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1. Discussion of Martingale Posterior Distributions by E. Fong, C. Holmes, and S. G. Walker

Author: Draper, David and Guo, Erdong
Subjects: Statistics - Methodology
Abstract: In this discussion note, we respond to the fascinating paper "Martingale Posterior Distributions" by E. Fong, C. Holmes, and S. G. Walker with a couple of comments. On the basis of previous research, a theorem is stated regarding the relationship between frequentist bootstrap and stick-breaking process., Comment: 2 pages. A written contribution to the RSS discussion meeting for JRSS-B
Published: 2023

2. Annealing Double-Head: An Architecture for Online Calibration of Deep Neural Networks

Author: Guo, Erdong, Draper, David, and De Iorio, Maria
Subjects: Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Model calibration, which is concerned with how frequently the model predicts correctly, not only plays a vital part in statistical model design, but also has substantial practical applications, such as optimal decision-making in the real world. However, it has been discovered that modern deep neural networks are generally poorly calibrated due to the overestimation (or underestimation) of predictive confidence, which is closely related to overfitting. In this paper, we propose Annealing Double-Head, a simple-to-implement but highly effective architecture for calibrating the DNN during training. To be precise, we construct an additional calibration head-a shallow neural network that typically has one latent layer-on top of the last latent layer in the normal model to map the logits to the aligned confidence. Furthermore, a simple Annealing technique that dynamically scales the logits by calibration head in training procedure is developed to improve its performance. Under both the in-distribution and distributional shift circumstances, we exhaustively evaluate our Annealing Double-Head architecture on multiple pairs of contemporary DNN architectures and vision and speech datasets. We demonstrate that our method achieves state-of-the-art model calibration performance without post-processing while simultaneously providing comparable predictive accuracy in comparison to other recently proposed calibration methods on a range of learning tasks., Comment: Revised Preprint. 19 pages, 10 figures, 4 tables. Typos fixed, and references added
Published: 2022

3. Neural Tangent Kernel of Matrix Product States: Convergence and Applications

Author: Guo, Erdong and Draper, David
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Quantum Physics
Abstract: In this work, we study the Neural Tangent Kernel (NTK) of Matrix Product States (MPS) and the convergence of its NTK in the infinite bond dimensional limit. We prove that the NTK of MPS asymptotically converges to a constant matrix during the gradient descent (training) process (and also the initialization phase) as the bond dimensions of MPS go to infinity by the observation that the variation of the tensors in MPS asymptotically goes to zero during training in the infinite limit. By showing the positive-definiteness of the NTK of MPS, the convergence of MPS during the training in the function space (space of functions represented by MPS) is guaranteed without any extra assumptions of the data set. We then consider the settings of (supervised) Regression with Mean Square Error (RMSE) and (unsupervised) Born Machines (BM) and analyze their dynamics in the infinite bond dimensional limit. The ordinary differential equations (ODEs) which describe the dynamics of the responses of MPS in the RMSE and BM are derived and solved in the closed-form. For the Regression, we consider Mercer Kernels (Gaussian Kernels) and find that the evolution of the mean of the responses of MPS follows the largest eigenvalue of the NTK. Due to the orthogonality of the kernel functions in BM, the evolution of different modes (samples) decouples and the "characteristic time" of convergence in training is obtained., Comment: 19 pages, 1 figure
Published: 2021

4. A Simple Necessary Condition For Independence of Real-Valued Random Variables

Author: Draper, David, Guo, Erdong, Lund, Robert, and Woody, Jon
Subjects: Mathematics - Probability, Mathematics - Statistics Theory, Statistics - Applications, Statistics - Methodology
Abstract: The standard method to check for the independence of two real-valued random variables -- demonstrating that the bivariate joint distribution factors into the product of its marginals -- is both necessary and sufficient. Here we present a simple necessary condition based on the support sets of the random variables, which -- if not satisfied -- avoids the need to extract the marginals from the joint in demonstrating dependence. We review, in an accessible manner, the measure-theoretic, topological, and probabilistic details necessary to establish the background for the old and new ideas presented here. We prove our result in both the discrete case (where the basic ideas emerge in a simple setting), the continuous case (where serious complications emerge), and for general real-valued random variables, and we illustrate the use of our condition in three simple examples., Comment: 25 pages, 5 figures
Published: 2021

5. The Practical Scope of the Central Limit Theorem

Author: Draper, David and Guo, Erdong
Subjects: Statistics - Other Statistics, Mathematics - Statistics Theory, Statistics - Applications, Statistics - Methodology
Abstract: The \textit{Central Limit Theorem (CLT)} is at the heart of a great deal of applied problem-solving in statistics and data science, but the theorem is silent on an important implementation issue: \textit{how much data do you need for the CLT to give accurate answers to practical questions?} Here we examine several approaches to addressing this issue -- along the way reviewing the history of this problem over the last 290 years -- and we illustrate the calculations with case-studies from finite-population sampling and gambling. A variety of surprises emerge., Comment: 47 pages, 17 figures
Published: 2021

6. Clinical application of a scale to assess genomic healthcare empowerment (GEmS): Process and illustrative case examples

Author: McConkie‐Rosell, Allyn, Schoch, Kelly, Sullivan, Jennifer, Spillmann, Rebecca C, Cope, Heidi, Tan, Queenie K‐G, Palmer, Christina GS, Hooper, Stephen R, Shashi, Vandana, Acosta, Maria T, Adam, Margaret, Adams, David R, Agrawal, Pankaj B, Alejandro, Mercedes E, Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Bademci, Guney, Baker, Eva, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak‐Toydemir, Pinar, Beck, Anita, Beggs, Alan H, Behrens, Edward, Bejerano, Gill, Bennet, Jimmy, Berg‐Rood, Beverly, Bernstein, Jonathan A, Berry, Gerard T, Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonnenmann, Carsten, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C, Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Byers, Peter, Byrd, William E, Carey, John, Carrasquillo, Olveen, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao‐Tuan, Clark, Gary D, Coakley, Terra R, Cobban, Laurel A, Cogan, Joy D, Coggins, Matthew, Sessions Cole, F, Colley, Heather A, Cooper, Cynthia M, Craigen, William J, Crouse, Andrew B, Cunningham, Michael, D'Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davids, Mariska, Dayal, Jyoti G, Deardorff, Matthew, Dell'Angelica, Esteban C, Dhar, Shweta U, Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Douine, Emilie D, Draper, David D, Duncan, Laura, Earl, Dawn, Eckstein, David J, Emrick, Lisa T, Eng, Christine M, Esteves, Cecilia, Estwick, Tyra, Falk, Marni, Fernandez, Liliana, Ferreira, Carlos, Fieg, Elizabeth L, Findley, Laurie C, Fisher, Paul G, Fogel, Brent L, Forghani, Irman, Fresard, Laure, Gahl, William A, Glass, Ian, and Godfrey, Rena A
Subjects: Pediatric, Biotechnology, Human Genome, Clinical Research, Genetics, Good Health and Well Being, Child, Delivery of Health Care, Family, Genomics, Humans, Parents, Exome Sequencing, exome and genomic sequencing, undiagnosed disorders, healthcare empowerment, genetic counseling, parental perspectives, rare disorders, Undiagnosed Disease Network, Clinical Sciences, Genetics & Heredity
Abstract: The Genome Empowerment Scale (GEmS), developed as a research tool, assesses perspectives of parents of children with undiagnosed disorders about to undergo exome or genome sequencing related to the process of empowerment. We defined genomic healthcare empowerment as follows: perceived ability to understand and seek new information related to the genomic sequencing, manage emotions related to the diagnostic process and outcomes, and utilize genomic sequencing information to the betterment of the individual/child and family. The GEmS consists of four scales, two are primarily emotion-focused (Meaning of a Diagnosis, and Emotional Management of the Process) and two are action-oriented (Seeking Information and Support, and Implications and Planning). The purpose of this research was to provide a strategy for interpreting results from the GEmS and present illustrative cases. These illustrations should serve to facilitate use of the GEmS in the clinical and research arena, particularly with respect to guiding genetic counseling processes for parents of children with undiagnosed conditions.
Published: 2022

7. Representation Theorem for Matrix Product States

Author: Guo, Erdong and Draper, David
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Quantum Physics
Abstract: In this work, we investigate the universal representation capacity of the Matrix Product States (MPS) from the perspective of boolean functions and continuous functions. We show that MPS can accurately realize arbitrary boolean functions by providing a construction method of the corresponding MPS structure for an arbitrarily given boolean gate. Moreover, we prove that the function space of MPS with the scale-invariant sigmoidal activation is dense in the space of continuous functions defined on a compact subspace of the $n$-dimensional real coordinate space $\mathbb{R^{n}}$. We study the relation between MPS and neural networks and show that the MPS with a scale-invariant sigmoidal function is equivalent to a one-hidden-layer neural network equipped with a kernel function. We construct the equivalent neural networks for several specific MPS models and show that non-linear kernels such as the polynomial kernel which introduces the couplings between different components of the input into the model appear naturally in the equivalent neural networks. At last, we discuss the realization of the Gaussian Process (GP) with infinitely wide MPS by studying their equivalent neural networks., Comment: 19 pages
Published: 2021

8. Infinitely Wide Tensor Networks as Gaussian Process

Author: Guo, Erdong and Draper, David
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract: Gaussian Process is a non-parametric prior which can be understood as a distribution on the function space intuitively. It is known that by introducing appropriate prior to the weights of the neural networks, Gaussian Process can be obtained by taking the infinite-width limit of the Bayesian neural networks from a Bayesian perspective. In this paper, we explore the infinitely wide Tensor Networks and show the equivalence of the infinitely wide Tensor Networks and the Gaussian Process. We study the pure Tensor Network and another two extended Tensor Network structures: Neural Kernel Tensor Network and Tensor Network hidden layer Neural Network and prove that each one will converge to the Gaussian Process as the width of each model goes to infinity. (We note here that Gaussian Process can also be obtained by taking the infinite limit of at least one of the bond dimensions $\alpha_{i}$ in the product of tensor nodes, and the proofs can be done with the same ideas in the proofs of the infinite-width cases.) We calculate the mean function (mean vector) and the covariance function (covariance matrix) of the finite dimensional distribution of the induced Gaussian Process by the infinite-width tensor network with a general set-up. We study the properties of the covariance function and derive the approximation of the covariance function when the integral in the expectation operator is intractable. In the numerical experiments, we implement the Gaussian Process corresponding to the infinite limit tensor networks and plot the sample paths of these models. We study the hyperparameters and plot the sample path families in the induced Gaussian Process by varying the standard deviations of the prior distributions. As expected, the parameters in the prior distribution namely the hyper-parameters in the induced Gaussian Process controls the characteristic lengthscales of the Gaussian Process., Comment: 20 pages, 4 figures
Published: 2021

9. The Bayesian Method of Tensor Networks

Author: Guo, Erdong and Draper, David
Subjects: Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract: Bayesian learning is a powerful learning framework which combines the external information of the data (background information) with the internal information (training data) in a logically consistent way in inference and prediction. By Bayes rule, the external information (prior distribution) and the internal information (training data likelihood) are combined coherently, and the posterior distribution and the posterior predictive (marginal) distribution obtained by Bayes rule summarize the total information needed in the inference and prediction, respectively. In this paper, we study the Bayesian framework of the Tensor Network from two perspective. First, we introduce the prior distribution to the weights in the Tensor Network and predict the labels of the new observations by the posterior predictive (marginal) distribution. Since the intractability of the parameter integral in the normalization constant computation, we approximate the posterior predictive distribution by Laplace approximation and obtain the out-product approximation of the hessian matrix of the posterior distribution of the Tensor Network model. Second, to estimate the parameters of the stationary mode, we propose a stable initialization trick to accelerate the inference process by which the Tensor Network can converge to the stationary path more efficiently and stably with gradient descent method. We verify our work on the MNIST, Phishing Website and Breast Cancer data set. We study the Bayesian properties of the Bayesian Tensor Network by visualizing the parameters of the model and the decision boundaries in the two dimensional synthetic data set. For a application purpose, our work can reduce the overfitting and improve the performance of normal Tensor Network model., Comment: 13 pages, 4 figures
Published: 2021

