Zhuang Ma, Xiaodong Li.

Source: Bernoulli, Volume 26, Number 1, 432--470.

Abstract:
Canonical correlation analysis (CCA) is a fundamental statistical tool for exploring the correlation structure between two sets of random variables. In this paper, motivated by the recent success of applying CCA to learn low dimensional representations of high dimensional objects, we propose two losses based on the principal angles between the model spaces spanned by the sample canonical variates and their population correspondents, respectively. We further characterize the non-asymptotic error bounds for the estimation risks under the proposed error metrics, which reveal how the performance of sample CCA depends adaptively on key quantities including the dimensions, the sample size, the condition number of the covariance matrices and particularly the population canonical correlation coefficients. The optimality of our uniform upper bounds is also justified by lower-bound analysis based on stringent and localized parameter spaces. To the best of our knowledge, for the first time our paper separates $p_{1}$ and $p_{2}$ for the first order term in the upper bounds without assuming the residual correlations are zeros. More significantly, our paper derives $(1-lambda_{k}^{2})(1-lambda_{k+1}^{2})/(lambda_{k}-lambda_{k+1})^{2}$ for the first time in the non-asymptotic CCA estimation convergence rates, which is essential to understand the behavior of CCA when the leading canonical correlation coefficients are close to $1$.

Xiucai Ding.

Source: Bernoulli, Volume 26, Number 1, 387--417.

Abstract:
We consider the recovery of a low rank $M imes N$ matrix $S$ from its noisy observation $ ilde{S}$ in the high dimensional framework when $M$ is comparable to $N$. We propose two efficient estimators for $S$ under two different regimes. Our analysis relies on the local asymptotics of the eigenstructure of large dimensional rectangular matrices with finite rank perturbation. We derive the convergent limits and rates for the singular values and vectors for such matrices.

Xuan Cao, Kshitij Khare, Malay Ghosh.

Source: Bayesian Analysis, Volume 15, Number 1, 241--262.

Abstract:
The choice of tuning parameters in Bayesian variable selection is a critical problem in modern statistics. In particular, for Bayesian linear regression with non-local priors, the scale parameter in the non-local prior density is an important tuning parameter which reflects the dispersion of the non-local prior density around zero, and implicitly determines the size of the regression coefficients that will be shrunk to zero. Current approaches treat the scale parameter as given, and suggest choices based on prior coverage/asymptotic considerations. In this paper, we consider the fully Bayesian approach introduced in (Wu, 2016) with the pMOM non-local prior and an appropriate Inverse-Gamma prior on the tuning parameter to analyze the underlying theoretical property. Under standard regularity assumptions, we establish strong model selection consistency in a high-dimensional setting, where $p$ is allowed to increase at a polynomial rate with $n$ or even at a sub-exponential rate with $n$ . Through simulation studies, we demonstrate that our model selection procedure can outperform other Bayesian methods which treat the scale parameter as given, and commonly used penalized likelihood methods, in a range of simulation settings.

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).

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).

Joseph Antonelli, Giovanni Parmigiani, Francesca Dominici.

Source: Bayesian Analysis, Volume 14, Number 3, 825--848.

Abstract:
In observational studies, estimation of a causal effect of a treatment on an outcome relies on proper adjustment for confounding. If the number of the potential confounders ( $p$ ) is larger than the number of observations ( $n$ ), then direct control for all potential confounders is infeasible. Existing approaches for dimension reduction and penalization are generally aimed at predicting the outcome, and are less suited for estimation of causal effects. Under standard penalization approaches (e.g. Lasso), if a variable $X_{j}$ is strongly associated with the treatment $T$ but weakly with the outcome $Y$ , the coefficient $eta_{j}$ will be shrunk towards zero thus leading to confounding bias. Under the assumption of a linear model for the outcome and sparsity, we propose continuous spike and slab priors on the regression coefficients $eta_{j}$ corresponding to the potential confounders $X_{j}$ . Specifically, we introduce a prior distribution that does not heavily shrink to zero the coefficients ( $eta_{j}$ s) of the $X_{j}$ s that are strongly associated with $T$ but weakly associated with $Y$ . We compare our proposed approach to several state of the art methods proposed in the literature. Our proposed approach has the following features: 1) it reduces confounding bias in high dimensional settings; 2) it shrinks towards zero coefficients of instrumental variables; and 3) it achieves good coverages even in small sample sizes. We apply our approach to the National Health and Nutrition Examination Survey (NHANES) data to estimate the causal effects of persistent pesticide exposure on triglyceride levels.

