Search

Continuous phase transitions where symmetry is spontaneously broken are ubiquitous in physics and often found between `Landau-compatible' phases where residual symmetries of one phase are a subset of the other. However, continuous `deconfined quantum critical' transitions between Landau-incompatible symmetry-breaking phases are known to exist in certain quantum systems, often with anomalous microscopic symmetries. In this paper, we investigate the need for such special conditions. We show that Landau-incompatible transitions can be found in a family of well-known classical statistical mechanical models with anomaly-free on-site microscopic symmetries, introduced by Jos\'{e}, Kadanoff, Kirkpatick and Nelson (Phys. Rev. B 16, 1217). The models are labeled by a positive integer $Q$ and constructed by a deformation of the 2d classical XY model, defined on any lattice, with an on-site potential that preserves a discrete $Q$-fold spin rotation and reflection symmetry. For a range of temperatures, even $Q$ models exhibit two Landau-incompatible partial symmetry-breaking phases and a direct transition between them for $Q \ge 4$. Characteristic features of Landau-incompatible transitions are easily seen, such as enhanced symmetries and melting of charged defects. For odd $Q$, and corresponding temperature ranges, two regions of a single partial symmetry-breaking phase are obtained, split by a stable `unnecessary critical' line. We present quantum models with anomaly-free symmetries that also exhibit similar phase diagrams., Comment: 6+8 pages, 3+7 figures (main + appendices)

Logged and disturbed forests are often viewed as degraded and depauperate environments compared with primary forest. However, they are dynamic ecosystems(1) that provide refugia for large amounts of biodiversity2,3, so we cannot afford to underestimate their conservation value4. Here we present empirically defined thresholds for categorizing the conservation value of logged forests, using one of the most comprehensive assessments of taxon responses to habitat degradation in any tropical forest environment. We analysed the impact of logging intensity on the individual occurrence patterns of 1,681 taxa belonging to 86 taxonomic orders and 126 functional groups in Sabah, Malaysia. Our results demonstrate the existence of two conservation-relevant thresholds. First, lightly logged forests (<29% biomass removal) retain high conservation value and a largely intact functional composition, and are therefore likely to recover their pre-logging values if allowed to undergo natural regeneration. Second, the most extreme impacts occur in heavily degraded forests with more than two-thirds (>68%) of their biomass removed, and these are likely to require more expensive measures to recover their biodiversity value. Overall, our data confirm that primary forests are irreplaceable5, but they also reinforce the message that logged forests retain considerable conservation value that should not be overlooked.

Many schools and their communities highly value the environmental dimension of their work and recognize that stimulating learning experiences and learning environments contribute to raising the standards of achievement. However, the contribution of the environmental dimension is not always fully recognized or reported. This document was produced to help schools evaluate their own work and present it effectively before, during, and after an inspection. The checklist suggests providing evidence of: (1) fieldwork and out-of-school learning; (2) environmental management; (3) extra-curricular activities; and (4) special projects, exhibitions, and awards. (CCM)

This checklist was produced to assist in the inspection and reporting process of the environmental dimension of schools. The term 'environmental dimension' is used to include both the curriculum and the context within which it is delivered. The checklist outlines the main features of the environmental dimension and makes some suggestions as to where in the inspection framework they could be reported. The checklist includes: (1) planned opportunities for out-of-classroom learning; (2) creative use of the school buildings and grounds; (3) study of environmental issues in the curriculum; and (4) the involvement of pupils in environmental decision-making.

Identifying linked cases of infection is a critical component of the public health response to viral infectious diseases. In a clinical context, there is a need to make rapid assessments of whether cases of infection have arrived independently onto a ward, or are potentially linked via direct transmission. Viral genome sequence data are of great value in making these assessments, but are often not the only form of data available. Here, we describe A2B-COVID, a method for the rapid identification of potentially linked cases of COVID-19 infection designed for clinical settings. Our method combines knowledge about infection dynamics, data describing the movements of individuals, and evolutionary analysis of genome sequences to assess whether data collected from cases of infection are consistent or inconsistent with linkage via direct transmission. A retrospective analysis of data from two wards at Cambridge University Hospitals NHS Foundation Trust during the first wave of the pandemic showed qualitatively different patterns of linkage between cases on designated COVID-19 and non-COVID-19 wards. The subsequent real-time application of our method to data from the second epidemic wave highlights its value for monitoring cases of infection in a clinical context.

SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures.

A key task in the study of networked systems is to derive local and global properties that impact connectivity, synchronizability, and robustness. Computing shortest paths or geodesics in the network yields measures of node centrality and network connectivity that can contribute to explain such phenomena. We derive an analytic distribution of shortest path lengths, on the giant component in the supercritical regime or on small components in the subcritical regime, of any sparse (possibly directed) graph with conditionally independent edges, in the infinite-size limit. We provide specific results for widely used network families like stochastic block models, dot-product graphs, random geometric graphs, and graphons. The survival function of the shortest path length distribution possesses a simple closed-form lower bound which is asymptotically tight for finite lengths, has a natural interpretation of traversing independent geodesics in the network, and delivers novel insight in the above network families. Notably, the shortest path length distribution allows us to derive, for the network families above, important graph properties like the bond percolation threshold, size of the giant component, average shortest path length, and closeness and betweenness centralities. We also provide a corroborative analysis of a set of 20 empirical networks. This unifying framework demonstrates how geodesic statistics for a rich family of random graphs can be computed cheaply without having access to true or simulated networks, especially when they are sparse but prohibitively large., Comment: 32 pages, 12 figures

Networks are a widely-used tool to investigate the large-scale connectivity structure in complex systems and graphons have been proposed as an infinite size limit of dense networks. The detection of communities or other meso-scale structures is a prominent topic in network science as it allows the identification of functional building blocks in complex systems. When such building blocks may be present in graphons is an open question. In this paper, we define a graphon-modularity and demonstrate that it can be maximised to detect communities in graphons. We then investigate specific synthetic graphons and show that they may show a wide range of different community structures. We also reformulate the graphon-modularity maximisation as a continuous optimisation problem and so prove the optimal community structure or lack thereof for some graphons, something that is usually not possible for networks. Furthermore, we demonstrate that estimating a graphon from network data as an intermediate step can improve the detection of communities, in comparison with exclusively maximising the modularity of the network. While the choice of graphon-estimator may strongly influence the accord between the community structure of a network and its estimated graphon, we find that there is a substantial overlap if an appropriate estimator is used. Our study demonstrates that community detection for graphons is possible and may serve as a privacy-preserving way to cluster network data.

We develop a Bayesian hierarchical model to identify communities of time series. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection and the selection of an optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of the S&P100 index and climate data from U.S. cities. Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

How people connect with one another is a fundamental question in the social sciences, and the resulting social networks can have a profound impact on our daily lives. Blau offered a powerful explanation: people connect with one another based on their positions in a social space. Yet a principled measure of social distance, allowing comparison within and between societies, remains elusive. We use the connectivity kernel of conditionally-independent edge models to develop a family of segregation statistics with desirable properties: they offer an intuitive and universal characteristic scale on social space (facilitating comparison across datasets and societies), are applicable to multivariate and mixed node attributes, and capture segregation at the level of individuals, pairs of individuals, and society as a whole. We show that the segregation statistics can induce a metric on Blau space (a space spanned by the attributes of the members of society) and provide maps of two societies. Under a Bayesian paradigm, we infer the parameters of the connectivity kernel from eleven ego-network datasets collected in four surveys in the United Kingdom and United States. The importance of different dimensions of Blau space is similar across time and location, suggesting a macroscopically stable social fabric. Physical separation and age differences have the most significant impact on segregation within friendship networks with implications for intergenerational mixing and isolation in later stages of life., Comment: Article: 23 pages, 3 figures. Supplementary material: 8 pages, 1 figure