10. Chlorophyll absorption and phytoplankton size information inferred from hyperspectral particulate beam attenuation.

Author: Houskeeper, Henry F, Draper, David, Kudela, Raphael M, and Boss, Emmanuel
Subjects: Absorption, Physicochemical, Algorithms, Chlorophyll, Geography, Phytoplankton, Reproducibility of Results, Spectrum Analysis, Optical Physics, Electrical and Electronic Engineering, Mechanical Engineering, Optics
Abstract: Electromagnetic theory predicts spectral dependencies in extinction efficiency near a narrow absorption band for a particle with an index of refraction close to that of the medium in which it is immersed. These absorption band effects are anticipated in oceanographic beam-attenuation (beam-c) spectra, primarily due to the narrow red peak in absorption produced by the phytoplankton photopigment, chlorophyll a (Chl a). Here we present a method to obtain Chl a absorption and size information by analyzing an eigendecomposition of hyperspectral beam-c residuals measured in marine surface waters by an automatic underway system. We find that three principal modes capture more than 99% of the variance in beam-c residuals at wavelengths near the Chl a red absorption peak. The spectral shapes of the eigenvectors resemble extinction efficiency residuals attributed to the absorption band effects. Projection of the eigenvectors onto the beam-c residuals produces a time series of amplitude functions with absolute values that are strongly correlated to concurrent Chl a absorption line height (aLH) measurements (r values of 0.59 to 0.83) and hence provide a method to estimate Chl a absorption. Multiple linear regression of aLH on the amplitude functions enables an independent estimate of aLH, with RMSE of 3.19⋅10-3m-1 (3.3%) or log10-RMSE of 18.6%, and a raw-scale R2 value of 0.90 based on the Tara Oceans Expedition data. Relationships between the amplitude functions and the beam-c exponential slopes are in agreement with theory relating beam-c to the particle size distribution. Compared to multispectral analysis of beam-c slope, hyperspectral analysis of absorption band effects is anticipated to be relatively insensitive to the addition of nonpigmented particles and to monodispersion.
Published: 2020

11. De Novo Pathogenic Variants in N-cadherin Cause a Syndromic Neurodevelopmental Disorder with Corpus Collosum, Axon, Cardiac, Ocular, and Genital Defects

Author: Accogli, Andrea, Calabretta, Sara, St-Onge, Judith, Boudrahem-Addour, Nassima, Dionne-Laporte, Alexandre, Joset, Pascal, Azzarello-Burri, Silvia, Rauch, Anita, Krier, Joel, Fieg, Elizabeth, Pallais, Juan C, Network, Undiagnosed Diseases, Acosta, Maria T, Adams, David R, Agrawal, Pankaj, Alejandro, Mercedes E, Allard, Patrick, Alvey, Justin, Andrews, Ashley, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Bademci, Guney, Baker, Eva, Balasubramanyam, Ashok, Baldridge, Dustin, Bale, Jim, Barbouth, Deborah, Batzli, Gabriel F, Bayrak-Toydemir, Pinar, Beggs, Alan H, Bejerano, Gill, Bellen, Hugo J, Bernstein, Jonathan A, Berry, Gerard T, Bican, Anna, Bick, David P, Birch, Camille L, Bivona, Stephanie, Bohnsack, John, Bonnenmann, Carsten, Bonner, Devon, Boone, Braden E, Bostwick, Bret L, Botto, Lorenzo, Briere, Lauren C, Brokamp, Elly, Brown, Donna M, Brush, Matthew, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Carey, John, Carrasquillo, Olveen, Chang, Ta Chen Peter, Chao, Hsiao-Tuan, Clark, Gary D, Coakley, Terra R, Cobban, Laurel A, Cogan, Joy D, Cole, F Sessions, Colley, Heather A, Cooper, Cynthia M, Cope, Heidi, Craigen, William J, D’Souza, Precilla, Dasari, Surendra, Davids, Mariska, Dayal, Jyoti G, Dell’Angelica, Esteban C, Dhar, Shweta U, Dorrani, Naghmeh, Dorset, Daniel C, Douine, Emilie D, Draper, David D, Duncan, Laura, Eckstein, David J, Emrick, Lisa T, Eng, Christine M, Esteves, Cecilia, Estwick, Tyra, Fernandez, Liliana, Ferreira, Carlos, Fieg, Elizabeth L, Fisher, Paul G, Fogel, Brent L, Forghani, Irman, Fresard, Laure, Gahl, William A, Godfrey, Rena A, Goldman, Alica M, Goldstein, David B, Gourdine, Jean-Philippe F, Grajewski, Alana, Groden, Catherine A, Gropman, Andrea L, Haendel, Melissa, Hamid, Rizwan, Hanchard, Neil A, and Hayes, Nichole
Subjects: Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Genetics, Biological Sciences, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Axons, Cadherins, Corpus Callosum, Eye, Frameshift Mutation, Genitalia, Heart Defects, Congenital, Heterozygote, Humans, Neurodevelopmental Disorders, Undiagnosed Diseases Network, ACOG, CDH2, N-cadherin, cardiac defects, cell-cell adhesion, corpus callosum, eye defects, genital defects, intellectual disability, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract: Cadherins constitute a family of transmembrane proteins that mediate calcium-dependent cell-cell adhesion. The extracellular domain of cadherins consists of extracellular cadherin (EC) domains, separated by calcium binding sites. The EC interacts with other cadherin molecules in cis and in trans to mechanically hold apposing cell surfaces together. CDH2 encodes N-cadherin, whose essential roles in neural development include neuronal migration and axon pathfinding. However, CDH2 has not yet been linked to a Mendelian neurodevelopmental disorder. Here, we report de novo heterozygous pathogenic variants (seven missense, two frameshift) in CDH2 in nine individuals with a syndromic neurodevelopmental disorder characterized by global developmental delay and/or intellectual disability, variable axon pathfinding defects (corpus callosum agenesis or hypoplasia, mirror movements, Duane anomaly), and ocular, cardiac, and genital anomalies. All seven missense variants (c.1057G>A [p.Asp353Asn]; c.1789G>A [p.Asp597Asn]; c.1789G>T [p.Asp597Tyr]; c.1802A>C [p.Asn601Thr]; c.1839C>G [p.Cys613Trp]; c.1880A>G [p.Asp627Gly]; c.2027A>G [p.Tyr676Cys]) result in substitution of highly conserved residues, and six of seven cluster within EC domains 4 and 5. Four of the substitutions affect the calcium-binding site in the EC4-EC5 interdomain. We show that cells expressing these variants in the EC4-EC5 domains have a defect in cell-cell adhesion; this defect includes impaired binding in trans with N-cadherin-WT expressed on apposing cells. The two frameshift variants (c.2563_2564delCT [p.Leu855Valfs∗4]; c.2564_2567dupTGTT [p.Leu856Phefs∗5]) are predicted to lead to a truncated cytoplasmic domain. Our study demonstrates that de novo heterozygous variants in CDH2 impair the adhesive activity of N-cadherin, resulting in a multisystemic developmental disorder, that could be named ACOG syndrome (agenesis of corpus callosum, axon pathfinding, cardiac, ocular, and genital defects).
Published: 2019

12. The Need for Spectrum and the Impact on Weather Observations

Author: Palmer, Robert, Whelan, David, Bodine, David, Kirstetter, Pierre, Kumjian, Matthew, Metcalf, Justin, Yeary, Mark, Yu, Tian-You, Rao, Ramesh, Cho, John, Draper, David, Durden, Stephen, English, Stephen, Kollias, Pavlos, Kosiba, Karen, Wada, Masakazu, Wurman, Joshua, Blackwell, William, Bluestein, Howard, Collis, Scott, Gerth, Jordan, Tuttle, Aaron, Wang, Xuguang, and Zrnić, Dusan
Published: 2021

13. Comment: A brief survey of the current state of play for Bayesian computation in data science at Big-Data scale

Author: Draper, David and Terenin, Alexander
Subjects: Statistics - Computation
Abstract: We wish to contribute to the discussion of "Comparing Consensus Monte Carlo Strategies for Distributed Bayesian Computation" by offering our views on the current best methods for Bayesian computation, both at big-data scale and with smaller data sets, as summarized in Table 1. This table is certainly an over-simplification of a highly complicated area of research in constant (present and likely future) flux, but we believe that constructing summaries of this type is worthwhile despite their drawbacks, if only to facilitate further discussion.
Published: 2017

14. Heterozygous variants in MYBPC1 are associated with an expanded neuromuscular phenotype beyond arthrogryposis

Author: Shashi, Vandana, Geist, Janelle, Lee, Youngha, Yoo, Yongjin, Shin, Unbeom, Schoch, Kelly, Sullivan, Jennifer, Stong, Nicholas, Smith, Edward, Jasien, Joan, Kranz, Peter, Lee, Yoonsung, Shin, Yong Beom, Wright, Nathan T, Choi, Murim, Kontrogianni‐Konstantopoulos, Aikaterini, Acosta, Maria T, Adams, David R, Aday, Aaron, Alejandro, Mercedes E, Allard, Patrick, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Bademci, Guney, Baker, Eva, Balasubramanyam, Ashok, Baldridge, Dustin, Barbouth, Deborah, Batzli, Gabriel F, Beggs, Alan H, Bellen, Hugo J, Bernstein, Jonathan A, Berry, Gerard T, Bican, Anna, Bick, David P, Birch, Camille L, Bivona, Stephanie, Bonnenmann, Carsten, Bonner, Devon, Boone, Braden E, Bostwick, Bret L, Briere, Lauren C, Brokamp, Elly, Brown, Donna M, Brush, Matthew, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Carrasquillo, Olveen, Chang, Ta Chen Peter, Chao, Hsiao‐Tuan, Clark, Gary D, Coakley, Terra R, Cobban, Laurel A, Cogan, Joy D, Cole, F Sessions, Colley, Heather A, Cooper, Cynthia M, Cope, Heidi, Craigen, William J, D'Souza, Precilla, Dasari, Surendra, Davids, Mariska, Davidson, Jean M, Dayal, Jyoti G, Dell'Angelica, Esteban C, Dhar, Shweta U, Dorrani, Naghmeh, Dorset, Daniel C, Douine, Emilie D, Draper, David D, Dries, Annika M, Duncan, Laura, Eckstein, David J, Emrick, Lisa T, Eng, Christine M, Enns, Gregory M, Esteves, Cecilia, Estwick, Tyra, Fernandez, Liliana, Ferreira, Carlos, Fieg, Elizabeth L, Fisher, Paul G, Fogel, Brent L, Forghani, Irman, Friedman, Noah D, Gahl, William A, Godfrey, Rena A, Goldman, Alica M, Goldstein, David B, Gourdine, Jean‐Philippe F, Grajewski, Alana, Groden, Catherine A, Gropman, Andrea L, Haendel, Melissa, Hamid, Rizwan, Hanchard, Neil A, High, Frances, and Holm, Ingrid A
Subjects: Biological Sciences, Medical Physiology, Biomedical and Clinical Sciences, Clinical Research, Rare Diseases, Aetiology, 2.1 Biological and endogenous factors, Musculoskeletal, Adult, Arthrogryposis, Carrier Proteins, Child, Fathers, Female, Humans, Infant, Male, Models, Molecular, Mutation, Neuromuscular Diseases, Pedigree, Phenotype, Protein Conformation, Whole Genome Sequencing, arthrogryposis, hypotonia, MYBPC1, myopathy, myosin binding protein-C, tremor, Undiagnosed Diseases Network, Genetics, Clinical Sciences, Genetics & Heredity, Clinical sciences
Abstract: Encoding the slow skeletal muscle isoform of myosin binding protein-C, MYBPC1 is associated with autosomal dominant and recessive forms of arthrogryposis. The authors describe a novel association for MYBPC1 in four patients from three independent families with skeletal muscle weakness, myogenic tremors, and hypotonia with gradual clinical improvement. The patients carried one of two de novo heterozygous variants in MYBPC1, with the p.Leu263Arg variant seen in three individuals and the p.Leu259Pro variant in one individual. Both variants are absent from controls, well conserved across vertebrate species, predicted to be damaging, and located in the M-motif. Protein modeling studies suggested that the p.Leu263Arg variant affects the stability of the M-motif, whereas the p.Leu259Pro variant alters its structure. In vitro biochemical and kinetic studies demonstrated that the p.Leu263Arg variant results in decreased binding of the M-motif to myosin, which likely impairs the formation of actomyosin cross-bridges during muscle contraction. Collectively, our data substantiate that damaging variants in MYBPC1 are associated with a new form of an early-onset myopathy with tremor, which is a defining and consistent characteristic in all affected individuals, with no contractures. Recognition of this expanded myopathic phenotype can enable identification of individuals with MYBPC1 variants without arthrogryposis.
Published: 2019

