An accurate and prompt diagnosis of pediatric pneumonia is imperative for successful treatment intervention. One approach to diagnose pneumonia cases is using radiographic data. In this article, we propose a novel parsimonious scalar-on-image classification model adopting the ideas of functional data analysis. Our main idea is to treat images as functional measurements and exploit underlying covariance structures to select basis functions; these bases are then used in approximating both image profiles and corresponding regression coefficient. We re-express the regression model into a standard generalized linear model where the functional principal component scores are treated as covariates. We apply the method to (1) classify pneumonia against healthy and viral against bacterial pneumonia patients, and (2) test the null effect about the association between images and responses. Extensive simulation studies show excellent numerical performance in terms of classification, hypothesis testing, and efficient computation.

Surveys provide secure, anonymous feedback from staff at all levels of healthcare organizations

(PRWeb May 06, 2020)

Read the full story at https://www.prweb.com/releases/health_worker_data_alliance_monitoring_emotional_physical_and_occupational_health_of_healthcare_workers_during_covid_19/prweb17101008.htm

Michael Schweinberger, Jonathan Stewart.

Source: The Annals of Statistics, Volume 48, Number 1, 374--396.

Abstract:
Statistical inference for exponential-family models of random graphs with dependent edges is challenging. We stress the importance of additional structure and show that additional structure facilitates statistical inference. A simple example of a random graph with additional structure is a random graph with neighborhoods and local dependence within neighborhoods. We develop the first concentration and consistency results for maximum likelihood and $M$-estimators of a wide range of canonical and curved exponential-family models of random graphs with local dependence. All results are nonasymptotic and applicable to random graphs with finite populations of nodes, although asymptotic consistency results can be obtained as well. In addition, we show that additional structure can facilitate subgraph-to-graph estimation, and present concentration results for subgraph-to-graph estimators. As an application, we consider popular curved exponential-family models of random graphs, with local dependence induced by transitivity and parameter vectors whose dimensions depend on the number of nodes.

Qiyang Han, Tengyao Wang, Sabyasachi Chatterjee, Richard J. Samworth.

Source: The Annals of Statistics, Volume 47, Number 5, 2440--2471.

Abstract:
We study the least squares regression function estimator over the class of real-valued functions on $[0,1]^{d}$ that are increasing in each coordinate. For uniformly bounded signals and with a fixed, cubic lattice design, we establish that the estimator achieves the minimax rate of order $n^{-min{2/(d+2),1/d}}$ in the empirical $L_{2}$ loss, up to polylogarithmic factors. Further, we prove a sharp oracle inequality, which reveals in particular that when the true regression function is piecewise constant on $k$ hyperrectangles, the least squares estimator enjoys a faster, adaptive rate of convergence of $(k/n)^{min(1,2/d)}$, again up to polylogarithmic factors. Previous results are confined to the case $dleq2$. Finally, we establish corresponding bounds (which are new even in the case $d=2$) in the more challenging random design setting. There are two surprising features of these results: first, they demonstrate that it is possible for a global empirical risk minimisation procedure to be rate optimal up to polylogarithmic factors even when the corresponding entropy integral for the function class diverges rapidly; second, they indicate that the adaptation rate for shape-constrained estimators can be strictly worse than the parametric rate.

Zhenguo Gao, Pang Du, Ran Jin, John L. Robertson.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 143--159.

Abstract:
Liver procurement experiments with surface-temperature monitoring motivated Gao et al. ( J. Amer. Statist. Assoc. 114 (2019) 773–781) to develop a variance change-point detection method under a smoothly-changing mean trend. However, the spotwise change points yielded from their method do not offer immediate information to surgeons since an organ is often transplanted as a whole or in part. We develop a new practical method that can analyze a defined portion of the organ surface at a time. It also provides a novel addition to the developing field of functional data monitoring. Furthermore, numerical challenge emerges for simultaneously modeling the variance functions of 2D locations and the mean function of location and time. The respective sample sizes in the scales of 10,000 and 1,000,000 for modeling these functions make standard spline estimation too costly to be useful. We introduce a multistage subsampling strategy with steps educated by quickly-computable preliminary statistical measures. Extensive simulations show that the new method can efficiently reduce the computational cost and provide reasonable parameter estimates. Application of the new method to our liver surface temperature monitoring data shows its effectiveness in providing accurate status change information for a selected portion of the organ in the experiment.

