A novel methodology to create generative statistical models of interconnects

This paper addresses the problem of constructing a generative statistical model for an interconnect starting from a limited set of S-parameter samples, which are obtained by simulating or measuring the interconnect for a few random realizations of its stochastic physical properties. These original samples are first converted into a pole-residue representation with common poles. The corresponding residues are modeled as a correlated stochastic process by means of principal component analysis and kernel density estimation. The obtained model allows generating new samples with similar statistics as the original data. A passivity check is performed over the generated samples to retain only passive data. The proposed approach is applied to a representative coupled microstrip line example

A novel generative stochastic model for high-speed interconnection links

In this paper we introduce a new modeling approach to create a generative model for stochastic link responses. The proposed scheme starts from a limited set of simulated or measured ‘training samples’, which are first represented by a rational model using vector fitting with common poles. Next, the generative model is built, leveraging the residues' stochastic distribution, via a principal component analysis and kernel density estimation. Then, in a post-processing phase, non-passive samples are discarded. The novel method is applied to a commercial connector footprint, a multi-conductor transmission line, and a complete link composed of the cascade connection of the former components

WZB117 (2-Fluoro-6-(m-hydroxybenzoyloxy) Phenyl m-Hydroxybenzoate) Inhibits GLUT1-mediated Sugar Transport by Binding Reversibly at the Exofacial Sugar Binding Site

WZB117 (2-fluoro-6-(m-hydroxybenzoyloxy) phenyl m-hydroxybenzoate) inhibits passive sugar transport in human erythrocytes and cancer cell lines and, by limiting glycolysis, inhibits tumor growth in mice. This study explores how WZB117 inhibits the erythrocyte sugar transporter glucose transport protein 1 (GLUT1) and examines the transporter isoform specificity of inhibition. WZB117 reversibly and competitively inhibits erythrocyte 3-O-methylglucose (3MG) uptake with Ki(app) = 6 mum but is a noncompetitive inhibitor of sugar exit. Cytochalasin B (CB) is a reversible, noncompetitive inhibitor of 3MG uptake with Ki(app) = 0.3 mum but is a competitive inhibitor of sugar exit indicating that WZB117 and CB bind at exofacial and endofacial sugar binding sites, respectively. WZB117 inhibition of GLUTs expressed in HEK293 cells follows the order of potency: insulin-regulated GLUT4 - GLUT1 - neuronal GLUT3. This may explain WZB117-induced murine lipodystrophy. Molecular docking suggests the following. 1) The WZB117 binding envelopes of exofacial GLUT1 and GLUT4 conformers differ significantly. 2) GLUT1 and GLUT4 exofacial conformers present multiple, adjacent glucose binding sites that overlap with WZB117 binding envelopes. 3) The GLUT1 exofacial conformer lacks a CB binding site. 4) The inward GLUT1 conformer presents overlapping endofacial WZB117, d-glucose, and CB binding envelopes. Interrogating the GLUT1 mechanism using WZB117 reveals that subsaturating WZB117 and CB stimulate erythrocyte 3MG uptake. Extracellular WZB117 does not affect CB binding to GLUT1, but intracellular WZB117 inhibits CB binding. These findings are incompatible with the alternating conformer carrier for glucose transport but are consistent with either a multisubunit, allosteric transporter, or a transporter in which each subunit presents multiple, interacting ligand binding sites

A generative modeling framework for statistical link analysis based on sparse data

This paper proposes a novel strategy for creating generative models of stochastic link responses starting from limited available data. Whereas state-of-the-art techniques, e.g., based on generalized polynomial chaos expansions, require a considerable amount of (expensive) input data, here we start from a small set of "training" responses. These responses are obtained either from simulations or measurements to construct a comprehensive stochastic model. Using this model, new response samples can be generated with a distribution as similar as possible to the real data distribution, for use in Monte Carlo-like analyses. The methodology first uses the standard Vector Fitting algorithm to fit the S-parameter data with rational functions having common poles. Then, a generative model for the residues is created by means of principal component analysis and kernel density estimation. An a posteriori selection of passive samples is performed on the generated data to ensure the new samples are physically consistent. The proposed modeling approach is applied to a commercial connector and to a set of differential striplines. Both are concatenated to produce the stochastic analysis of a complete link. Comparisons on the prediction of time-domain responses are also provided