15. IgG4‐related disease: Association with a rare gene variant expressed in cytotoxic T cells

Author: Newman, John H, Shaver, Aaron, Sheehan, Jonathan H, Mallal, Simon, Stone, John H, Pillai, Shiv, Bastarache, Lisa, Riebau, Derek, Allard‐Chamard, Hugues, Stone, William M, Perugino, Cory, Pilkinton, Mark, Smith, Scott A, McDonnell, Wyatt J, Capra, John A, Meiler, Jens, Cogan, Joy, Xing, Kelly, Mahajan, Vinay S, Mattoo, Hamid, Hamid, Rizwan, Phillips, John A, Adams, David R, Aday, Aaron, Alejandro, Mercedes E, Allard, Patrick, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Balasubramanyam, Ashok, Barseghyan, Hayk, Batzli, Gabriel F, Beggs, Alan H, Behnam, Babak, Bellen, Hugo J, Bernstein, Jonathan A, Bican, Anna, Bick, David P, Birch, Camille L, Bonner, Devon, Boone, Braden E, Bostwick, Bret L, Briere, Lauren C, Brown, Donna M, Brush, Matthew, Burke, Elizabeth A, Burrage, Lindsay C, Butte, Manish J, Chen, Shan, Clark, Gary D, Coakley, Terra R, Cooper, Cynthia M, Cope, Heidi, Craigen, William J, D'Souza, Precilla, Davids, Mariska, Davidson, Jean M, Dayal, Jyoti G, Dell'Angelica, Esteban C, Dhar, Shweta U, Dipple, Katrina M, Donnell‐Fink, Laurel A, Dorrani, Naghmeh, Dorset, Daniel C, Douine, Emilie D, Draper, David D, Dries, Annika M, Eckstein, David J, Emrick, Lisa T, Eng, Christine M, Enns, Gregory M, Eskin, Ascia, Esteves, Cecilia, Estwick, Tyra, Fernandez, Liliana, Ferreira, Carlos, Fisher, Paul G, Fogel, Brent L, Friedman, Noah D, Gahl, William A, Glanton, Emily, Godfrey, Rena A, Goldman, Alica M, Goldstein, David B, Gould, Sarah E, Gourdine, Jean‐Philippe F, Groden, Catherine A, Gropman, Andrea L, Haendel, Melissa, Hanchard, Neil A, Handley, Lori H, Herzog, Matthew R, High, Francis, Holm, Ingrid A, Hom, Jason, Howerton, Ellen M, Huang, Yong, Jamal, Fariha, Jiang, Yong‐hui, and Johnston, Jean M
Subjects: Biological Sciences, Genetics, Rare Diseases, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Adolescent, CD4-Positive T-Lymphocytes, Genetic Variation, Humans, Immunoglobulin G, Immunoglobulin G4-Related Disease, Male, Middle Aged, T-Lymphocytes, Cytotoxic, cytotoxic lymphocytes, heritable, IgG4-RD, Undiagnosed Disease Network, Medicinal and Biomolecular Chemistry, Clinical Sciences, Medicinal and biomolecular chemistry
Abstract: BackgroundFamily screening of a 48-year-old male with recently diagnosed IgG4-related disease (IgG4-RD) revealed unanticipated elevations in plasma IgG4 in his two healthy teenaged sons.MethodsWe performed gene sequencing, immune cell studies, HLA typing, and analyses of circulating cytotoxic CD4+ T lymphocytes and plasmablasts to seek clues to pathogenesis. DNA from a separate cohort of 99 patients with known IgG4-RD was also sequenced for the presence of genetic variants in a specific gene, FGFBP2.ResultsThe three share a previously unreported heterozygous single base deletion in fibroblast growth factor binding protein type 2 (FGFBP2), which causes a frameshift in the coding sequence. The FGFBP2 protein is secreted by cytotoxic T-lymphocytes and binds fibroblast growth factor. The variant sequence in the FGFBP2 protein is predicted to form a disordered random coil rather than a helical-turn-helix structure, unable to adopt a stable conformation. The proband and the two sons had 5-10-fold higher numbers of circulating cytotoxic CD4 + T cells and plasmablasts compared to matched controls. The three members also share a homozygous missense common variant in FGFBP2 found in heterozygous form in ~40% of the population. This common variant was found in 73% of an independent, well characterized IgG4-RD cohort, showing enrichment in idiopathic IgG4-RD.ConclusionsThe presence of a shared deleterious variant and homozygous common variant in FGFBP2 in the proband and sons strongly implicates this cytotoxic T cell product in the pathophysiology of IgG4-RD. The high prevalence of a common FGFBP2 variant in sporadic IgG4-RD supports the likelihood of participation in disease.
Published: 2019

16. Bi-allelic Variants in TONSL Cause SPONASTRIME Dysplasia and a Spectrum of Skeletal Dysplasia Phenotypes

Author: Burrage, Lindsay C, Reynolds, John J, Baratang, Nissan Vida, Phillips, Jennifer B, Wegner, Jeremy, McFarquhar, Ashley, Higgs, Martin R, Christiansen, Audrey E, Lanza, Denise G, Seavitt, John R, Jain, Mahim, Li, Xiaohui, Parry, David A, Raman, Vandana, Chitayat, David, Chinn, Ivan K, Bertuch, Alison A, Karaviti, Lefkothea, Schlesinger, Alan E, Earl, Dawn, Bamshad, Michael, Savarirayan, Ravi, Doddapaneni, Harsha, Muzny, Donna, Jhangiani, Shalini N, Eng, Christine M, Gibbs, Richard A, Bi, Weimin, Emrick, Lisa, Rosenfeld, Jill A, Postlethwait, John, Westerfield, Monte, Dickinson, Mary E, Beaudet, Arthur L, Ranza, Emmanuelle, Huber, Celine, Cormier-Daire, Valérie, Shen, Wei, Mao, Rong, Heaney, Jason D, Orange, Jordan S, Genomics, University of Washington Center for Mendelian, Network, Undiagnosed Diseases, Adams, David R, Aday, Aaron, Alejandro, Mercedes E, Allard, Patrick, Ashley, Euan A, Azamian, Mahshid S, Bacino, Carlos A, Baker, Eva, Balasubramanyam, Ashok, Barseghyan, Hayk, Batzli, Gabriel F, Beggs, Alan H, Behnam, Babak, Bellen, Hugo J, Bernstein, Jonathan A, Berry, Gerard T, Bican, Anna, Bick, David P, Birch, Camille L, Bonner, Devon, Boone, Braden E, Bostwick, Bret L, Briere, Lauren C, Brokamp, Elly, Brown, Donna M, Brush, Matthew, Burke, Elizabeth A, Butte, Manish J, Chen, Shan, Clark, Gary D, Coakley, Terra R, Cogan, Joy D, Colley, Heather A, Cooper, Cynthia M, Cope, Heidi, Craigen, William J, D’Souza, Precilla, Davids, Mariska, Davidson, Jean M, Dayal, Jyoti G, Dell’Angelica, Esteban C, Dhar, Shweta U, Dipple, Katrina M, Donnell-Fink, Laurel A, Dorrani, Naghmeh, Dorset, Daniel C, Douine, Emilie D, Draper, David D, Dries, Annika M, Duncan, Laura, Eckstein, David J, Emrick, Lisa T, Enns, Gregory M, Eskin, Ascia, and Esteves, Cecilia
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Genetics, Rare Diseases, Clinical Research, Congenital Structural Anomalies, Pediatric, Aetiology, 2.1 Biological and endogenous factors, Adolescent, Adult, Alleles, Animals, Cells, Cultured, Child, Child, Preschool, Chromosomal Instability, DNA Damage, Female, Fibroblasts, Genetic Association Studies, Genetic Variation, Humans, Mice, Mice, Knockout, Musculoskeletal Abnormalities, NF-kappa B, Osteochondrodysplasias, Exome Sequencing, Young Adult, Zebrafish, University of Washington Center for Mendelian Genomics, Undiagnosed Diseases Network, DNA repair, DNA replication, SPONASTRIME dysplasia, TONSL, exome sequencing, skeletal dysplasia, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract: SPONASTRIME dysplasia is an autosomal-recessive spondyloepimetaphyseal dysplasia characterized by spine (spondylar) abnormalities, midface hypoplasia with a depressed nasal bridge, metaphyseal striations, and disproportionate short stature. Scoliosis, coxa vara, childhood cataracts, short dental roots, and hypogammaglobulinemia have also been reported in this disorder. Although an autosomal-recessive inheritance pattern has been hypothesized, pathogenic variants in a specific gene have not been discovered in individuals with SPONASTRIME dysplasia. Here, we identified bi-allelic variants in TONSL, which encodes the Tonsoku-like DNA repair protein, in nine subjects (from eight families) with SPONASTRIME dysplasia, and four subjects (from three families) with short stature of varied severity and spondylometaphyseal dysplasia with or without immunologic and hematologic abnormalities, but no definitive metaphyseal striations at diagnosis. The finding of early embryonic lethality in a Tonsl-/- murine model and the discovery of reduced length, spinal abnormalities, reduced numbers of neutrophils, and early lethality in a tonsl-/- zebrafish model both support the hypomorphic nature of the identified TONSL variants. Moreover, functional studies revealed increased amounts of spontaneous replication fork stalling and chromosomal aberrations, as well as fewer camptothecin (CPT)-induced RAD51 foci in subject-derived cell lines. Importantly, these cellular defects were rescued upon re-expression of wild-type (WT) TONSL; this rescue is consistent with the hypothesis that hypomorphic TONSL variants are pathogenic. Overall, our studies in humans, mice, zebrafish, and subject-derived cell lines confirm that pathogenic variants in TONSL impair DNA replication and homologous recombination-dependent repair processes, and they lead to a spectrum of skeletal dysplasia phenotypes with numerous extra-skeletal manifestations.
Published: 2019

17. P\'olya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler

Author: Terenin, Alexander, Magnusson, Måns, Jonsson, Leif, and Draper, David
Subjects: Statistics - Machine Learning, Statistics - Computation
Abstract: Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language processing and machine learning. Most approaches to training the model rely on iterative algorithms, which makes it difficult to run LDA on big corpora that are best analyzed in parallel and distributed computational environments. Indeed, current approaches to parallel inference either don't converge to the correct posterior or require storage of large dense matrices in memory. We present a novel sampler that overcomes both problems, and we show that this sampler is faster, both empirically and theoretically, than previous Gibbs samplers for LDA. We do so by employing a novel P\'olya-urn-based approximation in the sparse partially collapsed sampler for LDA. We prove that the approximation error vanishes with data size, making our algorithm asymptotically exact, a property of importance for large-scale topic models. In addition, we show, via an explicit example, that - contrary to popular belief in the topic modeling literature - partially collapsed samplers can be more efficient than fully collapsed samplers. We conclude by comparing the performance of our algorithm with that of other approaches on well-known corpora.
Published: 2017
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18. A Space-Based Observational Strategy for Characterizing the First Stars and Galaxies Using the Redshifted 21-cm Global Spectrum

Author: Burns, Jack O., Bradley, Richard, Tauscher, Keith, Furlanetto, Steven, Mirocha, Jordan, Monsalve, Raul, Rapetti, David, Purcell, William, Newell, David, Draper, David, MacDowall, Robert, Bowman, Judd, Nhan, Bang, Wollack, Edward J., Fialkov, Anastasia, Jones, Dayton, Kasper, Justin C., Loeb, Abraham, Datta, Abhirup, Pritchard, Jonathan, Switzer, Eric, and Bicay, Michael
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, 85Axx
Abstract: The redshifted 21-cm monopole is expected to be a powerful probe of the epoch of the first stars and galaxies ($10
Published: 2017
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19. A Noninformative Prior on a Space of Distribution Functions

Author: Terenin, Alexander and Draper, David
Subjects: Mathematics - Statistics Theory
Abstract: In a given problem, the Bayesian statistical paradigm requires the specification of a prior distribution that quantifies relevant information about the unknowns of main interest external to the data. In cases where little such information is available, the problem under study may possess an invariance under a transformation group that encodes a lack of information, leading to a unique prior---this idea was explored at length by E.T. Jaynes. Previous successful examples have included location-scale invariance under linear transformation, multiplicative invariance of the rate at which events in a counting process are observed, and the derivation of the Haldane prior for a Bernoulli success probability. In this paper we show that this method can be extended, by generalizing Jaynes, in two ways: (1) to yield families of approximately invariant priors, and (2) to the infinite-dimensional setting, yielding families of priors on spaces of distribution functions. Our results can be used to describe conditions under which a particular Dirichlet Process posterior arises from an optimal Bayesian analysis, in the sense that invariances in the prior and likelihood lead to one and only one posterior distribution.
Published: 2017
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20. The Complex Genetic Legacy of Hybridization and Introgression between the Rare Ocotea loxensis van der Werff and the Widespread O. infrafoveolata van der Werff (Lauraceae).

