DeVos slammed for remarks on HBCU's, a new Senate measure could overturn Obama-era ESSA rules on accountability, and more.

Indiana's efforts to give students more control over their academic transcripts may prove a boon for researchers and school reformers, too.

Olivier Roustant, Fabrice Gamboa, Bertrand Iooss.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 386--412.

Abstract:
The so-called polynomial chaos expansion is widely used in computer experiments. For example, it is a powerful tool to estimate Sobol’ sensitivity indices. In this paper, we consider generalized chaos expansions built on general tensor Hilbert basis. In this frame, we revisit the computation of the Sobol’ indices with Parseval equalities and give general lower bounds for these indices obtained by truncation. The case of the eigenfunctions system associated with a Poincaré differential operator leads to lower bounds involving the derivatives of the analyzed function and provides an efficient tool for variable screening. These lower bounds are put in action both on toy and real life models demonstrating their accuracy.

Wanli Qiao.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 302--344.

Abstract:
Bandwidth selection is crucial in the kernel estimation of density level sets. A risk based on the symmetric difference between the estimated and true level sets is usually used to measure their proximity. In this paper we provide an asymptotic $L^{p}$ approximation to this risk, where $p$ is characterized by the weight function in the risk. In particular the excess risk corresponds to an $L^{2}$ type of risk, and is adopted to derive an optimal bandwidth for nonparametric level set estimation of $d$-dimensional density functions ($dgeq 1$). A direct plug-in bandwidth selector is developed for kernel density level set estimation and its efficacy is verified in numerical studies.

Xianzheng Huang, Haiming Zhou.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 970--1023.

Abstract:
We consider estimating the density of a response conditioning on an error-prone covariate. Motivated by two existing kernel density estimators in the absence of covariate measurement error, we propose a method to correct the existing estimators for measurement error. Asymptotic properties of the resultant estimators under different types of measurement error distributions are derived. Moreover, we adjust bandwidths readily available from existing bandwidth selection methods developed for error-free data to obtain bandwidths for the new estimators. Extensive simulation studies are carried out to compare the proposed estimators with naive estimators that ignore measurement error, which also provide empirical evidence for the effectiveness of the proposed bandwidth selection methods. A real-life data example is used to illustrate implementation of these methods under practical scenarios. An R package, lpme, is developed for implementing all considered methods, which we demonstrate via an R code example in Appendix B.2.

Danilo Covaes Nogarotto, Caio Lucidius Naberezny Azevedo, Jorge Luis Bazán.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 304--322.

Abstract:
The interest on the analysis of the zero–one augmented beta regression (ZOABR) model has been increasing over the last few years. In this work, we developed a Bayesian inference for the ZOABR model, providing some contributions, namely: we explored the use of Jeffreys-rule and independence Jeffreys prior for some of the parameters, performing a sensitivity study of prior choice, comparing the Bayesian estimates with the maximum likelihood ones and measuring the accuracy of the estimates under several scenarios of interest. The results indicate, in a general way, that: the Bayesian approach, under the Jeffreys-rule prior, was as accurate as the ML one. Also, different from other approaches, we use the predictive distribution of the response to implement Bayesian residuals. To further illustrate the advantages of our approach, we conduct an analysis of a real psychometric data set including a Bayesian residual analysis, where it is shown that misleading inference can be obtained when the data is transformed. That is, when the zeros and ones are transformed to suitable values and the usual beta regression model is considered, instead of the ZOABR model. Finally, future developments are discussed.

Saralees Nadarajah, Yuancheng Si.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 183--187.

Abstract:
Da Paz, Balakrishnan and Bazan [Braz. J. Probab. Stat. 33 (2019), 455–479] introduced the L-logistic distribution, studied its properties including estimation issues and illustrated a data application. This note derives a closed form expression for moment properties of the distribution. Some computational issues are discussed.

Lucas Pereira Lopes, Vicente Garibay Cancho, Francisco Louzada.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 801--825.

Abstract:
Multivariate options are adequate tools for multi-asset risk management. The pricing models derived from the pioneer Black and Scholes method under the multivariate case consider that the asset-object prices follow a Brownian geometric motion. However, the construction of such methods imposes some unrealistic constraints on the process of fair option calculation, such as constant volatility over the maturity time and linear correlation between the assets. Therefore, this paper aims to price and analyze the fair price behavior of the call-on-max (bivariate) option considering marginal heteroscedastic models with dependence structure modeled via copulas. Concerning inference, we adopt a Bayesian perspective and computationally intensive methods based on Monte Carlo simulations via Markov Chain (MCMC). A simulation study examines the bias, and the root mean squared errors of the posterior means for the parameters. Real stocks prices of Brazilian banks illustrate the approach. For the proposed method is verified the effects of strike and dependence structure on the fair price of the option. The results show that the prices obtained by our heteroscedastic model approach and copulas differ substantially from the prices obtained by the model derived from Black and Scholes. Empirical results are presented to argue the advantages of our strategy.

Christian Olivera, Ciprian Tudor.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 520--531.

Abstract:
Via a special transform and by using the techniques of the Malliavin calculus, we analyze the density of the solution to a stochastic differential equation with unbounded drift.

