Adaptive Resetting of Tuberoinfundibular Dopamine (TIDA) Network Activity during Lactation in Mice
Giving birth triggers a wide repertoire of physiological and behavioral changes in the mother to enable her to feed and care for her offspring. These changes require coordination and are often orchestrated from the CNS, through as of yet poorly understood mechanisms. A neuronal population with a central role in puerperal changes is the tuberoinfundibular dopamine (TIDA) neurons that control release of the pituitary hormone, prolactin, which triggers key maternal adaptations, including lactation and maternal care. Here, we used Ca2+ imaging on mice from both sexes and whole-cell recordings on female mouse TIDA neurons in vitro to examine whether they adapt their cellular and network activity according to reproductive state. In the high-prolactin state of lactation, TIDA neurons shift to faster membrane potential oscillations, a reconfiguration that reverses upon weaning. During the estrous cycle, however, which includes a brief, but pronounced, prolactin peak, oscillation frequency remains stable. An increase in the hyperpolarization-activated mixed cation current, Ih, possibly through unmasking as dopamine release drops during nursing, may partially explain the reconfiguration of TIDA rhythms. These findings identify a reversible plasticity in hypothalamic network activity that can serve to adapt the dam for motherhood.
SIGNIFICANCE STATEMENT Motherhood requires profound behavioral and physiological adaptations to enable caring for offspring, but the underlying CNS changes are poorly understood. Here, we show that, during lactation, neuroendocrine dopamine neurons, the "TIDA" cells that control prolactin secretion, reorganize their trademark oscillations to discharge in faster frequencies. Unlike previous studies, which typically have focused on structural and transcriptional changes during pregnancy and lactation, we demonstrate a functional switch in activity and one that, distinct from previously described puerperal modifications, reverses fully on weaning. We further provide evidence that a specific conductance (Ih) contributes to the altered network rhythm. These findings identify a new facet of maternal brain plasticity at the level of membrane properties and consequent ensemble activity.
A Patchwork of Useful Things
Adobe just announced what it calls the first digital economy index. It seems like it's modeled after other indices usually kept by the federal government to measure economic output and consumption. The Adobe index captures only consumer consumption behavior though. Some of its insights include new shopping behavior, such as which products have become hot items or decreased in popularity over time.
Ad Makers Use Deepfakes to 'Refresh' Old Content
With measures to stem the spread of COVID-19 putting a chokehold on their filming capabilities, advertising agencies are enhancing old content with new tech, including deepfakes. Deepfakes typically blend one person's likeness, or parts thereof, with the image of another person. Ad agencies are so restricted in how they can generate content, they'll explore anything that can be computer-generated.
Adaptive representation of dynamics during learning of a motor task
R Shadmehr
May 1, 1994; 14:3208-3224
Articles
The Pain of Sleep Loss: A Brain Characterization in Humans
Adam J. Krause
Mar 20, 2019; 39:2291-2300
BehavioralSystemsCognitive
Advances in Enteric Neurobiology: The "Brain" in the Gut in Health and Disease
Subhash Kulkarni
Oct 31, 2018; 38:9346-9354
Symposium and Mini-Symposium
Adaptive Bayesian Nonparametric Regression Using a Kernel Mixture of Polynomials with Application to Partial Linear Models
Fangzheng Xie, Yanxun Xu.
Source: Bayesian Analysis, Volume 15, Number 1, 159--186.
Abstract:
We propose a kernel mixture of polynomials prior for Bayesian nonparametric regression. The regression function is modeled by local averages of polynomials with kernel mixture weights. We obtain the minimax-optimal contraction rate of the full posterior distribution up to a logarithmic factor by estimating metric entropies of certain function classes. Under the assumption that the degree of the polynomials is larger than the unknown smoothness level of the true function, the posterior contraction behavior can adapt to this smoothness level provided an upper bound is known. We also provide a frequentist sieve maximum likelihood estimator with a near-optimal convergence rate. We further investigate the application of the kernel mixture of polynomials to partial linear models and obtain both the near-optimal rate of contraction for the nonparametric component and the Bernstein-von Mises limit (i.e., asymptotic normality) of the parametric component. The proposed method is illustrated with numerical examples and shows superior performance in terms of computational efficiency, accuracy, and uncertainty quantification compared to the local polynomial regression, DiceKriging, and the robust Gaussian stochastic process.