Population behaviours, such as voting and vaccination, depend on social networks. Social networks can differ depending on behaviour type and are typically hidden. However, we do often have large-scale behavioural data, albeit only snapshots taken at one timepoint. We present a method that jointly infers large-scale network structure and a networked model of human behaviour using only snapshot population behavioural data. This exploits the simplicity of a few parameter, geometric socio-demographic network model and a spin based model of behaviour. We illustrate, for the EU Referendum and two London Mayoral elections, how the model offers both prediction and the interpretation of our homophilic inclinations. Beyond offering the extraction of behaviour specific network structure from large-scale behavioural datasets, our approach yields a crude calculus linking inequalities and social preferences to behavioural outcomes. We give examples of potential network sensitive policies: how changes to income inequality, a social temperature and homophilic preferences might have reduced polarisation in a recent election.

Social network based information campaigns can be used for promoting beneficial health behaviours and mitigating polarisation (e.g. regarding climate change or vaccines). Network-based intervention strategies typically rely on full knowledge of network structure. It is largely not possible or desirable to obtain population-level social network data due to availability and privacy issues. It is easier to obtain information about individuals' attributes (e.g. age, income), which are jointly informative of an individual's opinions and their social network position. We investigate strategies for influencing the system state in a statistical mechanics based model of opinion formation. Using synthetic and data based examples we illustrate the advantages of implementing coarse-grained influence strategies on Ising models with modular structure in the presence of external fields. Our work provides a scalable methodology for influencing Ising systems on large graphs and the first exploration of the Ising influence problem in the presence of ambient (social) fields. By exploiting the observation that strong ambient fields can simplify control of networked dynamics, our findings open the possibility of efficiently computing and implementing public information campaigns using insights from social network theory without costly or invasive levels of data collection.

Heteroplasmy, multiple variants of mitochondrial DNA (mtDNA) in the same cytoplasm, may be naturally generated by mutations but is counteracted by a genetic mtDNA bottleneck during oocyte development. Engineered heteroplasmic mice with nonpathological mtDNA variants reveal a nonrandom tissue-specific mtDNA segregation pattern, with few tissues that do not show segregation. The driving force for this dynamic complex pattern has remained unexplained for decades, challenging our understanding of this fundamental biological problem and hindering clinical planning for inherited diseases. Here, we demonstrate that the nonrandom mtDNA segregation is an intracellular process based on organelle selection. This cell type-specific decision arises jointly from the impact of mtDNA haplotypes on the oxidative phosphorylation (OXPHOS) system and the cell metabolic requirements and is strongly sensitive to the nuclear context and to environmental cues.

We introduce topological invariants for gapless systems and study the associated boundary phenomena. More generally, the symmetry properties of the low-energy conformal field theory (CFT) provide discrete invariants, establishing the notion of symmetry-enriched quantum criticality. The charges of nonlocal scaling operators, or more generally of symmetry defects, are topological and imply the presence of localized edge modes. We primarily focus on the $1+1d$ case where the edge has a topological degeneracy, whose finite-size splitting can be exponential or algebraic in system size depending on the involvement of additional gapped sectors. An example of the former is given by tuning the spin-1 Heisenberg chain to a symmetry-breaking Ising phase. An example of the latter arises between the gapped Ising and cluster phases: this symmetry-enriched Ising CFT has an edge mode with finite-size splitting $\sim 1/L^{14}$. In addition to such new cases, our formalism unifies various examples previously studied in the literature. Similar to gapped symmetry-protected topological phases, a given CFT can split into several distinct symmetry-enriched CFTs. This raises the question of classification, to which we give a partial answer -- including a complete characterization of symmetry-enriched $1+1d$ Ising CFTs. Non-trivial topological invariants can also be constructed in higher dimensions, which we illustrate for a symmetry-enriched $2+1d$ CFT without gapped sectors., Comment: we generalized our framework to 2+1d, and added more examples