Rosineide F. da Paz, Narayanaswamy Balakrishnan, Jorge Luis Bazán.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 455--479.

Abstract:
Tadikamalla and Johnson [ Biometrika 69 (1982) 461–465] developed the $L_{B}$ distribution to variables with bounded support by considering a transformation of the standard Logistic distribution. In this manuscript, a convenient parametrization of this distribution is proposed in order to develop regression models. This distribution, referred to here as L-Logistic distribution, provides great flexibility and includes the uniform distribution as a particular case. Several properties of this distribution are studied, and a Bayesian approach is adopted for the parameter estimation. Simulation studies, considering prior sensitivity analysis, recovery of parameters and comparison of algorithms, and robustness to outliers are all discussed showing that the results are insensitive to the choice of priors, efficiency of the algorithm MCMC adopted, and robustness of the model when compared with the beta distribution. Applications to estimate the vulnerability to poverty and to explain the anxiety are performed. The results to applications show that the L-Logistic regression models provide a better fit than the corresponding beta regression models.

Phillip S. Kott.

Source: Statistics Surveys, Volume 12, 1--17.

Abstract:
Fitting complex survey data to regression equations is explored under a design-sensitive model-based framework. A robust version of the standard model assumes that the expected value of the difference between the dependent variable and its model-based prediction is zero no matter what the values of the explanatory variables. The extended model assumes only that the difference is uncorrelated with the covariates. Little is assumed about the error structure of this difference under either model other than independence across primary sampling units. The standard model often fails in practice, but the extended model very rarely does. Under this framework some of the methods developed in the conventional design-based, pseudo-maximum-likelihood framework, such as fitting weighted estimating equations and sandwich mean-squared-error estimation, are retained but their interpretations change. Few of the ideas here are new to the refereed literature. The goal instead is to collect those ideas and put them into a unified conceptual framework.

In the Gaussian sequence model $Y= heta_0 + varepsilon$ in $mathbb{R}^n$, we study the fundamental limit of approximating the signal $ heta_0$ by a class $Theta(d,d_0,k)$ of (generalized) splines with free knots. Here $d$ is the degree of the spline, $d_0$ is the order of differentiability at each inner knot, and $k$ is the maximal number of pieces. We show that, given any integer $dgeq 0$ and $d_0in{-1,0,ldots,d-1}$, the minimax rate of estimation over $Theta(d,d_0,k)$ exhibits the following phase transition: egin{equation*} egin{aligned} inf_{widetilde{ heta}}sup_{ hetainTheta(d,d_0, k)}mathbb{E}_ heta|widetilde{ heta} - heta|^2 asymp_d egin{cases} kloglog(16n/k), & 2leq kleq k_0,\ klog(en/k), & k geq k_0+1. end{cases} end{aligned} end{equation*} The transition boundary $k_0$, which takes the form $lfloor{(d+1)/(d-d_0) floor} + 1$, demonstrates the critical role of the regularity parameter $d_0$ in the separation between a faster $log log(16n)$ and a slower $log(en)$ rate. We further show that, once encouraging an additional '$d$-monotonicity' shape constraint (including monotonicity for $d = 0$ and convexity for $d=1$), the above phase transition is eliminated and the faster $kloglog(16n/k)$ rate can be achieved for all $k$. These results provide theoretical support for developing $ell_0$-penalized (shape-constrained) spline regression procedures as useful alternatives to $ell_1$- and $ell_2$-penalized ones.

The $p$-tensor Curie-Weiss model is a two-parameter discrete exponential family for modeling dependent binary data, where the sufficient statistic has a linear term and a term with degree $p geq 2$. This is a special case of the tensor Ising model and the natural generalization of the matrix Curie-Weiss model, which provides a convenient mathematical abstraction for capturing, not just pairwise, but higher-order dependencies. In this paper we provide a complete description of the limiting properties of the maximum likelihood (ML) estimates of the natural parameters, given a single sample from the $p$-tensor Curie-Weiss model, for $p geq 3$, complementing the well-known results in the matrix ($p=2$) case (Comets and Gidas (1991)). Our results unearth various new phase transitions and surprising limit theorems, such as the existence of a 'critical' curve in the parameter space, where the limiting distribution of the ML estimates is a mixture with both continuous and discrete components. The number of mixture components is either two or three, depending on, among other things, the sign of one of the parameters and the parity of $p$. Another interesting revelation is the existence of certain 'special' points in the parameter space where the ML estimates exhibit a superefficiency phenomenon, converging to a non-Gaussian limiting distribution at rate $N^{frac{3}{4}}$. We discuss how these results can be used to construct confidence intervals for the model parameters and, as a byproduct of our analysis, obtain limit theorems for the sample mean, which provide key insights into the statistical properties of the model.

This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users' travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.

