overcome LID-resistance, and Nurr1 agonism exacerbated LID severity in dyskinetic rats. Moreover, we found that expression of Nurr1 in l-DOPA naïve hemi-parkinsonian rats resulted in the formation of morphologic and electrophysiological signatures of maladaptive neuronal plasticity; a phenomenon associated with LID. Finally, we determined that ectopic Nurr1 expression can be found in the putamen of l-DOPA-treated PD patients. These data suggest that striatal Nurr1 is an important mediator of the formation of LID.

FAO and its partners have launched an initiative aimed at cutting food waste across the Asia-Pacific region. Save Food Asia-Pacific Campaign targets losses both straight after harvest and between the market and people’s plates. FAO estimates that reducing global food waste by just one quarter would be sufficient to feed the 870 million people suffering from chronic hunger in the world. [...]

The mission for an end to hunger in the world’s most populous region has received a boost, with member countries responding positively to a call by FAO for a “massive effort” to end hunger in Asia and the Pacific. 1. Asia-Pacific is home to nearly two-thirds of the world’s chronically hungry people. |True|     Asia-Pacific, with over 4.2 billion people, is home [...]

The 15th-century edition of Hafez's "Divan" will be sold at Sotheby's next month

The findings further refute the outdated notion that humans' capacity for complex artistic expression evolved exclusively in Europe

The Internet Archive describes the downloadable collection of more than one million books as a library, but critics call it piracy

This eruption is the longest since 2018 when the volcano caused a deadly tsunami

The 72-year-old animal was the third oldest in the North American population

Este año en el mes en que se llevó a cabo el mantenimiento anual de Logos Hope en Uruguay, la tripulante Cecilia* de Argentina se unió a un pequeño equipo que sirve en Asia Central. Mientras estuvo allí, pudo asistir a dos iglesias subterráneas que desbordaban de esperanza y fe.

On Patriots' Day, we celebrate musician, publisher, and patriot Josiah Flagg (1737-1794), a friend of Paul Revere and major figure in Early American music.

Visions 2020: Nydia Han and 6abc celebrates Asian Pacific American Heritage Month - 6abc  WPVI-TV

Hate crimes against Vancouver's Asian communities have increased since the early days of the outbreak and the #Elimin8hate campaign is an effort to combat that and comfort victims.

CNA Staff, Apr 23, 2020 / 05:13 pm (CNA).- A Catholic youth organization in Indonesia has instituted a movement to provide assistance to families struggling during the coronavirus pandemic.

Orang Muda Katolik, or Catholic Young People, recently began the initiative “Adopt One Brother,” which encourages youth to volunteer time and money to support poorer families, many of whom are now unemployed.

Indonesia has over 7,500 cases of COVID-19, and 647 deaths. According to data from the country’s Ministry of Labour, Aljazeera reported, 2.8 million Indonesians have lost their jobs because of the pandemic.

Stefanus Gusma, who leads OMK’s COVID-19 task force, said the initiative has spread to 26 of the country’s 34 provinces and involved thousands of OMK members. He said volunteers are encouraged to donate 200,000 to 500,000 rupiah ($12-32) per week.

"First, we mobilized our own members to help our fellow brothers and sisters who are experiencing difficulties. Then we extended our reach to anyone who was willing to help others,” Gusma told UCA News.

"After we receive their data, we contact them about where they would like their donations to go,” he said. “If a donor wants to donate to a family in East Nusa Tenggara province, we will coordinate with our members there to seek a family in need.”

With help from the local dioceses and governments, the organization has also distributed about 2,000 aid packages, electricity vouchers, and hygienic products.

According to UCA News, other OMK members said the organization has not only provided aid to families but to hospitals and orphanages as well. Maskendari, an OMK member in Pontianak, said the organization has distributed “hundreds of aid packages and thousands of personal protection items such as masks and bottles of hand sanitizer.”

“We want others to act, not only through our organization but also individually or with other groups,” Gusma told UCA News. "We want to show the importance of showing human solidarity in the midst of this current crisis," he added.

Orang Muda Katolik seeks to mentor young Catholics, aged between 15 and 35, by providing educational resources, coaching, and volunteer opportunities.

Bishop Pius Prapdi of Ketapang issued a letter to OMK at the end of March. He encouraged young Catholics to follow social distancing rules and other safety precautions. However, he also challenged the youth to find creative ways to help the community, like investigating free food assistance for those in need and checking-in on neighbors through social media.

