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"Harvey, A. C. (Andrew C.)"
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Dynamic Models for Volatility and Heavy Tails
The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling.
eBook
An in silico method to assess antibody fragment polyreactivity
Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models’ performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.
Off-target binding hinders the development of therapeutic antibodies and reproducibility in basic research settings. Here the authors develop a method to quantify and reduce the polyreactivity of antibody fragments based on protein sequence alone.
Journal Article
The Draft Genome Sequence of European Pear (Pyrus communis L. ‘Bartlett’)
We present a draft assembly of the genome of European pear (Pyrus communis) 'Bartlett'. Our assembly was developed employing second generation sequencing technology (Roche 454), from single-end, 2 kb, and 7 kb insert paired-end reads using Newbler (version 2.7). It contains 142,083 scaffolds greater than 499 bases (maximum scaffold length of 1.2 Mb) and covers a total of 577.3 Mb, representing most of the expected 600 Mb Pyrus genome. A total of 829,823 putative single nucleotide polymorphisms (SNPs) were detected using re-sequencing of 'Louise Bonne de Jersey' and 'Old Home'. A total of 2,279 genetically mapped SNP markers anchor 171 Mb of the assembled genome. Ab initio gene prediction combined with prediction based on homology searching detected 43,419 putative gene models. Of these, 1219 proteins (556 clusters) are unique to European pear compared to 12 other sequenced plant genomes. Analysis of the expansin gene family provided an example of the quality of the gene prediction and an insight into the relationships among one class of cell wall related genes that control fruit softening in both European pear and apple (Malus × domestica). The 'Bartlett' genome assembly v1.0 (http://www.rosaceae.org/species/pyrus/pyrus_communis/genome_v1.0) is an invaluable tool for identifying the genetic control of key horticultural traits in pear and will enable the wide application of marker-assisted and genomic selection that will enhance the speed and efficiency of pear cultivar development.
Journal Article
Somatic mutation landscapes at single-molecule resolution
Somatic mutations drive the development of cancer and may contribute to ageing and other diseases
1
,
2
. Despite their importance, the difficulty of detecting mutations that are only present in single cells or small clones has limited our knowledge of somatic mutagenesis to a minority of tissues. Here, to overcome these limitations, we developed nanorate sequencing (NanoSeq), a duplex sequencing protocol with error rates of less than five errors per billion base pairs in single DNA molecules from cell populations. This rate is two orders of magnitude lower than typical somatic mutation loads, enabling the study of somatic mutations in any tissue independently of clonality. We used this single-molecule sensitivity to study somatic mutations in non-dividing cells across several tissues, comparing stem cells to differentiated cells and studying mutagenesis in the absence of cell division. Differentiated cells in blood and colon displayed remarkably similar mutation loads and signatures to their corresponding stem cells, despite mature blood cells having undergone considerably more divisions. We then characterized the mutational landscape of post-mitotic neurons and polyclonal smooth muscle, confirming that neurons accumulate somatic mutations at a constant rate throughout life without cell division, with similar rates to mitotically active tissues. Together, our results suggest that mutational processes that are independent of cell division are important contributors to somatic mutagenesis. We anticipate that the ability to reliably detect mutations in single DNA molecules could transform our understanding of somatic mutagenesis and enable non-invasive studies on large-scale cohorts.
NanoSeq is used to detect mutations in single DNA molecules and analyses show that mutational processes that are independent of cell division are important contributors to somatic mutagenesis.