Sijia Xiang, Weixin Yao, Guangren Yang.

Source: Statistical Science, Volume 34, Number 3, 391--404.

Abstract:
Finite mixture models have offered a very important tool for exploring complex data structures in many scientific areas, such as economics, epidemiology and finance. Semiparametric mixture models, which were introduced into traditional finite mixture models in the past decade, have brought forth exciting developments in their methodologies, theories, and applications. In this article, we not only provide a selective overview of the newly-developed semiparametric mixture models, but also discuss their estimation methodologies, theoretical properties if applicable, and some open questions. Recent developments are also discussed.

The capacity to process information in conceptual form is a fundamental aspect of human cognition, yet little is known about how this type of information is encoded in the brain. Although the role of sensory and motor cortical areas has been a focus of recent debate, neuroimaging studies of concept representation consistently implicate a network of heteromodal areas that seem to support concept retrieval in general rather than knowledge related to any particular sensory-motor content. We used predictive machine learning on fMRI data to investigate the hypothesis that cortical areas in this "general semantic network" (GSN) encode multimodal information derived from basic sensory-motor processes, possibly functioning as convergence–divergence zones for distributed concept representation. An encoding model based on five conceptual attributes directly related to sensory-motor experience (sound, color, shape, manipulability, and visual motion) was used to predict brain activation patterns associated with individual lexical concepts in a semantic decision task. When the analysis was restricted to voxels in the GSN, the model was able to identify the activation patterns corresponding to individual concrete concepts significantly above chance. In contrast, a model based on five perceptual attributes of the word form performed at chance level. This pattern was reversed when the analysis was restricted to areas involved in the perceptual analysis of written word forms. These results indicate that heteromodal areas involved in semantic processing encode information about the relative importance of different sensory-motor attributes of concepts, possibly by storing particular combinations of sensory and motor features.

SIGNIFICANCE STATEMENT The present study used a predictive encoding model of word semantics to decode conceptual information from neural activity in heteromodal cortical areas. The model is based on five sensory-motor attributes of word meaning (color, shape, sound, visual motion, and manipulability) and encodes the relative importance of each attribute to the meaning of a word. This is the first demonstration that heteromodal areas involved in semantic processing can discriminate between different concepts based on sensory-motor information alone. This finding indicates that the brain represents concepts as multimodal combinations of sensory and motor representations.

Barry E. Stein
Jan 2, 2020; 40:3-11
Viewpoints

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

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

Georg S. Kranz
Nov 12, 2014; 34:15466-15475
Systems/Circuits

KM Harris
Jul 1, 1992; 12:2685-2705
Articles

Fiona Doetsch
Jul 1, 1997; 17:5046-5061
Articles

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

Italian translation of Press Release about BIS General Manager Agustín Carstens giving a speech on "Money in the digital age: what role for central banks?" (6 February 2018)

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

French translation of Press Release about BIS General Manager Agustín Carstens giving a speech on "Money in the digital age: what role for central banks?" (6 February 2018)

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

French translation of press release - the Basel Committee on Banking Supervision is finalising stress-testing principles, reviews ways to stop regulatory arbitrage behaviour, agrees on annual G-SIB list, discusses leverage ratio, crypto-assets, market risk framework and implementation, 20 September 2018.

Spanish translation of press release - the Basel Committee on Banking Supervision is finalising stress-testing principles, reviews ways to stop regulatory arbitrage behaviour, agrees on annual G-SIB list, discusses leverage ratio, crypto-assets, market risk framework and implementation, 20 September 2018.

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

Traditional views of sensorimotor adaptation (i.e., adaptation of movements to perturbed sensory feedback) emphasize the role of automatic, implicit correction of sensory prediction errors. However, latent memories formed during sensorimotor adaptation, manifest as improved relearning (e.g., savings), have recently been attributed to strategic corrections of task errors (failures to achieve task goals). To dissociate contributions of task errors and sensory prediction errors to latent sensorimotor memories, we perturbed target locations to remove or enforce task errors during learning and/or test, with male/female human participants. Adaptation improved after learning in all conditions where participants were permitted to correct task errors, and did not improve whenever we prevented correction of task errors. Thus, previous correction of task errors was both necessary and sufficient to improve adaptation. In contrast, a history of sensory prediction errors was neither sufficient nor obligatory for improved adaptation. Limiting movement preparation time showed that the latent memories driven by learning to correct task errors take at least two forms: a time-consuming but flexible component, and a rapidly expressible, inflexible component. The results provide strong support for the idea that movement corrections driven by a failure to successfully achieve movement goals underpin motor memories that manifest as savings. Such persistent memories are not exclusively mediated by time-consuming strategic processes but also comprise a rapidly expressible but inflexible component. The distinct characteristics of these putative processes suggest dissociable underlying mechanisms, and imply that identification of the neural basis for adaptation and savings will require methods that allow such dissociations.

SIGNIFICANCE STATEMENT Latent motor memories formed during sensorimotor adaptation manifest as improved adaptation when sensorimotor perturbations are reencountered. Conflicting theories suggest that this "savings" is underpinned by different mechanisms, including a memory of successful actions, a memory of errors, or an aiming strategy to correct task errors. Here we show that learning to correct task errors is sufficient to show improved subsequent adaptation with respect to naive performance, even when tested in the absence of task errors. In contrast, a history of sensory prediction errors is neither sufficient nor obligatory for improved adaptation. Finally, we show that latent sensorimotor memories driven by task errors comprise at least two distinct components: a time-consuming, flexible component, and a rapidly expressible, inflexible component.

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.

It has been proposed that people can generate probabilistic predictions at multiple levels of representation during language comprehension. We used magnetoencephalography (MEG) and electroencephalography (EEG), in combination with representational similarity analysis, to seek neural evidence for the prediction of animacy features. In two studies, MEG and EEG activity was measured as human participants (both sexes) read three-sentence scenarios. Verbs in the final sentences constrained for either animate or inanimate semantic features of upcoming nouns, and the broader discourse context constrained for either a specific noun or for multiple nouns belonging to the same animacy category. We quantified the similarity between spatial patterns of brain activity following the verbs until just before the presentation of the nouns. The MEG and EEG datasets revealed converging evidence that the similarity between spatial patterns of neural activity following animate-constraining verbs was greater than following inanimate-constraining verbs. This effect could not be explained by lexical-semantic processing of the verbs themselves. We therefore suggest that it reflected the inherent difference in the semantic similarity structure of the predicted animate and inanimate nouns. Moreover, the effect was present regardless of whether a specific word could be predicted, providing strong evidence for the prediction of coarse-grained semantic features that goes beyond the prediction of individual words.

SIGNIFICANCE STATEMENT Language inputs unfold very quickly during real-time communication. By predicting ahead, we can give our brains a "head start," so that language comprehension is faster and more efficient. Although most contexts do not constrain strongly for a specific word, they do allow us to predict some upcoming information. For example, following the context of "they cautioned the...," we can predict that the next word will be animate rather than inanimate (we can caution a person, but not an object). Here, we used EEG and MEG techniques to show that the brain is able to use these contextual constraints to predict the animacy of upcoming words during sentence comprehension, and that these predictions are associated with specific spatial patterns of neural activity.

We consider the question of how sensory networks enable the detection of sensory stimuli in a combinatorial coding space. We are specifically interested in the olfactory system, wherein recent experimental studies have reported the existence of rich, enigmatic response patterns associated with stimulus onset and offset. This study aims to identify the functional relevance of such response patterns (i.e., what benefits does such neural activity provide in the context of detecting stimuli in a natural environment). We study this problem through the lens of normative, optimization-based modeling. Here, we define the notion of a low-dimensional latent representation of stimulus identity, which is generated through action of the sensory network. The objective of our optimization framework is to ensure high-fidelity tracking of a nominal representation in this latent space in an energy-efficient manner. It turns out that the optimal motifs emerging from this framework possess morphologic similarity with prototypical onset and offset responses observed in vivo in locusts (Schistocerca americana) of either sex. Furthermore, this objective can be exactly achieved by a network with reciprocal excitatory–inhibitory competitive dynamics, similar to interactions between projection neurons and local neurons in the early olfactory system of insects. The derived model also makes several predictions regarding maintenance of robust latent representations in the presence of confounding background information and trade-offs between the energy of sensory activity and resultant behavioral measures such as speed and accuracy of stimulus detection.