How to improve the accuracy of height data from bird tracking devices?:An assessment of high-frequency GPS tracking and barometric altimetry in field conditions

Background: In the context of rapid development of wind energy infrastructure, information on the flight height of birds is vital to assess their collision risk with wind turbines. GPS tags potentially represent a powerful tool to collect flight height data, yet GPS positions are associated with substantial vertical error. Here, we assessed to what extent high-frequency GPS tracking with fix intervals of 2–3 s (GPS remaining turned on between fixes), or barometric altimetry using air pressure loggers integrated in GPS tags, improved the accuracy of height data compared to standard low-frequency GPS tracking (fix interval ≥ 5 min; GPS turned off between fixes). Results: Using data from 10 GPS tag models from three manufacturers in a field setting (194 tags deployed on free-living raptors), we estimated vertical accuracy based on periods when the birds were stationary on the ground (true height above ground was approximately zero), and the difference between GPS and barometric height in flight. In GPS height data, vertical accuracy was mainly driven by noise (little bias), while in barometric data, it was mostly affected by bias (little noise). In high-frequency GPS data, vertical accuracy was improved compared to low-frequency data in each tag model (mean absolute error (AE) reduced by 72% on average; range of mean AE 2–7 vs. 7–30 m). In barometric data, vertical accuracy did not differ between high- and low-frequency modes, with a bias of − 15 to − 5 m and mean AE of 7–15 m in stationary positions. However, the median difference between GPS and barometric data was smaller in flight positions than in stationary positions, suggesting that the bias in barometric height data was smaller in flight. Finally, simulations showed that the remaining vertical error in barometric and high-frequency GPS data had little effect on flight height distributions and the proportion of positions within the collision risk height range, as opposed to the extensive noise found in low-frequency GPS data in some tag models. Conclusions: Barometric altimetry may provide more accurate height data than standard low-frequency GPS tracking, but it involves the risk of a systematic error. Currently, high-frequency GPS tracking provides highest vertical accuracy and may thus substantially advance the study of wind turbine collision risk in birds.</p

Repercussion of megakaryocyte-specific Gata1 Loss on megakaryopoiesis and the hematopoietic precursor compartment

During hematopoiesis, transcriptional programs are essential for the commitment and differentiation of progenitors into the different blood lineages. GATA1 is a transcription factor expressed in several hematopoietic lineages and essential for proper erythropoiesis and megakaryopoiesis. Megakaryocyte-specific genes, such as GP1BA, are known to be directly regulated by GATA1. Mutations in GATA1 can lead to dyserythropoietic anemia and pseudo gray-platelet syndrome. Selective loss of Gata1 expression in adult mice results in macrothrombocytopenia with platelet dysfunction, characterized by an excess of immature megakaryocytes. To specifically analyze the impact of Gata1 loss in mature committed megakaryocytes, we generated Gata1-Lox|Pf4-Cre mice (Gata1cKOMK). Consistent with previous findings, Gata1cKOMK mice are macrothrombocytopenic with platelet dysfunction. Supporting this notion we demonstrate that Gata1 regulates directly the transcription of Syk, a tyrosine kinase that functions downstream of Clec2 and GPVI receptors in megakaryocytes and platelets. Furthermore, we show that Gata1cKOMK mice display an additional aberrant megakaryocyte differentiation stage. Interestingly, these mice present a misbalance of the multipotent progenitor compartment and the erythroid lineage, which translates into compensatory stress erythropoiesis and splenomegaly. Despite the severe thrombocytopenia, Gata1cKOMK mice display a mild reduction of TPO plasma levels, and Gata1cK-OMK megakaryocytes show a mild increase in Pf4 mRNA levels; such a misbalance might be behind the general hematopoietic defects observed, affecting locally normal TPO and Pf4 levels at hematopoietic stem cell niches. © 2016 Meinders et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited