Telomeres are specific nucleoprotein structures that are located at the ends of linear eukaryotic chromosomes and play crucial roles in genomic stability. Telomere DNA consists of simple repeats of a short G-rich sequence: TTAGGG in mammals and TTTAGGG in most plants. In recent years, the mammalian telomeric G-rich repeats have been shown to form G-quadruplex (G4) structures, which are crucial for modulating telomere functions. Surprisingly, even though plant telomeres are essential for plant growth, development, and environmental adaptions, only few reports exist on plant telomeric G4 DNA (pTG4). Here, using bulk and single-molecule assays, including CD spectroscopy, and single-molecule FRET approaches, we comprehensively characterized the structure and dynamics of a typical plant telomeric sequence, d[GGG(TTTAGGG)3]. We found that this sequence can fold into mixed G4s in potassium, including parallel and antiparallel structures. We also directly detected intermediate dynamic transitions, including G-hairpin, parallel G-triplex, and antiparallel G-triplex structures. Moreover, we observed that pTG4 is unfolded by the AtRecQ2 helicase but not by AtRecQ3. The results of our work shed light on our understanding about the existence, topological structures, stability, intermediates, unwinding, and functions of pTG4.

Knowledge of the molecular events in mitochondrial DNA (mtDNA) replication is crucial to understanding the origins of human disorders arising from mitochondrial dysfunction. Twinkle helicase is an essential component of mtDNA replication. Here, we employed atomic force microscopy imaging in air and liquids to visualize ring assembly, DNA binding, and unwinding activity of individual Twinkle hexamers at the single-molecule level. We observed that the Twinkle subunits self-assemble into hexamers and higher-order complexes that can switch between open and closed-ring configurations in the absence of DNA. Our analyses helped visualize Twinkle loading onto and unloading from DNA in an open-ringed configuration. They also revealed that closed-ring conformers bind and unwind several hundred base pairs of duplex DNA at an average rate of ∼240 bp/min. We found that the addition of mitochondrial single-stranded (ss) DNA–binding protein both influences the ways Twinkle loads onto defined DNA substrates and stabilizes the unwound ssDNA product, resulting in a ∼5-fold stimulation of the apparent DNA-unwinding rate. Mitochondrial ssDNA-binding protein also increased the estimated translocation processivity from 1750 to >9000 bp before helicase disassociation, suggesting that more than half of the mitochondrial genome could be unwound by Twinkle during a single DNA-binding event. The strategies used in this work provide a new platform to examine Twinkle disease variants and the core mtDNA replication machinery. They also offer an enhanced framework to investigate molecular mechanisms underlying deletion and depletion of the mitochondrial genome as observed in mitochondrial diseases.

Carlos Gustavo T. Moreira, Maria José Pacifico and Sergio Romaña Ibarra
Bull. Amer. Math. Soc. 57 (2018), 269-292.
Abstract, references and article information

(Institute of Atmospheric Physics, Chinese Academy of Sciences) Scientists investigate how nitrogen dynamics affects carbon and water budgets in China by incorporating the terrestrial nitrogen cycle into the Noah Land Surface Model.

Patrick Herr
Apr 1, 2020; 19:608-623
Research

Invitation Only Research Event

Event participants

Abdul Rahman Alageli, Associate Fellow, MENA Programme, Chatham House
Emaddedin Badi, Non-Resident Scholar, Middle East Institute
Tim Eaton, Senior Research Fellow, MENA Programme Chatham House
Valerie Stocker, Independent Researcher

17 March 2020

This paper explores armed group–community relations in Libya and the sources of revenue that have allowed armed groups to grow in power and influence. It draws out the implications for policy and identifies options for mitigating conflict dynamics.