This paper revisits the problem of computing empirical cumulative distribution functions (ECDF) efficiently on large, multivariate datasets. Computing an ECDF at one evaluation point requires $mathcal{O}(N)$ operations on a dataset composed of $N$ data points. Therefore, a direct evaluation of ECDFs at $N$ evaluation points requires a quadratic $mathcal{O}(N^2)$ operations, which is prohibitive for large-scale problems. Two fast and exact methods are proposed and compared. The first one is based on fast summation in lexicographical order, with a $mathcal{O}(N{log}N)$ complexity and requires the evaluation points to lie on a regular grid. The second one is based on the divide-and-conquer principle, with a $mathcal{O}(Nlog(N)^{(d-1){vee}1})$ complexity and requires the evaluation points to coincide with the input points. The two fast algorithms are described and detailed in the general $d$-dimensional case, and numerical experiments validate their speed and accuracy. Secondly, the paper establishes a direct connection between cumulative distribution functions and kernel density estimation (KDE) for a large class of kernels. This connection paves the way for fast exact algorithms for multivariate kernel density estimation and kernel regression. Numerical tests with the Laplacian kernel validate the speed and accuracy of the proposed algorithms. A broad range of large-scale multivariate density estimation, cumulative distribution estimation, survival function estimation and regression problems can benefit from the proposed numerical methods.

Dimitris Bertsimas, Bart Van Parys.

Source: The Annals of Statistics, Volume 48, Number 1, 300--323.

Abstract:
We present a novel binary convex reformulation of the sparse regression problem that constitutes a new duality perspective. We devise a new cutting plane method and provide evidence that it can solve to provable optimality the sparse regression problem for sample sizes $n$ and number of regressors $p$ in the 100,000s, that is, two orders of magnitude better than the current state of the art, in seconds. The ability to solve the problem for very high dimensions allows us to observe new phase transition phenomena. Contrary to traditional complexity theory which suggests that the difficulty of a problem increases with problem size, the sparse regression problem has the property that as the number of samples $n$ increases the problem becomes easier in that the solution recovers 100% of the true signal, and our approach solves the problem extremely fast (in fact faster than Lasso), while for small number of samples $n$, our approach takes a larger amount of time to solve the problem, but importantly the optimal solution provides a statistically more relevant regressor. We argue that our exact sparse regression approach presents a superior alternative over heuristic methods available at present.

Emmanuel J. Candès, Pragya Sur.

Source: The Annals of Statistics, Volume 48, Number 1, 27--42.

Abstract:
This paper rigorously establishes that the existence of the maximum likelihood estimate (MLE) in high-dimensional logistic regression models with Gaussian covariates undergoes a sharp “phase transition.” We introduce an explicit boundary curve $h_{mathrm{MLE}}$, parameterized by two scalars measuring the overall magnitude of the unknown sequence of regression coefficients, with the following property: in the limit of large sample sizes $n$ and number of features $p$ proportioned in such a way that $p/n ightarrow kappa $, we show that if the problem is sufficiently high dimensional in the sense that $kappa >h_{mathrm{MLE}}$, then the MLE does not exist with probability one. Conversely, if $kappa <h_{mathrm{MLE}}$, the MLE asymptotically exists with probability one.

Charles R. Doss, Jon A. Wellner.

Source: The Annals of Statistics, Volume 47, Number 5, 2950--2976.

Abstract:
We study a likelihood ratio test for the location of the mode of a log-concave density. Our test is based on comparison of the log-likelihoods corresponding to the unconstrained maximum likelihood estimator of a log-concave density and the constrained maximum likelihood estimator where the constraint is that the mode of the density is fixed, say at $m$. The constrained estimation problem is studied in detail in Doss and Wellner (2018). Here, the results of that paper are used to show that, under the null hypothesis (and strict curvature of $-log f$ at the mode), the likelihood ratio statistic is asymptotically pivotal: that is, it converges in distribution to a limiting distribution which is free of nuisance parameters, thus playing the role of the $chi_{1}^{2}$ distribution in classical parametric statistical problems. By inverting this family of tests, we obtain new (likelihood ratio based) confidence intervals for the mode of a log-concave density $f$. These new intervals do not depend on any smoothing parameters. We study the new confidence intervals via Monte Carlo methods and illustrate them with two real data sets. The new intervals seem to have several advantages over existing procedures. Software implementing the test and confidence intervals is available in the R package verb+logcondens.mode+.

Wei-Kuo Chen.

Source: The Annals of Statistics, Volume 47, Number 5, 2734--2756.

Abstract:
We consider the problem of detecting a deformation from a symmetric Gaussian random $p$-tensor $(pgeq3)$ with a rank-one spike sampled from the Rademacher prior. Recently, in Lesieur et al. (Barbier, Krzakala, Macris, Miolane and Zdeborová (2017)), it was proved that there exists a critical threshold $eta_{p}$ so that when the signal-to-noise ratio exceeds $eta_{p}$, one can distinguish the spiked and unspiked tensors and weakly recover the prior via the minimal mean-square-error method. On the other side, Perry, Wein and Bandeira (Perry, Wein and Bandeira (2017)) proved that there exists a $eta_{p}'<eta_{p}$ such that any statistical hypothesis test cannot distinguish these two tensors, in the sense that their total variation distance asymptotically vanishes, when the signa-to-noise ratio is less than $eta_{p}'$. In this work, we show that $eta_{p}$ is indeed the critical threshold that strictly separates the distinguishability and indistinguishability between the two tensors under the total variation distance. Our approach is based on a subtle analysis of the high temperature behavior of the pure $p$-spin model with Ising spin, arising initially from the field of spin glasses. In particular, we identify the signal-to-noise criticality $eta_{p}$ as the critical temperature, distinguishing the high and low temperature behavior, of the Ising pure $p$-spin mean-field spin glass model.