Additive Multivariate Gaussian Processes for Joint Species Distribution Modeling with Heterogeneous Data
Jarno Vanhatalo, Marcelo Hartmann, Lari Veneranta.
Source: Bayesian Analysis, Volume 15, Number 2, 415--447.
Abstract:
Species distribution models (SDM) are a key tool in ecology, conservation and management of natural resources. Two key components of the state-of-the-art SDMs are the description for species distribution response along environmental covariates and the spatial random effect that captures deviations from the distribution patterns explained by environmental covariates. Joint species distribution models (JSDMs) additionally include interspecific correlations which have been shown to improve their descriptive and predictive performance compared to single species models. However, current JSDMs are restricted to hierarchical generalized linear modeling framework. Their limitation is that parametric models have trouble in explaining changes in abundance due, for example, highly non-linear physical tolerance limits which is particularly important when predicting species distribution in new areas or under scenarios of environmental change. On the other hand, semi-parametric response functions have been shown to improve the predictive performance of SDMs in these tasks in single species models.
Here, we propose JSDMs where the responses to environmental covariates are modeled with additive multivariate Gaussian processes coded as linear models of coregionalization. These allow inference for wide range of functional forms and interspecific correlations between the responses. We propose also an efficient approach for inference with Laplace approximation and parameterization of the interspecific covariance matrices on the Euclidean space. We demonstrate the benefits of our model with two small scale examples and one real world case study. We use cross-validation to compare the proposed model to analogous semi-parametric single species models and parametric single and joint species models in interpolation and extrapolation tasks. The proposed model outperforms the alternative models in all cases. We also show that the proposed model can be seen as an extension of the current state-of-the-art JSDMs to semi-parametric models.
Additive models with trend filtering
Veeranjaneyulu Sadhanala, Ryan J. Tibshirani.
Source: The Annals of Statistics, Volume 47, Number 6, 3032--3068.
Abstract:
We study additive models built with trend filtering, that is, additive models whose components are each regularized by the (discrete) total variation of their $k$th (discrete) derivative, for a chosen integer $kgeq0$. This results in $k$th degree piecewise polynomial components, (e.g., $k=0$ gives piecewise constant components, $k=1$ gives piecewise linear, $k=2$ gives piecewise quadratic, etc.). Analogous to its advantages in the univariate case, additive trend filtering has favorable theoretical and computational properties, thanks in large part to the localized nature of the (discrete) total variation regularizer that it uses. On the theory side, we derive fast error rates for additive trend filtering estimates, and show these rates are minimax optimal when the underlying function is additive and has component functions whose derivatives are of bounded variation. We also show that these rates are unattainable by additive smoothing splines (and by additive models built from linear smoothers, in general). On the computational side, we use backfitting, to leverage fast univariate trend filtering solvers; we also describe a new backfitting algorithm whose iterations can be run in parallel, which (as far as we can tell) is the first of its kind. Lastly, we present a number of experiments to examine the empirical performance of trend filtering.
Adaptive estimation of the rank of the coefficient matrix in high-dimensional multivariate response regression models
Xin Bing, Marten H. Wegkamp.
Source: The Annals of Statistics, Volume 47, Number 6, 3157--3184.
Abstract:
We consider the multivariate response regression problem with a regression coefficient matrix of low, unknown rank. In this setting, we analyze a new criterion for selecting the optimal reduced rank. This criterion differs notably from the one proposed in Bunea, She and Wegkamp ( Ann. Statist. 39 (2011) 1282–1309) in that it does not require estimation of the unknown variance of the noise, nor does it depend on a delicate choice of a tuning parameter. We develop an iterative, fully data-driven procedure, that adapts to the optimal signal-to-noise ratio. This procedure finds the true rank in a few steps with overwhelming probability. At each step, our estimate increases, while at the same time it does not exceed the true rank. Our finite sample results hold for any sample size and any dimension, even when the number of responses and of covariates grow much faster than the number of observations. We perform an extensive simulation study that confirms our theoretical findings. The new method performs better and is more stable than the procedure of Bunea, She and Wegkamp ( Ann. Statist. 39 (2011) 1282–1309) in both low- and high-dimensional settings.
Adaptive risk bounds in univariate total variation denoising and trend filtering
Adityanand Guntuboyina, Donovan Lieu, Sabyasachi Chatterjee, Bodhisattva Sen.