¹⁸F-BMS-986192, an adnectin-based human programmed cell death ligand 1 (PD-L1) tracer, was developed to non-invasively determine whole-body PD-L1 expression by positron emission tomography (PET). We evaluated usability of ¹⁸F-BMS-986192 PET to detect different PD-L1 expression levels and therapy-induced changes of PD-L1 expression in tumors. Methods: In vitro binding assays with ¹⁸F-BMS-986192 were performed in human tumor cell lines with different total cellular and membrane PD-L1 protein expression levels. Subsequently, PET imaging was executed in immunodeficient mice xenografted with these cell lines. Mice were treated with interferon gamma (IFN) intraperitoneally for 3 days or with the mitogen-activated protein kinase kinase (MEK1/2) inhibitor selumetinib by oral gavage for 24 hours. Thereafter ¹⁸F-BMS-986192 was administered intravenously, followed by a 60-minute dynamic PET scan. Tracer uptake was expressed as percentage injected dose per gram tissue (%ID/g). Tissues were collected to evaluate ex vivo tracer biodistribution and to perform flow cytometric, Western blot, and immunohistochemical tumor analyses. Results: ¹⁸F-BMS-986192 uptake reflected PD-L1 membrane levels in tumor cell lines, and tumor tracer uptake in mice was associated with PD-L1 expression measured immunohistochemically. In vitro IFN treatment increased PD-L1 expression in the tumor cell lines and caused up to 12-fold increase in tracer binding. In vivo, IFN did neither affect PD-L1 tumor expression measured immunohistochemically nor ¹⁸F-BMS-986192 tumor uptake. In vitro, selumetinib downregulated cellular and membrane levels of PD-L1 of tumor cells by 50% as measured by Western blotting and flow cytometry. In mice, selumetinib lowered cellular, but not membrane PD-L1 levels of tumors and consequently no treatment-induced change in ¹⁸F-BMS-986192 tumor uptake was observed. Conclusion: ¹⁸F-BMS-986192 PET imaging allows detection of membrane-expressed PD-L1, as soon as 60 minutes after tracer injection. The tracer can discriminate a range of tumor cell PD-L1 membrane expression levels.

Treatment of hyperinsulinemic hypoglycemia is challenging. Surgical treatment of insulinomas and focal lesions in congenital hyperinsulinism (CHI) is invasive and carries major risks of morbidity. Medication to treat nesidioblastosis and diffuse CHI has varying efficacy and causes significant side effects. Here, we describe a novel method for therapy of hyperinsulinemic hyperglycemia, highly selectively killing beta cells by targeted photodynamic therapy (tPDT) with exendin-4-IRDye700DX, targeting the glucagon-like peptide 1 receptor (GLP-1R). A competitive binding assay was performed using Chinese hamster lung (CHL) cells transfected with the GLP-1R. The efficacy and specificity of tPDT with exendin-4-IRDye700DX was examined in vitro in cells with different levels of GLP-1R expression. Tracer biodistribution was determined in BALB/c nude mice bearing subcutaneous CHL-GLP-1R xenografts. Induction of cellular damage and the effect on tumor growth were analyzed to determine treatment efficacy. Exendin-4-IRDye700DX has a high affinity for the GLP-1R with an IC50 value of 6.3 nM. TPDT caused significant specific phototoxicity in GLP-1R positive cells (2.3 ± 0.8 % and 2.7 ± 0.3 % remaining cell viability in CHL-GLP-1R and INS-1 cells resp.). The tracer accumulates dose-dependently in GLP-1R positive tumors. In vivo tPDT induces cellular damage in tumors, shown by strong expression of cleaved-caspase-3 and leads to a prolonged median survival of the mice (36.5 vs. 22.5 days resp. p<0.05). These data show in vitro as well as in vivo evidence for the potency of tPDT using exendin-4-IRDye700DX. This could in the future provide a new, minimally invasive and highly specific treatment method for hyperinsulinemic hypoglycemia.

The cell cycle is a highly conserved process involving the coordinated separation of a single cell into two daughter cells. To relate transcriptional regulation across the cell cycle with oscillatory changes in protein abundance and activity, we carried out a proteome- and phospho-proteome-wide mass spectrometry profiling. We compared protein dynamics with gene transcription, revealing many transcriptionally regulated G2 mRNAs that only produce a protein shift after mitosis. Integration of CRISPR/Cas9 survivability studies further highlighted proteins essential for cell viability. Analyzing the dynamics of phosphorylation events and protein solubility dynamics over the cell cycle, we characterize predicted phospho-peptide motif distributions and predict cell cycle-dependent translocating proteins, as exemplified by the S-adenosylmethionine synthase MAT2A. Our study implicates this enzyme in translocating to the nucleus after the G1/S-checkpoint, which enables epigenetic histone methylation maintenance during DNA replication. Taken together, this data set provides a unique integrated resource with novel insights on cell cycle dynamics.

