Optimal immune-based therapies for type 1 diabetes (T1D) should restore self-tolerance without inducing chronic immunosuppression. CD4⁺Foxp3⁺ regulatory T cells (T_regs) are a key cell population capable of facilitating durable immune tolerance. However, clinical trials with expanded T_regs in T1D and solid-organ transplant recipients are limited by poor T_reg engraftment without host manipulation. We showed that T_reg engraftment and therapeutic benefit in nonautoimmune models required ablative host conditioning. Here, we evaluated T_reg engraftment and therapeutic efficacy in the nonobese diabetic (NOD) mouse model of autoimmune diabetes using nonablative, combinatorial regimens involving the anti-CD3 (αCD3), cyclophosphamide (CyP), and IAC (IL-2/JES6–1) antibody complex. We demonstrate that αCD3 alone induced substantial T-cell depletion, impacting both conventional T cells (T_conv) and T_regs, subsequently followed by more rapid rebound of T_regs. Despite robust depletion of host T_conv and host T_regs, donor T_regs failed to engraft even with interleukin-2 (IL-2) support. A single dose of CyP after αCD3 depleted rebounding host T_regs and resulted in a 43-fold increase in donor T_reg engraftment, yet polyclonal donor T_regs failed to reverse diabetes. However, infusion of autoantigen-specific T_regs after αCD3 alone resulted in robust T_reg engraftment within the islets and induced remission in all mice. This novel combinatorial therapy promotes engraftment of autoantigen-specific donor T_regs and controls islet autoimmunity without long-term immunosuppression.

Autoimmunity against pancreatic β-cell autoantigens is a characteristic of childhood type 1 diabetes (T1D). Autoimmunity usually appears in genetically susceptible children with the development of autoantibodies against (pro)insulin in early childhood. The offspring of mothers with T1D are protected from this process. The aim of this study was to determine whether the protection conferred by maternal T1D is associated with improved neonatal tolerance against (pro)insulin. Consistent with improved neonatal tolerance, the offspring of mothers with T1D had reduced cord blood CD4⁺ T-cell responses to proinsulin and insulin, a reduction in the inflammatory profile of their proinsulin-responsive CD4⁺ T cells, and improved regulation of CD4⁺ T cell responses to proinsulin at 9 months of age, as compared with offspring with a father or sibling with T1D. Maternal T1D was also associated with a modest reduction in CpG methylation of the INS gene in cord blood mononuclear cells from offspring with a susceptible INS genotype. Our findings support the concept that a maternal T1D environment improves neonatal immune tolerance against the autoantigen (pro)insulin.

OBJECTIVE

Sustained excess BMI increases the risk of type 1 diabetes (T1D) in autoantibody-positive relatives without diabetes of patients. We tested whether elevated BMI also accelerates the progression of islet autoimmunity before T1D diagnosis.

Jing Hughes
Apr 1, 2015; 38:e55-e57
e-Letters: Observations

The U.S. Food and Drug Administration announced March 24 that it is alerting health care professionals, patients and caregivers of EpiPen autoinjector errors related to device malfunctions and user administration.

Hey, it’s been quite some time without updates on this front, but our latest updates to our Android and iOS…

OBJECTIVE

We conducted an open-label, phase I study using autologous umbilical cord blood (UCB) infusion to ameliorate type 1 diabetes (T1D). Having previously reported on the first 15 patients reaching 1 year of follow-up, herein we report on the complete cohort after 2 years of follow-up.

Antonenko, Andrii, 1922-

Celiac disease.

Wien : M. Perles, 1878.

Carlsruhae : D.R. Marx, 1819.

Xuening Zhu.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1413--1448.

Abstract:
High dimensional time series receive considerable attention recently, whose temporal and cross-sectional dependency could be captured by the vector autoregression (VAR) model. To tackle with the high dimensionality, penalization methods are widely employed. However, theoretically, the existing studies of the penalization methods mainly focus on $i.i.d$ data, therefore cannot quantify the effect of the dependence level on the convergence rate. In this work, we use the spectral properties of the time series to quantify the dependence and derive a nonasymptotic upper bound for the estimation errors. By focusing on the nonconcave penalization methods, we manage to establish the oracle properties of the penalized VAR model estimation by considering the effects of temporal and cross-sectional dependence. Extensive numerical studies are conducted to compare the finite sample performance using different penalization functions. Lastly, an air pollution data of mainland China is analyzed for illustration purpose.

Mohsen Maleki, Arezo Hajrajabi, Reinaldo B. Arellano-Valle.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 273--290.

