London : John Murray, 1876.

[Edinburgh?] : [publisher not identified], [1901?]

Edinburgh : printed by J. Stark, [1836?]

London : J. Churchill, 1864.

Rockville, Maryland : National Institute on Drug Abuse, 1984.

Rockville, Maryland : National Institute on Drug Abuse, 1984.

Washington : Dept. of Transportation, National Highway Traffic Safety Administration, 1976.

Helsinki : Department of Pharmacology and Toxicology, University of Helsinki, 1985.

Durba Bhattacharya, Sourabh Bhattacharya.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 71--89.

Abstract:
Present day bio-medical research is pointing towards the fact that cognizance of gene–environment interactions along with genetic interactions may help prevent or detain the onset of many complex diseases like cardiovascular disease, cancer, type2 diabetes, autism or asthma by adjustments to lifestyle. In this regard, we propose a Bayesian semiparametric model to detect not only the roles of genes and their interactions, but also the possible influence of environmental variables on the genes in case-control studies. Our model also accounts for the unknown number of genetic sub-populations via finite mixtures composed of Dirichlet processes. An effective parallel computing methodology, developed by us harnesses the power of parallel processing technology to increase the efficiencies of our conditionally independent Gibbs sampling and Transformation based MCMC (TMCMC) methods. Applications of our model and methods to simulation studies with biologically realistic genotype datasets and a real, case-control based genotype dataset on early onset of myocardial infarction (MI) have yielded quite interesting results beside providing some insights into the differential effect of gender on MI.

The random-effects or normal-normal hierarchical model is commonly utilized in a wide range of meta-analysis applications. A Bayesian approach to inference is very attractive in this context, especially when a meta-analysis is based only on few studies. The bayesmeta R package provides readily accessible tools to perform Bayesian meta-analyses and generate plots and summaries, without having to worry about computational details. It allows for flexible prior specification and instant access to the resulting posterior distributions, including prediction and shrinkage estimation, and facilitating for example quick sensitivity checks. The present paper introduces the underlying theory and showcases its usage.

Xiaoqin Wang, Li Yin.

Source: The Annals of Statistics, Volume 48, Number 1, 138--160.

Abstract:
In sequential causal inference, two types of causal effects are of practical interest, namely, the causal effect of the treatment regime (called the sequential causal effect) and the blip effect of treatment on the potential outcome after the last treatment. The well-known $G$-formula expresses these causal effects in terms of the standard parameters. In this article, we obtain a new $G$-formula that expresses these causal effects in terms of the point observable effects of treatments similar to treatment in the framework of single-point causal inference. Based on the new $G$-formula, we estimate these causal effects by maximum likelihood via point observable effects with methods extended from single-point causal inference. We are able to increase precision of the estimation without introducing biases by an unsaturated model imposing constraints on the point observable effects. We are also able to reduce the number of point observable effects in the estimation by treatment assignment conditions.

Zhou Fan, Iain M. Johnstone.

Source: The Annals of Statistics, Volume 47, Number 5, 2855--2886.

Abstract:
We study the spectra of MANOVA estimators for variance component covariance matrices in multivariate random effects models. When the dimensionality of the observations is large and comparable to the number of realizations of each random effect, we show that the empirical spectra of such estimators are well approximated by deterministic laws. The Stieltjes transforms of these laws are characterized by systems of fixed-point equations, which are numerically solvable by a simple iterative procedure. Our proof uses operator-valued free probability theory, and we establish a general asymptotic freeness result for families of rectangular orthogonally invariant random matrices, which is of independent interest. Our work is motivated in part by the estimation of components of covariance between multiple phenotypic traits in quantitative genetics, and we specialize our results to common experimental designs that arise in this application.

Chengchun Shi, Rui Song, Wenbin Lu.

Source: The Annals of Statistics, Volume 47, Number 4, 2348--2377.

Abstract:
Precision medicine is an emerging medical paradigm that focuses on finding the most effective treatment strategy tailored for individual patients. In the literature, most of the existing works focused on estimating the optimal treatment regime. However, there has been less attention devoted to hypothesis testing regarding the optimal treatment regime. In this paper, we first introduce the notion of conditional qualitative treatment effects (CQTE) of a set of variables given another set of variables and provide a class of equivalent representations for the null hypothesis of no CQTE. The proposed definition of CQTE does not assume any parametric form for the optimal treatment rule and plays an important role for assessing the incremental value of a set of new variables in optimal treatment decision making conditional on an existing set of prescriptive variables. We then propose novel testing procedures for no CQTE based on kernel estimation of the conditional contrast functions. We show that our test statistics have asymptotically correct size and nonnegligible power against some nonstandard local alternatives. The empirical performance of the proposed tests are evaluated by simulations and an application to an AIDS data set.