Author: Draper, David, Riofrío, Lorena, Naranjo, Carlos, and Marques, Isabel
Subjects: BIOLOGICAL extinction, PLANT hybridization, GENETIC variation, PLANT species, GENE flow, INTROGRESSION (Genetics)
Abstract: Hybridization and introgression are complex evolutionary mechanisms that can increase species diversity and lead to speciation, but may also lead to species extinction. In this study, we tested the presence and genetic consequences of hybridization between the rare and Ecuadorian endemic O. loxensis van der Werff and the widespread O. infrafoveolata van der Werff (Lauraceae). Phenotypically, some trees are difficult to identify, and we expect that some might in fact be cryptic hybrids. Thus, we developed nuclear microsatellites to assess the existence of hybrids, as well as the patterns of genetic diversity and population structure in allopatric and sympatric populations. The results revealed high levels of genetic diversity, even in the rare O. loxensis, being usually significantly higher in sympatric than in allopatric populations. The Bayesian assignment of individuals into different genetic classes revealed a complex scenario with different hybrid generations occurring in all sympatric populations, but also in allopatric ones. The absence of some backcrossed hybrids suggests the existence of asymmetric gene flow, and that some hybrids might be more fitted than others might. The existence of current and past interspecific gene flow also explains the blurring of species boundaries in these species and could be linked to the high rates of species found in Ocotea. [ABSTRACT FROM AUTHOR]
Published: 2024
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21. IRF2BPL Is Associated with Neurological Phenotypes

Author: Marcogliese, Paul C, Shashi, Vandana, Spillmann, Rebecca C, Stong, Nicholas, Rosenfeld, Jill A, Koenig, Mary Kay, Martínez-Agosto, Julián A, Herzog, Matthew, Chen, Agnes H, Dickson, Patricia I, Lin, Henry J, Vera, Moin U, Salamon, Noriko, Graham, John M, Ortiz, Damara, Infante, Elena, Steyaert, Wouter, Dermaut, Bart, Poppe, Bruce, Chung, Hyung-Lok, Zuo, Zhongyuan, Lee, Pei-Tseng, Kanca, Oguz, Xia, Fan, Yang, Yaping, Smith, Edward C, Jasien, Joan, Kansagra, Sujay, Spiridigliozzi, Gail, El-Dairi, Mays, Lark, Robert, Riley, Kacie, Koeberl, Dwight D, Golden-Grant, Katie, Diseases, Program for Undiagnosed, Callens, Steven, Coucke, Paul, Hemelsoet, Dimitri, Terryn, Wim, Van Coster, Rudy, Network, Undiagnosed Diseases, Adams, David R, Alejandro, Mercedes E, Allard, Patrick, Azamian, Mahshid S, Bacino, Carlos A, Balasubramanyam, Ashok, Barseghyan, Hayk, Batzli, Gabriel F, Beggs, Alan H, Behnam, Babak, Bican, Anna, Bick, David P, Birch, Camille L, Bonner, Devon, Boone, Braden E, Bostwick, Bret L, Briere, Lauren C, Brown, Donna M, Brush, Matthew, Burke, Elizabeth A, Burrage, Lindsay C, Chen, Shan, Clark, Gary D, Coakley, Terra R, Cogan, Joy D, Cooper, Cynthia M, Cope, Heidi, Craigen, William J, D’Souza, Precilla, Davids, Mariska, Dayal, Jyoti G, Dell’Angelica, Esteban C, Dhar, Shweta U, Dillon, Ani, Dipple, Katrina M, Donnell-Fink, Laurel A, Dorrani, Naghmeh, Dorset, Daniel C, Douine, Emilie D, Draper, David D, Eckstein, David J, Emrick, Lisa T, Eng, Christine M, Eskin, Ascia, Esteves, Cecilia, Estwick, Tyra, Ferreira, Carlos, Fogel, Brent L, Friedman, Noah D, Gahl, William A, Glanton, Emily, Godfrey, Rena A, Goldstein, David B, Gould, Sarah E, Gourdine, Jean-Philippe F, and Groden, Catherine A
Subjects: Neurodegenerative, Brain Disorders, Neurosciences, Human Genome, Genetics, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Neurological, Program for Undiagnosed Diseases, Undiagnosed Diseases Network, C3HC4 RING finger, CG11138, Drosophila, EAP1, ataxia, developmental regression, hypotonia, neurodegeneration, pits, seizures, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract: Interferon regulatory factor 2 binding protein-like (IRF2BPL) encodes a member of the IRF2BP family of transcriptional regulators. Currently the biological function of this gene is obscure, and the gene has not been associated with a Mendelian disease. Here we describe seven individuals who carry damaging heterozygous variants in IRF2BPL and are affected with neurological symptoms. Five individuals who carry IRF2BPL nonsense variants resulting in a premature stop codon display severe neurodevelopmental regression, hypotonia, progressive ataxia, seizures, and a lack of coordination. Two additional individuals, both with missense variants, display global developmental delay and seizures and a relatively milder phenotype than those with nonsense alleles. The IRF2BPL bioinformatics signature based on population genomics is consistent with a gene that is intolerant to variation. We show that the fruit-fly IRF2BPL ortholog, called pits (protein interacting with Ttk69 and Sin3A), is broadly detected, including in the nervous system. Complete loss of pits is lethal early in development, whereas partial knockdown with RNA interference in neurons leads to neurodegeneration, revealing a requirement for this gene in proper neuronal function and maintenance. The identified IRF2BPL nonsense variants behave as severe loss-of-function alleles in this model organism, and ectopic expression of the missense variants leads to a range of phenotypes. Taken together, our results show that IRF2BPL and pits are required in the nervous system in humans and flies, and their loss leads to a range of neurological phenotypes in both species.
Published: 2018

22. Phenotypic expansion of CACNA1C-associated disorders to include isolated neurological manifestations

Author: Rodan, Lance H., Spillmann, Rebecca C., Kurata, Harley T., Lamothe, Shawn M., Maghera, Jasmine, Jamra, Rami Abou, Alkelai, Anna, Antonarakis, Stylianos E., Atallah, Isis, Bar-Yosef, Omer, Bilan, Frédéric, Bjorgo, Kathrine, Blanc, Xavier, Van Bogaert, Patrick, Bolkier, Yoav, Burrage, Lindsay C., Christ, Björn U., Granadillo, Jorge L., Dickson, Patricia, Donald, Kirsten A., Dubourg, Christèle, Eliyahu, Aviva, Emrick, Lisa, Engleman, Kendra, Gonfiantini, Michaela Veronika, Good, Jean-Marc, Kalser, Judith, Kloeckner, Chiara, Lachmeijer, Guus, Macchiaiolo, Marina, Nicita, Francesco, Odent, Sylvie, O’Heir, Emily, Ortiz-Gonzalez, Xilma, Pacio-Miguez, Marta, Palomares-Bralo, María, Pena, Loren, Platzer, Konrad, Quinodoz, Mathieu, Ranza, Emmanuelle, Rosenfeld, Jill A., Roulet-Perez, Eliane, Santani, Avni, Santos-Simarro, Fernando, Pode-Shakked, Ben, Skraban, Cara, Slaugh, Rachel, Superti-Furga, Andrea, Thiffault, Isabelle, van Jaabrsveld, Richard H., Vincent, Marie, Wang, Hong-Gang, Zacher, Pia, Alejandro, Mercedes E., Azamian, Mahshid S., Bacino, Carlos A., Balasubramanyam, Ashok, Chao, Hsiao-Tuan, Clark, Gary D., Craigen, William J., Dai, Hongzheng, Dhar, Shweta U., Emrick, Lisa T., Goldman, Alica M., Hanchard, Neil A., Jamal, Fariha, Karaviti, Lefkothea, Lalani, Seema R., Lee, Brendan H., Lewis, Richard A., Marom, Ronit, Moretti, Paolo M., Murdock, David R., Nicholas, Sarah K., Orengo, James P., Posey, Jennifer E., Potocki, Lorraine, Samson, Susan L., Scott, Daryl A., Tran, Alyssa A., Vogel, Tiphanie P., Wangler, Michael F., Yamamoto, Shinya, Eng, Christine M., Liu, Pengfei, Ward, Patricia A., Behrens, Edward, Deardorff, Matthew, Falk, Marni, Hassey, Kelly, Sullivan, Kathleen, Vanderver, Adeline, Goldstein, David B., Cope, Heidi, McConkie-Rosell, Allyn, Schoch, Kelly, Shashi, Vandana, Smith, Edward C., Sullivan, Jennifer A., Tan, Queenie K.-G., Walley, Nicole M., Agrawal, Pankaj B., Beggs, Alan H., Berry, Gerard T., Briere, Lauren C., Cobban, Laurel A., Coggins, Matthew, Cooper, Cynthia M., Fieg, Elizabeth L., High, Frances, Holm, Ingrid A., Korrick, Susan, Krier, Joel B., Lincoln, Sharyn A., Loscalzo, Joseph, Maas, Richard L., MacRae, Calum A., Pallais, J. Carl, Rao, Deepak A., Silverman, Edwin K., Stoler, Joan M., Sweetser, David A., Walker, Melissa, Walsh, Chris A., Esteves, Cecilia, Kelley, Emily G., Kohane, Isaac S., LeBlanc, Kimberly, McCray, Alexa T., Nagy, Anna, Dasari, Surendra, Lanpher, Brendan C., Lanza, Ian R., Morava, Eva, Oglesbee, Devin, Bademci, Guney, Barbouth, Deborah, Bivona, Stephanie, Carrasquillo, Olveen, Chang, Ta Chen Peter, Forghani, Irman, Grajewski, Alana, Isasi, Rosario, Lam, Byron, Levitt, Roy, Liu, Xue Zhong, McCauley, Jacob, Sacco, Ralph, Saporta, Mario, Schaechter, Judy, Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Zuchner, Stephan, Colley, Heather A., Dayal, Jyoti G., Eckstein, David J., Findley, Laurie C., Krasnewich, Donna M., Mamounas, Laura A., Manolio, Teri A., Mulvihill, John J., LaMoure, Grace L., Goldrich, Madison P., Urv, Tiina K., Doss, Argenia L., Acosta, Maria T., Bonnenmann, Carsten, D’Souza, Precilla, Draper, David D., Ferreira, Carlos, Godfrey, Rena A., Groden, Catherine A., Macnamara, Ellen F., Maduro, Valerie V., Markello, Thomas C., Nath, Avi, Novacic, Donna, Pusey, Barbara N., Toro, Camilo, Wahl, Colleen E., Baker, Eva, Burke, Elizabeth A., Adams, David R., Gahl, William A., Malicdan, May Christine V., Tifft, Cynthia J., Wolfe, Lynne A., Yang, John, Power, Bradley, Gochuico, Bernadette, Huryn, Laryssa, Latham, Lea, Davis, Joie, Mosbrook-Davis, Deborah, Rossignol, Francis, Ben Solomon, MacDowall, John, Thurm, Audrey, Zein, Wadih, Yousef, Muhammad, Adam, Margaret, Amendola, Laura, Bamshad, Michael, Beck, Anita, Bennett, Jimmy, Berg-Rood, Beverly, Blue, Elizabeth, Boyd, Brenna, Byers, Peter, Chanprasert, Sirisak, Cunningham, Michael, Dipple, Katrina, Doherty, Daniel, Earl, Dawn, Glass, Ian, Golden-Grant, Katie, Hahn, Sihoun, Hing, Anne, Hisama, Fuki M., Horike-Pyne, Martha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Lam, Christina, Maravilla, Kenneth, Mefford, Heather, Merritt, J. Lawrence, Mirzaa, Ghayda, Nickerson, Deborah, Raskind, Wendy, Rosenwasser, Natalie, Scott, C. Ron, Sun, Angela, Sybert, Virginia, Wallace, Stephanie, Wener, Mark, Wenger, Tara, Ashley, Euan A., Bejerano, Gill, Bernstein, Jonathan A., Bonner, Devon, Coakley, Terra R., Fernandez, Liliana, Fisher, Paul G., Fresard, Laure, Hom, Jason, Huang, Yong, Kohler, Jennefer N., Kravets, Elijah, Majcherska, Marta M., Martin, Beth A., Marwaha, Shruti, McCormack, Colleen E., Raja, Archana N., Reuter, Chloe M., Ruzhnikov, Maura, Sampson, Jacinda B., Smith, Kevin S., Sutton, Shirley, Tabor, Holly K., Tucker, Brianna M., Wheeler, Matthew T., Zastrow, Diane B., Zhao, Chunli, Byrd, William E., Crouse, Andrew B., Might, Matthew, Nakano-Okuno, Mariko, Whitlock, Jordan, Brown, Gabrielle, Butte, Manish J., Dell’Angelica, Esteban C., Dorrani, Naghmeh, Douine, Emilie D., Fogel, Brent L., Gutierrez, Irma, Huang, Alden, Krakow, Deborah, Lee, Hane, Loo, Sandra K., Mak, Bryan C., Martin, Martin G., Martínez-Agosto, Julian A., McGee, Elisabeth, Nelson, Stanley F., Nieves-Rodriguez, Shirley, Palmer, Christina G.S., Papp, Jeanette C., Parker, Neil H., Renteria, Genecee, Signer, Rebecca H., Sinsheimer, Janet S., Wan, Jijun, Wang, Lee-kai, Perry, Katherine Wesseling, Woods, Jeremy D., Alvey, Justin, Andrews, Ashley, Bale, Jim, Bohnsack, John, Botto, Lorenzo, Carey, John, Pace, Laura, Longo, Nicola, Marth, Gabor, Moretti, Paolo, Quinlan, Aaron, Velinder, Matt, Viskochil, Dave, Bayrak-Toydemir, Pinar, Mao, Rong, Westerfield, Monte, Bican, Anna, Brokamp, Elly, Duncan, Laura, Hamid, Rizwan, Kennedy, Jennifer, Kozuira, Mary, Newman, John H., PhillipsIII, John A., Rives, Lynette, Robertson, Amy K., Solem, Emily, Cogan, Joy D., Cole, F. Sessions, Hayes, Nichole, Kiley, Dana, Sisco, Kathy, Wambach, Jennifer, Wegner, Daniel, Baldridge, Dustin, Pak, Stephen, Schedl, Timothy, Shin, Jimann, Solnica-Krezel, Lilianna, Rush, Eric, Pitt, Geoffrey S., and Au, Ping Yee Billie
Published: 2021
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23. Heterozygous loss-of-function variants significantly expand the phenotypes associated with loss of GDF11

