Coarse-grained View Research Articles

Bexcitonics: Quasiparticle approach to open quantum dynamics.

We develop a quasiparticle approach to capture the dynamics of open quantum systems coupled to bosonic thermal baths of arbitrary complexity based on the Hierarchical Equations of Motion (HEOM). This is done by generalizing the HEOM dynamics and mapping it into that of the system in interaction with a few bosonic fictitious quasiparticles that we call bexcitons. Bexcitons arise from a decomposition of the bath correlation function into discrete features. Specifically, bexciton creation and annihilation couple the auxiliary density matrices in the HEOM. The approach provides a systematic strategy to construct exact quantum master equations that include the system-bath coupling to all orders even for non-Markovian environments. Specifically, by introducing different metrics and representations for the bexcitons it is possible to straightforwardly generate different variants of the HEOM, demonstrating that all these variants share a common underlying quasiparticle picture. Bexcitonic properties, while unphysical, offer a coarse-grained view of the correlated system-bath dynamics and its numerical convergence. For instance, we use it to analyze the instability of the HEOM when the bath is composed of underdamped oscillators and show that it leads to the creation of highly excited bexcitons. The bexcitonic picture can also be used to develop more efficient approaches to propagate the HEOM. As an example, we use the particle-like nature of the bexcitons to introduce mode-combination of bexcitons in both number and coordinate representation that uses the multi-configuration time-dependent Hartree to efficiently propagate the HEOM dynamics.

Proteome partitioning constraints in long-term laboratory evolution

Adaptive laboratory evolution experiments provide a controlled context in which the dynamics of selection and adaptation can be followed in real-time at the single-nucleotide level. And yet this precision introduces hundreds of degrees-of-freedom as genetic changes accrue in parallel lineages over generations. On short timescales, physiological constraints have been leveraged to provide a coarse-grained view of bacterial gene expression characterized by a small set of phenomenological parameters. Here, we ask whether this same framework, operating at a level between genotype and fitness, informs physiological changes that occur on evolutionary timescales. Using a strain adapted to growth in glucose minimal medium, we find that the proteome is substantially remodeled over 40 000 generations. The most striking change is an apparent increase in enzyme efficiency, particularly in the enzymes of lower-glycolysis. We propose that deletion of metabolic flux-sensing regulation early in the adaptation results in increased enzyme saturation and can account for the observed proteome remodeling.

Functional Ownership through Fractional Uniqueness

Ownership and borrowing systems, designed to enforce safe memory management without the need for garbage collection, have been brought to the fore by the Rust programming language. Rust also aims to bring some guarantees offered by functional programming into the realm of performant systems code, but the type system is largely separate from the ownership model, with type and borrow checking happening in separate compilation phases. Recent models such as RustBelt and Oxide aim to formalise Rust in depth, but there is less focus on integrating the basic ideas into more traditional type systems. An approach designed to expose an essential core for ownership and borrowing would open the door for functional languages to borrow concepts found in Rust and other ownership frameworks, so that more programmers can enjoy their benefits. One strategy for managing memory in a functional setting is through uniqueness types, but these offer a coarse-grained view: either a value has exactly one reference, and can be mutated safely, or it cannot, since other references may exist. Recent work demonstrates that linear and uniqueness types can be combined in a single system to offer restrictions on program behaviour and guarantees about memory usage. We develop this connection further, showing that just as graded type systems like those of Granule and Idris generalise linearity, a Rust-like ownership model arises as a graded generalisation of uniqueness. We combine fractional permissions with grading to give the first account of ownership and borrowing that smoothly integrates into a standard type system alongside linearity and graded types, and extend Granule accordingly with these ideas.

She has a stadium named after her

Abstract In English, sequences consisting of the verb have, a noun phrase and a past participle vary in meaning. This meaning variation has been discussed both in the context of grammatical description and language change, mostly based on a handful of examples. This study seeks to combine theoretical and methodological approaches from construction grammar and interactional linguistics in the description of this meaning variation. Theoretically, this implies distinguishing between abstracted meaning potential and situated meaning of linguistic elements. Methodologically, this means taking both a coarse-grained view by means of a quantitative corpus-based approach that abstracts over a number of instances and a fine-grained view by means of qualitative analysis of talk-in-interaction.