Abstract:
In this paper, we study the finite mixtures of autoregressive processes assuming that the distribution of innovations (errors) belongs to the class of scale mixture of skew-normal (SMSN) distributions. The SMSN distributions allow a simultaneous modeling of the existence of outliers, heavy tails and asymmetries in the distribution of innovations. Therefore, a statistical methodology based on the SMSN family allows us to use a robust modeling on some non-linear time series with great flexibility, to accommodate skewness, heavy tails and heterogeneity simultaneously. The existence of convenient hierarchical representations of the SMSN distributions facilitates also the implementation of an ECME-type of algorithm to perform the likelihood inference in the considered model. Simulation studies and the application to a real data set are finally presented to illustrate the usefulness of the proposed model.

Zhengwei Liu, Qi Li, Fukang Zhu.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 251--272.

Abstract:
To predict time series of counts with small values and remarkable fluctuations, an available model is the $r$ states random environment process based on the negative binomial thinning operator and the geometric marginal. However, we argue that the aforementioned model may suffer from the following two drawbacks. First, under the condition of no prior information, the overdispersed property of the geometric distribution may cause the predictions fluctuate greatly. Second, because of the constraints on the model parameters, some estimated parameters are close to zero in real-data examples, which may not objectively reveal the correlation relationship. For the first drawback, an $r$ states random environment process based on the binomial thinning operator and the Poisson marginal is introduced. For the second drawback, we propose a generalized $r$ states random environment integer-valued autoregressive model based on the binomial thinning operator to model fluctuations of data. Yule–Walker and conditional maximum likelihood estimates are considered and their performances are assessed via simulation studies. Two real-data sets are conducted to illustrate the better performances of the proposed models compared with some existing models.

Marco A. R. Ferreira.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 734--744.

Abstract:
We study the limiting behavior of the one-at-a-time Gibbs sampler for the intrinsic conditional autoregressive model with centering on the fly. The intrinsic conditional autoregressive model is widely used as a prior for random effects in hierarchical models for spatial modeling. This model is defined by full conditional distributions that imply an improper joint “density” with a multivariate Gaussian kernel and a singular precision matrix. To guarantee propriety of the posterior distribution, usually at the end of each iteration of the Gibbs sampler the random effects are centered to sum to zero in what is widely known as centering on the fly. While this works well in practice, this informal computational way to recenter the random effects obscures their implied prior distribution and prevents the development of formal Bayesian procedures. Here we show that the implied prior distribution, that is, the limiting distribution of the one-at-a-time Gibbs sampler for the intrinsic conditional autoregressive model with centering on the fly is a singular Gaussian distribution with a covariance matrix that is the Moore–Penrose inverse of the precision matrix. This result has important implications for the development of formal Bayesian procedures such as reference priors and Bayes-factor-based model selection for spatial models.

Arctic sea ice extent (SIE) in September 2019 ranked second-to-lowest in history and is trending downward. The understanding of how internal variability amplifies the effects of external $ ext{CO}_2$ forcing is still limited. We propose the VARCTIC, which is a Vector Autoregression (VAR) designed to capture and extrapolate Arctic feedback loops. VARs are dynamic simultaneous systems of equations, routinely estimated to predict and understand the interactions of multiple macroeconomic time series. Hence, the VARCTIC is a parsimonious compromise between fullblown climate models and purely statistical approaches that usually offer little explanation of the underlying mechanism. Our "business as usual" completely unconditional forecast has SIE hitting 0 in September by the 2060s. Impulse response functions reveal that anthropogenic $ ext{CO}_2$ emission shocks have a permanent effect on SIE - a property shared by no other shock. Further, we find Albedo- and Thickness-based feedbacks to be the main amplification channels through which $ ext{CO}_2$ anomalies impact SIE in the short/medium run. Conditional forecast analyses reveal that the future path of SIE crucially depends on the evolution of $ ext{CO}_2$ emissions, with outcomes ranging from recovering SIE to it reaching 0 in the 2050s. Finally, Albedo and Thickness feedbacks are shown to play an important role in accelerating the speed at which predicted SIE is heading towards 0.

Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstruction error from variational autoencoder (VAE) via maximizing the evidence lower bound. We revisit VAE from the perspective of information theory to provide some theoretical foundations on using the reconstruction error, and finally arrive at a simpler and more effective model for anomaly detection. In addition, to enhance the effectiveness of detecting anomalies, we incorporate a practical model uncertainty measure into the metric. We show empirically the competitive performance of our approach on benchmark datasets.