Modern biomedical applications often involve time-series data, from high-throughput phenotyping of model organisms, through to individual disease diagnosis and treatment using biomedical data streams. Data and tools for time-series analysis are developed and applied across the sciences and in industry, but meaningful cross-disciplinary interactions are limited by the challenge of identifying fruitful connections. Here we introduce the web platform, CompEngine, a self-organizing, living library of time-series data that lowers the barrier to forming meaningful interdisciplinary connections between time series. Using a canonical feature-based representation, CompEngine places all time series in a common space, regardless of their origin, allowing users to upload their data and immediately explore interdisciplinary connections to other data with similar properties, and be alerted when similar data is uploaded in the future. In contrast to conventional databases, which are organized by assigned metadata, CompEngine incentivizes data sharing by automatically connecting experimental and theoretical scientists across disciplines based on the empirical structure of their data. CompEngine's growing library of interdisciplinary time-series data also facilitates comprehensively characterization of algorithm performance across diverse types of data, and can be used to empirically motivate the development of new time-series analysis algorithms.

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147000 time series) and using a filtered version of the hctsa feature library (4791 features), we introduce a generically useful set of 22 CAnonical Time-series CHaracteristics, catch22. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.

We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection as well as the selection of an optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of the S&P100 index as well as climate data from US cities., Comment: 16 pages, 7 figures

In the last years complex networks tools contributed to provide insights on the structure of research, through the study of collaboration, citation and co-occurrence networks. The network approach focuses on pairwise relationships, often compressing multidimensional data structures and inevitably losing information. In this paper we propose for the first time a simplicial complex approach to word co-occurrences, providing a natural framework for the study of higher-order relations in the space of scientific knowledge. Using topological methods we explore the conceptual landscape of mathematical research, focusing on homological holes, regions with low connectivity in the simplicial structure. We find that homological holes are ubiquitous, which suggests that they capture some essential feature of research practice in mathematics. Holes die when a subset of their concepts appear in the same article, hence their death may be a sign of the creation of new knowledge, as we show with some examples. We find a positive relation between the dimension of a hole and the time it takes to be closed: larger holes may represent potential for important advances in the field because they separate conceptually distant areas. We also show that authors' conceptual entropy is positively related with their contribution to homological holes, suggesting that polymaths tend to be on the frontier of research., Comment: 24 pages, 12 figures

Although strategies that block FOXP3-dependent regulatory T cell function (CTLA4 blockade) and the inhibitory receptor PD1 have shown great promise in promoting antitumor immune responses in humans, their widespread implementation for cancer immunotherapy has been hampered by significant off-target autoimmune side effects that can be lethal. Our work has shown that absence of OX40 and CD30 costimulatory signals prevents CD4 T cell-driven autoimmunity in Foxp3-deficient mice, suggesting a novel way to block these side effects. In this study, we show that excellent antitumor CD8 T cell responses can be achieved in Foxp3(KO) mice deficient in OX40 and CD30 signals, particularly in the presence of concurrent PD1 blockade. Furthermore, excellent antitumor immune responses can also be achieved using combinations of Abs that block CTLA4, PD1, OX40, and CD30 ligands, without CD4 T cell-driven autoimmunity. By dissociating autoimmune side effects from anticancer immune responses, this potentially shifts this antitumor approach to patients with far less advanced disease.

This book was designed as a handy guide to figure drawing, by drawing tutor Helen Birch and explores 100 life drawings and portraits by contemporary artists. Drawn from Life is a celebration of playful, personal, and experimental approaches used by modern artists and illustrators. Traditional techniques and new media are organized as entries so that you can dip in and out for advice or flick through for inspiration and ideas. Drawn From Life highlights and examines the different techniques, qualities, and effects of each piece. Offering a bold, refreshing change from other run-of-the-mill figure drawing books, it adopts the unique approach of teaching drawing via the images themselves, focusing on the way portraits can be treated using a whole range of different techniques, rather than looking at how to master one single style. Practical tips are provided throughout the book on topics such as tone, perspective and colour. Readers are encouraged to discover the ways in which similar subjects and styles are executed by different artists, while also being inspired to use the tips in the book as a way of building on existing skills.