The effect of time series distance functions on functional climate networks

Complex network theory provides an important tool for the analysis of complex systems such as the Earth’s climate. In this context, functional climate networks can be constructed using a spatiotemporal climate dataset and a suitable time series distance function. The resulting coarse-grained view on climate variability consists of representing distinct areas on the globe (i.e., grid cells) by nodes and connecting pairs of nodes that present similar time series. One fundamental concern when constructing such a functional climate network is the definition of a metric that captures the mutual similarity between time series. Here we study systematically the effect of 29 time series distance functions on functional climate network construction based on global temperature data. We observe that the distance functions previously used in the literature commonly generate very similar networks while alternative ones result in rather distinct network structures and reveal different long-distance connection patterns. These patterns are highly important for the study of climate dynamics since they generally represent pathways for the long-distance transportation of energy and can be used to forecast climate variability on subseasonal to interannual or even decadal scales. Therefore, we propose the measures studied here as alternatives for the analysis of climate variability and to further exploit their complementary capability of capturing different aspects of the underlying dynamics that may help gaining a more holistic empirical understanding of the global climate system.

Skillful Climate Forecasts of the Tropical Indo-Pacific Ocean Using Model-Analogs

Seasonal forecasts made by coupled atmosphere–ocean general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields a forecast ensemble, without additional model integration. This technique is applied to four of the eight CGCMs comprising the North American Multimodel Ensemble (NMME) by selecting from prior long control runs those model states whose monthly tropical Indo-Pacific SST and SSH anomalies best resemble the observations at initialization time. Hindcasts are then made for leads of 1–12 months during 1982–2015. Deterministic and probabilistic skill measures of these model-analog hindcast ensembles are comparable to those of the initialized NMME hindcast ensembles, for both the individual models and the multimodel ensemble. In the eastern equatorial Pacific, model-analog hindcast skill exceeds that of the NMME. Despite initializing with a relatively large ensemble spread, model-analogs also reproduce each CGCM’s perfect-model skill, consistent with a coarse-grained view of tropical Indo-Pacific predictability. This study suggests that with little additional effort, sufficiently realistic and long CGCM simulations provide the basis for skillful seasonal forecasts of tropical Indo-Pacific SST anomalies, even without sophisticated data assimilation or additional ensemble forecast integrations. The model-analog method could provide a baseline for forecast skill when developing future models and forecast systems.

Application of local rules and cellular automata in representing protein translation and enhancing protein folding approximation

It is self-evident that the coarse-grained view of transcription and protein translation is a result of certain computations. Although there is no single definition of the term “computation,” protein translation can be implemented over mathematical models of computers. Protein folding, however, is a combinatorial problem; it is still unknown whether a fast, accurate, and optimal folding algorithm exists. The discovery of near-optimal folds depends on approximation algorithms and heuristic searches. The hydrophobic–hydrophilic (HP) model is a simplified representation of some of the realities of protein structure. Despite the simplified representation, the folding problem in the HP model was proven to be NP-complete. We use simple and local rules to model translation and folding of proteins. Local rules imply that at a certain level of abstraction an entity can move from a state to another based on its state and information collected from its neighborhood. Also, the rules are simple in a sense that they do not require complicated computation. We use one-dimensional cellular automata to describe translation of mRNA into protein. Cellular automata are discrete models of computation that use local interactions to produce a global behavior of some sort. We will also discuss how local rules can improve approximation algorithms of protein folding and give an example of a CA that accept a certain family of strings to achieve half H–H contacts.

The C. elegans Connectome Consists of Homogenous Circuits with Defined Functional Roles.

A major goal of systems neuroscience is to decipher the structure-function relationship in neural networks. Here we study network functionality in light of the common-neighbor-rule (CNR) in which a pair of neurons is more likely to be connected the more common neighbors it shares. Focusing on the fully-mapped neural network of C. elegans worms, we establish that the CNR is an emerging property in this connectome. Moreover, sets of common neighbors form homogenous structures that appear in defined layers of the network. Simulations of signal propagation reveal their potential functional roles: signal amplification and short-term memory at the sensory/inter-neuron layer, and synchronized activity at the motoneuron layer supporting coordinated movement. A coarse-grained view of the neural network based on homogenous connected sets alone reveals a simple modular network architecture that is intuitive to understand. These findings provide a novel framework for analyzing larger, more complex, connectomes once these become available.

Distribution of sulfur in styrene-butadiene rubber studied with anomalous small-angle X-ray scattering at sulfur K-edge

We have studied the structure of styrene-butadiene rubber with anomalous small-angle X-ray scattering (ASAXS) at sulfur K-edge. The use of ASAXS at sulfur K-edge selectively changes the scattering contrast relating to sulfur, thereby leading to the structural study of low-contrast samples that would otherwise be difficult to be conducted. The energy range around the K-edge of sulfur has not been applied to scattering study, owing to mainly the absence of beamline and detector appropriate for the energy range. We have developed ASAXS setting with a transmission geometry at BL27SU, SPring-8 and have successfully obtained the scattering information relating to inhomogeneous distribution of sulfur in styrene-butadiene rubber. A two-phase model is applied for elucidating a coarse-grained view of the inhomogeneous structure. The results indicate that inhomogeneous distribution of polymer and sulfur is closely related and the dense part of polymer corresponds to the part being sparse of sulfur. This study opens up the possibility to elucidate a complex structure of macromolecular systems with a low scattering contrast.