Source: The Annals of Statistics, Volume 48, Number 1, 205--229.
Abstract:
We study trend filtering, a relatively recent method for univariate nonparametric regression. For a given integer $rgeq1$, the $r$th order trend filtering estimator is defined as the minimizer of the sum of squared errors when we constrain (or penalize) the sum of the absolute $r$th order discrete derivatives of the fitted function at the design points. For $r=1$, the estimator reduces to total variation regularization which has received much attention in the statistics and image processing literature. In this paper, we study the performance of the trend filtering estimator for every $rgeq1$, both in the constrained and penalized forms. Our main results show that in the strong sparsity setting when the underlying function is a (discrete) spline with few “knots,” the risk (under the global squared error loss) of the trend filtering estimator (with an appropriate choice of the tuning parameter) achieves the parametric $n^{-1}$-rate, up to a logarithmic (multiplicative) factor. Our results therefore provide support for the use of trend filtering, for every $rgeq1$, in the strong sparsity setting.
Adaptive Invariance for Molecule Property Prediction. (arXiv:2005.03004v1 [q-bio.QM])
Effective property prediction methods can help accelerate the search for COVID-19 antivirals either through accurate in-silico screens or by effectively guiding on-going at-scale experimental efforts. However, existing prediction tools have limited ability to accommodate scarce or fragmented training data currently available. In this paper, we introduce a novel approach to learn predictors that can generalize or extrapolate beyond the heterogeneous data. Our method builds on and extends recently proposed invariant risk minimization, adaptively forcing the predictor to avoid nuisance variation. We achieve this by continually exercising and manipulating latent representations of molecules to highlight undesirable variation to the predictor. To test the method we use a combination of three data sources: SARS-CoV-2 antiviral screening data, molecular fragments that bind to SARS-CoV-2 main protease and large screening data for SARS-CoV-1. Our predictor outperforms state-of-the-art transfer learning methods by significant margin. We also report the top 20 predictions of our model on Broad drug repurposing hub.
Additive Bayesian variable selection under censoring and misspecification. (arXiv:1907.13563v3 [stat.ME] UPDATED)
We study the interplay of two important issues on Bayesian model selection (BMS): censoring and model misspecification. We consider additive accelerated failure time (AAFT), Cox proportional hazards and probit models, and a more general concave log-likelihood structure. A fundamental question is what solution can one hope BMS to provide, when (inevitably) models are misspecified. We show that asymptotically BMS keeps any covariate with predictive power for either the outcome or censoring times, and discards other covariates. Misspecification refers to assuming the wrong model or functional effect on the response, including using a finite basis for a truly non-parametric effect, or omitting truly relevant covariates. We argue for using simple models that are computationally practical yet attain good power to detect potentially complex effects, despite misspecification. Misspecification and censoring both have an asymptotically negligible effect on (suitably-defined) false positives, but their impact on power is exponential. We portray these issues via simple descriptions of early/late censoring and the drop in predictive accuracy due to misspecification. From a methods point of view, we consider local priors and a novel structure that combines local and non-local priors to enforce sparsity. We develop algorithms to capitalize on the AAFT tractability, approximations to AAFT and probit likelihoods giving significant computational gains, a simple augmented Gibbs sampler to hierarchically explore linear and non-linear effects, and an implementation in the R package mombf. We illustrate the proposed methods and others based on likelihood penalties via extensive simulations under misspecification and censoring. We present two applications concerning the effect of gene expression on colon and breast cancer.
Adaptive clinical trial designs for phase I cancer studies
Oleksandr Sverdlov, Weng Kee Wong, Yevgen Ryeznik.
Source: Statistics Surveys, Volume 8, 2--44.
Abstract:
Adaptive clinical trials are becoming increasingly popular research designs for clinical investigation. Adaptive designs are particularly useful in phase I cancer studies where clinical data are scant and the goals are to assess the drug dose-toxicity profile and to determine the maximum tolerated dose while minimizing the number of study patients treated at suboptimal dose levels.
In the current work we give an overview of adaptive design methods for phase I cancer trials. We find that modern statistical literature is replete with novel adaptive designs that have clearly defined objectives and established statistical properties, and are shown to outperform conventional dose finding methods such as the 3+3 design, both in terms of statistical efficiency and in terms of minimizing the number of patients treated at highly toxic or nonefficacious doses. We discuss statistical, logistical, and regulatory aspects of these designs and present some links to non-commercial statistical software for implementing these methods in practice.