SIGNIFICANCE STATEMENT A key area of study in olfactory coding involves understanding the transformation from high-dimensional sensory stimulus to low-dimensional decoded representation. Here, we examine not only the dimensionality reduction of this mapping but also its temporal dynamics, with specific focus on stimuli that are temporally continuous. Through optimization-based synthesis, we examine how sensory networks can track representations without prior assumption of discrete trial structure. We show that such tracking can be achieved by canonical network architectures and dynamics, and that the resulting responses resemble observations from neurons in the insect olfactory system. Thus, our results provide hypotheses regarding the functional role of olfactory circuit activity at both single neuronal and population scales.

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

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

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.

Enjoy a low-cost, healthy diet from your very own vegetable garden and get the chance to make money by selling your own products. Start your own vegetable garden to grow, prepare and eat your own delicious fruits and vegetables with these tips: Do your research: When you begin your own vegetable garden you should understand the type of soil you work [...]

Kalu, in the Amhara region of northern Ethiopia, is home to 28-year-old Yimam Ali. However, many young people from this region of Ethiopia move to the Middle East looking for work and a better life. The amount of job opportunities in the country has not matched its growth. 71 percent of Ethiopia’s population is under the age of 30 and many [...]

Women and children, especially pregnant women and children 0-2 years of age, are among the groups most vulnerable to food insecurity and chronic malnutrition. This is no different in Mozambique where, according to the FAO Technical Secretariat for Food Security and Nutrition 35% of the population is food insecure and 43% of children under 5 are chronically malnourished. Chronic undernutrition levels [...]

When approaching the small house, Hanen can be seen laughing with her family in the yard. Hanen is a 25-year-old biologist living in Ouled Taleb, Siliana, one of several areas in North West Tunisia that are particularly hard-hit by unemployment, low income and high rates of migration. Hanen comes from a poor rural family, who invested in their children’s education [...]

Bombing ground targets from the air is tricky and not always accurate. But a new type of bomb creates an unimaginable level of destruction

The USS Silversides is patrolling the Pacific during WWII when it finds itself in a terrifying situation: one of its torpedoes has jammed

Built nearly 4,700 years ago as a tomb for the pharaoh Djoser, the structure underwent more than a decade of on-and-off restorations

It’s not quite a meat dress, but Kaikaia gaga does boast some impressive horn-like appendages

Due to COVID-19, Art + Action's "Come to Your Census" project has pivoted from posters and events to social media and online outreach

A treasure trove of new fossils unearthed in Australia reveals some raunchily-positioned bugs

New research indicates that Iron Age Britons venerated brown hares and chickens long before modern Easter celebrations

Currently in the works, the new monument will honor her contributions and legacy with a contemplative space

Buried in a mass grave in Mexico City, the trio may have been part of the first generation abducted from their homeland and brought to the New World

A new book charts the history of counting the public, from the ancient censuses in Rome to the American version of decennial data collection

Darin Isaac was elected on Wednesday as the new chief of the Selkirk First Nation in Yukon. Isaac also held the position for two terms from 2005 to 2011. He has also served as a councillor for three terms.

The Computer Emergency Response Team of Mauritius (CERT-MU) in collaboration with the Command and Control Centre of Kenya organised a 3-day training programme on Cyber Defense Monitoring and Forensics at Voilà Hotel, Bagatelle from the 27^th February – 1^st March 2018. The training course provided an introduction to Network Security Monitoring (NSM), Security Information and Events Management (SIEM), Malware Analysis and Digital Forensics. Major part of the course was hands-on case studies and analysis exercises using real world data. The main focus of the training programme was on intensive hands-on sessions on addressing key challenges faced by local organizations in all sectors/industries. A wide range of commercial and open source tools were used to equip cyber defenders with the necessary skills to anticipate, detect, respond and contain adversaries. The training programme was followed by 23 participants from the public and private sector. 


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Service Team members share why they enjoy being onsite during Setup Week, when the vision of TeenStreet becomes a reality for them, and what part of the upcoming conference they are most excited for.