Spreading lengths of Hermite polynomials

The Renyi, Shannon and Fisher spreading lengths of the classical or hypergeometric orthogonal polynomials, which are quantifiers of their distribution all over the orthogonality interval, are defined and investigated. These information-theoretic measures of the associated Rakhmanov probability density, which are direct measures of the polynomial spreading in the sense of having the same units as the variable, share interesting properties: invariance under translations and reflections, linear scaling and vanishing in the limit that the variable tends towards a given definite value. The expressions of the Renyi and Fisher lengths for the Hermite polynomials are computed in terms of the polynomial degree. The combinatorial multivariable Bell polynomials, which are shown to characterize the finite power of an arbitrary polynomial, play a relevant role for the computation of these information-theoretic lengths. Indeed these polynomials allow us to design an error-free computing approach for the entropic moments (weighted L^q-norms) of Hermite polynomials and subsequently for the Renyi and Tsallis entropies, as well as for the Renyi spreading lengths. Sharp bounds for the Shannon length of these polynomials are also given by means of an information-theoretic-based optimization procedure. Moreover, it is computationally proved the existence of a linear correlation between the Shannon length (as well as the second-order Renyi length) and the standard deviation. Finally, the application to the most popular quantum-mechanical prototype system, the harmonic oscillator, is discussed and some relevant asymptotical open issues related to the entropic moments mentioned previously are posed.Comment: 16 pages, 4 figures. Journal of Computational and Applied Mathematics (2009), doi:10.1016/j.cam.2009.09.04

Rewriting a Deep Generative Model

A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules encoded by a deep generative model. To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We derive an algorithm for modifying one entry of the associative memory, and we demonstrate that several interesting structural rules can be located and modified within the layers of state-of-the-art generative models. We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects, and we show several proof-of-concept applications. Finally, results on multiple datasets demonstrate the advantage of our method against standard fine-tuning methods and edit transfer algorithms.Comment: ECCV 2020 (oral). Code at https://github.com/davidbau/rewriting. For videos and demos see https://rewriting.csail.mit.edu

Evaluation of the relationship between capillary and venous plasma glucose concentrations obtained by the HemoCue Glucose 201+ system during an oral glucose tolerance test

Abstract In 55 women with previous gestational diabetes mellitus, simultaneous capillary and venous plasma glucose concentrations were measured at 0, 30 and 120 min during a 75 g oral glucose tolerance test (OGTT). The aims of the study were to examine the relationship between capillary and venous glucose measurements, and to establish equations for the conversion of capillary and venous glucose concentrations using the HemoCue Glucose 201+ system. Additionally, the correlation between the capillary and venous glucose concentrations with the diagnostic cut-off limits proposed by the World Health Organization (WHO) in 1999 was evaluated. Capillary glucose concentrations were consistently higher than venous glucose concentrations at all time points of the OGTT (p < 0.001), and the correlations between the measurements were statistically highly significant (p < 0.001). The differences between the samples were greatest in the non-fasting state as revealed by the 95% prediction intervals (mmol/L) in Bland-Altman plots; ? 0.54 at 0 min, ? 2.01 at 30 min, and ? 1.35 at 120 min. Equivalence values for capillary plasma glucose concentrations derived from this study tended to be higher than those proposed by the WHO as diagnostic cut-off limits. Stratifying subjects by glucose tolerance status according to the WHO criteria revealed disagreements related to glucose values close to the diagnostic cut-off points. The study findings highlight the uncertainty associated with derived equivalence values. However, capillary plasma glucose measurements could be suitable for diagnostic purposes in epidemiological studies and when translating results on a group basis

Influenza A Virus Infection of Human Primary Dendritic Cells Impairs Their Ability to Cross-Present Antigen to CD8 T Cells

Influenza A virus (IAV) infection is normally controlled by adaptive immune responses initiated by dendritic cells (DCs). We investigated the consequences of IAV infection of human primary DCs on their ability to function as antigen-presenting cells. IAV was internalized by both myeloid DCs (mDCs) and plasmacytoid DCs but only mDCs supported viral replication. Although infected mDCs efficiently presented endogenous IAV antigens on MHC class II, this was not the case for presentation on MHC class I. Indeed, cross-presentation by uninfected cells of minute amounts of endocytosed, exogenous IAV was ∼300-fold more efficient than presentation of IAV antigens synthesized by infected cells and resulted in a statistically significant increase in expansion of IAV-specific CD8 T cells. Furthermore, IAV infection also impaired cross-presentation of other exogenous antigens, indicating that IAV infection broadly attenuates presentation on MHC class I molecules. Our results suggest that cross-presentation by uninfected mDCs is a preferred mechanism of antigen-presentation for the activation and expansion of CD8 T cells during IAV infection

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

Background: Multiple laboratory tests are used to diagnose and manage patients with diabetes mellitus. The quality of the scientific evidence supporting the use of these tests varies substantially. Approach: An expert committee compiled evidence-based recommendations for the use of laboratory testing for patients with diabetes. A new system was developed to grade the overall quality of the evidence and the strength of the recommendations. Draft guidelines were posted on the Internet and presented at the 2007 Arnold O. Beckman Conference. The document was modified in response to oral and written comments, and a revised draft was posted in 2010 and again modified in response to written comments. The National Academy of Clinical Biochemistry and the Evidence-Based Laboratory Medicine Committee of the American Association for Clinical Chemistry jointly reviewed the guidelines, which were accepted after revisions by the Professional Practice Committee and subsequently approved by the Executive Committee of the American Diabetes Association. Content: In addition to long-standing criteria based on measurement of plasma glucose, diabetes can be diagnosed by demonstrating increased blood hemoglobin A$_{1c}$ (HbA$_{1c}$) concentrations. Monitoring of glycemic control is performed by self-monitoring of plasma or blood glucose with meters and by laboratory analysis of HbA$_{1c}$. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of autoantibodies, urine albumin, insulin, proinsulin, C-peptide, and other analytes are addressed. Summary: The guidelines provide specific recommendations that are based on published data or derived from expert consensus. Several analytes have minimal clinical value at present, and their measurement is not recommended

Peripartum depression and anxiety as an integrative cross domain target for psychiatric preventative measures

Exposure to high levels of early life stress has been identified as a potent risk factor for neurodevelopmental delays in infants, behavioral problems and autism in children, but also for several psychiatric illnesses in adulthood, such as depression, anxiety, autism, and posttraumatic stress disorder. Despite having robust adverse effects on both mother and infant, the pathophysiology of peripartum depression and anxiety are poorly understood. The objective of this review is to highlight the advantages of using an integrated approach addressing several behavioral domains in both animal and clinical studies of peripartum depression and anxiety. It is postulated that a greater focus on integrated cross domain studies will lead to advances in treatments and preventative measures for several disorders associated with peripartum depression and anxiety. Exposure to high levels of early life stress has been identified as a potent risk factor for neurodevelopmental delays in infants, behavioral problems and autism in children, but also for several psychiatric illnesses in adulthood, such as depression, anxiety, autism, and posttraumatic stress disorder. Despite having robust adverse effects on both mother and infant, the pathophysiology of peripartum depression and anxiety are poorly understood. The objective of this review is to highlight the advantages of using an integrated approach addressing several behavioral domains in both animal and clinical studies of peripartum depression and anxiety. It is postulated that a greater focus on integrated cross domain studies will lead to advances in treatments and preventative measures for several disorders associated with peripartum depression and anxiety