WiScape: A Framework for Measuring the Performance of Wide-Area Wireless Networks

We present WiScape, a framework for benchmarking and understanding the behavior of wide-area wireless networks, e.g., city-wide or nation-wide cellular data networks using active participation from clients. The goal of WiScape is to provide a coarse-grained view of a wide-area wireless landscape that allows operators and users to understand broad performance characteristics of the network. In WiScape, a centralized controller instructs clients to collect minimal measurement samples over time and space in an opportunistic manner. To limit the overheads of this measurement framework, WiScape partitions the world into zones, contiguous areas with relatively similar user experiences, and partitions time into zone-specific epochs over which network statistics are relatively stable. For each epoch in each zone, WiScape takes a minimalistic view—it attempts to collect a small number of measurement samples to characterize the client experience in a zone at a specific epoch, thereby limiting the bandwidth and energy overheads for collecting measurements at client devices. For this effort, we have collected ground truth measurements for three commercial cellular networks across a nation-wide area in USA for a period of more than one year. We justify our design choices of WiScape through collected data, demonstrate that WiScape can provide an accurate performance characterization of the networks over a wide-area (within 4 percent error for more than 70 percent of instances) with a low overhead on the clients, and illustrate multiple applications of this framework through a sustained and ongoing measurement study.

Embedding effect systems in Haskell

Monads are now an everyday tool in functional programming for abstracting and delimiting effects. The link between monads and effect systems is well-known, but in their typical use, monads provide a much more coarse-grained view of effects. Effect systems capture fine-grained information about the effects, but monads provide only a binary view: effectful or pure . Recent theoretical work has unified fine-grained effect systems with monads using a monad-like structure indexed by a monoid of effect annotations (called parametric effect monads). This aligns the power of monads with the power of effect systems. This paper leverages recent advances in Haskell's type system (as provided by GHC) to embed this approach in Haskell, providing user-programmable effect systems. We explore a number of practical examples that make Haskell even better and safer for effectful programming. Along the way, we relate the examples to other concepts, such as Haskell's implicit parameters and coeffects .

Enhanced regime predictability in atmospheric low-order models due to stochastic forcing.

Regime predictability in atmospheric low-order models augmented with stochastic forcing is studied. Atmospheric regimes are identified as persistent or metastable states using a hidden Markov model analysis. A somewhat counterintuitive, coherence resonance-like effect is observed: regime predictability increases with increasing noise level up to an intermediate optimal value, before decreasing when further increasing the noise level. The enhanced regime predictability is due to increased persistence of the regimes. The effect is found in the Lorenz '63 model and a low-order model of barotropic flow over topography. The increased predictability is only present in the regime dynamics, that is, in a coarse-grained view of the system; predictability of individual trajectories decreases monotonically with increasing noise level. A possible explanation for the phenomenon is given and implications of the finding for weather and climate modelling and prediction are discussed.

Combining Theory and Experiments to Understand Sugar Regulation in Bacteria

In this review we discuss the interaction between experiments and theory in understanding regulatory systems in bacterial cells, using the galactose network of E. coli as a model system to illustrate this. Initially, experiments led to a coarse-grained network view of this system, which revealed the structure of entangled negative and positive feedback loops that regulates galactose metabolism. A dynamical model, based on this network picture and in vitro experiments, indicated that one of the main regulators would have little role to play in any steady-state scenarios. The dynamical experiments this result suggested revealed the intricate timing sequence of gene expression in situations of rapidly decreasing galactose. All this information is slowly coming together to form a picture of how the different elements of the galactose regulatory system work together to regulate metabolism in the presence of fluctuating and changing food sources.

Detecting and describing dynamic equilibria in adaptive networks

We review modeling attempts for the paradigmatic contact process (or SIS model) on adaptive networks. Elaborating on one particular proposed mechanism of topology change (rewiring) and its mean field analysis, we obtain a coarse-grained view of coevolving network topology in the stationary active phase of the system. Introducing an alternative framework applicable to a wide class of adaptive networks, active stationary states are detected, and an extended description of the resulting steady-state statistics is given for three different rewiring schemes. We find that slight modifications of the standard rewiring rule can result in either minuscule or drastic change of steady-state network topologies.