How smart can a micron-sized bag of chemicals be? How can an artificial or real cell make inferences about its environment? From which kinds of probability distributions can chemical reaction networks sample? We begin tackling these questions by showing three ways in which a stochastic chemical reaction network can implement a Boltzmann machine, a stochastic neural network model that can generate a wide range of probability distributions and compute conditional probabilities. The resulting models, and the associated theorems, provide a road map for constructing chemical reaction networks that exploit their native stochasticity as a computational resource. Finally, to show the potential of our models, we simulate a chemical Boltzmann machine to classify and generate MNIST digits in-silico.

Across a far-reaching diversity of scientific and industrial applications, a general key problem involves relating the structure of time-series data to a meaningful outcome, such as detecting anomalous events from sensor recordings, or diagnosing patients from physiological time-series measurements like heart rate or brain activity. Currently, researchers must devote considerable effort manually devising, or searching for, properties of their time series that are suitable for the particular analysis problem at hand. Addressing this non-systematic and time-consuming procedure, here we introduce a new tool, hctsa, that selects interpretable and useful properties of time series automatically, by comparing implementations over 7700 time-series features drawn from diverse scientific literatures. Using two exemplar biological applications, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in their time-series data.

This report records and discusses the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3). The report includes a description of the keynote presentation of the workshop, which served as an overview of sustainable scientific software. It also summarizes a set of lightning talks in which speakers highlighted to-the-point lessons and challenges pertaining to sustaining scientific software. The final and main contribution of the report is a summary of the discussions, future steps, and future organization for a set of self-organized working groups on topics including developing pathways to funding scientific software; constructing useful common metrics for crediting software stakeholders; identifying principles for sustainable software engineering design; reaching out to research software organizations around the world; and building communities for software sustainability. For each group, we include a point of contact and a landing page that can be used by those who want to join that group's future activities. The main challenge left by the workshop is to see if the groups will execute these activities that they have scheduled, and how the WSSSPE community can encourage this to happen.

Thrombosis is a common, life-threatening consequence of systemic infection; however, the underlying mechanisms that drive the formation of infection-associated thrombi are poorly understood. Here, using a mouse model of systemic Salmonella Typhimurium infection, we determined that inflammation in tissues triggers thrombosis within vessels via ligation of C-type lectin-like receptor-2 (CLEC-2) on platelets by podoplanin exposed to the vasculature following breaching of the vessel wall. During infection, mice developed thrombi that persisted for weeks within the liver. Bacteria triggered but did not maintain this process, as thrombosis peaked at times when bacteremia was absent and bacteria in tissues were reduced by more than 90% from their peak levels. Thrombus development was triggered by an innate, TLR4-dependent inflammatory cascade that was independent of classical glycoprotein VI-mediated (GPVI-mediated) platelet activation. After infection, IFN-ã release enhanced the number of podoplanin-expressing monocytes and Kupffer cells in the hepatic parenchyma and perivascular sites and absence of TLR4, IFN-ã, or depletion of monocytic-lineage cells or CLEC-2 on platelets markedly inhibited the process. Together, our data indicate that infection-driven thrombosis follows local inflammation and upregulation of podoplanin and platelet activation. The identification of this pathway offers potential therapeutic opportunities to control the devastating consequences of infection-driven thrombosis without increasing the risk of bleeding.

Fat-associated lymphoid clusters (FALCs) are a type of lymphoid tissue associated with visceral fat. Here we found that the distribution of FALCs was heterogeneous, with the pericardium containing large numbers of these clusters. FALCs contributed to the retention of B-1 cells in the peritoneal cavity through high expression of the chemokine CXCL13, and they supported B cell proliferation and germinal center differentiation during peritoneal immunological challenges. FALC formation was induced by inflammation, which triggered the recruitment of myeloid cells that expressed tumor-necrosis factor (TNF) necessary for signaling via the TNF receptors in stromal cells. Natural killer T cells (NKT cells) restricted by the antigen-presenting molecule CD1d were likewise required for the inducible formation of FALCs. Thus, FALCs supported and coordinated the activation of innate B cells and T cells during serosal immune responses.