Corporate Members Event Nominees Breakfast Briefing Partners and Major Corporates

Event participants

Christopher Sabatini, Senior Research Fellow for Latin America, US and the Americas Programme, Chatham House

Deanna L. Davis
Apr 1, 2020; 61:505-522
Research Articles

Myelin is a unique lipid-rich membrane structure that accelerates neurotransmission and supports neuronal function. Sphingolipids are critical myelin components. Yet sphingolipid content and synthesis have not been well characterized in oligodendrocytes, the myelin-producing cells of the CNS. Here, using quantitative real-time PCR, LC-MS/MS-based lipid analysis, and biochemical assays, we examined sphingolipid synthesis during the peak period of myelination in the postnatal rat brain. Importantly, we characterized sphingolipid production in isolated oligodendrocytes. We analyzed sphingolipid distribution and levels of critical enzymes and regulators in the sphingolipid biosynthetic pathway, with focus on the serine palmitoyltransferase (SPT) complex, the rate-limiting step in this pathway. During myelination, levels of the major SPT subunits increased and oligodendrocyte maturation was accompanied by extensive alterations in the composition of the SPT complex. These included changes in the relative levels of two alternative catalytic subunits, SPTLC2 and -3, in the relative levels of isoforms of the small subunits, ssSPTa and -b, and in the isoform distribution of the SPT regulators, the ORMDLs. Myelination progression was accompanied by distinct changes in both the nature of the sphingoid backbone and the N-acyl chains incorporated into sphingolipids. We conclude that the distribution of these changes among sphingolipid family members is indicative of a selective channeling of the ceramide backbone toward specific downstream metabolic pathways during myelination. Our findings provide insights into myelin production in oligodendrocytes and suggest how dysregulation of the biosynthesis of this highly specialized membrane could contribute to demyelinating diseases.

2 May 2019 , Volume 95, Number 3

In the latest issue a collection of articles explore how international norms are increasingly contested by both state and non-state actors.

Invitation Only Research Event

Event participants

Anita Isaacs, Professor of Political Science, Haverford College; Co-Director, Migration Encounters Project
Juan Ricardo Ortega, Principal Advisor for Central America, Inter-American Development Bank
Chair: Amy Pope, Associate Fellow, Chatham House; US Deputy Homeland Security Adviser for the Obama Administration (2015-17)

Invitation Only Research Event

Event participants

Laurence Broers, Associate Fellow, Russia and Eurasia Programme, Chatham House
Chair: Lubica Pollakova, Senior Programme Manager, Russia and Eurasia Programme

Knowledge of the molecular events in mitochondrial DNA (mtDNA) replication is crucial to understanding the origins of human disorders arising from mitochondrial dysfunction. Twinkle helicase is an essential component of mtDNA replication. Here, we employed atomic force microscopy imaging in air and liquids to visualize ring assembly, DNA binding, and unwinding activity of individual Twinkle hexamers at the single-molecule level. We observed that the Twinkle subunits self-assemble into hexamers and higher-order complexes that can switch between open and closed-ring configurations in the absence of DNA. Our analyses helped visualize Twinkle loading onto and unloading from DNA in an open-ringed configuration. They also revealed that closed-ring conformers bind and unwind several hundred base pairs of duplex DNA at an average rate of ∼240 bp/min. We found that the addition of mitochondrial single-stranded (ss) DNA–binding protein both influences the ways Twinkle loads onto defined DNA substrates and stabilizes the unwound ssDNA product, resulting in a ∼5-fold stimulation of the apparent DNA-unwinding rate. Mitochondrial ssDNA-binding protein also increased the estimated translocation processivity from 1750 to >9000 bp before helicase disassociation, suggesting that more than half of the mitochondrial genome could be unwound by Twinkle during a single DNA-binding event. The strategies used in this work provide a new platform to examine Twinkle disease variants and the core mtDNA replication machinery. They also offer an enhanced framework to investigate molecular mechanisms underlying deletion and depletion of the mitochondrial genome as observed in mitochondrial diseases.

The impact of climate change as a driver of human migration is expected by many to dwarf all others. Still, certain frequently repeated forecasts of the number of people who stand to be displaced by climate change are not informed by a complete understanding of migration dynamics, as this report explains.

London : Macmillan, 1902.

Rockville, Maryland : National Institute on Drug Abuse, 1985.

Rockville, Maryland : National Institute on Drug Abuse, 1979.

DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) dcs, containing the implementation of dynamic classifier selection methods (DCS); (ii) des, containing the implementation of dynamic ensemble selection methods (DES); (iii) static, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage (codecov.io) and is part of the scikit-learn-contrib supported projects. Documentation, code and examples can be found on its GitHub page: https://github.com/scikit-learn-contrib/DESlib.