Additive monotone regression in high and lower dimensions
Solveig Engebretsen, Ingrid K. Glad.
Source: Statistics Surveys, Volume 13, 1--51.
Abstract:
In numerous problems where the aim is to estimate the effect of a predictor variable on a response, one can assume a monotone relationship. For example, dose-effect models in medicine are of this type. In a multiple regression setting, additive monotone regression models assume that each predictor has a monotone effect on the response. In this paper, we present an overview and comparison of very recent frequentist methods for fitting additive monotone regression models. Three of the methods we present can be used both in the high dimensional setting, where the number of parameters $p$ exceeds the number of observations $n$, and in the classical multiple setting where $1<pleq n$. However, many of the most recent methods only apply to the classical setting. The methods are compared through simulation experiments in terms of efficiency, prediction error and variable selection properties in both settings, and they are applied to the Boston housing data. We conclude with some recommendations on when the various methods perform best.
Adaptive two-treatment three-period crossover design for normal responses
Uttam Bandyopadhyay, Shirsendu Mukherjee, Atanu Biswas.
Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 291--303.
Abstract:
In adaptive crossover design, our goal is to allocate more patients to a promising treatment sequence. The present work contains a very simple three period crossover design for two competing treatments where the allocation in period 3 is done on the basis of the data obtained from the first two periods. Assuming normality of response variables we use a reliability functional for the choice between two treatments. We calculate the allocation proportions and their standard errors corresponding to the possible treatment combinations. We also derive some asymptotic results and provide solutions on related inferential problems. Moreover, the proposed procedure is compared with a possible competitor. Finally, we use a data set to illustrate the applicability of the proposed design.
Adaptive estimation in the supremum norm for semiparametric mixtures of regressions
Heiko Werner, Hajo Holzmann, Pierre Vandekerkhove.
Source: Electronic Journal of Statistics, Volume 14, Number 1, 1816--1871.
Abstract:
We investigate a flexible two-component semiparametric mixture of regressions model, in which one of the conditional component distributions of the response given the covariate is unknown but assumed symmetric about a location parameter, while the other is specified up to a scale parameter. The location and scale parameters together with the proportion are allowed to depend nonparametrically on covariates. After settling identifiability, we provide local M-estimators for these parameters which converge in the sup-norm at the optimal rates over Hölder-smoothness classes. We also introduce an adaptive version of the estimators based on the Lepski-method. Sup-norm bounds show that the local M-estimator properly estimates the functions globally, and are the first step in the construction of useful inferential tools such as confidence bands. In our analysis we develop general results about rates of convergence in the sup-norm as well as adaptive estimation of local M-estimators which might be of some independent interest, and which can also be applied in various other settings. We investigate the finite-sample behaviour of our method in a simulation study, and give an illustration to a real data set from bioinformatics.
Addicted women : family dynamics, self perceptions, and support systems.
Rockville, Maryland : National Institute on Drug Abuse, 1979.
Addict aftercare : recovery training and self-help / Fred Zackon, William E. McAuliffe, James M.N. Ch'ien.
Adolescent drug abuse : analyses of treatment research / editors, Elizabeth R. Rahdert, John Grabowski.
Rockville, Maryland : National Institute on Drug Abuse, 1988.
Administrative scheme for the County of London made by the London County Council on 18th December, 1934, for discharging the functions transferred to the Council by Part I of the Local Government Act, 1929, and orders made bu the Minister of Health under
England : London County Council, Public Assistance Department, 1935.
Adelaide Festival 60 years : 1960-2020 / Edited by Catherine McKinnon ; Pictorial editors and research: Sheree Tirrell and Colin Koch.
Adult Safeguarding - A rights based approach in responding to Elder Abuse - Elicia White_SLIDES.
Administration issues for winding up the legal practice / presented by Sean Burton CA & Simone Ong CA, CTA, Nexia Edwards Marshall.
Adoption of New Science Standards May Start With Rhode Island
Rhode Island may become the first state to adopt the Next Generation Science Standards.
Eight States Add Citizenship Test as Graduation Requirement
Advocates have plans to push more state legislatures to pass laws requiring high schoolers to pass a citizenship test in order to graduate in coming years.