Data-space inversion (DSI) and related procedures represent a family of methods applicable for data assimilation in subsurface flow settings. These methods differ from model-based techniques in that they provide only posterior predictions for quantities (time series) of interest, not posterior models with calibrated parameters. DSI methods require a large number of flow simulations to first be performed on prior geological realizations. Given observed data, posterior predictions can then be generated directly. DSI operates in a Bayesian setting and provides posterior samples of the data vector. In this work we develop and evaluate a new approach for data parameterization in DSI. Parameterization reduces the number of variables to determine in the inversion, and it maintains the physical character of the data variables. The new parameterization uses a recurrent autoencoder (RAE) for dimension reduction, and a long-short-term memory (LSTM) network to represent flow-rate time series. The RAE-based parameterization is combined with an ensemble smoother with multiple data assimilation (ESMDA) for posterior generation. Results are presented for two- and three-phase flow in a 2D channelized system and a 3D multi-Gaussian model. The RAE procedure, along with existing DSI treatments, are assessed through comparison to reference rejection sampling (RS) results. The new DSI methodology is shown to consistently outperform existing approaches, in terms of statistical agreement with RS results. The method is also shown to accurately capture derived quantities, which are computed from variables considered directly in DSI. This requires correlation and covariance between variables to be properly captured, and accuracy in these relationships is demonstrated. The RAE-based parameterization developed here is clearly useful in DSI, and it may also find application in other subsurface flow problems.

Monika Bhattacharjee, Arup Bose.

Source: The Annals of Statistics, Volume 47, Number 6, 3470--3503.

Abstract:
Consider a high-dimensional linear time series model where the dimension $p$ and the sample size $n$ grow in such a way that $p/n o 0$. Let $hat{Gamma }_{u}$ be the $u$th order sample autocovariance matrix. We first show that the LSD of any symmetric polynomial in ${hat{Gamma }_{u},hat{Gamma }_{u}^{*},ugeq 0}$ exists under independence and moment assumptions on the driving sequence together with weak assumptions on the coefficient matrices. This LSD result, with some additional effort, implies the asymptotic normality of the trace of any polynomial in ${hat{Gamma }_{u},hat{Gamma }_{u}^{*},ugeq 0}$. We also study similar results for several independent MA processes. We show applications of the above results to statistical inference problems such as in estimation of the unknown order of a high-dimensional MA process and in graphical and significance tests for hypotheses on coefficient matrices of one or several such independent processes.

Ke Zhu.

Source: The Annals of Statistics, Volume 47, Number 6, 3185--3215.

Abstract:
This paper provides an entire inference procedure for the autoregressive model under (conditional) heteroscedasticity of unknown form with a finite variance. We first establish the asymptotic normality of the weighted least absolute deviations estimator (LADE) for the model. Second, we develop the random weighting (RW) method to estimate its asymptotic covariance matrix, leading to the implementation of the Wald test. Third, we construct a portmanteau test for model checking, and use the RW method to obtain its critical values. As a special weighted LADE, the feasible adaptive LADE (ALADE) is proposed and proved to have the same efficiency as its infeasible counterpart. The importance of our entire methodology based on the feasible ALADE is illustrated by simulation results and the real data analysis on three U.S. economic data sets.

Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark, Tyler H. McCormick.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 241--256.

Abstract:
The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data

Ying Chen, J. S. Marron, Jiejie Zhang.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1590--1616.

Abstract:
Electricity prices are high dimensional, serially dependent and have seasonal variations. We propose a Warping Functional AutoRegressive (WFAR) model that simultaneously accounts for the cross time-dependence and seasonal variations of the large dimensional data. In particular, electricity price curves are obtained by smoothing over the $24$ discrete hourly prices on each day. In the functional domain, seasonal phase variations are separated from level amplitude changes in a warping process with the Fisher–Rao distance metric, and the aligned (season-adjusted) electricity price curves are modeled in the functional autoregression framework. In a real application, the WFAR model provides superior out-of-sample forecast accuracy in both a normal functioning market, Nord Pool, and an extreme situation, the California market. The forecast performance as well as the relative accuracy improvement are stable for different markets and different time periods.

Konstantinos Fokianos, Bård Støve, Dag Tjøstheim, Paul Doukhan.

Source: Bernoulli, Volume 26, Number 1, 471--499.

Abstract:
We are studying linear and log-linear models for multivariate count time series data with Poisson marginals. For studying the properties of such processes we develop a novel conceptual framework which is based on copulas. Earlier contributions impose the copula on the joint distribution of the vector of counts by employing a continuous extension methodology. Instead we introduce a copula function on a vector of associated continuous random variables. This construction avoids conceptual difficulties related to the joint distribution of counts yet it keeps the properties of the Poisson process marginally. Furthermore, this construction can be employed for modeling multivariate count time series with other marginal count distributions. We employ Markov chain theory and the notion of weak dependence to study ergodicity and stationarity of the models we consider. Suitable estimating equations are suggested for estimating unknown model parameters. The large sample properties of the resulting estimators are studied in detail. The work concludes with some simulations and a real data example.