Parametrised state space models in the form of recurrent networks are often used in machine learning to learn from data streams exhibiting temporal dependencies. To break the black box nature of such models it is important to understand the dynamical features of the input-driving time series that are formed in the state space. We propose a framework for rigorous analysis of such state representations in vanishing memory state space models such as echo state networks (ESN). In particular, we consider the state space a temporal feature space and the readout mapping from the state space a kernel machine operating in that feature space. We show that: (1) The usual ESN strategy of randomly generating input-to-state, as well as state coupling leads to shallow memory time series representations, corresponding to cross-correlation operator with fast exponentially decaying coefficients; (2) Imposing symmetry on dynamic coupling yields a constrained dynamic kernel matching the input time series with straightforward exponentially decaying motifs or exponentially decaying motifs of the highest frequency; (3) Simple ring (cycle) high-dimensional reservoir topology specified only through two free parameters can implement deep memory dynamic kernels with a rich variety of matching motifs. We quantify richness of feature representations imposed by dynamic kernels and demonstrate that for dynamic kernel associated with cycle reservoir topology, the kernel richness undergoes a phase transition close to the edge of stability.

Stochastic gradient Langevin dynamics (SGLD) is a fundamental algorithm in stochastic optimization. Recent work by Zhang et al. (2017) presents an analysis for the hitting time of SGLD for the first and second order stationary points. The proof in Zhang et al. (2017) is a two-stage procedure through bounding the Cheeger's constant, which is rather complicated and leads to loose bounds. In this paper, using intuitions from stochastic differential equations, we provide a direct analysis for the hitting times of SGLD to the first and second order stationary points. Our analysis is straightforward. It only relies on basic linear algebra and probability theory tools. Our direct analysis also leads to tighter bounds comparing to Zhang et al. (2017) and shows the explicit dependence of the hitting time on different factors, including dimensionality, smoothness, noise strength, and step size effects. Under suitable conditions, we show that the hitting time of SGLD to first-order stationary points can be dimension-independent. Moreover, we apply our analysis to study several important online estimation problems in machine learning, including linear regression, matrix factorization, and online PCA.

Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category. We also review several prominent applications and widely used datasets and highlight directions for future research.

Yong-Chao Zhang, Na Zhang.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 1, 184--191.

Abstract:
We investigate the equivalence of dynamic and static asset allocations in the case where the price process of a risky asset is driven by a Poisson process. Under some mild conditions, we obtain a necessary and sufficient condition for the equivalence of dynamic and static asset allocations. In addition, we provide a simple sufficient condition for the equivalence.

Bomin Kim, Kevin H. Lee, Lingzhou Xue, Xiaoyue Niu.

Source: Statistics Surveys, Volume 12, 105--135.

Abstract:
We present a selective review of statistical modeling of dynamic networks. We focus on models with latent variables, specifically, the latent space models and the latent class models (or stochastic blockmodels), which investigate both the observed features and the unobserved structure of networks. We begin with an overview of the static models, and then we introduce the dynamic extensions. For each dynamic model, we also discuss its applications that have been studied in the literature, with the data source listed in Appendix. Based on the review, we summarize a list of open problems and challenges in dynamic network modeling with latent variables.

Kevin McGoff, Sayan Mukherjee, Natesh Pillai.

Source: Statistics Surveys, Volume 9, 209--252.

Abstract:
The topic of statistical inference for dynamical systems has been studied widely across several fields. In this survey we focus on methods related to parameter estimation for nonlinear dynamical systems. Our objective is to place results across distinct disciplines in a common setting and highlight opportunities for further research.

A. Philip Dawid, Vanessa Didelez

Source: Statist. Surv., Volume 4, 184--231.

Abstract:
We consider the problem of learning about and comparing the consequences of dynamic treatment strategies on the basis of observational data. We formulate this within a probabilistic decision-theoretic framework. Our approach is compared with related work by Robins and others: in particular, we show how Robins’s ‘ G -computation’ algorithm arises naturally from this decision-theoretic perspective. Careful attention is paid to the mathematical and substantive conditions required to justify the use of this formula. These conditions revolve around a property we term stability , which relates the probabilistic behaviours of observational and interventional regimes. We show how an assumption of ‘sequential randomization’ (or ‘no unmeasured confounders’), or an alternative assumption of ‘sequential irrelevance’, can be used to infer stability. Probabilistic influence diagrams are used to simplify manipulations, and their power and limitations are discussed. We compare our approach with alternative formulations based on causal DAGs or potential response models. We aim to show that formulating the problem of assessing dynamic treatment strategies as a problem of decision analysis brings clarity, simplicity and generality.