Author: Ravenscroft, Thomas A., Phillips, Jennifer B., Fieg, Elizabeth, Bajikar, Sameer S., Peirce, Judy, Wegner, Jeremy, Luna, Alia A., Fox, Eric J., Yan, Yi-Lin, Rosenfeld, Jill A., Zirin, Jonathan, Kanca, Oguz, Acosta, Maria T., Adam, Margaret, Adams, David R., Agrawal, Pankaj B., Alejandro, Mercedes E., Alvey, Justin, Amendola, Laura, Andrews, Ashley, Ashley, Euan A., Azamian, Mahshid S., Bacino, Carlos A., Bademci, Guney, Baker, Eva, Balasubramanya, Ashok, Baldridge, Dustin, Bale, Jim, Bamshad, Michael, Barbouth, Deborah, Bayrak-Toydemir, Pinar, Beck, Anita, Beggs, Alan H., Behrens, Edward, Bejerano, Gill, Bennet, Jimmy, Berg-Rood, Beverly, Bernstein, Jonathan A., Berry, Gerard T., Bican, Anna, Bivona, Stephanie, Blue, Elizabeth, Bohnsack, John, Bonnenmann, Carsten, Bonner, Devon, Botto, Lorenzo, Boyd, Brenna, Briere, Lauren C., Brokamp, Elly, Brown, Gabrielle, Burke, Elizabeth A., Burrage, Lindsay C., Butte, Manish J., Byers, Peter, Byrd, William E., Carey, John, Carrasquillo, Olveen, Chang, Ta Chen Peter, Chanprasert, Sirisak, Chao, Hsiao-Tuan, Clark, Gary D., Coakley, Terra R., Cobban, Laurel A., Cogan, Joy D., Coggins, Matthew, Cole, F. Sessions, Colley, Heather A., Cooper, Cynthia M., Cope, Heidi, Craigen, William J., Crouse, Andrew B., Cunningham, Michael, D’Souza, Precilla, Dai, Hongzheng, Dasari, Surendra, Davis, Joie, Dayal, Jyoti G., Deardorff, Matthew, Dell’Angelica, Esteban C., Dhar, Shweta U., Dipple, Katrina, Doherty, Daniel, Dorrani, Naghmeh, Doss, Argenia L., Douine, Emilie D., Draper, David D., Duncan, Laura, Earl, Dawn, Eckstein, David J., Emrick, Lisa T., Eng, Christine M., Esteves, Cecilia, Falk, Marni, Fernandez, Liliana, Ferreira, Carlos, Fieg, Elizabeth L., Findley, Laurie C., Fisher, Paul G., Fogel, Brent L., Forghani, Irman, Fresard, Laure, Gahl, William A., Glass, Ian, Gochuico, Bernadette, Godfrey, Rena A., Golden-Grant, Katie, Goldman, Alica M., Goldrich, Madison P., Goldstein, David B., Grajewski, Alana, Groden, Catherine A., Gutierrez, Irma, Hahn, Sihoun, Hamid, Rizwan, Hanchard, Neil A., Hassey, Kelly, Hayes, Nichole, High, Frances, Hing, Anne, Hisama, Fuki M., Holm, Ingrid A., Hom, Jason, Horike-Pyne, Martha, Huang, Alden, Huang, Yong, Huryn, Laryssa, Isasi, Rosario, Jamal, Fariha, Jarvik, Gail P., Jarvik, Jeffrey, Jayadev, Suman, Karaviti, Lefkothea, Kennedy, Jennifer, Kiley, Dana, Kohane, Isaac S., Kohler, Jennefer N., Krakow, Deborah, Krasnewich, Donna M., Kravets, Elijah, Korrick, Susan, Koziura, Mary, Krier, Joel B., Lalani, Seema R., Lam, Byron, Lam, Christina, LaMoure, Grace L., Lanpher, Brendan C., Lanza, Ian R., Latham, Lea, LeBlanc, Kimberly, Lee, Brendan H., Lee, Hane, Levitt, Roy, Lewis, Richard A., Lincoln, Sharyn A., Liu, Pengfei, Liu, Xue Zhong, Longo, Nicola, Loo, Sandra K., Loscalzo, Joseph, Maas, Richard L., MacDowall, John, Macnamara, Ellen F., MacRae, Calum A., Maduro, Valerie V., Majcherska, Marta M., Mak, Bryan C., Malicdan, May Christine V., Mamounas, Laura A., Manolio, Teri A., Mao, Rong, Maravilla, Kenneth, Markello, Thomas C., Marom, Ronit, Marth, Gabor, Martin, Beth A., Martin, Martin G., Martínez-Agosto, Julian A., Marwaha, Shruti, McCauley, Jacob, McConkie-Rosell, Allyn, McCormack, Colleen E., McCray, Alexa T., McGee, Elisabeth, Mefford, Heather, Merritt, J. Lawrence, Might, Matthew, Mirzaa, Ghayda, Morava, Eva, Moretti, Paolo, Moretti, Paolo M., Mosbrook-Davis, Deborah, Mulvihill, John J., Murdock, David R., Nagy, Anna, Nakano-Okuno, Mariko, Nath, Avi, Nelson, Stan F., Newman, John H., Nicholas, Sarah K., Nickerson, Deborah, Nieves-Rodriguez, Shirley, Novacic, Donna, Oglesbee, Devin, Orengo, James P., Pace, Laura, Pak, Stephen, Pallais, J. Carl, Palmer, Christina GS., Papp, Jeanette C., Parker, Neil H., Phillips, John A., III, Posey, Jennifer E., Potocki, Lorraine, Power, Bradley, Pusey, Barbara N., Quinlan, Aaron, Raskind, Wendy, Raja, Archana N., Rao, Deepak A., Renteria, Genecee, Reuter, Chloe M., Rives, Lynette, Robertson, Amy K., Rodan, Lance H., Rosenwasser, Natalie, Rossignol, Francis, Ruzhnikov, Maura, Sacco, Ralph, Sampson, Jacinda B., Samson, Susan L., Saporta, Mario, Scott, C. Ron, Schaechter, Judy, Schedl, Timothy, Schoch, Kelly, Scott, Daryl A., Shashi, Vandana, Shin, Jimann, Signer, Rebecca, Silverman, Edwin K., Sinsheimer, Janet S., Sisco, Kathy, Smith, Edward C., Smith, Kevin S., Solem, Emily, Solnica-Krezel, Lilianna, Solomon, Ben, Spillmann, Rebecca C., Stoler, Joan M., Sullivan, Jennifer A., Sullivan, Kathleen, Sun, Angela, Sutton, Shirley, Sweetser, David A., Sybert, Virginia, Tabor, Holly K., Tan, Amelia L.M., Tan, Queenie K.-G., Tekin, Mustafa, Telischi, Fred, Thorson, Willa, Thurm, Audrey, Tifft, Cynthia J., Toro, Camilo, Tran, Alyssa A., Tucker, Brianna M., Urv, Tiina K., Vanderver, Adeline, Velinder, Matt, Viskochil, Dave, Vogel, Tiphanie P., Wahl, Colleen E., Wallace, Stephanie, Walley, Nicole M., Walsh, Chris A., Walker, Melissa, Wambach, Jennifer, Wan, Jijun, Wang, Lee-kai, Wangler, Michael F., Ward, Patricia A., Wegner, Daniel, Wener, Mark, Wenger, Tara, Perry, Katherine Wesseling, Westerfield, Monte, Wheeler, Matthew T., Whitlock, Jordan, Wolfe, Lynne A., Woods, Jeremy D., Yamamoto, Shinya, Yang, John, Yousef, Muhammad, Zastrow, Diane B., Zein, Wadih, Zhao, Chunli, Zuchner, Stephan, Benke, Paul J., Cameron, Eric S., Strehlow, Vincent, Platzer, Konrad, Jamra, Rami Abou, Klöckner, Chiara, Osmond, Matthew, Licata, Thomas, Rojas, Samantha, Dyment, David, Chong, Josephine S.C., Lincoln, Sharyn, Postlethwait, John H., Krier, Joel, and Bellen, Hugo J.
Published: 2021
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24. GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model

