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"Frost, Robert"
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The Oxford history of Poland-Lithuania
The history of eastern Europe is dominated by the story of the rise of the Russian empire, yet Russia only emerged as a major power after 1700. For 300 years the greatest power in Eastern Europe was the union between the Kingdom of Poland and the Grand Suchy of Lithuania, one of the longest-lasting political unions in European history. Yet because it ended in the late-eighteenth century in what are misleadingly termed the Partitions of Poland, it barely features in standard accounts of European history. This book tells the story of the formation of a consensual, decentralised, multinational, and religiously plural state built from below as much as above, that was founded by peaceful negotiation, not war and conquest. From its inception in 1385-6, a vision of political union was developed that proved attractive to Poles, Lithuanians, Ruthenians, and Germans, a union which was extended to include Prussia in the 1450s and Livonia in the 1560s. Despite the often bitter disagreements over the nature of the union, these were nevertheless overcome by a republican vision of a union of peoples in one political community of citizens under an elected monarch. Robert Frost challenges interpretations of the union informed by the idea that the emergence of the sovereign nation state represents the essence of political modernity, and presents the Polish-Lithuanian union as a case study of a composite state. Publisher.
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Control of glucose homeostasis and insulin sensitivity by the Let-7 family of microRNAs
Diabetes mellitus is the most common metabolic disorder worldwide and a major risk factor for cardiovascular disease. MicroRNAs are negative regulators of gene expression that have been implicated in many biological processes, including metabolism. Here we show that the Let-7 family of microRNAs regulates glucose metabolism in multiple organs. Global and pancreas-specific overexpression of Let-7 in mice resulted in impaired glucose tolerance and reduced glucose-induced pancreatic insulin secretion. Mice overexpressing Let-7 also had decreased fat mass and body weight, as well as reduced body size. Global knockdown of the Let-7 family with an antimiR was sufficient to prevent and treat impaired glucose tolerance in mice with diet-induced obesity, at least in part by improving insulin sensitivity in liver and muscle. AntimiR treatment of mice on a high-fat diet also resulted in increased lean and muscle mass, but not increased fat mass, and prevented ectopic fat deposition in the liver. These findings demonstrate that Let-7 regulates multiple aspects of glucose metabolism and suggest antimiR-induced Let-7 knockdown as a potential treatment for type 2 diabetes mellitus. Furthermore, our Cre-inducible Let-7-transgenic mice provide a unique model for studying tissue-specific aspects of body growth and type 2 diabetes.
Journal Article
Phase 1 Trial of Antibody NI006 for Depletion of Cardiac Transthyretin Amyloid
In a phase 1 trial of NI006 for transthyretin amyloid cardiomyopathy, no apparent drug-related serious adverse events occurred. At doses of at least 10 mg per kilogram, amyloid-load surrogates appeared to be reduced.
Journal Article
Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error
We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior performance at a lower computational cost relative to other single sample approaches.
Journal Article
Analyzing cancer gene expression data through the lens of normal tissue-specificity
The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.
Journal Article
Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue
We describe a novel single sample gene set testing method for cancer transcriptomics data named tissue-adjusted pathway analysis of cancer (TPAC). The TPAC method leverages information about the normal tissue-specificity of human genes to compute a robust multivariate distance score that quantifies gene set dysregulation in each profiled tumor. Because the null distribution of the TPAC scores has an accurate gamma approximation, both population and sample-level inference is supported. As we demonstrate through an analysis of gene expression data for 21 solid human cancers from The Cancer Genome Atlas (TCGA) and associated normal tissue expression data from the Human Protein Atlas (HPA), TPAC gene set scores are more strongly associated with patient prognosis than the scores generated by existing single sample gene set testing methods.
Journal Article