References:
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Dawid, A. P. (2000). Causal inference without counterfactuals (with Discussion). Journal of the American Statistical Association 95 407–448.

Dawid, A. P. (2001). Separoids: A mathematical framework for conditional independence and irrelevance. Annals of Mathematics and Artificial Intelligence 32 335–372.

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Dawid, A. P. (2010). Beware of the DAG! In Proceedings of the NIPS 2008 Workshop on Causality. Journal of Machine Learning Research Workshop and Conference Proceedings ( D. Janzing, I. Guyon and B. Schölkopf, eds.) 6 59–86. http://tinyurl.com/33va7tm

Dawid, A. P. and Didelez, V. (2008). Identifying optimal sequential decisions. In Proceedings of the Twenty-Fourth Annual Conference on Uncertainty in Artificial Intelligence (UAI-08) ( D. McAllester and A. Nicholson, eds.). 113-120. AUAI Press, Corvallis, Oregon. http://tinyurl.com/3899qpp

Dechter, R. (2003). Constraint Processing. Morgan Kaufmann Publishers.

Didelez, V., Dawid, A. P. and Geneletti, S. G. (2006). Direct and indirect effects of sequential treatments. In Proceedings of the Twenty-Second Annual Conference on Uncertainty in Artificial Intelligence (UAI-06) ( R. Dechter and T. Richardson, eds.). 138-146. AUAI Press, Arlington, Virginia. http://tinyurl.com/32w3f4e

Didelez, V., Kreiner, S. and Keiding, N. (2010). Graphical models for inference under outcome dependent sampling. Statistical Science (to appear).

Didelez, V. and Sheehan, N. S. (2007). Mendelian randomisation: Why epidemiology needs a formal language for causality. In Causality and Probability in the Sciences, ( F. Russo and J. Williamson, eds.). Texts in Philosophy Series 5 263–292. College Publications, London.

Eichler, M. and Didelez, V. (2010). Granger-causality and the effect of interventions in time series. Lifetime Data Analysis 16 3–32.

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Geneletti, S. G. (2007). Identifying direct and indirect effects in a non–counterfactual framework. Journal of the Royal Statistical Society: Series B 69 199–215.

Geneletti, S. G. and Dawid, A. P. (2010). Defining and identifying the effect of treatment on the treated. In Causality in the Sciences ( P. M. Illari, F. Russo and J. Williamson, eds.) Oxford University Press (to appear).

Gill, R. D. and Robins, J. M. (2001). Causal inference for complex longitudinal data: The continuous case. Annals of Statistics 29 1785–1811.

Guo, H. and Dawid, A. P. (2010). Sufficient covariates and linear propensity analysis. In Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics, (AISTATS) 2010, Chia Laguna, Sardinia, Italy, May 13-15, 2010. Journal of Machine Learning Research Workshop and Conference Proceedings ( Y. W. Teh and D. M. Titterington, eds.) 9 281–288. http://tinyurl.com/33lmuj7

Henderson, R., Ansel, P. and Alshibani, D. (2010). Regret-regression for optimal dynamic treatment regimes. Biometrics (to appear). doi:10.1111/j.1541-0420.2009.01368.x

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Rosthøj, S., Fullwood, C., Henderson, R. and Stewart, S. (2006). Estimation of optimal dynamic anticoagulation regimes from observational data: A regret-based approach. Statistics in Medicine 25 4197–4215.

Shpitser, I. and Pearl, J. (2006a). Identification of conditional interventional distributions. In Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence (UAI-06) ( R. Dechter and T. Richardson, eds.). 437–444. AUAI Press, Corvallis, Oregon. http://tinyurl.com/2um8w47

Shpitser, I. and Pearl, J. (2006b). Identification of joint interventional distributions in recursive semi-Markovian causal models. In Proceedings of the Twenty-First National Conference on Artificial Intelligence 1219–1226. AAAI Press, Menlo Park, California.

Spirtes, P., Glymour, C. and Scheines, R. (2000). Causation, Prediction and Search, Second ed. Springer-Verlag, New York.