Trang Quynh Nguyen, Elizabeth A. Stuart.

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

Boyu Ren, Sergio Bacallado, Stefano Favaro, Tommi Vatanen, Curtis Huttenhower, Lorenzo Trippa.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 494--517.

Abstract:
Detecting associations between microbial compositions and sample characteristics is one of the most important tasks in microbiome studies. Most of the existing methods apply univariate models to single microbial species separately, with adjustments for multiple hypothesis testing. We propose a Bayesian analysis for a generalized mixed effects linear model tailored to this application. The marginal prior on each microbial composition is a Dirichlet process, and dependence across compositions is induced through a linear combination of individual covariates, such as disease biomarkers or the subject’s age, and latent factors. The latent factors capture residual variability and their dimensionality is learned from the data in a fully Bayesian procedure. The proposed model is tested in data analyses and simulation studies with zero-inflated compositions. In these settings and within each sample, a large proportion of counts per microbial species are equal to zero. In our Bayesian model a priori the probability of compositions with absent microbial species is strictly positive. We propose an efficient algorithm to sample from the posterior and visualizations of model parameters which reveal associations between covariates and microbial compositions. We evaluate the proposed method in simulation studies, and then analyze a microbiome dataset for infants with type 1 diabetes which contains a large proportion of zeros in the sample-specific microbial compositions.

Michael E. Sobel, Martin A. Lindquist.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 452--472.

Abstract:
Neuroscientists often use functional magnetic resonance imaging (fMRI) to infer effects of treatments on neural activity in brain regions. In a typical fMRI experiment, each subject is observed at several hundred time points. At each point, the blood oxygenation level dependent (BOLD) response is measured at 100,000 or more locations (voxels). Typically, these responses are modeled treating each voxel separately, and no rationale for interpreting associations as effects is given. Building on Sobel and Lindquist ( J. Amer. Statist. Assoc. 109 (2014) 967–976), who used potential outcomes to define unit and average effects at each voxel and time point, we define and estimate both “point” and “cumulated” effects for brain regions. Second, we construct a multisubject, multivoxel, multirun whole brain causal model with explicit parameters for regions. We justify estimation using BOLD responses averaged over voxels within regions, making feasible estimation for all regions simultaneously, thereby also facilitating inferences about association between effects in different regions. We apply the model to a study of pain, finding effects in standard pain regions. We also observe more cerebellar activity than observed in previous studies using prevailing methods.

Wanghuan Chu, Runze Li, Jingyuan Liu, Matthew Reimherr.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 276--298.

Abstract:
Motivated by an empirical analysis of data from a genome-wide association study on obesity, measured by the body mass index (BMI), we propose a two-step gene-detection procedure for generalized varying coefficient mixed-effects models with ultrahigh dimensional covariates. The proposed procedure selects significant single nucleotide polymorphisms (SNPs) impacting the mean BMI trend, some of which have already been biologically proven to be “fat genes.” The method also discovers SNPs that significantly influence the age-dependent variability of BMI. The proposed procedure takes into account individual variations of genetic effects and can also be directly applied to longitudinal data with continuous, binary or count responses. We employ Monte Carlo simulation studies to assess the performance of the proposed method and further carry out causal inference for the selected SNPs.

Joseph Antonelli, Maitreyi Mazumdar, David Bellinger, David Christiani, Robert Wright, Brent Coull.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 257--275.

Abstract:
Humans are routinely exposed to mixtures of chemical and other environmental factors, making the quantification of health effects associated with environmental mixtures a critical goal for establishing environmental policy sufficiently protective of human health. The quantification of the effects of exposure to an environmental mixture poses several statistical challenges. It is often the case that exposure to multiple pollutants interact with each other to affect an outcome. Further, the exposure-response relationship between an outcome and some exposures, such as some metals, can exhibit complex, nonlinear forms, since some exposures can be beneficial and detrimental at different ranges of exposure. To estimate the health effects of complex mixtures, we propose a flexible Bayesian approach that allows exposures to interact with each other and have nonlinear relationships with the outcome. We induce sparsity using multivariate spike and slab priors to determine which exposures are associated with the outcome and which exposures interact with each other. The proposed approach is interpretable, as we can use the posterior probabilities of inclusion into the model to identify pollutants that interact with each other. We utilize our approach to study the impact of exposure to metals on child neurodevelopment in Bangladesh and find a nonlinear, interactive relationship between arsenic and manganese.

Emily Berg, Danhyang Lee.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2158--2188.