Trang Quynh Nguyen, Elizabeth A. Stuart.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 518--520.

Yen-Chi Chen, Adrian Dobra.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 409--432.

Abstract:
Activity spaces are fundamental to the assessment of individuals’ dynamic exposure to social and environmental risk factors associated with multiple spatial contexts that are visited during activities of daily living. In this paper we survey existing approaches for measuring the geometry, size and structure of activity spaces, based on GPS data, and explain their limitations. We propose addressing these shortcomings through a nonparametric approach called density ranking and also through three summary curves: the mass-volume curve, the Betti number curve and the persistence curve. We introduce a novel mixture model for human activity spaces and study its asymptotic properties. We prove that the kernel density estimator, which at the present time, is one of the most widespread methods for measuring activity spaces, is not a stable estimator of their structure. We illustrate the practical value of our methods with a simulation study and with a recently collected GPS dataset that comprises the locations visited by 10 individuals over a six months period.

Chih-Yuan Hsu, Yi-Hau Chen, Ruoh-Rong Yu, Tsung-Wei Hung.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 160--177.

Abstract:
Workers in Taiwan overall have been suffering from long-lasting wage stagnation since the mid-1990s. In particular, there seems to be little mobility for the wages of Taiwanese workers to transit across wage quantile groups. It is of interest to see if certain groups of workers, such as female, lower educated and younger generation workers, suffer from the problem more seriously than the others. This work tries to apply a systematic statistical approach to study this issue, based on the longitudinal data from the Panel Study of Family Dynamics (PSFD) survey conducted in Taiwan since 1999. We propose the quantile transition regression model, generalizing recent methodology for quantile association, to assess the wage status transition with respect to the marginal wage quantiles over time as well as the effects of certain demographic and job factors on the wage status transition. Estimation of the model can be based on the composite likelihoods utilizing the binary, or ordinal-data information regarding the quantile transition, with the associated asymptotic theory established. A goodness-of-fit procedure for the proposed model is developed. The performances of the estimation and the goodness-of-fit procedures for the quantile transition model are illustrated through simulations. The application of the proposed methodology to the PSFD survey data suggests that female, private-sector workers with higher age and education below postgraduate level suffer from more severe wage status stagnation than the others.

Fan Li, Fan Li.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2389--2415.

Abstract:
Causal or unconfounded descriptive comparisons between multiple groups are common in observational studies. Motivated from a racial disparity study in health services research, we propose a unified propensity score weighting framework, the balancing weights, for estimating causal effects with multiple treatments. These weights incorporate the generalized propensity scores to balance the weighted covariate distribution of each treatment group, all weighted toward a common prespecified target population. The class of balancing weights include several existing approaches such as the inverse probability weights and trimming weights as special cases. Within this framework, we propose a set of target estimands based on linear contrasts. We further develop the generalized overlap weights, constructed as the product of the inverse probability weights and the harmonic mean of the generalized propensity scores. The generalized overlap weighting scheme corresponds to the target population with the most overlap in covariates across the multiple treatments. These weights are bounded and thus bypass the problem of extreme propensities. We show that the generalized overlap weights minimize the total asymptotic variance of the moment weighting estimators for the pairwise contrasts within the class of balancing weights. We consider two balance check criteria and propose a new sandwich variance estimator for estimating the causal effects with generalized overlap weights. We apply these methods to study the racial disparities in medical expenditure between several racial groups using the 2009 Medical Expenditure Panel Survey (MEPS) data. Simulations were carried out to compare with existing methods.

Arnak S. Dalalyan, Lionel Riou-Durand.

Source: Bernoulli, Volume 26, Number 3, 1956--1988.

Abstract:
Langevin diffusion processes and their discretizations are often used for sampling from a target density. The most convenient framework for assessing the quality of such a sampling scheme corresponds to smooth and strongly log-concave densities defined on $mathbb{R}^{p}$. The present work focuses on this framework and studies the behavior of the Monte Carlo algorithm based on discretizations of the kinetic Langevin diffusion. We first prove the geometric mixing property of the kinetic Langevin diffusion with a mixing rate that is optimal in terms of its dependence on the condition number. We then use this result for obtaining improved guarantees of sampling using the kinetic Langevin Monte Carlo method, when the quality of sampling is measured by the Wasserstein distance. We also consider the situation where the Hessian of the log-density of the target distribution is Lipschitz-continuous. In this case, we introduce a new discretization of the kinetic Langevin diffusion and prove that this leads to a substantial improvement of the upper bound on the sampling error measured in Wasserstein distance.

Galatia Cleanthous, Athanasios G. Georgiadis, Gerard Kerkyacharian, Pencho Petrushev, Dominique Picard.

Source: Bernoulli, Volume 26, Number 3, 1832--1862.