Author: Terenin, Alexander, Dong, Shawfeng, and Draper, David
Subjects: Statistics - Computation, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: Gibbs sampling is a widely used Markov chain Monte Carlo (MCMC) method for numerically approximating integrals of interest in Bayesian statistics and other mathematical sciences. Many implementations of MCMC methods do not extend easily to parallel computing environments, as their inherently sequential nature incurs a large synchronization cost. In the case study illustrated by this paper, we show how to do Gibbs sampling in a fully data-parallel manner on a graphics processing unit, for a large class of exchangeable models that admit latent variable representations. Our approach takes a systems perspective, with emphasis placed on efficient use of compute hardware. We demonstrate our method on a Horseshoe Probit regression model and find that our implementation scales effectively to thousands of predictors and millions of data points simultaneously.
Published: 2016
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25. Racial/Ethnic Disparity in NICU Quality of Care Delivery

Author: Profit, Jochen, Gould, Jeffrey B, Bennett, Mihoko, Goldstein, Benjamin A, Draper, David, Phibbs, Ciaran S, and Lee, Henry C
Subjects: Clinical Research, Preterm, Low Birth Weight and Health of the Newborn, Infant Mortality, Perinatal Period - Conditions Originating in Perinatal Period, Pediatric, Black or African American, California, Healthcare Disparities, Hispanic or Latino, Humans, Infant, Very Low Birth Weight, Intensive Care Units, Neonatal, Outcome and Process Assessment, Health Care, Prospective Studies, White People, Medical and Health Sciences, Psychology and Cognitive Sciences, Pediatrics
Abstract: BackgroundDifferences in NICU quality of care provided to very low birth weight (
Published: 2017

26. A Space-based Observational Strategy for Characterizing the First Stars and Galaxies Using the Redshifted 21 cm Global Spectrum

Author: Burns, Jack O, Bradley, Richard, Tauscher, Keith, Furlanetto, Steven, Mirocha, Jordan, Monsalve, Raul, Rapetti, David, Purcell, William, Newell, David, Draper, David, MacDowall, Robert, Bowman, Judd, Nhan, Bang, Wollack, Edward J, Fialkov, Anastasia, Jones, Dayton, Kasper, Justin C, Loeb, Abraham, Datta, Abhirup, Pritchard, Jonathan, Switzer, Eric, and Bicay, Michael
Subjects: cosmology: observations, dark ages, reionization, first stars, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Physical Chemistry (incl. Structural), Astronomy & Astrophysics
Abstract: The redshifted 21 cm monopole is expected to be a powerful probe of the epoch of the first stars and galaxies (10 < z < 35). The global 21 cm signal is sensitive to the thermal and ionization state of hydrogen gas and thus provides a tracer of sources of energetic photons-primarily hot stars and accreting black holes-which ionize and heat the high redshift intergalactic medium (IGM). This paper presents a strategy for observations of the global spectrum with a realizable instrument placed in a low-altitude lunar orbit, performing night-time 40-120 MHz spectral observations, while on the farside to avoid terrestrial radio frequency interference, ionospheric corruption, and solar radio emissions. The frequency structure, uniformity over large scales, and unpolarized state of the redshifted 21 cm spectrum are distinct from the spectrally featureless, spatially varying, and polarized emission from the bright foregrounds. This allows a clean separation between the primordial signal and foregrounds. For signal extraction, we model the foreground, instrument, and 21 cm spectrum with eigenmodes calculated via Singular Value Decomposition analyses. Using a Markov Chain Monte Carlo algorithm to explore the parameter space defined by the coefficients associated with these modes, we illustrate how the spectrum can be measured and how astrophysical parameters (e.g., IGM properties, first star characteristics) can be constrained in the presence of foregrounds using the Dark Ages Radio Explorer (DARE).
Published: 2017

27. Asynchronous Gibbs Sampling

Author: Terenin, Alexander, Simpson, Daniel, and Draper, David
Subjects: Statistics - Computation
Abstract: Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method often used in Bayesian learning. MCMC methods can be difficult to deploy on parallel and distributed systems due to their inherently sequential nature. We study asynchronous Gibbs sampling, which achieves parallelism by simply ignoring sequential requirements. This method has been shown to produce good empirical results for some hierarchical models, and is popular in the topic modeling community, but was also shown to diverge for other targets. We introduce a theoretical framework for analyzing asynchronous Gibbs sampling and other extensions of MCMC that do not possess the Markov property. We prove that asynchronous Gibbs can be modified so that it converges under appropriate regularity conditions -- we call this the exact asynchronous Gibbs algorithm. We study asynchronous Gibbs on a set of examples by comparing the exact and approximate algorithms, including two where it works well, and one where it fails dramatically. We conclude with a set of heuristics to describe settings where the algorithm can be effectively used.
Published: 2015

28. Causal Inference in Repeated Observational Studies: A Case Study of eBay Product Releases

Author: von Brzeski, Vadim, Taddy, Matt, and Draper, David
Subjects: Statistics - Applications
Abstract: Causal inference in observational studies is notoriously difficult, due to the fact that the experimenter is not in charge of the treatment assignment mechanism. Many potential con- founding factors (PCFs) exist in such a scenario, and if one seeks to estimate the causal effect of the treatment on a response, one needs to control for such factors. Identifying all relevant PCFs may be difficult (or impossible) given a single observational study. Instead, we argue that if one can observe a sequence of similar treatments over the course of a lengthy time period, one can identify patterns of behavior in the experimental subjects that are correlated with the response of interest and control for those patterns directly. Specifically, in our case-study we find and control for an early-adopter effect: the scenario in which the magnitude of the response is highly correlated with how quickly one adopts a treatment after its release. We provide a flexible hierarchical Bayesian framework that controls for such early-adopter effects in the analysis of the effects of multiple sequential treatments. The methods are presented and evaluated in the context of a detailed case-study involving product updates (newer versions of the same product) from eBay, Inc. The users in our study upgrade (or not) to a new version of the product at their own volition and timing. Our response variable is a measure of user actions, and we study the behavior of a large set of users (n = 10.5 million) in a targeted subset of eBay categories over a period of one year. We find that (a) naive causal estimates are hugely misleading and (b) our method, which is relatively insensitive to modeling assumptions and exhibits good out-of-sample predictive validation, yields sensible causal estimates that offer eBay a stable basis for decision-making.
Published: 2015

29. Cox's Theorem and the Jaynesian Interpretation of Probability

Author: Terenin, Alexander and Draper, David
Subjects: Mathematics - Statistics Theory, Statistics - Methodology
Abstract: There are multiple proposed interpretations of probability theory: one such interpretation is true-false logic under uncertainty. Cox's Theorem is a representation theorem that states, under a certain set of axioms describing the meaning of uncertainty, that every true-false logic under uncertainty is isomorphic to conditional probability theory. This result was used by Jaynes to develop a philosophical framework in which statistical inference under uncertainty should be conducted through the use of probability, via Bayes' Rule. Unfortunately, most existing correct proofs of Cox's Theorem require restrictive assumptions: for instance, many do not apply even to the simple example of rolling a pair of fair dice. We offer a new axiomatization by replacing various technical conditions with an axiom stating that our theory must be consistent with respect to repeated events. We discuss the implications of our results, both for the philosophy of probability and for the philosophy of statistics., Comment: This work is withdrawn due to a critical error which we are unable to repair without completely changing the framework. The first author deeply regrets this error, which was committed when he was still obtaining his master's degree and had yet to learn a proper degree of carefulness needed when devising theoretical arguments
Published: 2015

30. A nonparametric Bayesian analysis of heterogeneous treatment effects in digital experimentation

Author: Taddy, Matt, Gardner, Matt, Chen, Liyun, and Draper, David
Subjects: Statistics - Applications
Abstract: Randomized controlled trials play an important role in how Internet companies predict the impact of policy decisions and product changes. In these `digital experiments', different units (people, devices, products) respond differently to the treatment. This article presents a fast and scalable Bayesian nonparametric analysis of such heterogeneous treatment effects and their measurement in relation to observable covariates. New results and algorithms are provided for quantifying the uncertainty associated with treatment effect measurement via both linear projections and nonlinear regression trees (CART and Random Forests). For linear projections, our inference strategy leads to results that are mostly in agreement with those from the frequentist literature. We find that linear regression adjustment of treatment effect averages (i.e., post-stratification) can provide some variance reduction, but that this reduction will be vanishingly small in the low-signal and large-sample setting of digital experiments. For regression trees, we provide uncertainty quantification for the machine learning algorithms that are commonly applied in tree-fitting. We argue that practitioners should look to ensembles of trees (forests) rather than individual trees in their analysis. The ideas are applied on and illustrated through an example experiment involving 21 million unique users of EBay.com.
Published: 2014