Journal Article
A Biosynthetic Pathway for Anandamide
The endocannabinoid arachidonoyl ethanolamine (anandamide) is a lipid transmitter synthesized and released \"on demand\" by neurons in the brain. Anandamide is also generated by macrophages where its endotoxin (LPS)-induced synthesis has been implicated in the hypotension of septic shock and advanced liver cirrhosis. Anandamide can be generated from its membrane precursor, N-arachidonoyl phosphatidylethanolamine (NAPE) through cleavage by a phospholipase D (NAPE-PLD). Here we document a biosynthetic pathway for anandamide in mouse brain and RAW264.7 macrophages that involves the phospholipase C (PLC)-catalyzed cleavage of NAPE to generate a lipid, phosphoanandamide, which is subsequently dephosphorylated by phosphatases, including PTPN22, previously described as a protein tyrosine phosphatase. Bacterial endotoxin (LPS)-induced synthesis of anandamide in macrophages is mediated exclusively by the PLC/phosphatase pathway, which is up-regulated by LPS, whereas NAPE-PLD is down-regulated by LPS and functions as a salvage pathway of anandamide synthesis when the PLC/phosphatase pathway is compromised. Both PTPN22 and endocannabinoids have been implicated in autoimmune diseases, suggesting that the PLC/phosphatase pathway of anandamide synthesis may be a pharmacotherapeutic target.
Journal Article
A non-canonical role for desmoglein-2 in endothelial cells: implications for neoangiogenesis
Desmogleins (DSG) are a family of cadherin adhesion proteins that were first identified in desmosomes and provide cardiomyocytes and epithelial cells with the junctional stability to tolerate mechanical stress. However, one member of this family, DSG2, is emerging as a protein with additional biological functions on a broader range of cells. Here we reveal that DSG2 is expressed by non-desmosome-forming human endothelial progenitor cells as well as their mature counterparts [endothelial cells (ECs)] in human tissue from healthy individuals and cancer patients. Analysis of normal blood and bone marrow showed that DSG2 is also expressed by CD34
+
CD45
dim
hematopoietic progenitor cells. An inability to detect other desmosomal components, i.e., DSG1, DSG3 and desmocollin (DSC)2/3, on these cells supports a solitary role for DSG2 outside of desmosomes. Functionally, we show that CD34
+
CD45
dim
DSG2
+
progenitor cells are multi-potent and pro-angiogenic in vitro. Using a ‘knockout-first’ approach, we generated a
Dsg2
loss-of-function strain of mice (
Dsg2
lo/lo
) and observed that, in response to reduced levels of
Dsg2
: (i) CD31
+
ECs in the pancreas are hypertrophic and exhibit altered morphology, (ii) bone marrow-derived endothelial colony formation is impaired, (iii) ex vivo vascular sprouting from aortic rings is reduced, and (iv) vessel formation in vitro and in vivo is attenuated. Finally, knockdown of
DSG2
in a human bone marrow EC line reveals a reduction in an in vitro angiogenesis assay as well as relocalisation of actin and VE-cadherin away from the cell junctions, reduced cell–cell adhesion and increased invasive properties by these cells. In summary, we have identified DSG2 expression in distinct progenitor cell subpopulations and show that, independent from its classical function as a component of desmosomes, this cadherin also plays a critical role in the vasculature.
Journal Article
TIME SERIES MODELLING OF EPIDEMICS
This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020, Harvard Data Science Review, Special Issue 1—COVID-19, https://hdsr.mitpress.mit.edu/pub/ozgjx0yn) is extended to model the relationship between two or more series and the role of common trends is discussed. Data on daily deaths from COVID-19 in Italy and the UK provides an example of leading indicators when there is a balanced growth. When growth is not balanced, the model can be extended by including a non-stationary component in one of the series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy in the spring of 2020.
Journal Article
General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series
A class of model-based filters for extracting trends and cycles in economic time series is presented. These lowpass and bandpass filters are derived in a mutually consistent manner as the joint solution to a signal extraction problem in an unobserved-components model. The resulting trends and cycles are computed in finite samples using the Kalman filter and associated smoother. The filters form a class which is a generalization of the class of Butterworth filters, widely used in engineering. They are very flexible and have the important property of allowing relatively smooth cycles to be extracted from economic time series. Perfectly sharp, or ideal, bandpass filters emerge as a limiting case. Applying the method to quarterly series on U.S. investment and GDP shows a clearly defined cycle.
Journal Article
Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns
A stochastic volatility model may be estimated by a quasi-maximum likelihood procedure by transforming to a linear state-space form. The method is extended to handle correlation between the two disturbances in the model and applied to data on stock returns
Journal Article