Abstract:
We consider the problem of estimating the density of observations taking values in classical or nonclassical spaces such as manifolds and more general metric spaces. Our setting is quite general but also sufficiently rich in allowing the development of smooth functional calculus with well localized spectral kernels, Besov regularity spaces, and wavelet type systems. Kernel and both linear and nonlinear wavelet density estimators are introduced and studied. Convergence rates for these estimators are established and discussed.

Cristina Butucea, Amandine Dubois, Martin Kroll, Adrien Saumard.

Source: Bernoulli, Volume 26, Number 3, 1727--1764.

Abstract:
We address the problem of non-parametric density estimation under the additional constraint that only privatised data are allowed to be published and available for inference. For this purpose, we adopt a recent generalisation of classical minimax theory to the framework of local $alpha$-differential privacy and provide a lower bound on the rate of convergence over Besov spaces $mathcal{B}^{s}_{pq}$ under mean integrated $mathbb{L}^{r}$-risk. This lower bound is deteriorated compared to the standard setup without privacy, and reveals a twofold elbow effect. In order to fulfill the privacy requirement, we suggest adding suitably scaled Laplace noise to empirical wavelet coefficients. Upper bounds within (at most) a logarithmic factor are derived under the assumption that $alpha$ stays bounded as $n$ increases: A linear but non-adaptive wavelet estimator is shown to attain the lower bound whenever $pgeq r$ but provides a slower rate of convergence otherwise. An adaptive non-linear wavelet estimator with appropriately chosen smoothing parameters and thresholding is shown to attain the lower bound within a logarithmic factor for all cases.

Ester Mariucci, Kolyan Ray, Botond Szabó.

Source: Bernoulli, Volume 26, Number 2, 1070--1097.

Abstract:
The estimation of a log-concave density on $mathbb{R}$ is a canonical problem in the area of shape-constrained nonparametric inference. We present a Bayesian nonparametric approach to this problem based on an exponentiated Dirichlet process mixture prior and show that the posterior distribution converges to the log-concave truth at the (near-) minimax rate in Hellinger distance. Our proof proceeds by establishing a general contraction result based on the log-concave maximum likelihood estimator that prevents the need for further metric entropy calculations. We further present computationally more feasible approximations and both an empirical and hierarchical Bayes approach. All priors are illustrated numerically via simulations.

Ilaria Bianchini, Alessandra Guglielmi, Fernando A. Quintana.

Source: Bayesian Analysis, Volume 15, Number 1, 187--214.

Abstract:
We consider mixture models where location parameters are a priori encouraged to be well separated. We explore a class of determinantal point process (DPP) mixture models, which provide the desired notion of separation or repulsion. Instead of using the rather restrictive case where analytical results are partially available, we adopt a spectral representation from which approximations to the DPP density functions can be readily computed. For the sake of concreteness the presentation focuses on a power exponential spectral density, but the proposed approach is in fact quite general. We later extend our model to incorporate covariate information in the likelihood and also in the assignment to mixture components, yielding a trade-off between repulsiveness of locations in the mixtures and attraction among subjects with similar covariates. We develop full Bayesian inference, and explore model properties and posterior behavior using several simulation scenarios and data illustrations. Supplementary materials for this article are available online (Bianchini et al., 2019).

Peter M. Zygmunt
Jun 1, 2002; 22:4720-4727
Behavioral

Svetlana Mastitskaya
Apr 8, 2020; 40:3052-3062
Systems/Circuits

M Chalfie
Apr 1, 1985; 5:956-964
Articles

Maintenance of cardiorespiratory homeostasis depends on autonomic reflexes controlled by neuronal circuits of the brainstem. The neurophysiology and neuroanatomy of these reflex pathways are well understood, however, the mechanisms and functional significance of autonomic circuit modulation by glial cells remain largely unknown. In the experiments conducted in male laboratory rats we show that astrocytes of the nucleus of the solitary tract (NTS), the brain area that receives and integrates sensory information from the heart and blood vessels, respond to incoming afferent inputs with [Ca²⁺]_i elevations. Astroglial [Ca²⁺]_i responses are triggered by transmitters released by vagal afferents, glutamate acting at AMPA receptors and 5-HT acting at 5-HT_2A receptors. In conscious freely behaving animals blockade of Ca²⁺-dependent vesicular release mechanisms in NTS astrocytes by virally driven expression of a dominant-negative SNARE protein (dnSNARE) increased baroreflex sensitivity by 70% (p < 0.001). This effect of compromised astroglial function was specific to the NTS as expression of dnSNARE in astrocytes of the ventrolateral brainstem had no effect. ATP is considered the principle gliotransmitter and is released by vesicular mechanisms blocked by dnSNARE expression. Consistent with this hypothesis, in anesthetized rats, pharmacological activation of P2Y₁ purinoceptors in the NTS decreased baroreflex gain by 40% (p = 0.031), whereas blockade of P2Y₁ receptors increased baroreflex gain by 57% (p = 0.018). These results suggest that glutamate and 5-HT, released by NTS afferent terminals, trigger Ca²⁺-dependent astroglial release of ATP to modulate baroreflex sensitivity via P2Y₁ receptors. These data add to the growing body of evidence supporting an active role of astrocytes in brain information processing.