Abstract:
Quantiles of the distributions of several measures of erosion are important parameters in the Conservation Effects Assessment Project, a survey intended to quantify soil and nutrient loss on crop fields. Because sample sizes for domains of interest are too small to support reliable direct estimators, model based methods are needed. Quantile regression is appealing for CEAP because finding a single family of parametric models that adequately describes the distributions of all variables is difficult and small area quantiles are parameters of interest. We construct empirical Bayes predictors and bootstrap mean squared error estimators based on the linearly interpolated generalized Pareto distribution (LIGPD). We apply the procedures to predict county-level quantiles for four types of erosion in Wisconsin and validate the procedures through simulation.

Bret Zeldow, Vincent Lo Re III, Jason Roy.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1989--2010.

Abstract:
Bayesian Additive Regression Trees (BART) is a flexible machine learning algorithm capable of capturing nonlinearities between an outcome and covariates and interactions among covariates. We extend BART to a semiparametric regression framework in which the conditional expectation of an outcome is a function of treatment, its effect modifiers, and confounders. The confounders are allowed to have unspecified functional form, while treatment and effect modifiers that are directly related to the research question are given a linear form. The result is a Bayesian semiparametric linear regression model where the posterior distribution of the parameters of the linear part can be interpreted as in parametric Bayesian regression. This is useful in situations where a subset of the variables are of substantive interest and the others are nuisance variables that we would like to control for. An example of this occurs in causal modeling with the structural mean model (SMM). Under certain causal assumptions, our method can be used as a Bayesian SMM. Our methods are demonstrated with simulation studies and an application to dataset involving adults with HIV/Hepatitis C coinfection who newly initiate antiretroviral therapy. The methods are available in an R package called semibart.

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.

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).

Pantelis Samartsidis, Shaun R. Seaman, Anne M. Presanis, Matthew Hickman, Daniela De Angelis.

Source: Statistical Science, Volume 34, Number 3, 486--503.

Abstract:
Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is nonrandomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations. We detail the assumptions underlying each method, emphasize connections between the different approaches and provide guidelines regarding their practical implementation. Several open problems are identified thus highlighting the need for future research.

Catherine Mankiw
May 24, 2017; 37:5221-5231
Development Plasticity Repair

Hedok Lee
Aug 5, 2015; 35:11034-11044
Neurobiology of Disease

Fabrizio Benedetti
Nov 9, 2005; 25:10390-10402
Symposia and Mini-Symposia

Carrie J. McAdams
Jan 1, 1999; 19:431-441
Articles

RU Muller
Jul 1, 1987; 7:1951-1968
Articles

Adaptive coding of stimuli is well documented in perception, where it supports efficient encoding over a broad range of possible percepts. Recently, a similar neural mechanism has been reported also in value-based decision, where it allows optimal encoding of vast ranges of values in PFC: neuronal response to value depends on the choice context (relative coding), rather than being invariant across contexts (absolute coding). Additionally, value learning is sensitive to the amount of feedback information: providing complete feedback (both obtained and forgone outcomes) instead of partial feedback (only obtained outcome) improves learning. However, it is unclear whether relative coding occurs in all PFC regions and how it is affected by feedback information. We systematically investigated univariate and multivariate feedback encoding in various mPFC regions and compared three modes of neural coding: absolute, partially-adaptive and fully-adaptive.

Twenty-eight human participants (both sexes) performed a learning task while undergoing fMRI scanning. On each trial, they chose between two symbols associated with a certain outcome. Then, the decision outcome was revealed. Notably, in one-half of the trials participants received partial feedback, whereas in the other half they got complete feedback. We used univariate and multivariate analysis to explore value encoding in different feedback conditions.

We found that both obtained and forgone outcomes were encoded in mPFC, but with opposite sign in its ventral and dorsal subdivisions. Moreover, we showed that increasing feedback information induced a switch from absolute to relative coding. Our results suggest that complete feedback information enhances context-dependent outcome encoding.

SIGNIFICANCE STATEMENT This study offers a systematic investigation of the effect of the amount of feedback information (partial vs complete) on univariate and multivariate outcome value encoding, within multiple regions in mPFC and cingulate cortex that are critical for value-based decisions and behavioral adaptation. Moreover, we provide the first comparison of three possible models of neural coding (i.e., absolute, partially-adaptive, and fully-adaptive coding) of value signal in these regions, by using commensurable measures of prediction accuracy. Taken together, our results help build a more comprehensive picture of how the human brain encodes and processes outcome value. In particular, our results suggest that simultaneous presentation of obtained and foregone outcomes promotes relative value representation.