The generation of immune cells from BM precursors is a carefully regulated process. This is essential to limit the potential for oncogenesis and autoimmunity yet protect against infection. How infection modulates this is unclear. Salmonella can colonize systemic sites including the BM and spleen. This resolving infection has multiple IFN-γ-mediated acute and chronic effects on BM progenitors, and during the first week of infection IFN-γ is produced by myeloid, NK, NKT, CD4+ T cells, and some lineage-negative cells. After infection, the phenotype of BM progenitors rapidly but reversibly alters, with a peak ∼30-fold increase in Sca-1hi progenitors and a corresponding loss of Sca-1lo/int subsets. Most strikingly, the capacity of donor Sca-1hi cells to reconstitute an irradiated host is reduced; the longer donor mice are exposed to infection, and Sca-1hic-kitint cells have an increased potential to generate B1a-like cells. Thus, Salmonella can have a prolonged influence on BM progenitor functionality not directly related to bacterial persistence. These results reflect changes observed in leucopoiesis during aging and suggest that BM functionality can be modulated by life-long, periodic exposure to infection. Better understanding of this process could offer novel therapeutic opportunities to modulate BM functionality and promote healthy aging.

The advent of endoscopic sinus surgery led to a resurgence of interest in the detailed anatomy of the internal nose and paranasal sinuses. However, the official Terminologica Anatomica used by basic anatomists omits many of the structures of surgical importance. This led to numerous clinical anatomy papers and much discussion about the exact names and definitions for the structures of surgical relevance. This European Position Paper on the Anatomical Terminology of the Internal Nose and Paranasal Sinuses was conceived to re-evaluate the anatomical terms in common usage by endoscopic sinus surgeons and to compare this with the official Terminologica Anatomica. The text is a concise summary of all the structures encountered during routine endoscopic surgery in the nasal cavity, paranasal sinuses and at the interface with the orbit and skull base but does not provide a comprehensive text for advanced skull base surgery. It draws on a detailed review of the literature and provides a consensus where several options are available, defining the anatomical structure in simple terms and in English. It is recognised that this is an area of great variation and some indication of the frequency with which these variants are encountered is given in the text and table. All major anatomical points are illustrated, drawing on the expertise of the multi-national and multi-disciplinary contributors to this project.

We report statistical time-series analysis tools providing improvements in the rapid, precision extraction of discrete state dynamics from time traces of experimental observations of molecular machines. By building physical knowledge and statistical innovations into analysis tools, we provide techniques for estimating discrete state transitions buried in highly correlated molecular noise. We demonstrate the effectiveness of our approach on simulated and real examples of steplike rotation of the bacterial flagellar motor and the F1-ATPase enzyme. We show that our method can clearly identify molecular steps, periodicities and cascaded processes that are too weak for existing algorithms to detect, and can do so much faster than existing algorithms. Our techniques represent a step in the direction toward automated analysis of high-sample-rate, molecular-machine dynamics. Modular, open-source software that implements these techniques is provided., Wellcome Trust (London, England) (Massachusetts Institute of Technology. Postdoctoral Fellowship WT090651MF), Biotechnology and Biological Sciences Research Council (Great Britain) (Engineering and Physical Sciences Research Council. Grant BBD0201901)

A database of fetal heart rate (FHR) time series measured from 7221 patients during labor is analyzed with the aim of learning the types of features of these recordings that are informative of low cord pH. Our 'highly comparative' analysis involves extracting over 9000 time-series analysis features from each FHR time series, including measures of autocorrelation, entropy, distribution, and various model fits. This diverse collection of features was developed in previous work, and is publicly available. We describe five features that most accurately classify a balanced training set of 59 'low pH' and 59 'normal pH' FHR recordings. We then describe five of the features with the strongest linear correlation to cord pH across the full dataset of FHR time series. The features identified in this work may be used as part of a system for guiding intervention during labor in future. This work successfully demonstrates the utility of comparing across a large, interdisciplinary literature on time-series analysis to automatically contribute new scientific results for specific biomedical signal processing challenges., Comment: 7 pages, 4 figures

A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation.