SIGNIFICANCE STATEMENT Cardiorespiratory reflexes maintain autonomic balance and ensure cardiovascular health. Impaired baroreflex may contribute to the development of cardiovascular disease and serves as a robust predictor of cardiovascular and all-cause mortality. The data obtained in this study suggest that astrocytes are integral components of the brainstem mechanisms that process afferent information and modulate baroreflex sensitivity via the release of ATP. Any condition associated with higher levels of "ambient" ATP in the NTS would be expected to decrease baroreflex gain by the mechanism described here. As ATP is the primary signaling molecule of glial cells (astrocytes, microglia), responding to metabolic stress and inflammatory stimuli, our study suggests a plausible mechanism of how the central component of the baroreflex is affected in pathological conditions.

One of the first signs of viral infection is body-wide aches and pain. Although this type of pain usually subsides, at the extreme, viral infections can induce painful neuropathies that can last for decades. Neither of these types of pain sensitization is well understood. A key part of the response to viral infection is production of interferons (IFNs), which then activate their specific receptors (IFNRs) resulting in downstream activation of cellular signaling and a variety of physiological responses. We sought to understand how type I IFNs (IFN-α and IFN-β) might act directly on nociceptors in the dorsal root ganglion (DRG) to cause pain sensitization. We demonstrate that type I IFNRs are expressed in small/medium DRG neurons and that their activation produces neuronal hyper-excitability and mechanical pain in mice. Type I IFNs stimulate JAK/STAT signaling in DRG neurons but this does not apparently result in PKR-eIF2α activation that normally induces an anti-viral response by limiting mRNA translation. Rather, type I IFNs stimulate MNK-mediated eIF4E phosphorylation in DRG neurons to promote pain hypersensitivity. Endogenous release of type I IFNs with the double-stranded RNA mimetic poly(I:C) likewise produces pain hypersensitivity that is blunted in mice lacking MNK-eIF4E signaling. Our findings reveal mechanisms through which type I IFNs cause nociceptor sensitization with implications for understanding how viral infections promote pain and can lead to neuropathies.

SIGNIFICANCE STATEMENT It is increasingly understood that pathogens interact with nociceptors to alert organisms to infection as well as to mount early host defenses. Although specific mechanisms have been discovered for diverse bacterial and fungal pathogens, mechanisms engaged by viruses have remained elusive. Here we show that type I interferons, one of the first mediators produced by viral infection, act directly on nociceptors to produce pain sensitization. Type I interferons act via a specific signaling pathway (MNK-eIF4E signaling), which is known to produce nociceptor sensitization in inflammatory and neuropathic pain conditions. Our work reveals a mechanism through which viral infections cause heightened pain sensitivity

Nestin, an intermediate filament protein widely used as a marker of neural progenitors, was recently found to be expressed transiently in developing cortical neurons in culture and in developing mouse cortex. In young cortical cultures, nestin regulates axonal growth cone morphology. In addition, nestin, which is known to bind the neuronal cdk5/p35 kinase, affects responses to axon guidance cues upstream of cdk5, specifically, to Sema3a. Changes in growth cone morphology require rearrangements of cytoskeletal networks, and changes in microtubules and actin filaments are well studied. In contrast, the roles of intermediate filament proteins in this process are poorly understood, even in cultured neurons. Here, we investigate the molecular mechanism by which nestin affects growth cone morphology and Sema3a sensitivity. We find that nestin selectively facilitates the phosphorylation of the lissencephaly-linked protein doublecortin (DCX) by cdk5/p35, but the phosphorylation of other cdk5 substrates is not affected by nestin. We uncover that this substrate selectivity is based on the ability of nestin to interact with DCX, but not with other cdk5 substrates. Nestin thus creates a selective scaffold for DCX with activated cdk5/p35. Last, we use cortical cultures derived from Dcx KO mice to show that the effects of nestin on growth cone morphology and on Sema3a sensitivity are DCX-dependent, thus suggesting a functional role for the DCX-nestin complex in neurons. We propose that nestin changes growth cone behavior by regulating the intracellular kinase signaling environment in developing neurons. The sex of animal subjects is unknown.

SIGNIFICANCE STATEMENT Nestin, an intermediate filament protein highly expressed in neural progenitors, was recently identified in developing neurons where it regulates growth cone morphology and responsiveness to the guidance cue Sema3a. Changes in growth cone morphology require rearrangements of cytoskeletal networks, but the roles of intermediate filaments in this process are poorly understood. We now report that nestin selectively facilitates phosphorylation of the lissencephaly-linked doublecortin (DCX) by cdk5/p35, but the phosphorylation of other cdk5 substrates is not affected. This substrate selectivity is based on preferential scaffolding of DCX, cdk5, and p35 by nestin. Additionally, we demonstrate a functional role for the DCX-nestin complex in neurons. We propose that nestin changes growth cone behavior by regulating intracellular kinase signaling in developing neurons.