Abhirup Datta, Sudipto Banerjee, James S. Hodges, Leiwen Gao.

Source: Bayesian Analysis, Volume 14, Number 4, 1221--1244.

Abstract:
Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and networks. Theoretical results establish a link between the parameters in our model and the variance and covariances of the random effects. Simulation studies demonstrate that the improved interpretability of our model reaps benefits in terms of accurately recovering the latent spatial random effects as well as for inference on the spatial covariance parameters. Under modest spatial correlation, our model far outperforms the CAR models, while the performances are similar when the spatial correlation is strong. We also assess sensitivity to the choice of the ordering in the DAG construction using theoretical and empirical results which testify to the robustness of our model. We also present a large-scale public health application demonstrating the competitive performance of the model.

Guillaume Kon Kam King, Antonio Canale, Matteo Ruggiero.

Source: Bayesian Analysis, Volume 14, Number 4, 1121--1141.

Abstract:
Motivated by the problem of forecasting demand and offer curves, we introduce a class of nonparametric dynamic models with locally-autoregressive behaviour, and provide a full inferential strategy for forecasting time series of piecewise-constant non-decreasing functions over arbitrary time horizons. The model is induced by a non Markovian system of interacting particles whose evolution is governed by a resampling step and a drift mechanism. The former is based on a global interaction and accounts for the volatility of the functional time series, while the latter is determined by a neighbourhood-based interaction with the past curves and accounts for local trend behaviours, separating these from pure noise. We discuss the implementation of the model for functional forecasting by combining a population Monte Carlo and a semi-automatic learning approach to approximate Bayesian computation which require limited tuning. We validate the inference method with a simulation study, and carry out predictive inference on a real dataset on the Italian natural gas market.

Yixin Wang, Andrew C. Miller, David M. Blei.

Source: Statistical Science, Volume 34, Number 2, 229--233.

Susan Gruber, Mark J. van der Laan.

Source: Statistical Science, Volume 34, Number 1, 82--85.

Abstract:
Dorie and co-authors (DHSSC) are to be congratulated for initiating the ACIC Data Challenge. Their project engaged the community and accelerated research by providing a level playing field for comparing the performance of a priori specified algorithms. DHSSC identified themes concerning characteristics of the DGP, properties of the estimators, and inference. We discuss these themes in the context of targeted learning.

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)

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 Basel Committee is publishing "Sound Practices: implications of fintech developments for banks and bank supervisors", February 2018.

Discurso del Dr. Agustín Carstens, Director General del Banco de Pagos Internacionales, en la Celebración del 25 Aniversario de la Autonomía del Banco de México, Ciudad de México, 22 de noviembre de 2019.

Apple's latest iOS 13.5 beta release includes a new Health app feature that allows iPhone and Apple Watch users to automatically send Medical ID information to first responders.

Wink customers will soon have to pay a monthly subscription fee to access any of the smart home hardware that they have purchased.

Being able to have an overview of changes to designs since initial release is an important aspect to time-efficient engineering. Making use of an automatically updated revision table helps with that.  What version are we using? Who has made these

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Combined auditory brainstem response/otoacoustic emission testing is used to screen for auditory neuropathy spectrum disorder (ANSD), which leads to failure to develop normal auditory behavior and oral language. Prevalence estimates have been obtained for infants in NICUs.

ANSD risk in well-infant nurseries has not been examined. This study contributes ANSD prevalence estimates for infants admitted to well-infant nurseries and compares the costs of 2 different 2-technology screening protocols. (Read the full article)

Clinical decision support systems offer a way to help physicians use evidence-based guidelines for screening. Screening patients for common developmental, psychosocial, and behavioral issues informs the clinical decision-making process and may improve patient outcomes.

The Child Health Improvement through Computer Automation system, a clinical decision support system and an electronic medical record, is able to effectively screen patient families in the waiting room by using a tailored questionnaire. The study reveals positive screening rates for identifiable risks in a very large representative urban population by using Child Health Improvement through Computer Automation’s questionnaire. (Read the full article)

In preterm infants receiving supplemental oxygen, manual control of the inspired oxygen fraction is often difficult and time consuming, which may increase the risk of complications. We developed a system for automatic oxygen control and proved its efficacy in the past.