31. Quality of life after pharmacomechanical catheter-directed thrombolysis for proximal deep venous thrombosis

Author: Sichlau, Michael, Vlahos, Athanasios, Smith, Steven, Thalheimer, Quinn, Singh, Nisha, Harting, Rekha, Gocke, John, Guth, Scott, Shah, Neel, Brady, Paul, Schatz, Marvin, Horrow, Mindy, Markazi, Peyman, Forouzan, Leli, Matalon, Terence A.S., Hertzog, David, Goday, Swapna, Kennedy, Margaret, Kaplan, Robert, Campbell, Thomas, Hartman, Jamie, Nahum, Elmer, Venkat, Arvind, Krishnamurthy, Venkataramu, Rectenwald, John, Henke, Peter, Eliason, Jonathan, Willatt, Jonathon, Escobar, Guillermo, Samuels, Shaun, Katzen, Barry, Benenati, James, Powell, Alex, Pena, Constantino, Wallach, Howard, Gandhi, Ripal, Schneider, Joseph, Kim, Stanley, Hashemi, Farrah, Boyle, Joseph, Patel, Nilesh, Verta, Michael, Leung, Daniel, Garcia, Marc, Blatt, Phillip, Khatri, Jamil, Epstein, Dave, Ryan, Randall, Sweeny, Tom, Stillabower, Michael, Kimbiris, George, Raman, Tuhina, Sierzenski, Paul, Getto, Lelia, Dignazio, Michael, Horvath, Mark, Gornik, Heather, Bartholomew, John, Shishehbor, Mehdi, Peacock, Frank, Joseph, Douglas, Kim, Soo Hyum, Mahlay, Natalia Fendrikova, Clair, Daniel, Lyden, Sean, Kapoor, Baljendra, McLennon, Gordon, Pierce, Gregory, Newman, James, Spain, James, Gill, Amanjiit, Hamilton, Aaron, Rizzo, Anthony, Park, Woosup, Dietzek, Alan, Galin, Ira, Plummer, Dahlia, Hsu, Richard, Broderick, Patrick, Keller, Andrew, Sayeed, Sameer, Slater, Dennis, Lustberg, Herb, Akus, Jan, Sidman, Robert, Dhami, Mandeep, Kohanski, Phillip, Bulgaru, Anca, Dulala, Renuka, Burch, James, Kapur, Dinesh, Yang, Jie, Ranson, Mark, Wladis, Alan, Varnagy, David, Mekhail, Tarek, Winter, Robert, Perez-Izquierdo, Manuel, Motew, Stephen, Royd-Kranis, Robin, Workman, Raymond, Kribbs, Scott, Hogsette, Gerald, Moore, Phillip, Thomason, Bradley, Means, William, Bonsall, Richard, Stewart, John, Golwya, Daniel, Azene, Ezana, Bottner, Wayne, Bishop, William, Clayton, Dave, Gundersen, Lincoln, Riherd, Jody, Shakhnovich, Irina, Ziegelbein, Kurt, Chang, Thomas, Sharma, Karun, Allison, Sandra, Banovac, Fil, Cohen, Emil, Furlong, Brendan, Kessler, Craig, McCullough, Mike, Spies, Jim, Lin, Judith, Kaatz, Scott, Getzen, Todd, Miller, Joseph, Schwartz, Scott, Kabbani, Loay, McVinnie, David, Rundback, John, Manno, Joseph, Schwab, Richard, Cole, Randolph, Herman, Kevin, Singh, David, Barkama, Ravit, Patel, Amish, Comerota, Anthony, Pigott, John, Seiwert, Andrew, Whalen, Ralph, Russell, Todd, Assi, Zakaria, Kazanjian, Sahira, Yobbagy, Jonathan, Kaminski, Brian, Kaufman, Allan, Begeman, Garett, DiSalle, Robert, Thakur, Subash, Kim, Paul, Jacquet, Marc, Dykes, Thomas, Gerding, Joseph, Baker, Christopher, Debiasto, Mark, Mittleider, Derek, Higgins, George, III, Amberson, Steven, Pezzuti, Roger, Gallagher, Thomas, Schainfeld, Robert, Wicky, Stephan, Kalva, Sanjeeva, Walker, Gregory, Salazar, Gloria, Pomerantz, Benjamin, Patel, Virenda, Kabrhel, Christopher, Iqbal, Shams, Gangull, Suvranu, Oklu, Rahmi, Brannan, Scott, Misra, Sanjay, Bjarnason, Haraldur, Ashrani, Aneel, Caccavale, Michael, Fleming, Chad, Friese, Jeremy, Heit, John, Kalra, Manju, Macedo, Thanila, McBane, Robert, McKusick, Michael, Stockland, Andrew, Woodrum, David, Wysokinski, Waldemar, Verma, Adarsh, Davis, Andrew, Chung, Jerry, Nicker, David, Anderson, Brian, Stein, Robert, Weiss, Michael, Patel, Parag, Rilling, William, Tutton, Sean, Hieb, Robert, Hohenwalter, Eric, Colella, M. Riccardo, Gosset, James, White, Sarah, Lewis, Brian, Brown, Kellie, Rossi, Peter, Seabrook, Gary, Guimaraes, Marcelo, Selby, J. Bayne, McGary, William, Hannegan, Christopher, Robison, Jacob, Brothers, Thomas, Elliott, Bruce, Garg, Nitin, Anderson, M. Bret, Uflacker, Renan, Schonholz, Claudio, Raney, Laurence, Greenberg, Charles, Kaufman, John, Keller, Frederick, Kolbeck, Kenneth, Landry, Gregory, Mitchell, Erica, Barton, Robert, DeLoughery, Thomas, Kalbfleisch, Norman, Minjarez, Renee, Lakin, Paul, Liem, Timothy, Moneta, Gregory, Farsad, Khashayar, Fleischman, Ross, French, Loren, Marques, Vasco, Al−Hassani, Yasir, Sawar, Asad, Taylor, Frank, Patel, Rajul, Malhotra, Rahul, Hashemi, Farah, Padnick, Marvin, Gurley, Melissa, Cucher, Fred, Sterrenberg, Ronald, Deepthi, G. Reshmaal, Cumaranatunge, Gomes, Bhatla, Sumit, Jacobs, Darick, Dolen, Eric, Gamboa, Pablo, Dean, L. Mark, Davis, Thomas, Lippert, John, Khanna, Sanjeev, Schirf, Brian, Silber, Jeffrey, Wood, Donald, McGraw, J. Kevin, LaPerna, Lucy, Willette, Paul, Murphy, Timothy, Cerezo, Joselyn, Dhangana, Rajoo, Ahn, Sun Ho, Dubel, Gregory, Haas, Richard, Jay, Bryan, Prince, Ethan, Soares, Gregory, Klinger, James, Lambiase, Robert, Jay, Gregory, Tubbs, Robert, Beland, Michael, Hampson, Chris, O'Hara, Ryan, Thompson, Chad, Frodsham, Aaron, Gardiner, Fenwick, Jaffan, Abdel, Keating, Lawrence, Zafar, Abdul, Alicic, Radica, Raabe, Rodney, Brower, Jayson, McClellan, David, Pellow, Thomas, Zylak, Christopher, Davis, Joseph, Reilly, M. Kathleen, Symington, Kenneth, Seibold, Camerson, Nachreiner, Ryan, Murray, Daniel, Murray, Stephen, Saha, Sandeep, Luna, Gregory, Hodgson, Kim, McLafferty, Robert, Hood, Douglas, Moore, Colleen, Griffen, David, Hurst, Darren, Lubbers, David, Kim, Daniel, Warren, Brent, Engel, Jeremy, Suresh, D.P., VanderWoude, Eric, Razdan, Rahul, Hutchins, Mark, Rounsborg, Terry, Midathada, Madhu, Moravec, Daniel, Tilford, Joni, Beckman, Joni, Razavi, Mahmood, Openshaw, Kurt, Flanigan, D. Preston, Loh, Christopher, Dorne, Howard, Chan, Michael, Thomas, Jamie, Psaila, Justin, Ringold, Michael, Fisher, Jay, Lipcomb, Any, Oskin, Timothy, Wible, Brandt, Coleman, Brendan, Elliott, David, Gaddis, Gary, Cochran, C. Doug, Natarajan, Kannan, Bick, Stewart, Cooke, Jeffrey, Hedderman, Ann, Greist, Anne, Miller, Lorrie, Martinez, Brandon, Flanders, Vincent, Underhill, Mark, Hofmann, Lawrence, Sze, Daniel, Kuo, William, Louie, John, Hwang, Gloria, Hovsepian, David, Kothary, Nishita, Berube, Caroline, Schreiber, Donald, Jeffrey, Brooke, Schor, Jonathan, Deitch, Jonathan, Singh, Kuldeep, Hahn, Barry, Ardolic, Brahim, Gupta, Shilip, Bashir, Riyaz, Rao, Angara Koneti, Garg, Manish, Patil, Pravin, Zack, Chad, Cohen, Gary, Schmieder, Frank, Lakhter, Valdimir, Sacks, David, Guay, Robert, Scott, Mark, Cunningham, Karekin, Sigal, Adam, Cescon, Terrence, Leasure, Nick, Dhurairaj, Thiruvenkatasamy, Muck, Patrick, Knochel, Kurt, Lohr, Joann, Barreau, Jose, Recht, Matthew, Bhaskaran, Jayapandia, Brahmamdam, Ranga, Draper, David, Mehta, Apurva, Maher, James, Sharafuddin, Melhem, Lentz, Steven, Nugent, Andrew, Sharp, William, Kresowik, Timothy, Nicholson, Rachel, Sun, Shiliang, Youness, Fadi, Pascarella, Luigi, Ray, Charles, Knuttinen, Martha-Gracia, Bui, James, Gaba, Ron, Dobiesz, Valerie, Shamim, Ejaz, Nimmagadda, Sangeetha, Peace, David, Zain, Aarti, Palumto, Alison, Haskal, Ziv, Hirshon, Jon Mark, Richard, Howard, Verceles, Avelino, Wong-You-Chong, Jade, Othee, Bertrand, Patel, Rahul, Iliescu, Bogdan, Williams, David, Gemmete, Joseph, Cwikiel, Wojciech, Cho, Kyung, Schields, James, Vellody, Ranjith, Novelli, Paula, Dasika, Narasimham, Wakefield, Thomas, Desmond, Jeffrey, Froehlich, James, Khaja, Minhajuddin, Hunter, David, Golzarian, Jafar, Cressman, Erik, Dotta, Yvonne, Schmiechen, Nate, Marek, John, Garcia, David, Tawil, Isaac, Langsfeld, Mark, Moll, Stephan, Mauro, Matthew, Stavas, Joseph, Burke, Charles, Dixon, Robert, Yu, Hyeon, Keagy, Blair, Kim, Kyuny, Kasthuri, Raj, Key, Nigel, Chaer, Rabih, Makaroun, Michael, Rhee, Robert, Cho, Jae−Sung, Baril, Donald, Marone, Luke, Hseih, Margaret, Feterik, Kristian, Smith, Roy, Jeyabalan, Geetha, Rogers, Jennifer, Vinik, Russel, Kinikini, Dan, Kraiss, Larry, Mueller, Michelle, Pendleton, Robert, Rondina, Matthew, Sarfati, Mark, Wanner, Nathan, Johnson, Stacy, Hopkins, Christy, Ihnat, Daniel, Angle, John, Matsumoto, Alan, Harthun, Nancy, Turba, Ulku, Saad, Wael, Uthlaut, Brian, Nannapaneni, Srikant, Ling, David, Sabri, Saher, Kern, John, Macik, B. Gail, Hoke, George, Park, Auh Wahn, Stone, James, Sneed, Benjamin, Syverud, Scott, Davidson, Kelly, Sharma, Aditya, Wilkins, Luke, Black, Carl, Asay, Mark, Hatch, Daniel, Smilanich, Robert, Patten, Craig, Brown, S. Douglas, Nielsen, Ryan, Alward, William, Collins, John, Nokes, Matthew, Geary, Randolph, Edwards, Matthew, Godshall, Christopher, Levy, Pavel, Winokur, Ronald, Sista, Akhilesh, Madoff, David, Lee, Kyungmouk, Pua, Bradley, DeSancho, Maria, Milizia, Raffaele, Gao, Jing, McLean, Gordon, Khalid, Sanualah, Vedantham, Suresh, Lewis, Larry, Saad, Nael, Thoelke, Mark, Pallow, Robert, Klein, Seth, Sicard, Gregorio, Cohen, David J., Comerota, Anthony J., Gornik, Heather L., Jaff, Michael R., Julian, Jim, Kahn, Susan R., Kearon, Clive, Kee, Stephen, Kindzelski, Andrei L., Lewis, Lawrence, Magnuson, Elizabeth, Razavi, Mahmood K., Murphy, Timothy P., Julian, Jim A., Gu, Chu-Shu, Magnuson, Elizabeth A., Goldhaber, Samuel Z., Schneider, Joseph R., Sista, Akhilesh K., McLafferty, Robert B., Kaufman, John A., Wible, Brandt C., and Blinder, Morey
Published: 2020
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32. The Association of Level of Care With NICU Quality

Author: Profit, Jochen, Gould, Jeffrey B, Bennett, Mihoko, Goldstein, Benjamin A, Draper, David, Phibbs, Ciaran S, and Lee, Henry C
Subjects: Paediatrics, Biomedical and Clinical Sciences, Pediatric, Health Services, Preterm, Low Birth Weight and Health of the Newborn, Clinical Research, Prevention, Patient Safety, Perinatal Period - Conditions Originating in Perinatal Period, Infant Mortality, Pediatric Research Initiative, Good Health and Well Being, California, Cross-Sectional Studies, Humans, Infant, Newborn, Infant, Very Low Birth Weight, Intensive Care Units, Neonatal, Outcome Assessment, Health Care, Quality Indicators, Health Care, Medical and Health Sciences, Psychology and Cognitive Sciences, Pediatrics, Biomedical and clinical sciences, Health sciences, Psychology
Abstract: BackgroundRegionalized care delivery purportedly optimizes care to vulnerable very low birth weight (VLBW;
Published: 2016

33. Distributed Cognition and Process Management Enabling Individualized Translational Research: The NIH Undiagnosed Diseases Program Experience