Recent advances in our ability to watch the molecular and cellular processes of life in action-such as atomic force microscopy, optical tweezers and Forster fluorescence resonance energy transfer-raise challenges for digital signal processing (DSP) of the resulting experimental data. This article explores the unique properties of such biophysical time series that set them apart from other signals, such as the prevalence of abrupt jumps and steps, multi-modal distributions and autocorrelated noise. It exposes the problems with classical linear DSP algorithms applied to this kind of data, and describes new nonlinear and non-Gaussian algorithms that are able to extract information that is of direct relevance to biological physicists. It is argued that these new methods applied in this context typify the nascent field of biophysical DSP. Practical experimental examples are supplied.

By considering the task of finding the shortest walk through a network we find an algorithm for which the run time is not as O(2^n), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the number of dense regions in the original graph. Since we exploit a form of local community detection as a preprocessing, this work gives support to the project of developing heuristic algorithms for detecting dense regions in networks: preprocessing of this kind can accelerate optimization tasks on networks. Our work also suggests a class of empirical conjectures for how structural features of efficient networked systems might scale with system size.

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording, and analyzing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series and over 9000 time-series analysis algorithms are analyzed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines, and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heart beat intervals, speech signals, and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

info:eu-repo/semantics/nonPublished

info:eu-repo/semantics/published

Formic acid contributes significantly to acid rain in remote environments. Direct sources of formic acid include human activities, biomass burning and plant leaves. Aside from these direct sources, sunlight-induced oxidation of non-methane hydrocarbons (largely of biogenic origin) is probably the largest source. However, model simulations substantially underpredict atmospheric formic acid levels, indicating that not all sources have been included in the models. Here, we use satellite measurements of formic acid concentrations to constrain model simulations of the global formic acid budget. According to our simulations, 100- 120Tg of formic acid is produced annually, which is two to three times more than that estimated from known sources. We show that 90% of the formic acid produced is biogenic in origin, and largely sourced from tropical and boreal forests. We suggest that terpenoids- volatile organic compounds released by plants- are the predominant precursors. Model comparisons with independent observations of formic acid strengthen our conclusions, and provide indirect validation for the satellite measurements. Finally, we show that the larger formic acid emissions have a substantial impact on rainwater acidity, especially over boreal forests in the summer, where formic acid reduces pH by 0.25- 0.5., SCOPUS: ar.j, SCOPUS: ar.j, info:eu-repo/semantics/published

Removing noise from signals which are piecewise constant (PWC) is a challenging signal processing problem that arises in many practical scientific and engineering contexts. In the first paper (part I) of this series of two, we presented background theory building on results from the image processing community to show that the majority of these algorithms, and more proposed in the wider literature, are each associated with a special case of a generalized functional, that, when minimized, solves the PWC denoising problem. It shows how the minimizer can be obtained by a range of computational solver algorithms. In this second paper (part II), using this understanding developed in part I, we introduce several novel PWC denoising methods, which, for example, combine the global behaviour of mean shift clustering with the local smoothing of total variation diffusion, and show example solver algorithms for these new methods. Comparisons between these methods are performed on synthetic and real signals, revealing that our new methods have a useful role to play. Finally, overlaps between the generalized methods of these two papers and others such as wavelet shrinkage, hidden Markov models, and piecewise smooth filtering are touched on.