“Catholic Young People can also help others in a safe way,” he wrote. “With creativity, young people can become leaders in this situation and go through critical times together.”

“Pope Francis invites young people to become the main actors (protagonists) in renewing the world, let us in this crisis period stop for a moment to reflect back on what we have made for ourselves, the environment, the Church and the citizens of the world.”

Are we really going to bet that we can go back to life as normal without proper coronavirus tracking in place?

Javier left Mexico eight months ago to be a missionary in South Asia. Here he shares why he thought it would be an easier job.

CNA Staff, May 4, 2020 / 12:01 pm (CNA).- The Congregation for the Doctrine of the Faith has ordered 15 psychiatric hospitals in Belgium which belong to the Brothers of Charity to cease identifying as Catholic institutions after they allowed the euthanization of patients in 2017.

The hospitals are managed by a civil non-profit corporation with the same name as the Brothers of Charity religious congregation which owns them.

The CDF decision was communicated in a letter dated March 30, stating that "with deep sadness" the "psychiatric hospitals managed by the Provincialate of the Brothers of Charity association in Belgium will no longer be able to consider themselves Catholic institutions."

In a statement responding to the CDF's decision, the superior general of the Brothers of Charity, Br. René Stockman, said that "with a heavy heart" the religious congregation "must let go of its psychiatric centers in Belgium."

Br. Stockman pointed out that it is "painful" that the psychiatric centers of the Brothers of Charity in Belgium have lost their Catholic status, considering also that the brothers "were among the pioneers in the field of mental health care in Belgium."

At the same time, Stockman said he recognizes that "the congregation [the Brothers of Charity] has no choice but to remain faithful to the charism of charity, which cannot be reconciled with the practice of euthanasia on psychiatric patients."

The decision by the Vatican's doctrinal office ends three years of disputes between the Brothers of Charity and the corporation which manages their hospitals in Belgium.

In 2017, the board decided to allow euthanasia to be carried out in its hospitals in Belgium, where the euthanasia law is among the most broad.

At the time of the decision, the board of the corporation was composed of 15 members, with only three of them religious brothers of the congregation. 

Two of the three religious brothers among the board members, Luc Lemmens, 61, and Veron Raes, 57, supported the euthanasia decision. Their terms on the board ended at the end of September 2018 and were not renewed.

The religious congregation, especially Stockman, protested the decision, reiterating the Brothers of Charity's rejection of euthanasia in their hospitals.

The brothers appealed to the Vatican, which asked the psychiatric hospitals to change their protocol allowing euthanasia as “a medical act” under certain conditions.

The hospital management responded with a long statement in September 2017, in which it contested a lack of dialogue and maintained the hospital was "perfectly consistent" with Christian doctrine.

The CDF's direction that the hospitals must no longer identify as Catholic was communicated in a letter signed by CDF prefect Cardinal Luis Francisco Ladaria Ferrer and secretary Archbishop Giacomo Morandi.

The letter retraced the developments of the story, recalling that the document allowing euthanasia in the brothers' hospitals "refers neither to God, nor to Holy Scripture, nor to the Christian vision of Man."

According to the letter, the CDF had spoken with the Brothers of Charity and had also informed Pope Francis of the gravity of the situation.

Other audiences had also taken place beginning June 2017, including with the Congregation for Institutes of Consecrated Life and Societies of Apostolic Life, the Secretariat of State, the representatives of the Brothers of Charity and the managing corporation, as well as representatives of the Belgian bishops' conference.

The Holy See also sent Bishop Jan Hendriks, auxiliary of Amsterdam, as an apostolic visitor, but he did not register any steps forward nor a desire to find "a viable solution that avoids any form of responsibility of the institution for euthanasia."

The request of the CDF to the Brothers of Charity and to the managing corporation was clear: “affirm in writing and in an unequivocal way their adherence to the principles of the sacredness of human life and the unacceptability of euthanasia, and, as a consequence, the absolute refusal to carry it out in the institutions they depend on."

The corporation "did not give assurance on these points."

The CDF therefore reiterated that "euthanasia remains an inadmissible act, even in extreme cases," and strengthened the statement by citing St. John Paul II's 1995 encyclical Evangelium vitae, and a Jan. 30 speech by Pope Francis to the CDF.