Detection of Fuzzy Association Rules by Fuzzy Transforms

We present a new method based on the use of fuzzy transforms for detecting coarse-grained association rules in the datasets. The fuzzy association rules are represented in the form of linguistic expressions and we introduce a pre-processing phase to determine the optimal fuzzy partition of the domains of the quantitative attributes. In the extraction of the fuzzy association rules we use the AprioriGen algorithm and a confidence index calculated via the inverse fuzzy transform. Our method is applied to datasets of the 2001 census database of the district of Naples (Italy); the results show that the extracted fuzzy association rules provide a correct coarse-grained view of the data association rule set.

Correction: Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

Correction: Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

Correction: Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

Correction: Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

Regime prediction and predictability in nonlinear dynamical systems

Prediction and predictability properties of nonlinear dynamical systems are diagnosed and analysed empirically using nonlinear time series analysis techniques. The notion of predictability is relaxed from accurate prediction of individual trajectories to a coarse-grained view in which only probabilities of visiting certain regions of state space or regimes are forecast. The regimes and the transition probabilities between them are determined simultaneously by fitting a hidden Markov model to a time series of the system. Predictive information is then refined by building a nearest-neighbour model of the regime posterior distribution. The ideas are exemplified on the stochastically forced Lorenz system.

Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

BackgroundMetabolic phenotyping has become an important ‘bird's-eye-view’ technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. Although genotyping technology has expanded greatly over the past decade, metabolic phenotyping has languished due to the difficulty of ‘top-down’ chemical analyses. Here, we describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems.Methodology/Principal FindingsThe analysis method includes a stable isotope labeling technique for use in living organisms; a systematic method for simultaneously identifying a large number of metabolites by using a newly developed HSQC-based metabolite chemical shift database combined with heteronuclear multidimensional NMR spectroscopy; Principal Components Analysis; and a visualization method using a coarse-grained overview of the metabolic system. The database contains more than 1000 1H and 13C chemical shifts corresponding to 142 metabolites measured under identical physicochemical conditions. Using the stable isotope labeling technique in Arabidopsis T87 cultured cells and Bombyx mori, we systematically detected >450 HSQC peaks in each 13C-HSQC spectrum derived from model plant, Arabidopsis T87 cultured cells and the invertebrate animal model Bombyx mori. Furthermore, for the first time, efficient 13C labeling has allowed reliable signal assignment using analytical separation techniques such as 3D HCCH-COSY spectra in higher organism extracts.Conclusions/SignificanceOverall physiological changes could be detected and categorized in relation to a critical developmental phase change in B. mori by coarse-grained representations in which the organization of metabolic pathways related to a specific developmental phase was visualized on the basis of constituent changes of 56 identified metabolites. Based on the observed intensities of 13C atoms of given metabolites on development-dependent changes in the 56 identified 13C-HSQC signals, we have determined the changes in metabolic networks that are associated with energy and nitrogen metabolism.

Structural Characterization of the Ribosomal P1A−P2B Protein Dimer by Small-Angle X-ray Scattering and NMR Spectroscopy

The five ribosomal P-proteins, denoted P0-(P1-P2)2, constitute the stalk structure of the large subunit of eukaryotic ribosomes. In the yeast Saccharomyces cerevisiae, the group of P1 and P2 proteins is differentiated into subgroups that form two separate P1A-P2B and P1B-P2A heterodimers on the stalk. So far, structural studies on the P-proteins have not yielded any satisfactory information using either X-ray crystallography or NMR spectroscopy, and the structures of the ribosomal stalk and its individual constituents remain obscure. Here we outline a first, coarse-grained view of the P1A-P2B solution structure obtained by a combination of small-angle X-ray scattering and heteronuclear NMR spectroscopy. The complex has an elongated shape with a length of 10 nm and a cross section of approximately 2.5 nm. 15N NMR relaxation measurements establish that roughly 30% of the residues are present in highly flexible segments, which belong primarily to the linker region and the C-terminal part of the polypeptide chain. Secondary structure predictions and NMR chemical shift analysis, together with previous results from CD spectroscopy, indicate that the structured regions involve alpha-helices. NMR relaxation data further suggest that several helices are arranged in a nearly parallel or antiparallel topology. These results provide the first structural comparison between eukaryotic P1 and P2 proteins and the prokaryotic L12 counterpart, revealing considerable differences in their overall shapes, despite similar functional roles and similar oligomeric arrangements. These results present for the first time a view of the structure of the eukaryotic stalk constituents, which is the only domain of the eukaryotic ribosome that has escaped successful structural characterization.