Calcineurin inhibitors, such as tacrolimus (FK506) and cyclosporine, are widely used as standard immunosuppressants in organ transplantation recipients. However, these drugs can cause severe pain in patients, commonly referred to as calcineurin inhibitor-induced pain syndrome (CIPS). Although calcineurin inhibition increases NMDAR activity in the spinal cord, the underlying mechanism remains enigmatic. Using an animal model of CIPS, we found that systemic administration of FK506 in male and female mice significantly increased the amount of α2-1–GluN1 complexes in the spinal cord and the level of α2-1–bound GluN1 proteins in spinal synaptosomes. Treatment with FK506 significantly increased the frequency of mEPSCs and the amplitudes of monosynaptic EPSCs evoked from the dorsal root and puff NMDAR currents in spinal dorsal horn neurons. Inhibiting α2-1 with gabapentin or disrupting the α2-1–NMDAR interaction with α2-1Tat peptide completely reversed the effects of FK506. In α2-1 gene KO mice, treatment with FK506 failed to increase the frequency of NMDAR-mediated mEPSCs and the amplitudes of evoked EPSCs and puff NMDAR currents in spinal dorsal horn neurons. Furthermore, systemic administration of gabapentin or intrathecal injection of α2-1Tat peptide reversed thermal and mechanical hypersensitivity in FK506-treated mice. In addition, genetically deleting GluN1 in dorsal root ganglion neurons or α2-1 genetic KO similarly attenuated FK506-induced thermal and mechanical hypersensitivity. Together, our findings indicate that α2-1–bound NMDARs mediate calcineurin inhibitor-induced tonic activation of presynaptic and postsynaptic NMDARs at the spinal cord level and that presynaptic NMDARs play a prominent role in the development of CIPS.

SIGNIFICANCE STATEMENT Calcineurin inhibitors are immunosuppressants used to prevent rejection of transplanted organs and tissues. However, these drugs can cause severe, unexplained pain. We showed that calcineurin inhibition enhances physical interaction between α2-1 and NMDARs and their synaptic trafficking in the spinal cord. α2-1 is essential for calcineurin inhibitor-induced aberrant activation of presynaptic and postsynaptic NMDARs in the spinal cord. Furthermore, inhibiting α2-1 or disrupting α2-1–NMDAR interaction reduces calcineurin inhibitor-induced pain hypersensitivity. Eliminating NMDARs in primary sensory neurons or α2-1 KO also attenuates calcineurin inhibitor-induced pain hypersensitivity. This new information extends our mechanistic understanding of the role of endogenous calcineurin in regulating synaptic plasticity and nociceptive transmission and suggests new strategies for treating this painful condition.

“The timing couldn’t have been worse for refugees from Syria passing through Macedonia.” An OM worker shares about conditions in a Macedonian refugee transit camp.

A l’approche de la décision de la SADC, il est important qu’elle porte toute son attention sur les mesures qui permettent de garantir l’équité de traitement entre les protagonistes. Sans modifier le texte de la feuille de route, les autorités ont la possibilité de prouver leur volonté de garantir la neutralité du processus, afin que l’opposition soit libre de faire le choix d’entrer ou non dans cette transition sur une base équilibrée. Le rejet des autorités de ces mesures exposerait leur absence de volonté de voir se dérouler une transition et des élections crédibles. Il démontrerait également leur choix de plonger le pays dans l’instabilité plutôt que d’accepter des mesures qui renforcent la transition. Le refus de l’opposition d’adhérer au processus ne pourrait plus être justifié par un déséquilibre de la solution proposée.

During the last few months, new options for resolving the Madagascar crisis have emerged in the form of the most recent roadmap proposed by the mediation team of the Southern African Development Community (SADC). Accepted by the authorities and rejected by elements of the opposition, the roadmap remains the subject of debate and there is still no agreement on how to achieve a peaceful transition. In a few weeks from now, the SADC is due to make a statement on the document and accept, reject or amend it. There are other options for changing the course of events without changing the roadmap and these should be explored as quickly as possible.

Rome Newsroom, Apr 30, 2020 / 02:01 pm (CNA).- In the central Italian countryside, at the edge of the Umbrian woods just outside Norcia, a group of Benedictine monks prays and works from well before the sun rises until it sets.

This much has not changed in the monks’ lives during Italy’s coronavirus lockdown; but what has is the visitors they receive at the monastery.

“Usually we have some guests coming from all over the world... visitors coming from Italy or the U.S., friends or retreatants,” Fr. Benedict Nivakoff, O.S.B., told CNA by phone.

“And so, the total absence of those people, of that presence, has just focused our prayer all the more and we try to do what we are called to do more seriously,” he said.

“The main thing is a greater intensity of prayer for all those who are suffering.”

Nivakoff is the prior of the monks living at the site of St. Benedict’s birth. After religious life was suppressed in the area in the 1800s, a group led by Fr. Cassian Folsom was given permission to re-establish the monastery and moved there in 2000.

The prior said when the coronavirus was at its height in Italy, the monks did a traditional procession around the property with relics of the true cross.

“And that’s a way of praying for people, invoking the saints and calling down God’s help and his mercy on the country and on the world,” he said.

St. Benedict himself “experienced plagues, famines, sickness, death, not to mention relentless attacks of the devil on him and on his monks. He saw all of those as occasions for the monks themselves and for him to renew his trust and his faith in God,” Nivakoff said.

There is a “sad and persistent temptation,” he explained, to think “the world can solve these problems, but in fact, this world is passing away and God is the only answer to the suffering that we see.”