Removing noise from piecewise constant (PWC) signals is a challenging signal processing problem arising in many practical contexts. For example, in exploration geosciences, noisy drill hole records need to be separated into stratigraphic zones, and in biophysics, jumps between molecular dwell states have to be extracted from noisy fluorescence microscopy signals. Many PWC denoising methods exist, including total variation regularization, mean shift clustering, stepwise jump placement, running medians, convex clustering shrinkage and bilateral filtering; conventional linear signal processing methods are fundamentally unsuited. This paper (part I, the first of two) shows that most of these methods are associated with a special case of a generalized functional, minimized to achieve PWC denoising. The minimizer can be obtained by diverse solver algorithms, including stepwise jump placement, convex programming, finite differences, iterated running medians, least angle regression, regularization path following and coordinate descent. In the second paper, part II, we introduce novel PWC denoising methods, and comparisons between these methods performed on synthetic and real signals, showing that the new understanding of the problem gained in part I leads to new methods that have a useful role to play.

SCOPUS: no.j, info:eu-repo/semantics/published

Annual scholarly and creative journal sponsored by the Ball State University Honors College.; Contents of this issue: I Knit Chicago -- Trains -- A Day Like Any Other -- Daniels -- My Two Americas -- Untitled -- Chair -- Wakonda -- Arrows Toward There -- Articles of Memory -- Murphysboro, IL -- Portrait of You as a Canyon -- Pompeii Abandonment -- De Milo -- Granular -- The Moon Lies -- Cooper River Bridge -- Manifest -- First Fig, First Sonnet -- From Within -- Home -- Death of Naivety -- Wolfgang -- Pointillism -- Who We Wanted to Be -- Souls: Our System -- On Interpreting -- At the Library -- Untitled, This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

Between stillness and motion there is only self. There is no difference between the personal and the universal. Size means nothing, neither does scale. We are constantly shifting and changing. Since the world is made of people, it too is constantly in flux. Flux is stillness. People are not prone to growth. Growth is willful action. Change does not equal growth. More does not equal growth. When anything grows, it begins at the most basic level possible. We are currently defined by the image. Art governs the image. Art is willful action. To grow, we must instead define the image, not be defined by it. To grow, Art must instead define the image, not be defined by it. To define is to transcend. To transcend is to move toward unity. All things microscopic and macroscopic are unified by motion. Art must first understand motion. Motion transcends all. Growth is motion., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

Between stillness and motion there is only self. There is no difference between the personal and the universal. Size means nothing, neither does scale. We are constantly shifting and changing. Since the world is made of people, it too is constantly in flux. Flux is stillness. People are not prone to growth. Growth is willful action. Change does not equal growth. More does not equal growth. When anything grows, it begins at the most basic level possible. We are currently defined by the image. Art governs the image. Art is willful action. To grow, we must instead define the image, not be defined by it. To grow, Art must instead define the image, not be defined by it. To define is to transcend. To transcend is to move toward unity. All things microscopic and macroscopic are unified by motion. Art must first understand motion. Motion transcends all. Growth is motion., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

Between stillness and motion there is only self. There is no difference between the personal and the universal. Size means nothing, neither does scale. We are constantly shifting and changing. Since the world is made of people, it too is constantly in flux. Flux is stillness. People are not prone to growth. Growth is willful action. Change does not equal growth. More does not equal growth. When anything grows, it begins at the most basic level possible. We are currently defined by the image. Art governs the image. Art is willful action. To grow, we must instead define the image, not be defined by it. To grow, Art must instead define the image, not be defined by it. To define is to transcend. To transcend is to move toward unity. All things microscopic and macroscopic are unified by motion. Art must first understand motion. Motion transcends all. Growth is motion., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

Between stillness and motion there is only self. There is no difference between the personal and the universal. Size means nothing, neither does scale. We are constantly shifting and changing. Since the world is made of people, it too is constantly in flux. Flux is stillness. People are not prone to growth. Growth is willful action. Change does not equal growth. More does not equal growth. When anything grows, it begins at the most basic level possible. We are currently defined by the image. Art governs the image. Art is willful action. To grow, we must instead define the image, not be defined by it. To grow, Art must instead define the image, not be defined by it. To define is to transcend. To transcend is to move toward unity. All things microscopic and macroscopic are unified by motion. Art must first understand motion. Motion transcends all. Growth is motion., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about