A multicenter study adds evidence for the proposed automatic oxygen control system to significantly improve oxygen administration to preterm infants receiving mechanical ventilation or nasal continuous positive airway pressure while reducing workload compared with routine manual oxygen control. (Read the full article)

A substantial gap exists between what is recommended for effective primary care of children and what takes place. Patient-centered health information technologies have been used to gather information and counsel parents, however, have not been integrated directly with electronic health records nor been speech-based to improve decision-making at the point-of-care.

This study shows that a ubiquitous technology, the telephone, can be successfully used to automatically assess and counsel parents before pediatric primary care visits as well as inform their primary care clinicians in a way that is feasible and effective for multiple important issues. (Read the full article)

Urinary tract infection is the most common serious bacterial illness among febrile infants and young children. Automated urine cytometry may supplant traditional urinalysis, but diagnostic performance at unique pediatric cutpoints has not been described for this labor-saving technique.

We describe new, clinically useful cutpoints for automated leukocyte and bacterial counts. The sensitivity and specificity of bacterial counts ≥250 cells/μL exceed those of other methods. However, point-of-care dipstick tests for leukocyte esterase or nitrite have acceptable performance. (Read the full article)

Encephalitis is a serious and disabling condition. There are infectious and immune-mediated causes of encephalitis, but many cases remain undiagnosed.

This large single-center study on childhood encephalitis provides insight into the relative frequency and clinicoradiologic phenotypes of infectious, autoantibody-associated, and unknown encephalitis. Risk factors for an abnormal outcome are also defined. (Read the full article)

Clinical pain assessment methods in youth are vulnerable to underestimation bias and underrecognition. Facial expressions are sensitive, specific biomarkers of the presence and severity of pain. Computer vision–based pattern recognition enables measurement of pain-related facial expressions from video.

This study demonstrates initial validity for developing computer vision algorithms for automated pain assessment in children. The system developed and tested in this study could provide standardized, continuous, and valid patient monitoring that is potentially scalable. (Read the full article)

Adding second-stage automated auditory brainstem response (ABR) testing for infants who failed the initial OAE test in a two-stage neonatal hearing screening has been shown to reduce false referrals to the hearing clinic.

Infants with hearing loss may be missed by a 2-stage hearing screening because they pass the automated ABR test. In our study, a significant number of infants with hearing loss >45 decibel hearing level passed screening with automated ABR. (Read the full article)

Pediatric autoimmune hepatitis is an uncommon condition; children and youth can present with a diverse and insidious clinical course and biochemical features. Response to treatment is generally good, and transplantation is rarely needed.

This population-based study adds knowledge regarding the incidence of pediatric autoimmune hepatitis in Canada, as well as a description of diagnostic and therapeutic approaches among centers. Long-term outcomes are also described. (Read the full article)

Objectives: Antibiotic combination therapy is used for severe infections caused by multidrug-resistant (MDR) Gram-negative bacteria. Yet, data of which combinations are most effective is lacking. This study aimed to evaluate the in vitro efficacy of polymyxin B in combination with 13 other antibiotics against four clinical strains of MDR Pseudomonas aeruginosa.

Methods: We evaluated the interactions of polymyxin B in combination with amikacin, aztreonam, cefepime, chloramphenicol, ciprofloxacin, fosfomycin, meropenem, minocycline, rifampicin, temocillin, thiamphenicol or trimethoprim by automated time-lapse microscopy using predefined cut-off values indicating inhibition of growth (≤10⁶ CFU/mL) at 24 h. Promising combinations were subsequently evaluated in static time-kill experiments.

Results: All strains were intermediate or resistant to polymyxin B, anti-pseudomonal β-lactams, ciprofloxacin and amikacin. Genes encoding β-lactamases (e.g., bla_PAO and bla_OXA-50) and mutations associated with permeability and efflux were detected in all strains. In the time-lapse microscopy experiments, positive interactions were found with 39 of 52 antibiotic combination/bacterial strain setups. Enhanced activity was found against all four strains with polymyxin B used in combination with aztreonam, cefepime, fosfomycin, minocycline, thiamphenicol and trimethoprim. Time kill experiments showed additive or synergistic activity with 27 of the 39 tested polymyxin B combinations, most frequently with aztreonam, cefepime, and meropenem.

Conclusion: Positive interactions were frequently found with the tested combinations, also against strains that harboured several resistance mechanisms to the single drugs and with antibiotics that are normally not active against P. aeruginosa. Further study is needed to explore the clinical utility of these combinations.

An AI-powered digital assistant to take notes for you? It’s already happening in the workplace, but classroom note taking could prove harder to automate.

Automated EFT payments enabled by technology from fintech company Ozow may be a secure alternative method to transact than card payments.