Prohibitin (PHB) is a critical protein involved in many cellular activities. In brain, PHB resides in mitochondria, where it forms a large protein complex with PHB2 in the inner TFmembrane, which serves as a scaffolding platform for proteins involved in mitochondrial structural and functional integrity. PHB overexpression at moderate levels provides neuroprotection in experimental brain injury models. In addition, PHB expression is involved in ischemic preconditioning, as its expression is enhanced in preconditioning paradigms. However, the mechanisms of PHB functional regulation are still unknown. Observations that nitric oxide (NO) plays a key role in ischemia preconditioning compelled us to postulate that the neuroprotective effect of PHB could be regulated by NO. Here, we test this hypothesis in a neuronal model of ischemia–reperfusion injury and show that NO and PHB are mutually required for neuronal resilience against oxygen and glucose deprivation stress. Further, we demonstrate that NO post-translationally modifies PHB through protein S-nitrosylation and regulates PHB neuroprotective function, in a nitric oxide synthase-dependent manner. These results uncover the mechanisms of a previously unrecognized form of molecular regulation of PHB that underlies its neuroprotective function.

SIGNIFICANCE STATEMENT Prohibitin (PHB) is a critical mitochondrial protein that exerts a potent neuroprotective effect when mildly upregulated in mice. However, how the neuroprotective function of PHB is regulated is still unknown. Here, we demonstrate a novel regulatory mechanism for PHB that involves nitric oxide (NO) and shows that PHB and NO interact directly, resulting in protein S-nitrosylation on residue Cys⁶⁹ of PHB. We further show that nitrosylation of PHB may be essential for its ability to preserve neuronal viability under hypoxic stress. Thus, our study reveals a previously unknown mechanism of functional regulation of PHB that has potential therapeutic implications for neurologic disorders.

Priming refers to the influence that a previously encountered object exerts on future responses to similar objects. For many years, visual priming has been known as a facilitation and sometimes an inhibition effect that lasts for an extended period of time. It contrasts with the recent finding of an oscillated priming effect where facilitation and inhibition alternate over time periodically. Here we developed a computational model of visual priming that combines rhythmic sampling of the environment (attentional oscillation) with active preparation for future events (temporal expectation). Counterintuitively, it shows that both the sustained and oscillated priming effects can emerge from an interaction between attentional oscillation and temporal expectation. The interaction also leads to novel predictions, such as the change of visual priming effects with temporal expectation and attentional oscillation. Reanalysis of two published datasets and the results of two new experiments of visual priming tasks with male and female human participants provide support for the model's relevance to human behavior. More generally, our model offers a new perspective that may unify the increasing findings of behavioral and neural oscillations with the classic findings in visual perception and attention.

SIGNIFICANCE STATEMENT There is increasing behavioral and neural evidence that visual attention is a periodic process that sequentially samples different alternatives in the theta frequency range. It contrasts with the classic findings of sustained facilitatory or inhibitory attention effects. How can an oscillatory perceptual process give rise to sustained attention effects? Here we make this connection by proposing a computational model for a "fruit fly" visual priming task and showing both the sustained and oscillated priming effects can have the same origin: an interaction between rhythmic sampling of the environment and active preparation for future events. One unique contribution of our model is to predict how temporal contexts affects priming. It also opens up the possibility of reinterpreting other attention-related classic phenomena.

As an important cognitive bias, the framing effect shows that our decision preferences are sensitive to the verbal description (i.e., frame) of options. This study focuses on the neural underpinnings of the social framing effect, which is based on decision-making regarding other people. A novel paradigm was used in which participants made a trade-off between economic benefits and the feelings of others. This decision was described as either a "harm" to, or "not helping," other persons in two conditions (Harm frame vs Help frame). Both human males and females were recruited. Participants behaved more prosocially for Harm frame compared with Help frame, resulting in a significant social framing effect. Using functional magnetic resonance imaging, Experiment 1 showed that the social framing effect was associated with stronger activation in the temporoparietal junction (TPJ), especially its right part. The functional connectivity between the right TPJ (rTPJ) and medial prefrontal cortex predicted the social framing effect on the group level. In Experiment 2, we used transcranial direct current stimulation to modulate the activity of the rTPJ and found that the social framing effect became more prominent under anodal (excitatory) stimulation, while the nonsocial framing effect elicited by the economic gain/loss gambling frame remained unaffected. The rTPJ results might be associated with moral conflicts modulated by the social consequences of an action or different levels of mentalizing with others under different frame conditions, but alternative interpretations are also worth noting. These findings could help elucidate the psychological mechanisms of the social framing effect.