The CDF stressed that "Catholic teaching affirms the sacred value of human life," the "importance of caring for and accompanying the sick and disabled," as well as "the Christian value of suffering, the moral unacceptability of euthanasia" and "the impossibility of introducing this practice in Catholic hospitals, not even in extreme cases, as well as of collaborating in this regard with civil institutions."

The Brothers of Charity is a religious congregation of lay brothers founded in 1807 in Belgium, whose specialization is care for the sick and those with psychiatric diseases.

At the congregation's July 2018 general chapter the group stressed that the Brothers of Charity "believes in sacredness and absolute respect for every human life, from conception to natural death. The general chapter requires that each brother, associate member and others associated with the mission of the congregation adhere to the doctrine of the Catholic Church on ethical issues."

Eoin Hayes's first-half strike earned the Group B points for Irish side Region 2 as Russia's South Region failed to convert their possession into a single shot on goal.

AN item for the “there’s always one” file. Only days into the great lockdown and some people are just not coping. Take the holidaymaker – British, of course – who decided she would flout the rules and have a dip at Paradise Park in Tenerife.

School board members in Tucson, Ariz., acted after learning that a controversial economics textbook that hadn't been properly vetted.

Macroglossia is a clinical feature of several disorders and a common reason for additional diagnostic investigations during infancy. Limited research has been done on the evaluation of macroglossia when other features are not suggestive of Beckwith-Wiedemann syndrome.

All patients with apparently isolated macroglossia should have at least initial evaluation with abdominal ultrasounds and molecular studies for Beckwith-Wiedemann syndrome before a final diagnosis is given. Other common diagnoses included isolated macroglossia, chromosomal abnormalities, hypothyroidism, and mucopolysaccharidoses. (Read the full article)

Breastfeeding and oral sucrose have shown an analgesic effect in premature newborn infants for minor painful procedures. Studies suggest that the analgesic properties of breast milk are superior to oral sucrose in term neonates.

For premature infants from 32 to 37 weeks, there is no significant difference in analgesic effect between breast milk and oral sucrose. Breast milk is a safe and natural method for pain relief in late preterm infants. (Read the full article)

Asian Americans have a reduced risk of cerebral palsy (CP) compared with whites. Whether this is true for all Asian ethnic subgroups is unknown. Differences in sociodemographic factors may explain disparities in CP prevalence between Asians and whites.

East Asian, Filipino, Indian, Pacific Islander, and Southeast Asian children were 13% to 38% less likely to have CP than white children. Differences in maternal age and education, gender, and birth weight did not explain these differences in CP rates. (Read the full article)

Some but not all randomized trials have shown effects of probiotics on incidence and duration of diarrhea and respiratory tract infections among children in developing countries. Calcium improves resistance to intestinal infections in adults, but efficacy in children is unknown.

Lactobacillus reuteri DSM17938 may prevent diarrhea, especially in children with lower nutritional status. Regular calcium milk, alone or with Lactobacillus casei CRL431, did not reduce diarrhea. None of the interventions affected respiratory tract infections in these Indonesian children. (Read the full article)

Pain during routine infant immunization causes parental anxiety. Oral sucrose solutions are effective pain-reduction strategies. Few studies have measured a combined strategy of a physical intervention along with sucrose to decrease the infant’s pain response.

We demonstrate that a physical, nonpharmacological intervention called the 5 S’s (swaddling, side/stomach position, shushing, swinging, and sucking) provides significant pain reduction with or without sucrose during routine 2- and 4-month vaccinations. (Read the full article)

Preterm infants are at a high risk for retinopathy of prematurity (ROP). Several postnatal factors are well known to be associated with ROP. However, the relationships between antenatal or maternal risk factors and ROP are poorly understood.

This study used a large cohort database to study the influence of maternal gestational hypertension and preeclampsia on ROP in preterm infants. The results showed that preeclampsia, but not gestational hypertension, was associated with a reduced risk of ROP in preterm births. (Read the full article)

Cholestasis predisposes to the development of fat-soluble vitamin (FSV) deficiency. D-α tocopheryl polyethylene glycol-1000 succinate and coadministered FSVs are absorbed in spite of cholestasis.

Infants with biliary atresia with total bilirubin >2 mg/dL are at risk for fat-soluble vitamin (FSV) deficiency. A multivitamin preparation containing d-α tocopheryl polyethylene glycol-1000 succinate alone is not effective in treating biochemical FSV insufficiency in cholestatic infants. (Read the full article)