“So St. Benedict’s message, if you will, would be that all these things that happen can work for the good, and that is for the good of … each man and woman, each monk, in drawing closer to God.”

The monks in Norcia experienced tragedy first-hand four and a half years ago when several earthquakes, including one of 6.6-magnitude, struck central Italy and Norcia in August and October 2016.

The earthquakes destroyed hundreds of homes and the monk’s own buildings, including the Basilica of St. Benedict.

They have been rebuilding, but construction has been on hold during Italy’s lockdown, Nivakoff said, noting that it may, God willing, be able to start back up in a few weeks.

“The earthquake taught us many things and maybe one of the more relevant lessons for today is to resist the temptation that everything should go back exactly as it was,” he said.

“We thought after the earthquake, ‘well the answer is [to rebuild] everything as good if not better than before.’”

“But at the root of that is a fallacy, that this is a world, and we are men touched by original sin, who will only really have happiness and completion and real restoration in heaven,” the prior said.

He noted, “we can and do and need to work to improve things and to bring order where there is chaos and disorder but not at the risk of making this world into the destination and the goal,” because “it isn’t; it’s our temporary place so that we might get to heaven.”

“The earthquake really helped us to see that in a visible form, because the ground was literally shaking beneath our feet,” he said, “and the buildings we had called home to us and to our neighbors, our families, our friends, all the people here in Italy that we know, in central Italy, as all that fell apart.”

He said this “has called for trust and faith that is hard to muster in these days when the faith is so minimal.”

According to Nivakoff, “there are so many” lessons from monastic life that could help people quarantined in their homes right now, but he emphasized “two principle challenges to solitude.”

The first is for those who are in quarantine with others. As for monks who live with other monks, charity is very important when living in the midst of many people, he said.

“This really calls for lots and lots of patience, [and] to remember that patience with others always begins with patience with ourselves,” he explained. “Accepting our sins, accepting our faults, accepting that God is patient with us, and being patient with ourselves, helps us to be more patient with others.”

He added that silence can be a really useful tool in these circumstances: “Not speaking, not responding to the irritating or difficult or perhaps provocative things … people we live with say.”

“Especially under quarantine, the people we live with are probably going to still be with us in a few hours and maybe our passions will have calmed down by then” to respond in a better way, he said.

The second principle he drew on is for those who are living alone, such as the elderly or the young.

“For them, the quarantine really means an eremitical lifestyle. And for them the hardest temptations are sadness, acedia,” Nivakoff said.

“Sadness, which can be good because it can help us to lament our sins, lament not being with God, but at the same time can be a very inward looking and very self-pitying emotion, that stems from expectations not fulfilled.”

He recommended lots of humility and accepting that you are not in charge, not placing hope in things one does not have any control over.

“We have a lot more control over whether we say our prayers at noon than whether the government stops the lockdown in one week,” he pointed out. “The ways to combat sadness are this: to make goals that depend on me, and to put our trust and hope in God.”

Nivakoff also noted that there is a lot of talk right now about the importance of regaining the liberties men and women have had and avoiding “overreach of the government.”

“And that might be true, but from a Christian perspective, it is that we men and women need to accept the limitations that this disease brings on us,” he said.

“So even this terrible virus we need to see as permitted by [God] for some good purpose and the most traditional understanding of that is for some kind of purification.”

“So, we ask for God’s mercy because we need it.”

So during the coronavirus pandemic, the monks continue their prayer and their work taking care of the animals, gardening, cooking, cleaning, and managing the nearby forest.

To support themselves the monks also brew beer, and because it is sold through the internet, the coronavirus has not negatively impacted sales.

“And thank God, that model has really been blessed at this time because with so many people not being able to leave their home, many have taken it as an occasion to sample some monastic beer,” Nivakoff said.

“We continue to export from Italy to the United States and beer is available and it seems to delight many hearts there and we are very happy.”

School-based health clinics for teachers and their families can significantly lower a district's health care costs and slightly reduce teacher absenteeism, a new study finds.

Community groups and sports not connected to school can help students stay more connected academically during a critical transition period, according to a study of low-income students in New York City.

Internet use has rapidly become a commonplace activity, especially among adolescents. Poor mental health and several somatic health problems are associated with heavy Internet use by adolescents.

Results of this study provide evidence of a U-shaped relationship between intensity of Internet use and poorer mental health of adolescents. Heavy Internet users were also confirmed to be at increased risk for somatic health problems in this nationally representative sample of adolescents. (Read the full article)

Blood pressure and heart rate are altered by sleep state and postnatal age in healthy term and preterm infants. Preterm infants have altered blood pressure responses to head-up tilting during sleep.

Preterm birth has marked effects on the maturation of baroreflex sensitivity during sleep, which may contribute to the greater vulnerability of preterm infants to sudden infant death syndrome. (Read the full article)

Overweight has been associated with an increased risk of asthma in children, although the published literature is contradictory. How change in overweight status during childhood affects asthma risk has not been well studied.

Among children whose weight has normalized, high BMI during the first 4 years of life does not increase the risk of asthma at school age. Current high BMI is associated with increased risk of asthma and sensitization to inhalant allergens. (Read the full article)