Paul J. Birrell, Lorenz Wernisch, Brian D. M. Tom, Leonhard Held, Gareth O. Roberts, Richard G. Pebody, Daniela De Angelis.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 74--93.

Abstract:
A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated, potentially biased and originating from multiple sources. This seriously challenges the capacity for real-time monitoring. Here, we assess the feasibility of real-time inference based on such data by constructing an analytic tool combining an age-stratified SEIR transmission model with various observation models describing the data generation mechanisms. As batches of data become available, a sequential Monte Carlo (SMC) algorithm is developed to synthesise multiple imperfect data streams, iterate epidemic inferences and assess model adequacy amidst a rapidly evolving epidemic environment, substantially reducing computation time in comparison to standard MCMC, to ensure timely delivery of real-time epidemic assessments. In application to simulated data designed to mimic the 2009 A/H1N1 epidemic, SMC is shown to have additional benefits in terms of assessing predictive performance and coping with parameter nonidentifiability.

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 $1$.

Zhi-Qiang Wang, Nian-Sheng Tang.

Source: Bayesian Analysis, Volume 15, Number 2, 579--604.

Abstract:
Bayesian inference on quantile regression (QR) model with mixed discrete and non-ignorable missing covariates is conducted by reformulating QR model as a hierarchical structure model. A probit regression model is adopted to specify missing covariate mechanism. A hybrid algorithm combining the Gibbs sampler and the Metropolis-Hastings algorithm is developed to simultaneously produce Bayesian estimates of unknown parameters and latent variables as well as their corresponding standard errors. Bayesian variable selection method is proposed to recognize significant covariates. A Bayesian local influence procedure is presented to assess the effect of minor perturbations to the data, priors and sampling distributions on posterior quantities of interest. Several simulation studies and an example are presented to illustrate the proposed methodologies.

Amanda Avona
May 29, 2019; 39:4323-4331
Systems/Circuits

P Seguela
Feb 1, 1993; 13:596-604
Articles

Peter Klivenyi
Jan 1, 2000; 20:1-7
Cellular

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

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

The parabrachial (PB) complex mediates both ascending nociceptive signaling and descending pain modulatory information in the affective/emotional pain pathway. We have recently reported that chronic pain is associated with amplified activity of PB neurons in a rat model of neuropathic pain. Here we demonstrate that similar activity amplification occurs in mice, and that this is related to suppressed inhibition to lateral parabrachial (LPB) neurons from the CeA in animals of either sex. Animals with pain after chronic constriction injury of the infraorbital nerve (CCI-Pain) displayed higher spontaneous and evoked activity in PB neurons, and a dramatic increase in after-discharges, responses that far outlast the stimulus, compared with controls. LPB neurons in CCI-Pain animals showed a reduction in inhibitory, GABAergic inputs. We show that, in both rats and mice, LPB contains few GABAergic neurons, and that most of its GABAergic inputs arise from CeA. These CeA GABA neurons express dynorphin, somatostatin, and/or corticotropin releasing hormone. We find that the efficacy of this CeA-LPB pathway is suppressed in chronic pain. Further, optogenetically stimulating this pathway suppresses acute pain, and inhibiting it, in naive animals, evokes pain behaviors. These findings demonstrate that the CeA-LPB pathway is critically involved in pain regulation, and in the pathogenesis of chronic pain.

SIGNIFICANCE STATEMENT We describe a novel pathway, consisting of inhibition by dynorphin, somatostatin, and corticotropin-releasing hormone-expressing neurons in the CeA that project to the parabrachial nucleus. We show that this pathway regulates the activity of pain-related neurons in parabrachial nucleus, and that, in chronic pain, this inhibitory pathway is suppressed, and that this suppression is causally related to pain perception. We propose that this amygdalo-parabrachial pathway is a key regulator of both chronic and acute pain, and a novel target for pain relief.

