Software Engineer (10 positions), Google Inc. Mountain View, CA: Design, dev, mod, and/or test sw needed for various Google projects. MS in CS, Engg, Comp Info Syst, Math, Phys, or a related field and a course, research proj, internship, thesis, or 1 yr exp in: C, C++, Java, Jscript, Obj-C, Cocoa, H

An insurance payment due to Victor Oladipo's injury gives Pacers lowest payroll in NBA

Mark Herrmann spent six years with the Krannert School of Management before coming to work for the John Purdue Club

In one weekend Stoffel Vandoorne manages to qualify on pole position in two races and crash out of both of them.

Ethiopian Prime Minister Abiy Ahmed has said that opposition politicians were trying to exploit uncertainty created by the coronavirus pandemic to seize power, risking instability.

Monday night's game figures to have a big impact on the NFC playoff race.

Dan Greenberg, co-founder and CEO of native advertising solutions provider Sharethrough, discusses how buyers’ increased desire for contextual targeting and native advertising will benefit premium publishers in 2018.

Joe Carroll
Feb 1, 2003; 2:117-126
Research

9 April 2020

Dongming Wang, Rina Dong and Chenqi Mou
Math. Comp. 89 (2020), 1993-2015.
Abstract, references and article information

Jiangxue Fang
Trans. Amer. Math. Soc. 373 (2020), 3445-3481.
Abstract, references and article information

Yu. K. Dem'yanovich
St. Petersburg Math. J. 31 (2020), 435-453.
Abstract, references and article information

Tonie Fares and Pascal Lefèvre
Proc. Amer. Math. Soc. 148 (2020), 2487-2496.
Abstract, references and article information

S. Waleed Noor and Osmar R. Severiano
Proc. Amer. Math. Soc. 148 (2020), 2469-2476.
Abstract, references and article information

Joachim Kock and David I. Spivak
Proc. Amer. Math. Soc. 148 (2020), 2317-2329.
Abstract, references and article information

(Princeton University, Engineering School) New findings about critical cellular structures have upended common assumptions about their formation and composition and provided new insight how molecular machines are built in living cells.

Do you know which main gases are contained in the composition of air? Under climate change and global warming, carbon dioxide ...

(American Geriatrics Society) The COVID-19 pandemic has placed unprecedented pressure on societies worldwide, given the pandemic's rapid, often deadly spread. In health care, the pandemic has raised the pressing question of how society should allocate scarce resources during a crisis. This is the question experts addressed today in a new position statement published by the American Geriatrics Society (AGS) in the Journal of the American Geriatrics Society (DOI: 10.1111/jgs.16537).

Cri-Du-Chat Syndrome (CdCs) is a rare genetic disease caused by a deletion in the short arm of chromosome 5 (5p) with a variable clinical spectrum. To date no study in literature has ever investigated the alterations of brain glucose metabolism in these subjects by means of [¹⁸F]fluoro-2-deoxy-d-glucose Positron Emission Tomography/Computed Tomography (¹⁸F-FDG PET/CT). The aims of this study were to detect difference in brain FDG metabolism in patients affected by CdCs with different clinical presentations and identify possible "brain metabolic phenotypes" of this syndrome. Methods: 6 patients (age: 5 M and 1 F, age range: 10-27) with CdCs were assessed for presence of cognitive and behavioral symptoms with a battery of neuropsychological tests and then classified as patient with a severe or mild phenotype. Then, patients underwent a brain ¹⁸F-FDG PET/CT scan. PET/CT findings were compared to a control group, matched for age and sex, by using statistical parametric mapping (SPM). Association of different clinical phenotypes and ¹⁸F-FDG PET/CT findings was investigated. Results: Four patients presented a severe phenotype, whereas 2 patients demonstrated mild phenotype. SPM single subject and group analysis compared to the control cohort revealed a significant hypometabolism in the left temporal lobe (BAs 20, 36 and 38), in the right frontal subcallosal gyrus (BA 34) and caudate body, and in the cerebellar tonsils (p<0.001). Hypermetabolism (P = 0.001) was revealed in the right superior and precentral frontal gyrus (BA 6) in patient group compared to the control cohort. In SPM single subject analysis the hypermetabolic areas were detected only in patients with a severe phenotype. Conclusion: This study revealed different patterns of brain glucose metabolism in patients with severe and mild phenotype compared to control subjects. In particular, the hypermetabolic abnormalities in the brain, evaluated by¹⁸F-FDG PET/CT, seem to correlate with the severe phenotype in patients with CdCs.

