Counterexamples Related to a Characterization of Multivariate Regular Variation

Counterexamples Related to a Characterization of Multivariate Regular Variatio

Heavy-Tailed Insurance Portfolios: Buffer Capital and Ruin Probabilities

Heavy-Tailed Insurance Portfolios: Buffer Capital and Ruin Probabilitie

Measuring degree-degree association in networks

The Pearson correlation coefficient is commonly used for quantifying the global level of degree-degree association in complex networks. Here, we use a probabilistic representation of the underlying network structure for assessing the applicability of different association measures to heavy-tailed degree distributions. Theoretical arguments together with our numerical study indicate that Pearson's coefficient often depends on the size of networks with equal association structure, impeding a systematic comparison of real-world networks. In contrast, Kendall-Gibbons' $\tau_{b}$ is a considerably more robust measure of the degree-degree association

Эпидемиологическая характеристика парвовирусной В19 инфекции у детей с гематологическими заболеваниями

ДЕТИ, СЛУЖБЫ ОХРАНЫ ЗДОРОВЬЯКРОВИ БОЛЕЗНИПАРВОВИРУСНЫЕ ИНФЕКЦИИЭПИДЕМИОЛОГИ

The Bivariate Normal Copula

We collect well known and less known facts about the bivariate normal distribution and translate them into copula language. In addition, we prove a very general formula for the bivariate normal copula, we compute Gini's gamma, and we provide improved bounds and approximations on the diagonal.Comment: 24 page

Biologic Agents to Promote Periodontal Regeneration and Bone Augmentation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142088/1/cap0080.pd

DeepHistoClass: A novel strategy for confident classification of immunohistochemistry images using Deep Learning

© 2021 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.mcpro.2021.100140A multitude of efforts worldwide aim to create a single cell reference map of the human body, for fundamental understanding of human health, molecular medicine and targeted treatment. Antibody-based proteomics using immunohistochemistry (IHC) has proven to be an excellent technology for integration with large-scale single cell transcriptomics datasets. The golden standard for evaluation of IHC staining patterns is manual annotation, which is expensive and may lead to subjective errors. Artificial intelligence holds much promise for efficient and accurate pattern recognition, but confidence in prediction needs to be addressed. Here, the aim was to present a reliable and comprehensive framework for automated annotation of IHC images. We developed a multi-label classification of 7,848 complex IHC images of human testis corresponding to 2,794 unique proteins, generated as part of the Human Protein Atlas (HPA) project. Manual annotation data for eight different cell types was generated as a basis for training and testing a proposed Hybrid Bayesian Neural Network. By combining the deep learning model with a novel uncertainty metric; DeepHistoClass (DHC) confidence score; the average diagnostic performance improved from 86.9% to 96.3%. This metric not only reveals which images are reliably classified by the model, but can also be utilized for identification of manual annotation errors. The proposed streamlined workflow can be developed further for other tissue types in health and disease, and has important implications for digital pathology initiatives or large-scale protein mapping efforts such as the HPA project.Knut and Alice Wallenberg Foundation

Transfer Functions for Protein Signal Transduction: Application to a Model of Striatal Neural Plasticity

We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation.Comment: 13 pages, 5 tables, 15 figure