Sterne, J. A. C., May, M., Costagliola, D., de Wolf, F., Phillips, A. N., Harris, R., Funk, M. J., Geskus, R. B., Gill, J., Dabis, F., Miro, J. M., Justice, A. C., Ledergerber, B., Fatkenheuer, G., Hogg, R. S., D’Arminio-Monforte, A., Saag, M., Smith, C., Staszewski, S., Egger, M., Cole, S. R. and When To Start Consortium (2009). Timing of initiation of antiretroviral therapy in AIDS-Free HIV-1-infected patients: A collaborative analysis of 18 HIV cohort studies. Lancet 373 1352–1363.

Taubman, S. L., Robins, J. M., Mittleman, M. A. and Hernán, M. A. (2009). Intervening on risk factors for coronary heart disease: An application of the parametric g-formula. International Journal of Epidemiology 38 1599–1611.

Tian, J. (2008). Identifying dynamic sequential plans. In Proceedings of the Twenty-Fourth Annual Conference on Uncertainty in Artificial Intelligence (UAI-08) ( D. McAllester and A. Nicholson, eds.). 554–561. AUAI Press, Corvallis, Oregon. http://tinyurl.com/36ufx2h

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Hyperbolic balance laws with uncertain (random) parameters and inputs are ubiquitous in science and engineering. Quantification of uncertainty in predictions derived from such laws, and reduction of predictive uncertainty via data assimilation, remain an open challenge. That is due to nonlinearity of governing equations, whose solutions are highly non-Gaussian and often discontinuous. To ameliorate these issues in a computationally efficient way, we use the method of distributions, which here takes the form of a deterministic equation for spatiotemporal evolution of the cumulative distribution function (CDF) of the random system state, as a means of forward uncertainty propagation. Uncertainty reduction is achieved by recasting the standard loss function, i.e., discrepancy between observations and model predictions, in distributional terms. This step exploits the equivalence between minimization of the square error discrepancy and the Kullback-Leibler divergence. The loss function is regularized by adding a Lagrangian constraint enforcing fulfillment of the CDF equation. Minimization is performed sequentially, progressively updating the parameters of the CDF equation as more measurements are assimilated.

Uncertainties in a structure is inevitable, which generally lead to variation in dynamic response predictions. For a complex structure, brute force Monte Carlo simulation for response variation analysis is infeasible since one single run may already be computationally costly. Data driven meta-modeling approaches have thus been explored to facilitate efficient emulation and statistical inference. The performance of a meta-model hinges upon both the quality and quantity of training dataset. In actual practice, however, high-fidelity data acquired from high-dimensional finite element simulation or experiment are generally scarce, which poses significant challenge to meta-model establishment. In this research, we take advantage of the multi-level response prediction opportunity in structural dynamic analysis, i.e., acquiring rapidly a large amount of low-fidelity data from reduced-order modeling, and acquiring accurately a small amount of high-fidelity data from full-scale finite element analysis. Specifically, we formulate a composite neural network fusion approach that can fully utilize the multi-level, heterogeneous datasets obtained. It implicitly identifies the correlation of the low- and high-fidelity datasets, which yields improved accuracy when compared with the state-of-the-art. Comprehensive investigations using frequency response variation characterization as case example are carried out to demonstrate the performance.

Marianna Pensky.

Source: The Annals of Statistics, Volume 47, Number 4, 2378--2403.

Abstract:
In the present paper, we consider a dynamic stochastic network model. The objective is estimation of the tensor of connection probabilities $mathbf{{Lambda}}$ when it is generated by a Dynamic Stochastic Block Model (DSBM) or a dynamic graphon. In particular, in the context of the DSBM, we derive a penalized least squares estimator $widehat{oldsymbol{Lambda}}$ of $mathbf{{Lambda}}$ and show that $widehat{oldsymbol{Lambda}}$ satisfies an oracle inequality and also attains minimax lower bounds for the risk. We extend those results to estimation of $mathbf{{Lambda}}$ when it is generated by a dynamic graphon function. The estimators constructed in the paper are adaptive to the unknown number of blocks in the context of the DSBM or to the smoothness of the graphon function. The technique relies on the vectorization of the model and leads to much simpler mathematical arguments than the ones used previously in the stationary set up. In addition, all results in the paper are nonasymptotic and allow a variety of extensions.

Jared D. Fisher, Davide Pettenuzzo, Carlos M. Carvalho.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 299--338.