9 April 2020

Perlecan is a critical proteoglycan found in the extracellular matrix (ECM) of cartilage. In healthy cartilage, perlecan regulates cartilage biomechanics and we previously demonstrated perlecan deficiency leads to reduced cellular and ECM stiffness in vivo. This change in mechanics may lead to the early onset osteoarthritis seen in disorders resulting from perlecan knockdown such as Schwartz-Jampel syndrome (SJS). To identify how perlecan knockdown affects the material properties of developing cartilage, we used imaging and liquid chromatography–tandem mass spectrometry (LC-MS/MS) to study the ECM in a murine model of SJS, Hspg2C1532Y-Neo. Perlecan knockdown led to defective pericellular matrix formation, whereas the abundance of bulk ECM proteins, including many collagens, increased. Post-translational modifications and ultrastructure of collagens were not significantly different; however, LC-MS/MS analysis showed more protein was secreted by Hspg2C1532Y-Neo cartilage in vitro, suggesting that the incorporation of newly synthesized ECM was impaired. In addition, glycosaminoglycan deposition was atypical, which may explain the previously observed decrease in mechanics. Overall, these findings provide insight into the influence of perlecan on functional cartilage assembly and the progression of osteoarthritis in SJS.

9 April 2020

Atherosclerosis-related CVD causes nearly 20 million deaths annually. Most patients are treated after plaques develop, so therapies must regress existing lesions. Current therapies reduce plaque volume, but targeting all apoB-containing lipoproteins with intensive combinations that include alirocumab or evinacumab, monoclonal antibodies against cholesterol-regulating proprotein convertase subtilisin/kexin type 9 and angiopoietin-like protein 3, may provide more benefit. We investigated the effect of such lipid-lowering interventions on atherosclerosis in APOE*3-Leiden.CETP mice, a well-established model for hyperlipidemia. Mice were fed a Western-type diet for 13 weeks and thereafter matched into a baseline group (euthanized at 13 weeks) and five groups that received diet alone (control) or with treatment [atorvastatin; atorvastatin and alirocumab; atorvastatin and evinacumab; or atorvastatin, alirocumab, and evinacumab (triple therapy)] for 25 weeks. We measured effects on cholesterol levels, plaque composition and morphology, monocyte adherence, and macrophage proliferation. All interventions reduced plasma total cholesterol (37% with atorvastatin to 80% with triple treatment; all P < 0.001). Triple treatment decreased non-HDL-C to 1.0 mmol/l (91% difference from control; P < 0.001). Atorvastatin reduced atherosclerosis progression by 28% versus control (P < 0.001); double treatment completely blocked progression and diminished lesion severity. Triple treatment regressed lesion size versus baseline in the thoracic aorta by 50% and the aortic root by 36% (both P < 0.05 vs. baseline), decreased macrophage accumulation through reduced proliferation, and abated lesion severity. Thus, high-intensive cholesterol-lowering triple treatment targeting all apoB-containing lipoproteins regresses atherosclerotic lesion area and improves lesion composition in mice, making it a promising potential approach for treating atherosclerosis.

The composition-function relationship of HDL particles and its effects on the mechanisms driving coronary heart disease (CHD) is poorly understood. We tested the hypothesis that the functionality of HDL particles is significantly influenced by their lipid composition. Using a novel 3D-separation method, we isolated five different-sized HDL subpopulations from CHD patients who had low preβ-1 functionality (low-F) (ABCA1-dependent cholesterol-efflux normalized for preβ-1 concentration) and controls who had either low-F or high preβ-1 functionality (high-F). Molecular numbers of apoA-I, apoA-II, and eight major lipid classes were determined in each subpopulation by LC-MS. The average number of lipid molecules decreased from 422 in the large spherical α-1 particles to 57 in the small discoid preβ-1 particles. With decreasing particle size, the relative concentration of free cholesterol (FC) decreased in α-mobility but not in preβ-1 particles. Preβ-1 particles contained more lipids than predicted; 30% of which were neutral lipids (cholesteryl ester and triglyceride), indicating that these particles were mainly remodeled from larger particles not newly synthesized. There were significant correlations between HDL-particle functionality and the concentrations of several lipids. Unexpectedly, the phospholipid:FC ratio was significantly correlated with large-HDL-particle functionality but not with preβ-1 functionality. There was significant positive correlation between particle functionality and total lipids in high-F controls, indicating that the lipid-binding capacity of apoA-I plays a major role in the cholesterol efflux capacity of HDL particles. Functionality and lipid composition of HDL particles are significantly correlated and probably both are influenced by the lipid-binding capacity of apoA-I.