Dopaminergic neurons innervate extensive areas of the brain and release dopamine (DA) onto a wide range of target neurons. However, DA release is also precisely regulated. In Drosophila melanogaster brain explant preparations, DA is released specifically onto α3/α'3 compartments of mushroom body (MB) neurons that have been coincidentally activated by cholinergic and glutamatergic inputs. The mechanism for this precise release has been unclear. Here we found that coincidentally activated MB neurons generate carbon monoxide (CO), which functions as a retrograde signal evoking local DA release from presynaptic terminals. CO production depends on activity of heme oxygenase in postsynaptic MB neurons, and CO-evoked DA release requires Ca²⁺ efflux through ryanodine receptors in DA terminals. CO is only produced in MB areas receiving coincident activation, and removal of CO using scavengers blocks DA release. We propose that DA neurons use two distinct modes of transmission to produce global and local DA signaling.

SIGNIFICANCE STATEMENT Dopamine (DA) is needed for various higher brain functions, including memory formation. However, DA neurons form extensive synaptic connections, while memory formation requires highly specific and localized DA release. Here we identify a mechanism through which DA release from presynaptic terminals is controlled by postsynaptic activity. Postsynaptic neurons activated by cholinergic and glutamatergic inputs generate carbon monoxide, which acts as a retrograde messenger inducing presynaptic DA release. Released DA is required for memory-associated plasticity. Our work identifies a novel mechanism that restricts DA release to the specific postsynaptic sites that require DA during memory formation.

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.

From a media bed unit start-up in Bangkok to a fully developed 120 households deep water culture (DWC) unit in Ethiopia, aquaponics is showcasing its true potential to produce sustainable food anytime, anywhere. A marriage between aquaculture (raising aquatic animals such as fish, snails or prawns in tanks) and hydroponics (cultivating plants in water), aquaponics is a ‘clean and green’ [...]

What does William Shakespeare have in common with Mexican beekeeper Francisco Lenin Bartolo Reyes? Both men understand the importance of the honey bee, a small but invaluable ally of the human race.

The exact molecule behind the Minamata mercury disaster, caused by a chemical plant’s wastewater, remains a point of disagreement

Storms reveal, then hide, the ship's sand-covered remains every decade or so

Some 50 artists around the Prenzlauer Berg district displayed works of art for passersby to enjoy

The story behind the image, and its removal, led to mixed reactions from the public, including native communities

Over the past three years, the disease has claimed the lives of seven of the famously resilient ponies

The simultaneous mating calls of the male pug-nosed tree frog confuses bats but not female frogs

Narrated by Smithsonian Distinguished Scholar Richard Kurin, the 24-part video series blends history with modern mainstays

Airing on the aquarium's Twitch channel at 4 p.m. EST today, the two-hour session will focus on the video game's diverse insect population

With Washington, D.C. COVID-19 state of emergency, Smithsonian officials say museums to close Saturday, March 14; events canceled through May 3

In an official statement, the Institution announced temporary closures beginning Saturday, March 14

How do you select one image to represent half a century of photography and art? You don’t

When this publication first appeared five decades ago, it was happy to join the fray

Do some armchair traveling and see the breathtaking vistas of all 50 states while learning about their histories

From Vietnam to Antarctica, this year's winners bring you amazing glimpses of a changing world—and the indefatigable human spirit

The new viruses are not harmful to humans or closely related to SARS-CoV-2, the coronavirus that causes COVID-19

Test your mettle with this puzzle created exclusively for our readers

Find the capital cities, working from a list of state names

These paintings, sculptures and illustrations honor the bonds of motherhood

Set of American Flamingos at Rio Lagartos Biosphere Reserve in Mexico, march in a synchronized formation whilst trying to find some food

Tour a gallery of presidential portraits, print a 3-D model of a fossil or volunteer to transcribe historical documents

In a nation under quarantine, chronicling a crisis demands careful strategy

See how researchers at Smithsonian's National Zoo are trying to glean insight into elephant foraging behavior and more.

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.