Abstract:
We introduce a fast, closed-form, simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian dynamic linear models of West and Harrison ( Bayesian Forecasting and Dynamic Models (1997) Springer), and it can objectively determine, through a fully automated procedure, both the optimal set of regressors to include in the predictive system and the degree to which the model coefficients, volatilities and covariances should vary over time. When applied to a portfolio of five stock and bond returns, we find that our method leads to large forecast gains, both in statistical and economic terms. In particular, we find that relative to a standard no-predictability benchmark, the optimal combination of predictors, stochastic volatility and time-varying covariances increases the annualized certainty equivalent returns of a leverage-constrained power utility investor by more than 500 basis points.

Ian L. Dryden, Kwang-Rae Kim, Charles A. Laughton, Huiling Le.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2213--2234.

Abstract:
Molecular dynamics simulations produce huge datasets of temporal sequences of molecules. It is of interest to summarize the shape evolution of the molecules in a succinct, low-dimensional representation. However, Euclidean techniques such as principal components analysis (PCA) can be problematic as the data may lie far from in a flat manifold. Principal nested spheres gives a fundamentally different decomposition of data from the usual Euclidean subspace based PCA [ Biometrika 99 (2012) 551–568]. Subspaces of successively lower dimension are fitted to the data in a backwards manner with the aim of retaining signal and dispensing with noise at each stage. We adapt the methodology to 3D subshape spaces and provide some practical fitting algorithms. The methodology is applied to cluster analysis of peptides, where different states of the molecules can be identified. Also, the temporal transitions between cluster states are explored.

Ying Chen, J. S. Marron, Jiejie Zhang.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1590--1616.

Abstract:
Electricity prices are high dimensional, serially dependent and have seasonal variations. We propose a Warping Functional AutoRegressive (WFAR) model that simultaneously accounts for the cross time-dependence and seasonal variations of the large dimensional data. In particular, electricity price curves are obtained by smoothing over the $24$ discrete hourly prices on each day. In the functional domain, seasonal phase variations are separated from level amplitude changes in a warping process with the Fisher–Rao distance metric, and the aligned (season-adjusted) electricity price curves are modeled in the functional autoregression framework. In a real application, the WFAR model provides superior out-of-sample forecast accuracy in both a normal functioning market, Nord Pool, and an extreme situation, the California market. The forecast performance as well as the relative accuracy improvement are stable for different markets and different time periods.

Yanjun Qian, Jianhua Z. Huang, Chiwoo Park, Yu Ding.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1537--1563.

Abstract:
In situ transmission electron microscope (TEM) adds a promising instrument to the exploration of the nanoscale world, allowing motion pictures to be taken while nano objects are initiating, crystalizing and morphing into different sizes and shapes. To enable in-process control of nanocrystal production, this technology innovation hinges upon a solution addressing a statistical problem, which is the capability of online tracking a dynamic, time-varying probability distribution reflecting the nanocrystal growth. Because no known parametric density functions can adequately describe the evolving distribution, a nonparametric approach is inevitable. Towards this objective, we propose to incorporate the dynamic evolution of the normalized particle size distribution into a state space model, in which the density function is represented by a linear combination of B-splines and the spline coefficients are treated as states. The closed-form algorithm runs online updates faster than the frame rate of the in situ TEM video, making it suitable for in-process control purpose. Imposing the constraints of curve smoothness and temporal continuity improves the accuracy and robustness while tracking the probability distribution. We test our method on three published TEM videos. For all of them, the proposed method is able to outperform several alternative approaches.

Binyan Jiang, Ziqi Chen, Chenlei Leng.

Source: Bernoulli, Volume 26, Number 2, 1234--1268.

Abstract:
High-dimensional data that evolve dynamically feature predominantly in the modern data era. As a partial response to this, recent years have seen increasing emphasis to address the dimensionality challenge. However, the non-static nature of these datasets is largely ignored. This paper addresses both challenges by proposing a novel yet simple dynamic linear programming discriminant (DLPD) rule for binary classification. Different from the usual static linear discriminant analysis, the new method is able to capture the changing distributions of the underlying populations by modeling their means and covariances as smooth functions of covariates of interest. Under an approximate sparse condition, we show that the conditional misclassification rate of the DLPD rule converges to the Bayes risk in probability uniformly over the range of the variables used for modeling the dynamics, when the dimensionality is allowed to grow exponentially with the sample size. The minimax lower bound of the estimation of the Bayes risk is also established, implying that the misclassification rate of our proposed rule is minimax-rate optimal. The promising performance of the DLPD rule is illustrated via extensive simulation studies and the analysis of a breast cancer dataset.