9 April 2020

Deborah Young-Hyman
Dec 1, 2016; 39:2126-2140
Psychosocial Research and Care in Diabetes

Ofri Mosenzon
Aug 1, 2016; 39:S146-S153
II. Diabetes Treatment Options

Deborah Young-Hyman
Dec 1, 2016; 39:2126-2140
Psychosocial Research and Care in Diabetes

Mexicans migrate to Canada in much smaller numbers than to the United States, yet over the last 30 years the country has become an increasingly attractive destination. Canada prioritizes highly skilled, educated Mexicans for permanent residency, but also attracts temporary workers from Mexico. This article examines Mexican migration to Canada and how it has been shaped by visa requirements, trade policy, and more.

Margaret A. Powers
Apr 1, 2016; 34:70-80
Position Statements

The West Virginia House of Delegates passed its version of a sweeping education omnibus bill, which would allow the state's first charter schools.

Thor Nystrom ranks the top UDFA signings by position, plus CFL draft picks and the best available unsigned prospects. (Getty Images)

Marburg : Elwert'sche, 1879.

Leipzig : F.C.W. Vogel, 1889.

Bonnae : Typis Joan. Frid. Abshoven, 1790.

Detroit, Mich. : G.S. Davis, 1891.

Glasgow : J. Maclehose, 1887.

Paris : chez Mequignon-Marvis, 1816.

The West Virginia House of Delegates passed its version of a sweeping education omnibus bill, which would allow the state's first charter schools.

Patrick L. Combettes, Christian L. Müller.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 207--238.

Abstract:
We introduce a flexible optimization model for maximum likelihood-type estimation (M-estimation) that encompasses and generalizes a large class of existing statistical models, including Huber’s concomitant M-estimator, Owen’s Huber/Berhu concomitant estimator, the scaled lasso, support vector machine regression, and penalized estimation with structured sparsity. The model, termed perspective M-estimation, leverages the observation that convex M-estimators with concomitant scale as well as various regularizers are instances of perspective functions, a construction that extends a convex function to a jointly convex one in terms of an additional scale variable. These nonsmooth functions are shown to be amenable to proximal analysis, which leads to principled and provably convergent optimization algorithms via proximal splitting. We derive novel proximity operators for several perspective functions of interest via a geometrical approach based on duality. We then devise a new proximal splitting algorithm to solve the proposed M-estimation problem and establish the convergence of both the scale and regression iterates it produces to a solution. Numerical experiments on synthetic and real-world data illustrate the broad applicability of the proposed framework.

Tensor Train decomposition is used across many branches of machine learning. We present T3F—a library for Tensor Train decomposition based on TensorFlow. T3F supports GPU execution, batch processing, automatic differentiation, and versatile functionality for the Riemannian optimization framework, which takes into account the underlying manifold structure to construct efficient optimization methods. The library makes it easier to implement machine learning papers that rely on the Tensor Train decomposition. T3F includes documentation, examples and 94% test coverage.

High dimensional data often contain multiple facets, and several clustering patterns can co-exist under different variable subspaces, also known as the views. While multi-view clustering algorithms were proposed, the uncertainty quantification remains difficult --- a particular challenge is in the high complexity of estimating the cluster assignment probability under each view, and sharing information among views. In this article, we propose an approximate Bayes approach --- treating the similarity matrices generated over the views as rough first-stage estimates for the co-assignment probabilities; in its Kullback-Leibler neighborhood, we obtain a refined low-rank matrix, formed by the pairwise product of simplex coordinates. Interestingly, each simplex coordinate directly encodes the cluster assignment uncertainty. For multi-view clustering, we let each view draw a parameterization from a few candidates, leading to dimension reduction. With high model flexibility, the estimation can be efficiently carried out as a continuous optimization problem, hence enjoys gradient-based computation. The theory establishes the connection of this model to a random partition distribution under multiple views. Compared to single-view clustering approaches, substantially more interpretable results are obtained when clustering brains from a human traumatic brain injury study, using high-dimensional gene expression data.