Author: Links, Amanda E, Draper, David, Lee, Elizabeth, Guzman, Jessica, Valivullah, Zaheer, Maduro, Valerie, Lebedev, Vlad, Didenko, Maxim, Tomlin, Garrick, Brudno, Michael, Girdea, Marta, Dumitriu, Sergiu, Haendel, Melissa A, Mungall, Christopher J, Smedley, Damian, Hochheiser, Harry, Arnold, Andrew M, Coessens, Bert, Verhoeven, Steven, Bone, William, Adams, David, Boerkoel, Cornelius F, Gahl, William A, and Sincan, Murat
Subjects: Health Services and Systems, Health Sciences, Good Health and Well Being, information system, ontology-based phenotyping, precision medicine, process management system, translational research, Biomedical and clinical sciences, Health sciences
Abstract: The National Institutes of Health Undiagnosed Diseases Program (NIH UDP) applies translational research systematically to diagnose patients with undiagnosed diseases. The challenge is to implement an information system enabling scalable translational research. The authors hypothesized that similar complex problems are resolvable through process management and the distributed cognition of communities. The team, therefore, built the NIH UDP integrated collaboration system (UDPICS) to form virtual collaborative multidisciplinary research networks or communities. UDPICS supports these communities through integrated process management, ontology-based phenotyping, biospecimen management, cloud-based genomic analysis, and an electronic laboratory notebook. UDPICS provided a mechanism for efficient, transparent, and scalable translational research and thereby addressed many of the complex and diverse research and logistical problems of the NIH UDP. Full definition of the strengths and deficiencies of UDPICS will require formal qualitative and quantitative usability and process improvement measurement.
Published: 2016

34. Power-Expected-Posterior Priors for Variable Selection in Gaussian Linear Models

Author: Fouskakis, Dimitris, Ntzoufras, Ioannis, and Draper, David
Subjects: Statistics - Computation
Abstract: In the context of the expected-posterior prior (EPP) approach to Bayesian variable selection in linear models, we combine ideas from power-prior and unit-information-prior methodologies to simultaneously produce a minimally-informative prior and diminish the effect of training samples. The result is that in practice our power-expected-posterior (PEP) methodology is sufficiently insensitive to the size n* of the training sample, due to PEP's unit-information construction, that one may take n* equal to the full-data sample size n and dispense with training samples altogether. In this paper we focus on Gaussian linear models and develop our method under two different baseline prior choices: the independence Jeffreys (or reference) prior, yielding the J-PEP posterior, and the Zellner g-prior, leading to Z-PEP. We find that, under the reference baseline prior, the asymptotics of PEP Bayes factors are equivalent to those of Schwartz's BIC criterion, ensuring consistency of the PEP approach to model selection. We compare the performance of our method, in simulation studies and a real example involving prediction of air-pollutant concentrations from meteorological covariates, with that of a variety of previously-defined variants on Bayes factors for objective variable selection. Our prior, due to its unit-information structure, leads to a variable-selection procedure that (1) is systematically more parsimonious than the basic EPP with minimal training sample, while sacrificing no desirable performance characteristics to achieve this parsimony; (2) is robust to the size of the training sample, thus enjoying the advantages described above arising from the avoidance of training samples altogether; and (3) identifies maximum-a-posteriori models that achieve good out-of-sample predictive performance.
Published: 2013
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35. Combining Immature and Total Neutrophil Counts to Predict Early Onset Sepsis in Term and Late Preterm Newborns

Author: Newman, Thomas B, Draper, David, Puopolo, Karen M, Wi, Soora, and Escobar, Gabriel J
Subjects: Paediatrics, Biomedical and Clinical Sciences, Hematology, Clinical Research, Vaccine Related, Sepsis, Infectious Diseases, Inflammatory and immune system, Age Factors, Cross-Sectional Studies, Humans, Infant, Newborn, Infant, Newborn, Diseases, Infant, Premature, Leukocyte Count, Neutrophils, ROC Curve, Retrospective Studies, Risk, complete blood count, sepsis, neutrophils, leukocytes, sensitivity, Paediatrics and Reproductive Medicine, Public Health and Health Services, Pediatrics, Clinical sciences
Abstract: BackgroundThe absolute neutrophil count and the immature/total neutrophil ratio (I/T) provide information about the risk of early onset sepsis in newborns. However, it is not clear how to combine their potentially overlapping information into a single likelihood ratio.MethodsWe obtained electronic records of blood cultures and of complete blood counts with manual differentials drawn 4 hours of age and when the pretest probability of infection is close to the treatment threshold.
Published: 2014

36. Baby-MONITOR: A Composite Indicator of NICU Quality

Author: Profit, Jochen, Kowalkowski, Marc A, Zupancic, John AF, Pietz, Kenneth, Richardson, Peter, Draper, David, Hysong, Sylvia J, Thomas, Eric J, Petersen, Laura A, and Gould, Jeffrey B
Subjects: Perinatal Period - Conditions Originating in Perinatal Period, Pediatric, Preterm, Low Birth Weight and Health of the Newborn, Infant Mortality, Female, Humans, Infant, Newborn, Infant, Very Low Birth Weight, Intensive Care Units, Neonatal, Intensive Care, Neonatal, Male, Quality Indicators, Health Care, infant, newborn, quality of care, performance measurement, Medical and Health Sciences, Psychology and Cognitive Sciences, Pediatrics
Abstract: Background and objectivesNICUs vary in the quality of care delivered to very low birth weight (VLBW) infants. NICU performance on 1 measure of quality only modestly predicts performance on others. Composite measurement of quality of care delivery may provide a more comprehensive assessment of quality. The objective of our study was to develop a robust composite indicator of quality of NICU care provided to VLBW infants that accurately discriminates performance among NICUs.MethodsWe developed a composite indicator, Baby-MONITOR, based on 9 measures of quality chosen by a panel of experts. Measures were standardized, equally weighted, and averaged. We used the California Perinatal Quality Care Collaborative database to perform across-sectional analysis of care given to VLBW infants between 2004 and 2010. Performance on the Baby-MONITOR is not an absolute marker of quality but indicates overall performance relative to that of the other NICUs. We used sensitivity analyses to assess the robustness of the composite indicator, by varying assumptions and methods.ResultsOur sample included 9023 VLBW infants in 22 California regional NICUs. We found significant variations within and between NICUs on measured components of the Baby-MONITOR. Risk-adjusted composite scores discriminated performance among this sample of NICUs. Sensitivity analysis that included different approaches to normalization, weighting, and aggregation of individual measures showed the Baby-MONITOR to be robust (r = 0.89-0.99).ConclusionsThe Baby-MONITOR may be a useful tool to comprehensively assess the quality of care delivered by NICUs.
Published: 2014

37. Stratification of Risk of Early-Onset Sepsis in Newborns ≥34 Weeks’ Gestation

Author: Escobar, Gabriel J, Puopolo, Karen M, Wi, Soora, Turk, Benjamin J, Kuzniewicz, Michael W, Walsh, Eileen M, Newman, Thomas B, Zupancic, John, Lieberman, Ellice, and Draper, David
Subjects: Paediatrics, Reproductive Medicine, Biomedical and Clinical Sciences, Hematology, Pediatric Research Initiative, Infectious Diseases, Prevention, Perinatal Period - Conditions Originating in Perinatal Period, Clinical Research, Infant Mortality, Preterm, Low Birth Weight and Health of the Newborn, Pediatric, Sepsis, Reproductive health and childbirth, Good Health and Well Being, Age of Onset, Algorithms, Anti-Bacterial Agents, Case-Control Studies, Decision Support Techniques, Female, Humans, Infant, Newborn, Infant, Premature, Infant, Premature, Diseases, Logistic Models, Male, Multivariate Analysis, Prognosis, Reproducibility of Results, Retrospective Studies, Risk Assessment, Risk Factors, Watchful Waiting, early-onset sepsis, late preterm infant, predictive modeling, term newborn, Medical and Health Sciences, Psychology and Cognitive Sciences, Pediatrics, Biomedical and clinical sciences, Health sciences, Psychology
Abstract: ObjectiveTo define a quantitative stratification algorithm for the risk of early-onset sepsis (EOS) in newborns ≥ 34 weeks' gestation.MethodsWe conducted a retrospective nested case-control study that used split validation. Data collected on each infant included sepsis risk at birth based on objective maternal factors, demographics, specific clinical milestones, and vital signs during the first 24 hours after birth. Using a combination of recursive partitioning and logistic regression, we developed a risk classification scheme for EOS on the derivation dataset. This scheme was then applied to the validation dataset.ResultsUsing a base population of 608,014 live births ≥ 34 weeks' gestation at 14 hospitals between 1993 and 2007, we identified all 350 EOS cases
Published: 2014

38. A Community Error Inventory for Satellite Microwave Observation Error Representation and Uncertainty Quantification.

Author: Xun Yang, John, You, Yalei, Blackwell, William, Cheng Da, Kalnay, Eugenia, Grassotti, Christopher, Quanhua (Mark) Liu, Ferraro, Ralph, Huan Meng, Cheng-Zhi Zou, Shu-Peng Ho, Jifu Yin, Petkovic, Veljko, Hewison, Timothy, Posselt, Derek, Gambacorta, Antonia, Draper, David, Misra, Sidharth, Kroodsma, Rachael, and Min Chen
Subjects: ERRORS-in-variables models, MICROSPACECRAFT, MICROWAVES, INVENTORIES, NUMERICAL weather forecasting
Abstract: Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly developed algorithms. SatERR takes a bottom-up approach: errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT. [ABSTRACT FROM AUTHOR]
Published: 2024
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39. Genetic diversity and structure in two epiphytic orchids from the montane forests of southern Ecuador: The role of overcollection on Masdevallia rosea in comparison with the widespread Pleurothallis lilijae

Author: Riofrío, María, primary, Naranjo, Carlos, additional, Mendoza, Alberto, additional, Draper, David, additional, and Marques, Isabel, additional
Published: 2023
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40. DISCUSSION

Author: Draper, David
Published: 2017

41. Bayesian Statistics

Author: Draper, David
Abstract: © 2012 Springer Science+Business Media, LLC. All rights reserved. Article Outline: Glossary Definition of the Subject and Introduction The Bayesian Statistical Paradigm Three Examples Comparison with the Frequentist Statistical Paradigm Future Directions Bibliography
Published: 2012

42. Statistical Analysis of Performance Indicators in UK Higher Education

Author: Draper, David and Gittoes, Mark
Published: 2004

43. SatERR: A Community Error Inventory for Satellite Microwave Observation Error Representation and Uncertainty Quantification

Author: Yang, John Xun, primary, You, Yalei, additional, Blackwell, William, additional, Da, Cheng, additional, Kalnay, Eugenia, additional, Grassotti, Christopher, additional, Liu, Quanhua (Mark), additional, Ferraro, Ralph, additional, Meng, Huan, additional, Zou, Cheng-Zhi, additional, Ho, Shu-Peng, additional, Yin, Jifu, additional, Petkovic, Veljko, additional, Hewison, Timothy, additional, Posselt, Derek, additional, Gambacorta, Antonia, additional, Draper, David, additional, Misra, Sidharth, additional, Kroodsma, Rachael, additional, and Chen, Min, additional
Published: 2023
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44. Abstract 2762: Inhibition of murine myeloid suppressor cells increases CD8+ T cell activation in Vitro

Author: Zaitouna, Anita J., primary, Lapinski, Philip, additional, Rowse, Amber, additional, Draper, David, additional, and Wise, Scott, additional
Published: 2023
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45. Abstract 2749: Preclinical assessment of chimeric antigen receptor (CAR) T persistence and functionality in the disseminated NALM6-Luc human B cell acute lymphoblastic leukemia (ALL) model

Author: Draper, David W., primary, Germain, Derrik, additional, Roys, Stacey, additional, Nelson, Olivia, additional, and Wise, Scott, additional
Published: 2023
Full Text: View/download PDF

46. Abstract 5945: Transcriptional analysis of TME in MC38 colon carcinoma model following checkpoint inhibition

Author: Singh, Priyanka, primary, Saims, Dan, additional, Draper, David, additional, and Barnes, Sheri, additional
Published: 2023
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47. Stochastic Optimization: A Review

Author: Fouskakis, Dimitris and Draper, David
Published: 2002
Full Text: View/download PDF

48. A Thermodynamic Framework for Mg 2+ Binding to RNA

Author: Misra, Vinod K. and Draper, David E.
Published: 2001

49. [Bayesian Model Averaging: A Tutorial]: Comment

Author: Draper, David
Published: 1999

50. Crystal Structure of a Conserved Ribosomal Protein-RNA Complex

Author: Conn, Graeme L., Draper, David E., Lattman, Eaton E., and Gittis, Apostolos G.
Published: 1999

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