Kelly C. M. Gonçalves, Hélio S. Migon, Leonardo S. Bastos.

Source: Bayesian Analysis, Volume 15, Number 2, 335--362.

Abstract:
The paper introduces a new class of models, named dynamic quantile linear models, which combines dynamic linear models with distribution-free quantile regression producing a robust statistical method. Bayesian estimation for the dynamic quantile linear model is performed using an efficient Markov chain Monte Carlo algorithm. The paper also proposes a fast sequential procedure suited for high-dimensional predictive modeling with massive data, where the generating process is changing over time. The proposed model is evaluated using synthetic and well-known time series data. The model is also applied to predict annual incidence of tuberculosis in the state of Rio de Janeiro and compared with global targets set by the World Health Organization.

Alberto Cassese, Weixuan Zhu, Michele Guindani, Marina Vannucci.

Source: Bayesian Analysis, Volume 14, Number 2, 553--572.

Abstract:
In many applications, investigators monitor processes that vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or “normal” behavior. In this manuscript, we consider the monitoring of pneumonia and influenza (P&I) mortality, to detect influenza outbreaks in the continental United States, and propose a Bayesian nonparametric model selection approach to take into account the spatio-temporal dependence of outbreaks. More specifically, we introduce a zero-inflated conditionally identically distributed species sampling prior which allows borrowing information across time and to assign data to clusters associated to either a null or an alternate process. Spatial dependences are accounted for by means of a Markov random field prior, which allows to inform the selection based on inferences conducted at nearby locations. We show how the proposed modeling framework performs in an application to the P&I mortality data and in a simulation study, and compare with common threshold methods for detecting outbreaks over time, with more recent Markov switching based models, and with spike-and-slab Bayesian nonparametric priors that do not take into account spatio-temporal dependence.

R Shadmehr
May 1, 1994; 14:3208-3224
Articles

We consider the question of how sensory networks enable the detection of sensory stimuli in a combinatorial coding space. We are specifically interested in the olfactory system, wherein recent experimental studies have reported the existence of rich, enigmatic response patterns associated with stimulus onset and offset. This study aims to identify the functional relevance of such response patterns (i.e., what benefits does such neural activity provide in the context of detecting stimuli in a natural environment). We study this problem through the lens of normative, optimization-based modeling. Here, we define the notion of a low-dimensional latent representation of stimulus identity, which is generated through action of the sensory network. The objective of our optimization framework is to ensure high-fidelity tracking of a nominal representation in this latent space in an energy-efficient manner. It turns out that the optimal motifs emerging from this framework possess morphologic similarity with prototypical onset and offset responses observed in vivo in locusts (Schistocerca americana) of either sex. Furthermore, this objective can be exactly achieved by a network with reciprocal excitatory–inhibitory competitive dynamics, similar to interactions between projection neurons and local neurons in the early olfactory system of insects. The derived model also makes several predictions regarding maintenance of robust latent representations in the presence of confounding background information and trade-offs between the energy of sensory activity and resultant behavioral measures such as speed and accuracy of stimulus detection.

SIGNIFICANCE STATEMENT A key area of study in olfactory coding involves understanding the transformation from high-dimensional sensory stimulus to low-dimensional decoded representation. Here, we examine not only the dimensionality reduction of this mapping but also its temporal dynamics, with specific focus on stimuli that are temporally continuous. Through optimization-based synthesis, we examine how sensory networks can track representations without prior assumption of discrete trial structure. We show that such tracking can be achieved by canonical network architectures and dynamics, and that the resulting responses resemble observations from neurons in the insect olfactory system. Thus, our results provide hypotheses regarding the functional role of olfactory circuit activity at both single neuronal and population scales.

Recent literature has highlighted that international trade is mostly priced in a few key vehicle currencies, and is increasingly dominated by intermediate goods and global value chains (GVCs). Taking these features into account, this paper reexamines the business cycle dynamics of international trade and its relationship with monetary policy and exchange rates.

Bank for International Settlements BIS Working Papers by David Cook and Nikhil Patel

Bank of England Working Papers by Aydan Dogan and Ida Hjortsoe

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Delayed umbilical cord clamping in premature infants has been associated with decreased rates of intraventricular hemorrhage; however, the mechanisms that explain this finding have not been described.

Premature infants with delayed umbilical cord clamping have improved superior vena cava blood flow over the first days of life. This may provide one of the mechanism(s) by which this technique reduces the incidence in intraventricular hemorrhage in this at-risk population. (Read the full article)