Boyu Ren, Sergio Bacallado, Stefano Favaro, Tommi Vatanen, Curtis Huttenhower, Lorenzo Trippa.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 494--517.

Abstract:
Detecting associations between microbial compositions and sample characteristics is one of the most important tasks in microbiome studies. Most of the existing methods apply univariate models to single microbial species separately, with adjustments for multiple hypothesis testing. We propose a Bayesian analysis for a generalized mixed effects linear model tailored to this application. The marginal prior on each microbial composition is a Dirichlet process, and dependence across compositions is induced through a linear combination of individual covariates, such as disease biomarkers or the subject’s age, and latent factors. The latent factors capture residual variability and their dimensionality is learned from the data in a fully Bayesian procedure. The proposed model is tested in data analyses and simulation studies with zero-inflated compositions. In these settings and within each sample, a large proportion of counts per microbial species are equal to zero. In our Bayesian model a priori the probability of compositions with absent microbial species is strictly positive. We propose an efficient algorithm to sample from the posterior and visualizations of model parameters which reveal associations between covariates and microbial compositions. We evaluate the proposed method in simulation studies, and then analyze a microbiome dataset for infants with type 1 diabetes which contains a large proportion of zeros in the sample-specific microbial compositions.

John R. Tipton, Mevin B. Hooten, Connor Nolan, Robert K. Booth, Jason McLachlan.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2363--2388.

Abstract:
Multivariate compositional count data arise in many applications including ecology, microbiology, genetics and paleoclimate. A frequent question in the analysis of multivariate compositional count data is what underlying values of a covariate(s) give rise to the observed composition. Learning the relationship between covariates and the compositional count allows for inverse prediction of unobserved covariates given compositional count observations. Gaussian processes provide a flexible framework for modeling functional responses with respect to a covariate without assuming a functional form. Many scientific disciplines use Gaussian process approximations to improve prediction and make inference on latent processes and parameters. When prediction is desired on unobserved covariates given realizations of the response variable, this is called inverse prediction. Because inverse prediction is often mathematically and computationally challenging, predicting unobserved covariates often requires fitting models that are different from the hypothesized generative model. We present a novel computational framework that allows for efficient inverse prediction using a Gaussian process approximation to generative models. Our framework enables scientific learning about how the latent processes co-vary with respect to covariates while simultaneously providing predictions of missing covariates. The proposed framework is capable of efficiently exploring the high dimensional, multi-modal latent spaces that arise in the inverse problem. To demonstrate flexibility, we apply our method in a generalized linear model framework to predict latent climate states given multivariate count data. Based on cross-validation, our model has predictive skill competitive with current methods while simultaneously providing formal, statistical inference on the underlying community dynamics of the biological system previously not available.

Piotr Kokoszka, Neda Mohammadi Jouzdani.

Source: Bernoulli, Volume 26, Number 3, 2383--2399.

Abstract:
This paper is concerned with frequency domain theory for functional time series, which are temporally dependent sequences of functions in a Hilbert space. We consider a variance decomposition, which is more suitable for such a data structure than the variance decomposition based on the Karhunen–Loéve expansion. The decomposition we study uses eigenvalues of spectral density operators, which are functional analogs of the spectral density of a stationary scalar time series. We propose estimators of the variance components and derive convergence rates for their mean square error as well as their asymptotic normality. The latter is derived from a frequency domain invariance principle for the estimators of the spectral density operators. This principle is established for a broad class of linear time series models. It is a main contribution of the paper.

Akio Fujiwara, Koichi Yamagata.

Source: Bernoulli, Volume 26, Number 3, 2105--2142.

Abstract:
We herein develop a theory of contiguity in the quantum domain based upon a novel quantum analogue of the Lebesgue decomposition. The theory thus formulated is pertinent to the weak quantum local asymptotic normality introduced in the previous paper [Yamagata, Fujiwara, and Gill, Ann. Statist. 41 (2013) 2197–2217], yielding substantial enlargement of the scope of quantum statistics.

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