SIGNIFICANCE STATEMENT Previous studies have suggested that the framing effect is generated from an interaction between the amygdala and anterior cingulate cortex. This opinion, however, is based on findings from nonsocial framing tasks. Recent research has highlighted the importance of distinguishing between the social and nonsocial framing effects. The current study focuses on the social framing effect and finds out that the temporoparietal junction and its functional connectivity with the medial prefrontal cortex play a significant role. Additionally, modulating the activity of this region leads to changes in social (but not nonsocial) framing effect. Broadly speaking, these findings help understand the difference in neural mechanisms between social and nonsocial decision-making. Meanwhile, they might be illuminating to promote helping behavior in society.

The cats also cross the road an average of 4.5 times in six days, putting themselves in danger

Speech by Mr Stefan Ingves, Governor of the Sveriges Riksbank, at the Sveriges Riksbank, Stockholm, 3 April 2020.

BIS Bulletin No 7, April 2020. Past epidemics had long-lasting effects on economies through illness and the loss of lives, while Covid-19 is marked by widespread containment measures and relatively lower fatalities among young people. The short-term costs of Covid-19 will probably dwarf those of past epidemics, due to the unprecedented and synchronised global sudden stop in economic activity induced by containment measures. The current estimated impact on global GDP growth for 2020 is around -4%, with substantial downside risks if containment policies are prolonged. Output losses are larger for major economies.

BCBS Press release "Basel Committee issues progress report on banks' implementation of the 'Principles for effective risk data aggregation and reporting'", 29 April 2020

With the business world -- and the world at large, for that matter -- changing at what feels like a moment's notice, businesses and brands have never been required to be as limber as in this current moment. Marketing leaders want hard evidence and objective facts for decision making. It wasn't long ago that multi-touch attribution was the prized child of the hype cycle among marketers.

This report outlines the progress made by banks in implementing the Basel Committee's Principles for effective risk data aggregation and risk reporting ("the Principles" or "BCBS 239")1 based on supervisors' assessments conducted in 2019.

Daily data on effective exchange rate in nominal terms have been updated. Broad indices cover 60 economies, with data from 1996. Narrow indices cover 26 economies with data from 1983.

Banks' performance on equity and debt markets since the Covid-19 outbreak has been on a par with that experienced after the collapse of Lehman Brothers in 2008. During the initial phase, the market sell-off swept over all banks, which underperformed significantly relative to other sectors. Still, markets showed some differentiation by bank nationality, and credit default swap (CDS) spreads rose the most for those banks that had entered the crisis with the highest level of credit risk. The subsequent stabilisation, brought about by forceful policy measures since mid-March, has favoured banks with higher profitability and healthier balance sheets. Less profitable banks saw their long-term rating outlooks revised to negative. And the CDS spreads of the riskiest banks continued increasing even through the stabilisation phase.

Bank of Italy Working Papers by Luca Metelli, Filippo Natoli and Luca Rossi

Bank of Italy Working Papers by Andrea Gazzani and Alejandro Vicondoa

Central Bank of Chile Working Papers by Gee Hee Hong, Matthew Klepacz, Ernesto Pasten and Raphael Schoenle

A Special Purpose Machine, which is intended to perform a specific task, have a wide range of scope in the Industrial Applications like quantity packaging and bottle filling. It includes limit switches, sensors, logic controls where the process can be

Author information

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As per the normal review of market volatility to ensure adequate collateral coverage, the Chicago Mercantile Exchange Inc., Clearing House Risk Management staff approved the performance bond requirements for the following products listed in the advisory at the link below.

The rates will be effective after the close of business on April 22, 2020.

For the full text of this advisory, please click here.

As per the normal review of market volatility to ensure adequate collateral coverage, the Chicago Mercantile Exchange Inc., Clearing House Risk Management staff approved the performance bond requirements for the following products listed in the advisory at the link below.

The rates will be effective after the close of business on 04/22/2020.

Click here for the full text of the advisory

20-170

Pursuant to Clearing Advisory 20-152 that was published on April 8th, the clearing house will switch the options pricing and valuation model to Bachelier to accommodate negative prices in the underlying futures and allow for listing of option contracts with negative strikes for the set of products specified in the link below. The switch will be effective for the margin cycle run at the end of trading tomorrow April 22, 2020 and will remain in place until further notice.

Click here for the full text of the advisory

20-170

As per the normal review of market volatility to ensure adequate collateral coverage, the Chicago Mercantile Exchange Inc., Clearing House Risk Management staff approved the performance bond requirements for the following products listed in the advisory at the link below.

The rates will be effective after the close of business on 4/23/2020.

For the full text of this advisory, please click here.