Budget Cuts Lead Wyoming to Scale Back Relationship With Accrediting Agency
AdvancED, the national accreditation company, has for the last two years operated Wyoming's entire accreditation process but the state will now do the work on its own.
Is Addiction Hereditary?
Addiction is a major health problem, both mentally and physically. In fact, it is probably one of the most complicated illnesses to deal with because it is has to be dealt with on both a physical and psychological level. Approximately one in eight adults struggle with drug and alcohol addiction at the same time and […]
Addressing Trauma in Young Children in Immigrant and Refugee Families through Early Childhood Programs
During this webinar, speakers provide an overview of an MPI policy brief that seeks to raise awareness of the intersection of trauma and early childhood development, and how U.S. early childhood programs could more effectively address this trauma in young children in refugee and immigrant households. The participants discuss efforts to integrate trauma-informed approaches into early childhood systems and how home visiting services can effectively address trauma and mental health through a two-generation approach.
Addressing Trauma in Young Children in Immigrant and Refugee Families through Early Childhood Programs
During this webinar, speakers discuss a MPI policy brief that explores the intersection of trauma and early childhood development, exploring how migration-related trauma and stressors can influence the wellbeing of young children of immigrants, and points to key opportunities for states to support, through early childhood and other programs.
Adolescent Obesity and Early-Onset Type 2 Diabetes
Type 2 diabetes (T2D) is increasingly diagnosed at younger ages. We investigated the association of adolescent obesity with incident T2D at early adulthood. A nationwide, population-based study evaluated 1,462,362 adolescents (59% men, mean age 17.4 years) during 1996–2016. Data were linked to the Israeli National Diabetes Registry. Weight and height were measured at study entry. Cox proportional models were applied. During 15,810,751 person-years, 2,177 people (69% men) developed T2D (mean age at diagnosis 27 years). There was an interaction among BMI, sex, and incident T2D (Pinteraction = 0.023). In a model adjusted for sociodemographic variables, the hazard ratios for diabetes diagnosis were 1.7 (95% CI 1.4–2.0), 2.8 (2.3–3.5), 5.8 (4.9–6.9), 13.4 (11.5–15.7), and 25.8 (21.0–31.6) among men in the 50th–74th percentile, 75th–84th percentile, overweight, mild obesity, and severe obesity groups, respectively, and 2.2 (1.6–2.9), 3.4 (2.5–4.6), 10.6 (8.3–13.6), 21.1 (16.0–27.8), and 44.7 (32.4–61.5), respectively, in women. An inverse graded relationship was observed between baseline BMI and mean age of T2D diagnosis: 27.8 and 25.9 years among men and women with severe obesity, respectively, and 29.5 and 28.5 years among low-normal BMI (5th–49th percentile; reference), respectively. The projected fractions of adult-onset T2D that were attributed to high BMI (≥85th percentile) at adolescence were 56.9% (53.8–59.9%) and 61.1% (56.8–65.2%) in men and women, respectively. Severe obesity significantly increases the risk for incidence of T2D in early adulthood in both sexes. The rise in adolescent severe obesity is likely to increase diabetes incidence in young adults in coming decades.
ADA tip sheet includes CDC guidance on identifying counterfeit N95 masks
The American Dental Association has created a tip sheet with guidance from the National Institute for Occupational Safety and Health group at the Centers for Disease Control and Prevention to help health care professionals avoid buying or using counterfeit N95 respirators, which are often simply referred to as masks.
ADA urges CDC to update guidance for dental personnel
The American Dental Association is urging the Centers for Disease Control and Prevention to “quickly provide guidance” on how to safely reopen dental practices during the deceleration phase of the COVID-19 outbreak.
ADA asks Congress to help dental community in next COVID-19 legislation
As Congress works on the next COVID-19 relief package, the ADA is asking lawmakers to include a number of provisions to assist the dental profession in recovery efforts.
ADASRI manuscript wins 2020 William J. Gies Award in clinical research
A manuscript authored by the American Dental Association Science & Research Institute and Council on Scientific Affairs won the 2020 William J. Gies Award in clinical research from the American and International Associations for Dental Research.
ADA Member Advantage endorses Best Card for credit card processing
ADA Member Advantage announced May 1 that it has selected Best Card as its exclusively endorsed credit card processing solution for Association members.